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As Generative AI fundamentally alters the higher education landscape, faculty are facing an unprecedented need for rapid professional growth and re-skilling. This session argues that traditional, top-down professional development can create resistance to change in the era of AI; instead, we must cultivate "Third Space" environments such as Communities of Practice (CoPs) that prioritize social and emotional connection over outcomes driven results. We explore how an intersegmental AI community of practice, developed through the California Learning Lab, involving California Community Colleges, CSUs, and UCs served as a vital laboratory for faculty to navigate the "identity crisis" brought on by AI.

By centering the partnership across institutional connections, we demonstrate how the CoP model facilitates a shift in professional roles, moving from siloed subject-matter experts to collaborative facilitators and knowledge partners. We will discuss the CoP as a high-trust space where faculty can move beyond "plagiarism anxiety" to experiment with new pedagogical identities and work modalities. Attendees will learn how this intersegmental model fosters a culture of shared intelligence, providing the emotional and intellectual scaffolding necessary for the long-term evolution of our academic workforce. We will share specific strategies for building these collaborative hubs to ensure that faculty development remains human-centered and participatory in the face of AI disruption.

In 2024, the University of Rhode Island (URI) established an Academic Affairs AI Task Force to examine and support AI’s evolving role across the institution. This session offers a candid case study of what AI implementation looks like in a unionized, shared-governance environment where faculty perspectives range from enthusiasm to principled resistance.

This work has been sustained by a small, cross-functional core team that has carried significant responsibility alongside primary institutional roles. Through subcommittees of faculty, staff, and administrators, URI initially organized its work across three domains: teaching and learning, research, and university operations. As the work evolved, a fourth domain, ethics, was added to ensure the framework remained grounded in institutional values and responsible use. The resulting AI Task Force Report now serves as a strategic roadmap for prioritizing and implementing AI initiatives across academic and administrative contexts.

Participants will explore lessons learned about change management amid technological uncertainty, communication across institutional levels and state system structures, faculty-centered implementation, and the importance of creating space for informed choice, including principled refusal, while advancing mission-aligned innovation. URI welcomes dialogue with colleagues navigating similar challenges and hopes to serve as a thought partner for institutions developing their own AI governance frameworks.

Faculty across disciplines face the same challenge: high interest in AI, but no coherent model for integrating it in ways that are pedagogically sound, operationally practical, and institutionally responsible.

This session presents the Teaching with AI Framework, a structured, human-centered approach built on two complementary pillars. Pedagogical Integration defines how learning is intentionally designed and experienced, treating AI as a bounded cognitive partner that expands inquiry, supports reflection, and preserves student responsibility for reasoning, verification, and judgment. Instrumental Augmentation defines how teaching work is executed and optimized, leveraging AI to enhance every phase of the instructional lifecycle, from planning and content design to assessment, feedback, and reflective improvement.

Grounded in evidence from a Faculty AI Fellowship at Florida Gulf Coast University's Whitaker College of Engineering, the presenter will share the seven principles of Pedagogical Integration and demonstrate how Instrumental Augmentation is operationalized through two concrete tools: an instructor-facing AI Workspace and a student-facing AI Personalized Assistant.

Participants will engage in a guided design activity, leaving with a clear mental model, practical structures, and an actionable plan for responsible AI integration in their own courses and institutions.

This session presents a theory-to-practice framework for the responsible integration of artificial intelligence into graduate advising systems serving nontraditional students, including adult learners balancing work, family, and academic responsibilities. Drawing on student development theory, including self-authorship, transition theory, belonging, and ecological support models, the session examines how AI can be used as a pre-appointment and pathway-planning support tool while preserving human-led developmental advising. Participants will explore a scalable advising model in which AI supports logistical functions such as course sequencing, milestone mapping, and resource navigation, while advisors remain central to meaning-making, identity development, ethical reflection, and complex decision support. Special attention will be given to equity, targeted universalism, and safeguards to ensure that AI systems do not replace relational advising or unintentionally reinforce disparities for historically marginalized and first-generation graduate students. The session will conclude with an interactive design activity in which participants apply the framework to their own institutional contexts.

This presentation explores the innovative development and implementation of a course-specific AI chatbot designed to address challenges in subject-specific education, such as unreliable general AI tools and declining textbook usage. Unlike generic platforms, this chatbot was trained on instructor-curated content, ensuring alignment with course objectives, reliable information, and transparency through source citations. Integrated into the institution’s LMS, it provides students with on-demand, trustworthy, and engaging support. The pilot trial demonstrated high student engagement, with participants rating the chatbot as easy to use and highly useful for quick access to accurate information. Usage data revealed consistent interaction, with many students favoring the chatbot over traditional resources. This model empowers faculty to maintain control of AI outputs, ensuring academic integrity and alignment with learning goals. The session will highlight the broader implications for higher education, offering a framework for scalable, discipline-specific AI tools. Attendees will gain actionable insights into developing and implementing similar AI solutions, while addressing challenges like over-reliance, reduced critical thinking, and ethical considerations in AI-enhanced learning.

In an effort to demystify AI tools, I have required students to use Claude in several of my classes, one of which is Classical Influences on Modern Literature, specifically emphasizing comedy. Students begin the semester by reading theories of humor, plus some examples of ancient Roman jokes. I introduce them to large language models by making this two-part assignment. First, prompt Claude with this request: Tell me a joke that involves people in a bar. Second, analyze the joke based on what they have learned.

I have done this only a few times, but it is possible to see the progression of an LLM in terms of what it produces. Some early jokes it returned were non-sequiturs. Later jokes made more sense, but were not necessarily more humorous.

This short assignment is low-stakes; it allows me to assess their writing, allows them to understand how I grade, and gives them specific exposure to, and permission to use, an LLM. This assignment is also a springboard to talk about why so many modern jokes take place in a bar and the difference between creating humor and parroting jokes someone has heard. I also require students to write about their experiences with Claude at the end of the semester.

This presentation offers a case-study of classroom use of this emerging technology and my own explorations of Claude. I argue that while instructors’ apprehension surrounding AI tools is justified, there is ample room for using LLMs to show students what the tools can and cannot do.

What happens when students stop playing sustainability games and start building them—with AI as a creative partner?

This session presents a pedagogical model where learners in higher education become designers of sustainability-focused digital games, using generative AI tools as collaborative co-creators throughout the design process. Rather than passively consuming pre-built simulations, students grapple with ecological systems, make curricular decisions, and communicate complex sustainability ideas to authentic audiences, all while reflecting critically on how AI shapes their creative choices.

Drawing on constructionist learning theory, experiential learning frameworks, and emerging research on AI in education, this session demonstrates how game design pedagogy can simultaneously develop systems thinking, digital literacy, sustainability competencies, and responsible AI use. Participants will explore how this approach reframes traditional assignments, repositions students as public educators, and addresses the persistent knowledge-action gap in environmental education.

Attendees will leave with a concrete framework for implementing student-as-game-designer projects, practical strategies for integrating AI as a design collaborator, and evidence-informed guidance on assessing learning outcomes across cognitive, affective, and behavioral dimensions.

This presentation introduces an evolutionary humanities approach that integrates writing, reflection, and interdisciplinary inquiry to help students understand AI as part of the ongoing co-evolution of humans, culture, and cognitive technologies. Rather than framing generative AI primarily as a policy or academic integrity problem, this approach situates it within the longer history of human information systems—language, writing, print, and digital networks—and the evolving forms of collective intelligence they enable. Drawing on examples from first-year writing and interdisciplinary humanities courses, the presentation highlights pedagogical practices such as writing-as-inquiry, reflective metacognition, rhetorical analysis of technology narratives, guided experimentation with AI tools, and collaborative knowledge-building in the classroom that actively engage students in examining their relationship to emerging cognitive technologies. Students engage scholarship on cultural evolution and collective intelligence while also practicing forms of collective inquiry through dialogue, peer inquiry, and shared interpretation of complex questions. By situating AI within a broader human story of evolving knowledge systems and planetary-scale intelligence, this approach helps students develop historical perspective, intellectual agency, and a sense of shared responsibility within evolving human knowledge networks.

As institutions moved from ground level exploration with AI toward coordinated, campus-wide strategies, faculty AI readiness has emerged as a central challenge. Many colleges and universities encounter fragmented efforts and trepidation around how to best support faculty in meaningful and sustainable ways.

This session presents a scalable model for preparing AI-ready faculty through structured, cohort-based professional development. Drawing from the design and implmentation of the SOAR (Strategies for Optimal AI Resilience) AI academy, a cross-institional grant project, the session highlights an approach that integrates AI literacy, hands-on experimentation and real-time implementation.

Attendees will be introduced to the key elements of the project, including the SOAR AI academy, cohort based engagement, and an AI Coaches model. Data and outcomes from this 18-month project will be highlighted as far as what was most impactful to the project outcomes.

Through facilitated discussion and collaborative exchange, attendees will consider the design of the SOAR model and if it, or elements of it could apply at their own institutions. Attendees will also be invited to share what they are currently doing in related areas at their instituions as well as challenges so that all in attendance can benefit from the takeaways. Attendees will leave with a flexible blueprint (SOAR) for designing and/or strengthening AI-focused professional development.

As generative AI rapidly transforms higher education, institutions face an urgent imperative: how do we govern its use responsibly, equitably, and transparently? This session examines practical frameworks for developing institutional AI policy that centers inclusive design ethics and algorithmic accountability. Drawing on the EThICAL model (Excite, Tailor, Identify, Collect, Apply, Learn, Iterate) and Inclusive Design for Learning principles, a governance roadmap tested across institutional contexts will be shared. Participants will discuss and explore how to align AI adoption with context sensitive institutional mission and global standards such as the NIST AI Risk Management Framework and the EU AI Act, while mitigating risks of bias, data privacy violations, and automated exclusion. The session moves from theory to actionable policy and practice, offering adaptable tools for AI use policies, ethics review committees, and faculty governance structures that balance innovation with responsibility and equity.

Many faculty and students face GenAI use as an event horizon: a cataclysmic, unavoidable future taking over their options and decisions in the present. How do we shift faculty and student mindsets from this anxiety and avoidance to intentional, growth-minded learning?

At John Jay College of Criminal Justice, we have designed an innovative approach to developing critical thinking skills with intentional use of GenAI. Through inviting instructors to “flip” the Bloom Revised Taxonomy, we engage Gen Z and Gen Alpha students in a different journey through critical thinking skills development while training faculty to make intentional choices of GenAI tools and strategies.

We teach this approach in a seminar that has engaged more than 50 full time and part time faculty across 17 academic departments. With 6 different faculty cohorts, both in person and online, we have reduced faculty anxiety about GenAI, increasing faculty willingness to shift from GenAI-avoidant mindsets to GenAI-intentional use perspectives. Our seminar participants have now generated more than 100 assignments, a Pressbook guide and an OER assignment repository.

In our session, we will share our framework and assessment data while walking participants through the assignment design process. Participants will discuss the flipped Bloom Revised Taxonomy and explore the faculty design assignments and self-assessment rubric. We will share our Pressbook and assignment repository for further practice and discovery.

Whether AI enhances or erodes critical thinking depends not on whether students use it, but on how the interaction is structured. This session introduces the AI Cognitive Pyramid, a role-based framework that classifies generative AI use in higher education according to where the substantive cognitive work resides during a learning episode.

The pyramid defines five roles (executor, scaffold, reflector, evidence, and opponent) ordered by cognitive offloading risk. When AI functions as an executor, producing finished outputs in place of the learner, it eliminates the generation, evaluation, and effortful processing that produce durable learning. When structured as opponent or evidence, AI use structurally requires learners to generate claims, evaluate evidence, and defend reasoning, which are the cognitive operations central to critical thinking.

Drawing on cognitive offloading theory, desirable difficulties research, and recent experimental evidence (including Bastani et al., 2025; Fan et al., 2025; Gerlich, 2025), the session presents the theoretical grounding for the framework before engaging participants in classifying real assignment examples by role, identifying role drift, and applying design principles to their own courses.

Participants leave with a classification tool, discipline-specific design principles, and a practical understanding of how assessment structure determines whether students build or bypass the cognitive work that learning requires.

As generative AI makes it easier for students to produce polished work, long-standing challenges in higher education—such as shallow discussion, uneven preparation, and unclear expectations—are becoming more visible. Faculty are often positioned as enforcers of academic integrity, yet the more productive response is redesign.

This session introduces a six-week UDL + AI Design Studio that reframes Universal Design for Learning (UDL) as a practical design cycle—WHY (purpose and engagement), WHAT (conceptual clarity), and HOW (supported practice and demonstration). Rather than redesigning entire courses, participants focus on one high-leverage teaching challenge and develop a ready-to-implement solution. AI is used as a design partner to help instructors efficiently draft, refine, and stress-test prompts, scaffolds, and assessments.

This model simplifies UDL without diminishing its rigor and shifts AI from a compliance concern to a capacity-building tool. Attendees will see a concrete redesign example and leave with a fresh way to understand UDL—not as a checklist of strategies, but as a streamlined design process for improving engagement, clarity, and student performance.

This session explores how generative AI can function not as a writer or evaluator, but as a constrained rhetorical audience in first-year multimodal composition. Drawing on a classroom-library collaboration at Goldey-Beacom College, the presentation shares findings from a qualitative study of 26 student visual advocacy projects in which students compared their intended rhetorical meanings with AI-generated interpretations of their work. Findings show that AI consistently identified surface meaning and logos-driven elements while often flattening ethos, pathos, cultural context, and lived experience. Rather than treating these misreadings as failures, students used them as opportunities for revision, reflection, and stronger audience awareness. By framing machine interpretation as reverse evaluation, this session offers a practical, human-centered model for ethical AI literacy that helps students engage emerging technologies critically while strengthening rhetorical judgment and multimodal clarity in algorithmically mediated environments.

Generative AI is becoming an essential skill in today’s workforce. Organizations ranging from large corporations such as Amazon and Google to small businesses increasingly expect employees to use AI tools effectively, and in some cases, such skills are tied to career advancement. In this context, restricting or prohibiting AI use in the classroom may not reflect the realities students will face after graduation. A more effective approach is to guide students in using AI correctly by developing the foundational knowledge needed to evaluate and verify AI-generated outputs.

Learning how to apply AI is not limited to technical programs; every discipline can help students engage with AI effectively. This session demonstrates how instructors can use course content to guide students in applying AI within a disciplinary context. For example, in an Individual Tax course, students use AI to build automated worksheets based on IRS forms and instructions, then verify calculations and interpret results. Participants will gain practical strategies to help students use AI for analysis, verification, and problem solving across disciplines.

Liberal arts colleges are committed to preparing students for lives of purpose and meaningful contribution. As artificial intelligence reshapes the nature of work, they face a defining challenge: how to connect this mission more directly to students’ future pathways. While many responses emphasize co-curricular programming or skills-based initiatives, these approaches leave largely untouched the core site of a liberal arts education—the academic program itself. The work our faculty do through teaching and mentoring to prepare students for meaningful lives must become more intentional, visible, and fully integrated within the curriculum.

This roundtable advances The Meaningful Work Initiative, an emerging model that reframes career development as a curricular question. The central claim is that preparing students for work in an AI-shaped world cannot be addressed through additional programming or pre-professional initiatives alone; instead, it requires more purposeful integration of career development into the academic experience itself.

Participants will explore how disciplines can help students interpret changing forms of knowledge, authorship, and expertise and how courses, assignments, and learning goals can be redesigned to connect academic work to questions of purpose and career pathways. The session will also consider the role of academic leadership in supporting this shift across departments and programs.

Artificial intelligence is moving rapidly into the everyday life of higher education. Faculty are rethinking assessment and feedback, student-support teams are exploring new tools, and institutional leaders are being asked to make policy and implementation decisions with urgency. Yet the central question is not simply whether AI should be used. The deeper question is how AI can be integrated in ways that strengthen learning, preserve trust, support equity, and protect the human judgment at the heart of education. This session addresses that question through a theory-to-practice model. I will introduce a human-centered, ethics-informed framework for responsible AI integration in higher education, grounded in inclusive practice, critical reflection, and evidence-based implementation. The session will then move from conceptual framing to practical application by examining realistic higher education scenarios involving teaching, assessment, feedback, communication, and academic support. Participants will use the framework to evaluate where AI adds value, where it creates risk, and how educators and institutions can make better decisions about adoption. Rather than treating AI as either a threat or a shortcut, this session helps participants develop a more disciplined and human-centered way of thinking about technological change. Attendees will leave with language, questions, a decision structure they can use immediately in their own settings

AI tools create work that looks professional but often fails in important ways: calculation errors, outdated information, missing context, fabricated sources. That gap between "looks right" and "is right" is exactly the professional judgment students need before graduating into fields where they'll be expected to use these tools competently.

Assignments that ban AI miss the chance to build that judgment. Assignments that ignore it produce graduates who can't evaluate what the tools give them. Co-led by an instructional designer and a faculty member, this roundtable explores creating classroom assignments that require students to use AI, then check its work, look for issues, and explain their reasoning. AI gets things wrong in every field, and catching those mistakes takes the same kind of critial thinking faculty are already trying to teach.

Discussion Questions

1. Where does AI tend to get things wrong in your field, and what do students need to know to catch those mistakes?

2. What's the difference between an assignment that asks students to come up with an answer and one that asks them to judge whether an answer is any good?

3. How do we design assignments where using AI actually makes the work harder, because students have to check it, fix it, or critque it?

4. What needs to change about academic integrity and grading when AI is part of the assignment instead of something we're trying to keep out?

What if AI use in the classroom was itself a civics lesson? As two social studies teacher educators, we rethought AI use through core civics tenets of participation, deliberation, and critical literacy. Rather than handing down rules, we deliberated on the use of AI. We asked students to think like citizens: who decides, on what grounds, and with what accountability? We also integrated AI as a recurring theme, examining it through historical, ethical, pedagogical, and practical lenses — including the hype-and-disappointment cycle of ed tech, AI’s role in learning vs. shortcutting, and its application in elementary social studies planning.

Key activities included a co-constructed AI Responsibilities Contract built through student surveys and structured dialogue, a student “moral compass” exercise, critical evaluation of AI in teaching and planning, and structured deliberations on the ethics of AI use in assignments. These approaches demonstrate that AI integration in social studies teacher education isn’t just a logistics problem — it’s a site for teaching civics and citizenship meaning-making.

In this session, we will share the civics tenets that framed our approach and describe the specific activities aligned with them. Participants will leave with concrete tools and a framework for the question we're all grappling with: How do I approach AI use in a university course? We will also provide space for participants to share what they are doing in their own teaching.

Most colleges and universities that have developed AI governance frameworks built them to manage risk: what tools are approved, what uses are prohibited, how to avoid liability. These documents serve an important compliance function, but they reveal more than their authors may intend. Read through the lens of organizational digital maturity research, an AI governance framework becomes a diagnostic artifact — one that signals whether an institution is reacting to the current moment or building the capacity to navigate whatever comes next.

This session introduces the digital maturity framework developed by Kane et al. (2019), research conducted and published before generative AI became publicly available. Their central finding holds: organizations that thrive through technological disruption are not the ones that respond fastest to each new tool, but the ones whose structures, cultures, and strategies are designed to adapt continuously. The session applies this lens to AI governance in higher education, drawing on an ongoing content analysis of AI governance frameworks at over 100 U.S. institutions spanning research universities, regional comprehensives, community colleges, and minority-serving institutions. Participants will assess their own institution's governance posture using maturity indicators drawn from the research, moving from theory to a practical self-assessment they can take back to campus.

Higher education’s dominant response to generative AI has been swift and understandable, but completely wrong. Institutions deployed AI detection as a first line of defense against academic dishonesty and built punitive consequences into policies.  The research on those tools is now substantial, and its findings are damning.  Detection tools produce false positives at rates that should disqualify them from all high-stakes use. They disproportionately flag non-native English speakers, neurodivergent writers, and students from educational backgrounds that emphasize clarity over elaboration.  The students most likely to be falsely accused already face the greatest systemic barriers in higher education, making this about equity, not just integrity.

This session contends that the problem is a misdiagnosis. Institutions have identified the symptom while misidentifying the cause. The actual issue is poor assessment design, and this problem was present in our assessment practices long before AI arrived to expose it. When an assessment can be completed by a generative AI without genuine engagement with the learning it was designed to measure, the assessment was measuring the wrong thing. AI did not create that problem; it exposed it.  Responsible use of AI in education begins by correcting the misdiagnosis and resolving the actual issue rather than penalizing the learner. This session offers the evidence, a reframe, and a live demonstration of what better assessment design looks like.

As universities move toward coordinated AI strategies, governance is often framed as a policy problem. Drawing on findings from the first year of a multi-year research program, this session argues that the central challenge is not the absence of governance, but the ambiguity through which it operates. Based on qualitative interviews with administrators, IT leaders, and faculty, this session examines how AI-related expectations are being interpreted and enacted across campus units.

Findings show that ambiguity shapes decision-making at every level. Units rely on different logics, processes, and priorities, leading to fragmented approaches. In practice, governance is distributed across structures such as IT review processes, faculty syllabus guidance, and academic integrity systems. Some institutions embed AI into existing IT governance, while others distinguish between policy and procedure to remain responsive.

At the same time, this ambiguity is pushed onto students, who must navigate inconsistent expectations and determine what counts as acceptable use across courses and contexts. Faculty responses range from integration to prohibition, and enforcement varies widely. The result is a governance landscape where “it depends” functions as policy, shifting responsibility onto students and amplifying hidden curricula.

This session reframes AI governance as a sociotechnical design problem and offers a framework for building adaptive, transparent, and student-centered approaches.

Artificial intelligence can now generate fluent answers to complex questions in seconds. When answers are easy to produce, the central challenge of education shifts from producing information to evaluating it.

This session explores how faculty can redesign learning environments so that student judgment—rather than answer production—becomes the central outcome of learning in AI-rich classrooms.

Drawing on classroom implementation in a political science course and faculty development work across disciplines, this session introduces the A.S.K.E.D. framework (Articulate, Support, Knock Around, Evaluate, Decide), a structured cycle that requires students to interrogate AI-generated claims, test competing explanations, and defend decisions under uncertainty.

Participants will see examples from an AI-supported campaign simulation used in an undergraduate political science course where students receive conflicting advice from AI advisors and must make strategic policy decisions. The session focuses on practical strategies faculty can adapt to redesign assignments, discussions, and assessments so that AI becomes a pressure test for reasoning rather than a shortcut to answers.

The intersection of generative artificial intelligence and higher education presents a critical inflection point for design disciplines historically rooted in manual execution. As advanced Large Language Models (LLMs) automate entry-level production tasks—such as responsive layout synthesis and front-end coding—the traditional collegiate graphic design curriculum risks imminent obsolescence. This Innovation Session introduces an early-stage, transformative pedagogical framework: "Design Engineering."

We will explore how to aggressively transition design education away from isolated aesthetic execution and toward systems architecture, semantic data structuring, and cross-disciplinary API integration. By utilizing standardized middleware like the Model Context Protocol (MCP) to establish a communication layer between visual design systems and generative AI coding engines, students learn to bridge the cognitive gap between human visual intent and deterministic machine logic. This session will outline how teaching algorithmic literacy and "Context as a Service" empowers students to orchestrate AI models rather than compete against them. We will share insights on updating aging syllabi, bridging faculty skill gaps through co-teaching models, and evolving the traditional static portfolio into a dynamic "Systems Portfolio." Participants will leave with a clear, strategic roadmap for equipping students to thrive in an AI-saturated labor market.

AI offers new forms of support to marginalized students who may lack access to tutoring, mentoring, career coaching, and academic or financial capital. Yet these same tools can also reproduce and more deeply entrench inequality through bias, misinformation, paywalls, and overreliance. This discussion session examines how institutions can responsibly use AI to support marginalized learners while avoiding further harm to the very students they seek to serve. Drawing on current scholarship, research, and participants’ own experiences, attendees will explore practical strategies for equitable AI implementation that empowers rather than reinscribes disempowerment. Through engagement with the presenter, whose work at a Historically Black University has brought these concerns directly to the fore, participants will compare institutional experiences and debate tensions between access, integrity, and equity. Participants will leave with a stronger understanding of both the promise and dangers of AI for marginalized learners, along with actionable ideas for policy and practice.

Higher education professionals across institutions are being asked to work differently as AI becomes more integrated into daily operations. Departments across colleges and universities are navigating this shift in different ways, and marketing and communications functions are often among the first to feel its impact as they are asked to produce more content, move faster, and support increasingly complex priorities.

Communications professionals are experimenting with AI tools for content development, analytics, project management, and operational efficiency. At the same time, leaders are confronting larger questions about role evolution, staff development, quality control, and how to maintain institutional voice in an increasingly automated environment.

This session explores how communications leaders are determining where AI can improve efficiency, where human expertise still matters most, and how to make thoughtful decisions about integrating new tools into daily work.

Using marketing and communications as a practical lens, presenters will explore broader workforce questions many institutions are facing as AI changes roles, workflows, and expectations across higher education.

Presenters will share practical examples of how they evaluate tools, redesign workflows, and approach AI adoption thoughtfully rather than reactively.

Participants will leave with practical ideas for evaluating AI tools, adapting workflows, and identifying where human expertise should remain central.

Although there is growing guidance to help faculty make decisions about integrating AI into individual courses, none has fully addressed AI holistically across all levels of academic practice from program design to course development to day-to-day instruction. In addition, the impact of AI on online education often receives disproportionate attention due to the asynchronous nature of learning remotely. However, online education is an ideal modality for implementing intentional, design-forward solutions that strengthen both learning and integrity.

Through an extensive review of current research and emerging practices, we have identified six essential pedagogical moves that online educators should adopt to respond proactively and sustainably to the realities of generative AI. For each move, we outline critical actions at the program, course design, and instructional levels. This framework demonstrates the interdependence of these roles and the actions each can take, and highlights how strategic decisions at the program level cascade into course design and ultimately can help shape facilitation and assessment. This approach also means that integrity is a structural feature and not just an add-on. Embedded throughout the framework are proactive strategies to support meaningful learning, valid assessment and academic integrity.

Explore how these six moves apply to your role and how online education can constructively address generative AI’s opportunities and challenges.

Policy neutrality in higher education AI governance is not neutral in practice. It functions as a risk-transfer mechanism that hides who absorbs enforcement error, who does the unpaid repair work, and who is missing from the drafting room. This 60-minute Theory to Practice session introduces a six-question Intersectional AI Policy Audit for higher education governance. Grounded in intersectionality, Black feminist thought, data feminism, and design justice, the audit examines authorship and absence, enforcement asymmetry, burden of appeal, neutral defaults, consent architecture, and governance loops. It is designed for AI-related institutional documents across academic and operational domains, including AI use policies, syllabus statements, support and triage workflows, and vendor data clauses. In breakouts, participants apply the audit to artifacts, identify where burdens and protections are distributed inequitably, and draft one concrete governance revision tied to a named theoretical principle. Participants leave with a reusable audit worksheet, a facilitator guide for campus governance groups, and an annotated reading list.

Faculty work has never been more demanding or more fragmented. Assessment reporting, grant prospecting, literature reviews, curriculum mapping, committee documentation: the cumulative weight of these tasks consumes the time and energy faculty need for the work that drew them to the professoriate in the first place.

Most faculty who use generative AI treat it as a single-task tool: draft this email, summarize this article. That approach is useful but limited, both by the technology's constraints and by the narrowness of how it is being applied. This session introduces a different model: the multi-agent persona system (MAPS), in which a faculty member designs a personalized AI framework with specialized personas for different domains of their work. A writing persona handles drafting and revision. A project management persona tracks deadlines and priorities. New personas can be created as unanticipated needs arise. Environments like Anthropic's Cowork already support this kind of architecture.

The result is AI that functions not as a one-off tool but as adaptive infrastructure across every dimension of faculty work: assessment, research, teaching, and service. The session addresses faculty concerns about academic integrity, equity of access, and data privacy head-on, demonstrating how intentional, faculty-controlled AI use is itself a response to those concerns.

The core takeaway: when faculty design AI to work the way they work, they don't lose control. They gain capacity.

To address the rapid integration of generative AI in higher education, this session showcases the "14-Day AI Challenge," an innovative "sprint" model designed to build faculty technical confidence through intentional, daily practice. We analyze the transformative journey of faculty members from diverse disciplines as they moved from basic prompting to complex tasks such as redesigning assessments for "AI-resiliency".

The challenge demonstrated a significant shift in perspective, moving faculty from initial hesitation to viewing AI as a formidable "thought partner" and collaborator. Participants reported substantial productivity gains, frequently saving 3 - 4 hours weekly on administrative tasks like professional correspondence and grant outlining. However, the initiative’s most profound impact was a pedagogical pivot. Faculty moved away from recall-based assignments toward higher-order thinking, incorporating metacognitive reflections and "human-in-the-loop" strategies to mitigate risks like "cognitive surrender" and AI hallucinations.

Participants will explore qualitative data from faculty logs, highlighting the "best of" the 14-day tasks, such as using the "Expand" prompt for creative depth and "Explain" for simplifying complex jargon. I will share a practical roadmap for implementing this low-stakes, high-impact development model to foster a culture of responsible AI integration that prioritizes critical judgment over automated dependency.

Static study materials often leave students stranded at a "knowledge wall" during independent study, where real-time faculty feedback is unavailable. Consequently, students often default to rote memorization rather than developing the deep reasoning skills required for professional practice. This session introduces a "Faculty as the Architect" model, a framework that empowers educators to move beyond being mere users of generative AI to designing the underlying pedagogical logic of discipline-specific tutors.

Using a successful case study from a professional program as a blueprint, we will explore the "Five Pillars of Logic." These universal engineering principles: Socratic Protocol, Safety Alert Protocol, Metacognitive Scaffolding, Knowledge Bridges, and Blueprint Tethering transform a general large language model into a disciplined coach. Participants will learn how these bots are "tethered" to specific course documents to ensure content validity and "coached" to mirror a faculty member’s Socratic voice. We will share data demonstrating how this intervention shifted student perception of AI from an "ethical fear" to a "critical reasoning partner," resulting in an increase in student confidence when navigating complex concepts. This session provides a transferable roadmap for any discipline seeking to bridge the "after-hours" learning gap with intentional, faculty-led AI design using a FREE online application.

Students are already using AI. The question is not whether it belongs in the classroom, but how we design learning around it.

Many assignments today produce work that is polished and structured, but missing depth and original thought. Students often rely on AI to generate responses instead of using it to develop their own ideas. This is not just a student issue. It reflects how assignments are designed.

This session focuses on how faculty can shift AI from an answer generator to a learning partner. Using examples from entrepreneurship courses, participants will see how research, AI tools such as NotebookLM, and presentation-based assignments can be combined to improve engagement and understanding.

One example includes a project where students research a failed company, use AI to explore their sources, and deliver a 60 second elevator pitch. This approach helps students take complex ideas and communicate them clearly while still requiring their own thinking.

Participants will leave with practical ways to redesign assignments, increase engagement, and better align their courses with how students are already learning.  With giving them experience on how they will work in their future careers.

As generative AI continues to reshape higher education, institutions are grappling with how students, faculty, and staff perceive and use these tools in academic and professional contexts. Since the public release of generative AI tools in November 2022, no comprehensive, campus-wide assessment of AI perceptions and use has been conducted on the campus at this regional university in the Midwest. As a result, students, faculty, and staff have largely navigated AI independently, developing practices, assumptions, and concerns without coordinated institutional guidance. Moreover, without a clear understanding of how students, faculty, and staff perceive and use AI, efforts to support AI literacy on campus have often been inconsistent and not always effective. This study presents preliminary findings from a first campus-wide survey conducted at a regional university in the Midwest, examining perceptions and reported uses of AI across students, faculty, and staff.

By looking at these areas across students, faculty, and staff, the study offers a clearer picture of how AI is being used and understood on campus. Instead of relying on scattered experiences, institution can use these insights to make more informed decisions to support faculty, students, and staff, shape policies, and plan training.

To date, most colleges and universities have opted to use a stoplight approach to AI policy development: "red-lighting" instances when AI use is prohibited outright, "yellow-lighting" case-by-case situations when cautious and approved use of AI is permissible, and "green-lighting" instances where AI use is encouraged and sometimes even required. Complicating matters further is that AI policy is often left to individual faculty to decide, leaving students to have vastly different experiences between classes in a given semester and between different semesters.

This discussion session engages AI committee chairs, co-chairs, and members (including ex-officio members) in exploring how best to develop, evaluate, and update AI policy at the institutional, divisional, departmental, and individual levels. Drawing on extensive research conducted of existing college and university AI policy design along with first-hand consulting and advising experience, it offers guidance on effective strategies for how colleges and universities: ensuring broad committee representation, gathering data on known AI use (administrators, faculty, student affairs, and students alike), forming initial AI policies, and developing a cadence for regularly revisiting and potentially updating AI policies.

This discussion offers concrete and practical guidance for attendees around transparency, ethical considerations, institutional governance, and policy-making.

As institutions move from isolated experimentation toward coordinated AI strategies, many struggle to align policy, pedagogy, and institutional mission. The result is fragmented AI efforts that prioritize policy compliance while leaving teaching practices, faculty decision making, and learning goals underdeveloped. This session introduces CORE‑AI (Collaborative Research on Education with AI), a multi‑institutional research‑to‑practice collaboration that positions faculty development as the connective infrastructure linking policy, pedagogy, and inquiry in support of ethical, learning‑centered AI integration.

Grounded in practitioner inquiry and the COVER model for ethical decision making, CORE‑AI integrates faculty development and cross‑institutional research to examine how educators respond to generative AI in practice. Our research provides evidence of increased instructor confidence, expanded attention to equity and access, and stronger emphasis on process‑oriented assessment and student learning.

Participants will engage with a transferable model for building institutional “ethical infrastructure” that centers faculty development as the mechanism for aligning institutional priorities with classroom practice. Attendees will map current AI initiatives across policy, pedagogy, and inquiry; identify gaps in coordination; and design targeted faculty development strategies that support coherent, mission‑aligned AI integration within their own institutional contexts.

Feminist pedagogy recognizes that questioning dominant, hierarchical power structures from within the very frameworks that produce these systems is a contradiction not easily overcome (Lorde, 2004; Vergès, 2021). Artificial Intelligence (AI) further compounds these challenges in the feminist classroom: When students delegate decolonial feminist critique to an AI, the tool undermines learners’ development of critical thinking. More significantly, the AI pretends neutrality while in fact reproducing dominant patriarchal, colonial frameworks (Kubes, 2025), rendering the power dynamics under scrutiny invisible.

This session presents PersephonAI, a custom AI bot for women’s & gender studies courses designed with the expectation that it would withhold rather than provide, question rather than answer, and position productive discomfort as the site of learning. The bot was designed to require students to articulate their own claims and ground their analysis in assigned course materials. What emerged was an iterative struggle, as PersephonAI had to be reconfigured multiple times before it could sustain that pedagogical stance.

Drawing on preliminary data from a 20-student undergraduate class in spring 2026, this session traces the difference between design intention and AI output, and what that gap reveals about dominant structures inherent in AI platforms. Participants engage with PersephonAI firsthand and reflect on the ways in which AI defaults to hierarchical knowledge models.

Despite strong institutional interest in AI integration, faculty consistently report insufficient knowledge and confidence to incorporate AI meaningfully into their teaching, and most institutions lack the frameworks, resources, or professional development models to support faculty effectively. This session presents a faculty learning community model grounded in an explicit AI literacy framework and open educational resources (OER) designed for reuse and adaptation across institutional and disciplinary contexts.

Drawing on faculty surveys and interviews, we present evidence of faculty needs and challenges in AI integration and demonstrate how anchoring professional development in clearly defined AI literacy competencies produces more targeted, impactful outcomes than ad hoc training. We share our AI literacy framework, facilitation guide, and learning community activities—all freely available under a Creative Commons license.

Because this model addresses educator support for AI literacy rather than discipline-specific AI use, it transfers broadly across fields and roles. Participants will leave equipped to adapt these resources to their own institutional contexts. Beyond our own model and resources, this session creates space for participants to exchange needs, challenges, and effective approaches with one another—fostering cross-institutional collaboration and idea sharing.

As artificial intelligence (AI) tools rapidly enter higher education, institutions face a pressing challenge: how to move beyond experimentation toward evidence-based instructional models that support meaningful learning, ethical engagement, and workforce readiness. This session shares findings from a multi-institutional study examining adult learners’ perceptions of a scaffolded AI-integrated curriculum grounded in the Technology Acceptance Model (TAM). Results showed significant gains in student confidence, perceived value, and intended use of AI tools. The session highlights the curriculum framework and instructional strategies used in the intervention, including scaffolded AI integration, ethical reflection, instructor modeling, and metacognitive activities aligned with authentic academic and professional tasks. Participants will examine how these design elements support responsible AI use and consider how the framework can be adapted across disciplines and institutional contexts. Although implemented in writing courses at two-year institutions, the underlying design principles are transferable across disciplines and institutional contexts. Participants will leave with a research-informed framework they can adapt to support ethical AI integration, student confidence, and meaningful learning across disciplines.

This poster will explore how higher education faculty perceive and engage with GenAI and how these perceptions frame pedagogical decision-making. Drawing on a mixed-method approach and combining survey questionnaires with in-depth interviews, this research applies the Dynamic Systems Model of Role Identity (Kaplan & Garner, 2017) to illuminate the complex interplay between one’s beliefs, values, goals, self-perceptions, action-possibilities, and their associated emotions, all nested within a given domain and social-cultural context. Attendees will gain a nuanced understanding as to how faculty role-identity frames classroom policies and curricular choices and will leave the session with new insight into the practical, ethical, and pedagogical implications of integrating GenAI into one’s classroom across the higher education landscape. The poster will help participants consider how AI adoption can be integrated thoughtfully into classroom and institutional culture and practices while balancing innovation and pedagogical effectiveness.

Open-ended student course evaluations contain valuable insights into instructional practices and student experiences in chemistry lecture courses, yet they are often underutilized due to their volume and the lack of accessible analytic methods. INSIGHT (Instructional NLP for Student Input and Guiding High-Impact Teaching), funded by the CA Learning Lab, addresses this challenge by applying natural language processing (NLP) as an interpretable, faculty-facing tool for analyzing qualitative course evaluation data.

We applied NLP techniques to responses from large-enrollment, lower-division chemistry courses, using thematic classification aligned with chemistry education research constructs, including clarity of instruction, assessment and feedback, opportunities for practice, relevance, and classroom climate. Rather than automating judgments, INSIGHT surfaces patterns in student feedback while preserving transparency and instructor interpretability.

Effectiveness was evaluated using a mixed-methods approach. NLP classifications were compared with human-coded data to assess alignment and reliability, while faculty surveys and interviews examined perceived accuracy, clarity, and instructional usefulness. Pre/post measures captured shifts in instructors’ perceptions and intended teaching changes. INSIGHT supports data-informed curricular decisions and promotes reflective, high-impact teaching practices.

This session examines an AI-mediated discussion board assignment that responds to a growing instructional challenge: students using generative AI to produce discussion post responses. Rather than restricting this practice, the assignment incorporated AI directly into the activity. In a second-year writing course, students used AI tools to create distinct personas and engage in multi-post, roleplay-based debates about whether AI can replace human companionship. This approach expanded the traditional discussion board format by encouraging sustained participation, perspective-taking, and creative interaction.

The assignment generated a high level of activity (126 posts from 22 students), far exceeding typical participation benchmarks. Analysis of the discussion, however, reveals a key distinction between participation and interaction. Many contributions took the form of self-replies, with students staging debates between their own AI-generated personas, while peer-to-peer exchange was less frequent. In addition, similar arguments appeared across multiple threads, suggesting that increased output did not necessarily produce more varied or extended dialogue.

This session presents both the design and outcomes of the assignment, using these results to examine how AI reshapes discussion practices. It concludes with practical revisions that prioritize meaningful interaction and stronger responses over simply producing more posts.

Gen AI is not simply a new tool; it is a disruption that exposes gaps in how higher education prepares faculty to teach. Many instructors are asked to respond quickly to AI without the pedagogical grounding, collaborative space, or institutional structures needed to do so thoughtfully. The result is often reactive policies, isolated experimentation, or avoidance.

This session introduces an AI & Pedagogy Faculty Learning Community as a different response. Rather than traditional training, this model focuses on culture building. Designed as a sustained, cohort-based experience, the FLC creates space where faculty collectively make sense of AI’s implications for learning, test new approaches, and reflect on what should change and what should remain constant in an AI-mediated environment.

The FLC is structured with a lead faculty facilitator alongside pedagogical experts. This model encourages faculty leadership and builds a strong sense of community across disciplines while working toward a shared, student-centered purpose. It focuses on pedagogy rather than tools, positioning AI as a catalyst for rethinking feedback, authorship, creativity, and the instructor’s role.

Drawing on an institutional case from Bentley University and emerging findings, we show how this approach builds pedagogical confidence, cross-disciplinary collaboration, and a more intentional teaching culture. Attendees will leave with a research-informed framework for leading similar cultural shifts.

How can instructors support students in developing productive AI habits while addressing AI overuse in constructive ways? Explore strategies that go beyond detection tools to address why and how students misuse AI. We'll discuss practical proactive techniques to build good habits and responsive approaches that use conversation when AI misuse is suspected. You'll leave this webinar with concrete tools and frameworks that you can use across disciplines.

Multilingual learners make up about 10.6% (5.3 million) of U.S. public school students (NCES, 2024), underscoring the need for teacher education programs to better prepare educators for linguistically diverse classrooms. Equipping teacher educators with the skills to use artificial intelligence (AI) effectively is important. This presentation examines how a U.S. graduate program transformed its curriculum to prepare educators for K–12 multilingual settings by integrating technologies and AI tools. The program also supports teacher educators in developing AI literacy, pedagogical strategies, and ethical frameworks for working with multilingual learners. The session highlights the integration of tools such as adaptive language platforms, automated feedback systems, and generative AI to meet diverse student needs. It also emphasizes the importance of preparing teacher educators to model and scaffold AI use for sustainable implementation. At the same time, the presentation addresses risks of the digital divide if access, and infrastructure are not considered. Without careful training, AI may amplify inequities related to bias, privacy, and unequal access. The research question that guides this study is how do teacher education programs effectively train prospective teachers to use AI without contributing to a digital divide in multilingual education contexts? Participants will leave with strategies to evaluate and apply digital and AI tools in equitable, responsible ways.

San Diego State University’s Academic Applications of Artificial Intelligence initiative is a comprehensive, data-informed set of resources to support  the adaptations necessary for ethical, integrated, and cohesive use of generative AI. This session will focus on two recent projects, both of which concern whole-program design. The first is the AI-Ready Curricular Options Network Resource (ACORN) Toolkit and the second is Leading AI-Ready Programs experience, which prepares leaders to implement the Toolkit in support of department-level collaboration and planning to ensure students experience clear, consistent, and discipline-relevant approaches to AI. The ACORN project began in the summer of 2025 with a small taskforce of 8 faculty and 2 students. Since then, they have created, stress tested and launched a Toolkit in support of coherent, strategic, program-level approaches to AI literacy instruction. The Toolkit contains both preparatory and “core” tools. Those in academic leadership positions, such as department chairs, can use this toolkit to facilitate working collaboratively with their faculty to reduce gaps in AI coverage, avoid redundancies, and increase career readiness. After reviewing the toolkit and explaining how the leadership program prepared the way for ACORN implementation, we will walk through the Group AI Literacy Development Tool, which is central to the ACORN process.

Generative Artificial Intelligence (AI) has heightened concern about academic integrity in higher education, yet student use of AI in academic writing may be more nuanced than dominant narratives suggest. Drawing on findings from an exploratory study of undergraduate and graduate students, this session examines how students report using AI in academic writing, how they perceive its impact, and where they want more guidance. Findings suggest that many students use AI selectively for revision, grammar, clarity, and idea support rather than full content generation. At the same time, students express strong interest in learning how to use AI responsibly and effectively. Using these findings as a starting point, this session moves beyond the question of whether students are using AI to consider what these patterns reveal for teaching, learning, assessment, and institutional response. Participants will explore practical implications for assignment-level guidance, student-facing AI literacy support, faculty development, and greater consistency in institutional messaging. The session is designed to help institutions move beyond reactive, detection-centered responses toward more educationally grounded and context-sensitive approaches to AI in academic writing.

As AI tools become increasingly integrated into higher education, conversations often center the perspectives of faculty and administrators, while student perspectives remain underrepresented, and consequently, underexplored. This session centers student voices by examining how students interpret and engage with AI tools in introductory sociology courses.

Drawing on reflection data from scaffolded, AI-integrated assignments, this session highlights the diversity of student attitudes toward AI, ranging from enthusiasm and curiosity to skepticism and concern—challenging the assumption that student perspectives and engagement are uniform. Instead, the findings demonstrate the nuanced ways students engage with AI in their learning. Notably, assignment design plays a key role in shaping these perspectives, with structured evaluation activities prompting more critical and reflective engagement.

This session will facilitate an open forum dialogue inviting participants to reflect on their own assumptions about students and AI, as well as how their students engage with and perceive AI tools. Participants will consider how student perspectives can shape assignment design and classroom or institutional AI policies, and what teaching AI literacy looks like across disciplines. The discussion will also encourage reflection on how to support students’ metacognitive engagement with AI and identify directions for future research.

As artificial intelligence rapidly reshapes higher education, faculty are navigating a critical tension: how to leverage AI without compromising their professional identity and pedagogical integrity, all while fostering student learning. This session introduces the concept of the “AI-augmented professor”, which reframes AI not as a replacement but as a strategic partner in reclaiming time for high-impact, student-centered work.

Drawing on emerging practices and lived faculty experience, we explore key questions like: What aspects of academic work should be protected versus delegated? How do we mitigate risks such as cognitive offloading, de-skilling and over-reliance on AI tools? And what does ethical augmentation look like in teaching, research and service?

Participants will engage with a practical, adaptable framework (Boundaries, Not Barriers) to intentionally integrate AI while preserving the relational, creative and critical dimensions of academic work. Through interactive reflection and applied examples, this session equips faculty with strategies to use AI to enhance (rather than undermine) student engagement and intellectual rigor. At the same time, it helps administrators support faculty in making  intentional, sustainable decisions about their work in an AI-enabled environment.

The emergence of generative AI created urgent questions for Samuel Merritt University (SMU), a health sciences institution: how AI can support clinical reasoning, how to prepare students for ethical AI use in patient care, how faculty could leverage AI for teaching and scholarship, and how the institution could respond while preserving academic integrity.

From 2023 to 2026, SMU developed a staged institutional response led through Academic and Student Affairs. In 2023-2024, we assessed faculty needs and perceptions of AI, demystified generative AI, and engaged faculty early adopters to model practical uses. In 2024-2025, we provided AI literacy training to about 80% of faculty, created rich faculty resources, established a student AI use policy, and added AI orientation for new students. In 2025-2026 participating in the AAC&U AI Institute catalyzed scholarship of teaching and learning (SoTL) activity, including developing research infrastructure and incentives for AI teaching innovation.

Upon discovering Dr. Julaine Fowlin's Ground, Build, Propel framework (2026), we retrospectively analyzed our activities, finding alignment with 10 of the framework's 13 action steps, as well as critical gaps that now inform strategy. Our poster shows the framework's utility as both validation and diagnostic tool. Gap analysis now guides our strategic priorities: curricular redesign support, sustained foundational training, advocacy for premium tools, and expanding SoTL projects.

“What are you going to do with that?” For decades, this question has served as a stand in for broader cultural skepticism about the practical value of studying the humanities. With the rapid acceleration of artificial intelligence, however, the question has taken on new urgency in pedagogical conversations about preparing students for the future of work. At the same time, employers increasingly emphasize skills such as critical thinking, ethical reasoning, communication, and adaptability, competencies long cultivated in humanities education.

This session explores how connecting humanities coursework to recognized career competencies can address undergraduate anxiety about artificial intelligence and the future of work while offering instructors across disciplines a model to adapt in their own courses. The presentation argues that humanistic inquiry and interpretation are increasingly complementary to and help shape new forms of work emerging in AI influenced labor markets.

Drawing on classroom practices that integrate NACE competencies into course assignments, the session shows how humanities courses help students critically examine AI while highlighting how humanistic study cultivates durable skills for changing labor markets. The result is a pedagogical approach that helps instructors address student anxiety about employability while showing how the humanities prepare students for an AI shaped future of work.

In this session, we explore how generative AI can become an unexpected catalyst for connection, collaboration, and belonging. While most conversations about belonging in higher education center on students, we shift the focus to educator communities.

Generative AI is reshaping education, and educators are navigating this shift with strikingly mixed emotions. Regardless of where educators stand, many share a common experience: pressure to keep up, emotional ambivalence, and overwhelming urgency. These shared struggles, paradoxically, present an opportunity for educators to unite and collaborate.

The session’s participants will examine why educators’ community and belonging are especially critical during rapid technological change, and how generative AI itself can serve as a catalyst for building communities of practice, turning isolated anxiety into collective resilience. We present four distinct educator community models, each unique in funding source, scale, structure, and goals: a peer-led fellowship model, an institutional ecosystem model, a collaborative community model, and a grassroots cross-campus model. Participants will reflect on what types of communities they currently have, what they would like to create, and why, weighing each model's tradeoffs within their own institutional contexts.

We invite educators to join the session as they are—curious, skeptical, enthusiastic, or uncertain—and leave with both a framework and a community to move forward together.

With the integration of generative AI (GenAI) into education, institutions need research-based ways to use these tools that encourage meaningful learning and critical thinking. This session examines a project involving five public institutions that tested the use of ChatGPT, Claude, Gemini, and Perplexity in online and in-person courses across disciplines. Drawing on scholarship in AI literacy, critical thinking, and reflective practice as lenses for responsible AI integration, the session will outline the project design, including implementation of AI learning experiences and collection of faculty and student feedback to examine challenges and opportunities.

The project combined faculty development and classroom implementation through action research, student partners, and surveys examining AI literacy, usability, and participant experiences. Fifty faculty members were recruited in its initial phase, with approximately 35 implementing course-based experiences in Spring 2026 across disciplines, including STEM, health professions, culinary arts, and business. Presenters will share project insights, patterns observed in faculty-designed learning experiences, and lessons learned related to pedagogy and AI integration.

This session prioritizes AI integration as a pedagogical and curricular goal. Participants will leave with practical strategies, illustrative examples, and an evidence-based framework for critically evaluating AI integration in higher education.

Before a lecture begins or an assignment is due, the syllabus is often the very first teaching impression students receive. It signals what kind of learning space they are entering, whether they belong there, and whether their instructor is supportive and sees them as capable. In the AI era, this document deserves more than a new policy paragraph. Building on a holistic view of the syllabus as a teaching tool and roadmap, the current virtual workshop extends that framework to address AI literacy, transparency, equity, and student belonging. What makes this session especially valuable is the blend of 3 perspectives: From developmental science, the syllabus is seen as a relational tool that can reduce uncertainty, build trust, and invite students into the learning process. From educational science, it becomes a clear, evidence-informed guide for learning goals, assessment, and course design in a time of rapid technological change. From academic leadership, it serves as a practical tool for aligning responsible AI practices across disciplines while staying responsive to the needs of students and faculty. Designed for novice or seasoned instructors, curriculum designers, and academic leaders, this engagingly hands-on workshop turns AI uncertainty into practical action. Grounded in best practices and dialogue across disciplines, participants will leave with a concrete syllabus revision they can apply right away to support student learning, responsible AI use, and human connection.

Even in an era of widespread AI, there are still many situations—including particular entire courses—where it’s beneficial to exclude this technology from the teaching and learning process. That said, teaching without AI has to mean something different from just doing what we used to do; we need to consider the situation students encounter outside the classroom, as well as ways that AI is shaping what they bring.

This will be a practical, discussion-driven workshop for faculty members navigating community building, assignment design, and fostering a commitment to academic integrity in the new educational and technological environment. We’ll cover guidance on AI policies and limitations and biases in detection tools, and provide space to reflect on how AI literacy fits into your teaching goals, even in courses where AI use is discouraged or restricted. The session will focus on effective teaching strategies, and we’ll facilitate activities that guide faculty to examine their current assignments, in-class exercises, and pedagogy in order to design new approaches that meet the moment.

Small liberal arts institutions face a particular challenge in responding to AI: limited resources, close-knit faculty governance, and a mission centered on human relationships and critical thinking. At the University of Puget Sound, a cross-functional AI Working Group spent a year developing an institutional strategy grounded not in technology adoption but in ethical principles derived directly from the university's mission and values. This session presents that process as a model for values-driven AI governance at institutions where community trust and faculty engagement are essential to any durable innovation.

The working group began with a thorough review of AI literature and peer institution policies before developing seven ethical principles (human-centered focus, accountability, transparency, privacy and security, fairness, equal access, and sustainability) through extensive consultation with faculty and staff senates, student government, department chairs, and 521 campus-wide survey respondents. Those principles became the evaluative framework for everything that followed, including a practical governance structure with risk-based criteria for determining which AI uses require formal review.

The session will walk attendees through the key design decisions in this process, the tradeoffs between governance models, and the ethical principles as a transferable tool.

This session showcases an emerging, AI-enabled innovation at Texas Tech University that reimagines journalism as community information infrastructure rather than content production. We present Retriever Technology and NewsHound - a pair of AI tools that enable  both students and local journalists to shift from repetitive reporting tasks to higher-value investigative and community-engaged work. Retriever is a web-scraping and AI-assisted system that automatically collects public data (e.g., local government meetings) and transforms it into structured, publishable information, while NewsHound helps investigative reporters with every phase of an investigative process from coming up with ideas, to refining their hypotheses, to finding sources, to organization and writing, and finally, to fact checking. They both allow students and reporters all over the country to handle intensely complex subjects in significantly less time.

These initiatives equip students with AI literacy, data skills, and ethical decision-making capacities while directly supporting regional news ecosystems. The session situates this innovation within broader national efforts to rebuild local journalism through AI-enabled tools. Participants will explore how institutions can integrate AI into co-curricular and applied learning environments to enhance student success, workforce readiness, and civic impact, especially in resource-constrained regions.

Most conversations about AI in higher education begin with the wrong question. “Should students use AI?” positions faculty as enforcers and frames AI as something that happens to a course. A more useful question is: when does AI use support the learning I care about, and when does it undermine it?

The Pedagogy-First AI Tool is a three-gate decision framework grounded in established learning research. It guides faculty through a structured process for making intentional AI decisions at the assignment level, treating both AI use and non-use as legitimate pedagogical choices.

Gate 1 asks to what level students need to develop independent capability. Gate 2 asks which of five learning moments the assignment represents: Exploration, Practice, Repetition, Feedback, or Assessment. Gate 3 is the hinge: is AI acting as a scaffold, helping students reach work they could not access alone, or as a shortcut, doing the cognitive work they need to do themselves? A scaffold lifts students toward the learning goal. A shortcut replaces the work of getting there.

Piloted at a cross-disciplinary faculty workshop with 40 participants, the tool resonated across skeptics and AI enthusiasts. Its primary value, faculty reported, was in giving them language to explain their AI decisions.

This session introduces the tool, walks through an example, and invites participants to apply it. The goal is an AI decision faculty can explain to students, colleagues, and themselves.

As colleges and universities move from isolated AI experimentation toward broader adoption, many institutional strategies still begin with technology infrastructure, vendor integration, or policy containment. This session introduces a different starting point: a people-first framework for institutional AI coherence.

Drawing on cross-functional leadership spanning faculty development, student AI literacy, curriculum redesign, governance policy, analytics-informed decision making, and pilots, this session outlines five interconnected layers of AI integration: (1) faculty and staff capacity-building, (2) student fluency and ethical formation, (3) policy and governance alignment, (4) operational and data integration, and (5) institutional narrative and culture.

This model begins with human capability and institutional mission, positioning governance and operations as extensions of academic purpose rather than drivers of change. Participants will examine how fragmentation across these layers creates risk, inequity, and inefficiency, and how intentional alignment produces clarity, trust, and sustainable transformation through designing systems that center institutional mission and long-term transformation goals.

Attendees will leave with a practical diagnostic framework to assess their institution’s AI maturity and identify next-step priorities for responsible, mission-aligned adoption.

As generative AI (GenAI) reshapes higher education, its potential to advance student success through co-curricular structures remains underexplored. This session presents findings from a qualitative study with undergraduate medical students, examining self-directed learning (SDL), individual learning plans (ILPs), and GenAI integration. Students identified SDL as essential to success but reported persistent challenges with goal setting, time management, and progress monitoring. ILPs provided critical scaffolding for these skills, while GenAI enhanced efficiency through personalized planning. Simultaneously, students described tensions between independence and needing guidance, alongside concerns about overreliance on GenAI. These findings suggest GenAI’s greatest potential lies not in replacing instruction but in redesigning co-curricular systems to support student learning.

This interactive session positions GenAI as a tool for supporting student success. Participants will explore how GenAI can be intentionally embedded in co-curricular practices—such as advising and scaffolding—to promote metacognition and lifelong learning. Through collaborative design, attendees will leave with adaptable strategies for leveraging GenAI to strengthen student agency while preserving deep learning and academic integrity. Knowing how to leverage GenAI while supporting student learning is a valuable skill across fields and job roles in higher education.

This session showcases a rapid, low-cost model for deploying AI-enabled student success tools—designed and built entirely by institutional practitioners without formal coding expertise. Grounded in a “no-code, faculty-driven innovation” framework, the session highlights three early-stage solutions: a student-facing degree tracker that enhances transparency and self-advising; a faculty-authored degree audit logic system that aligns curriculum, policy, and progression requirements; and a transfer credit evaluation tool that streamlines articulation and reduces administrative friction.

Collectively, these tools demonstrate how generative AI and no-code platforms can decentralize innovation capacity, enabling faculty and staff to directly translate academic policy into functional, student-centered systems. The approach reduces reliance on traditional IT development pipelines while accelerating time-to-implementation and improving responsiveness to student needs.

Attendees will gain insight into the design process, governance considerations, and practical constraints of building AI-enabled tools in resource-conscious environments. The session invites discussion on scalability, data integrity, and the evolving role of faculty as co-creators of digital infrastructure that advances learning, persistence, and institutional effectiveness.

During the spring 2025 semester, my 300-level special topics Media Studies course engaged in a service learning project designed to allow them to interrogate generative AI, experiment with GenAI tools, and ultimately discern which tools, if any, were appropriate to integrate into their production workflows. The class itself tackled the topic of artificial intelligence from a number of different angles: we examined 1) the history of AI, its parameters, and implications, 2) the nature of human creativity and theories surrounding its technological augmentation, 3) contemporary uses of GenAI and the ethical issues at stake; and finally 4) we integrated AI tools into creative workflows.

These decisions about AI usage were concrete; the class partnered with a local nonprofit to create videos for K-5 students about safety and etiquette on bike trails. Students decided which tools to use for which steps of the production process, while also critically examining the ontology of those tools and the broader implications of their use.

In this presentation, I will discuss course design, key assignments I used to achieve learning outcomes, and the results of the project the students worked on in this second half of the semester. Ultimately, I argue, critical media literacy will depend on students’ ability to understand these tools and their contexts, experiment in a supportive environment, and most importantly develop a personalized process of discernment.

As AI tools become more embedded in higher education, faculty face a growing challenge: students can generate answers quickly but often lack the ability to evaluate their correctness or explain their reasoning. This workshop introduces the TAKE AI.M Learning Approach. It is a teaching model, which reframes AI as a tool for comparison and reflection rather than a replacement for thinking.

The model structures learning through five phases:

- Take a Position (students commit to an initial solution),

- AI Assistance (students compare their work with AI-generated outputs),

- Knowledge Gained (students reconcile differences and identify new understanding),

- Engineering Judgment (students evaluate correctness and physical or conceptual validity), and

- AI.M Reflection (students apply a focused reflective lens such as misconceptions, modeling, method, etc.).

Participants will experience the model through a hands-on activity adapted from a STEM perspective. The session will then offer a guide for participants to configure their own assignments using the TAKE AI.M framework.

The workshop emphasizes practical implementation, assessment strategies, and methods for fostering student accountability and critical thinking in AI-assisted learning environments.

As AI becomes embedded in higher education, instructors in statistics and data literacy face a new challenge: students increasingly rely on AI for answers, often bypassing the conceptual understanding needed for sound quantitative reasoning. This session introduces an innovative solution using generative AI not as the answer generator but as the learner. In a graduate statistics course, AI was configured to behave like a novice undergraduate student: asking clarifying questions, struggling with assumptions, misinterpreting SPSS output, and demonstrating common misconceptions. Graduate students were tasked with teaching AI through simulated dialogue, which required the graduate students to explain concepts clearly, justify analytic decisions, and diagnose misunderstandings in real time.

This session expands the model to broader statistics education. Participants will explore how AI‑as‑student simulations can deepen content mastery, reduce math/statistics anxiety, improve communication skills, and prepare learners for future teaching, consulting, or interdisciplinary collaboration. The roundtable highlights how AI can shift from a shortcut to a pedagogical partner, enriching both teaching and learning in quantitative courses across disciplines

This session will speak about Babson College’s work developing Roger: The Entrepreneurial Thinking and Learning Agent, a centralized, institutionally governed AI Platform designed to expand access, support learning, and embed Babson’s entrepreneurial thinking.

This strategic initiative was driven by the desire to provide students with equitable access to AI tools across the campus community, while also providing a safe, Babson-specific environment. Roger enables the Babson community to access multiple AI models within a single platform without relying on fragmented tools.

Furthermore, agents developed within the platform are designed to support student learning. They guide students through discovery questions, iteration, and reflection, ultimately helping students learn and understand AI, not just generate outputs.

In this session, the Babson team will share how the initial pilot with over 300 users revealed usage patterns and informed overall platform design and deployment.

As AI reshapes how work gets done, many institutions are wrestling with a familiar tension: employers are calling for new and different kinds of skills but translating those signals into curriculum and student preparation is often unclear and unwieldy. This session is designed as a informal discussion, inviting participants to pause and examine where their own institutions stand. We will use a set of guiding questions to explore how employer voice is, or can, inform AI‑related skill development, what has worked in practice, and where challenges persist. Participants will consider what it looks like to move from one‑time employer input to more sustained dialogue that can evolve alongside changing workforce expectations.

As generative AI reshapes teaching, assessment, and disciplinary identity, many faculty appear to be resisting change, but they are actually grieving change. The perceived loss of long-standing pedagogical norms, academic integrity conventions, and professional authority has triggered responses that mirror well-established psychological stages of grief: denial, anger, bargaining, disengagement, and, in some cases, gradual acceptance.

This roundtable reframes faculty resistance to AI not as a deficit of innovation or willingness, but as a predictable, human response to rapid identity-level disruption. Participants will explore how these stages manifest in practice: faculty attempts to AI-proof courses, retreat to analog assessments, and withdrawal from institutional AI initiatives. These behaviors are often rooted in cognitive overload, time scarcity, and the absence of structured support, not simple opposition to technology.

Discussion will center on practical, humane approaches to supporting faculty through this transition, including how to reduce cognitive burden in professional development, create psychologically safe spaces for experimentation, and align institutional expectations with realistic faculty capacity. Participants will leave with a diagnostic framework for recognizing grief-stage responses and a set of actionable strategies that can be implemented at the department, college, or institutional level.

How does a university move from reactive AI use to a sustained, institution-wide culture of responsible AI integration? Presenters representing administrative and faculty leadership from Boise State University will share a multi-year arc of strategic AI transformation. The presentation will illustrate both the vertical structures that create coherence in AI guidelines and the horizontal, grassroots approaches that generate genuine faculty buy-in. We will discuss four major initiatives, namely the formation of the AI Coordinating Committee (AICC), the launch of a department-embedded AI Liaisons network, two iterations of an AI Institute for Teaching & Learning, and an upcoming campus-wide AI Conversations event, alongside efficacy research findings and the development of a series of white papers on major institutional questions related to AI that support departmental decision-making and policy development. Presenters will map how each initiative addressed a different layer of the institutional challenge: governance and accountability (AICC), distributed peer capacity (Liaisons), deep faculty development grounded in AI Frameworks for teaching (Institute), and broad, values-centered dialogue (AI Conversations). Participants will use a facilitated network-mapping activity to identify their own institution's strengths, gaps, and potential next steps in AI transformation, leaving with a visual model they can adapt and use.

AI is quickly taking on an infrastructural role in higher education, and is now embedded in procurement, advising, assessment, and student services. In the absence of robust federal or state AI regulation, public universities act as powerful de facto brokers between AI vendors and democratic publics. Yet the people most affected by emerging technological systems on campus have almost no voice in how they are chosen, implemented, or governed. Outside of temporary advisory groups, students have few ways to contribute to discussions about these transformations.

This session presents a model Student Technology Council (STC) as one institutional response: a formal student-led governance body with a standing mandate to engage university administration on matters of AI procurement, data governance, and technology policy. Drawing on a multi-year research, participatory design, and impact project at the University of Virginia, this session outlines the theoretical and legal case for student co-governance, presents the structure of the council designed by students for UVA, and discusses the possibilities for replication in other institutional contexts. The bulk of the session is structured discussion in which participants work through the institutional questions the STC raises for their own campuses — where it would sit, what objections it might face, and how the argument for student co-governance reshapes the technology decisions they are already making.

This session challenges the assumption that educational technology is built by technologists. Through live demonstrations, participants will see tools created at Bridgewater State University by faculty and staff, not developers, using vibe coding, a practice of building functional software through natural language prompting with AI. No prior coding knowledge required, only pedagogical expertise.

The tools on display were built to address real gaps: a UDL-aligned instructional design tool that generates syllabi, rubrics, and assignments; an ADA Title II-compliant onboarding portal built for accessibility from the ground up; and a converter that makes PDFs accessible to all learners. Each reflects what happens when the person who understands UDL, culturally responsive teaching, and learner variability is also the person who builds the solution.

As institutions race to meet the DOJ's ADA Title II requirements, and as faculty continue to cite time, resources, and institutional support as barriers to UDL adoption, vibe coding offers a new form of educator agency. The gap between knowing what learners need and having the tools to meet those needs has fundamentally shifted.

Participants will leave with a framework for identifying gaps in their own ecosystems, and the confidence that they already have what it takes to begin.

Reflection is a key component of experiential learning activities such as undergraduate research. Critical reflection can be difficult for students who are not trained to do so or who consider a lab notebook to be reflection. Additionally, students need to know the competencies employers are seeking to translate this high-impact practice into language for future success. Finally, scalable reflection and individualized coaching are necessary with increasing demands on faculty and staff to include more students in their activities.

The Purdue Office of Undergraduate Research developed a chatbot to encourage students to share more about their activities using a combination of elaboration and critical reflection questions. After students share their experience, the chatbot connects them to career readiness and university-developed competencies. The result provides suggestions on how to develop competencies not described, and allows students to elaborate if they can demonstrate those competencies. From the interactions, the chatbot provides points that can be included in personal marketing documents about the student’s research experiences. This approach supports equitable access to structured reflection by providing consistent, on-demand guidance that complements mentoring across student experiences.

We will share the chatbot, our considerations while preparing it for campus-wide deployment, and how to complement this tool with conversations with mentors and advisors.

This session presents an experimental approach to open educational resource (OER) development that applies AI-assisted prototyping to produce a research support book for graduate students in the life sciences. The project conceptualizes AI interaction as an iterative process with distinct phases of prototype fidelity and resolution, rather than treating generative AI as a text generator.

The experiment used Claude Code, an agentic AI tool, in a command-line environment, enabling direct file manipulation, package installation, and programmatic iteration. Quire, an open-source publishing tool, was selected for multi-format output.

The experiment unfolded in two sequential phases. The low-fidelity, low-resolution phase validated core platform functionality using template content, establishing technical stability before content development. The high-fidelity, high-resolution phase generated and refined close to one hundred pages covering key stages of the graduate research lifecycle.

Outputs were evaluated across three dimensions: prompt adherence, content quality and accuracy, and visual clarity and accessibility. Two librarians are independently conducting qualitative review, while an institutional plagiarism checker and computational tools examine linguistic patterns across chapters and word segments.

This session invites attendees to consider AI not as a shortcut but as a prototyping partner, offering a transferable framework for responsible OER production.

Sim Studios transforms professional readiness education by using AI to address a persistent challenge: giving every student authentic, high-stakes meeting practice when traditional role-play allows only one participant at a time. Drawing on 30 years of entertainment industry experience, I developed simulations based on real professional contexts—not generic AI role-play. Each bot represents a distinct executive profile with communication styles and priorities validated by industry professionals.

Students upload their creative work, select meeting types and executive profiles, and conduct research—building agency and investment. They choose between Guided Mode (with coaching support) or Seamless Mode (fully authentic, uninterrupted experience), meeting learners at different readiness levels.

After each meeting, students receive targeted feedback on professional competencies such as strategic thinking, relationship-building, and career positioning, along with executive profile reveals and reflection prompts.

Originally designed for screenwriters, Sim Studios now supports multiple creative fields and extends to any discipline requiring professional communication, from business to healthcare to education. This session presents a faculty-driven model that other institutions can adapt.

Higher education increasingly expects students to conduct interviews—with community partners, professionals, and stakeholders—yet most students receive little opportunity for realistic, low-risk practice or meaningful feedback. Role-plays often feel artificial, and access to real stakeholders is uneven and inequitable.

This session introduces an AI-mediated stakeholder interview simulation designed to help students develop ethical, dialogic interviewing skills through iterative practice, immediate feedback, and structured reflection. Participants will experience short excerpts of the simulation, examine AI-generated feedback on questioning, probing, listening, and ethical stance-taking, and explore how these tools can strengthen teaching and assessment without replacing human judgment.

Drawing from pilot implementations, the session highlights how responsible AI design—grounded personas, transparent feedback criteria, and human oversight—can support authentic learning while addressing common concerns about automation, bias, and academic integrity. Participants will leave with concrete integration pathways and collaboration opportunities and a clear sense of where AI adds value and where human teaching remains essential.

Current AI detection tools are mostly unreliable in real-world academic environments and likely to be less effective as generative AI evolves. Given such a reality, this session starts with full acceptance of the proposition: assume you can’t detect when students have used generative AI. If we embrace this as a fundamental reality of our current situation, what practices and policies must be reformed or even abandoned? What strategies, frameworks, and attitudes might be adopted, revived, or re-emphasized to best ensure equitable and engaged student learning in this new era?

After briefly expanding on this thought experiment, presenters will lead participants through a progressive discussion to reflect on the logical consequences of such a reality and what actions might continue to center effective student learning and development in the age of AI. The presenters – an Executive Director of a faculty development center and leader in a university’s AI policy committee, a faculty development manager, and an instructional technologist enaged in AI research – offer varied perspectives for action, and the discussion will be as good for instructors as it is for administrators, instructional support staff, and those in IT.

As institutions move from AI exploration to implementation, many struggle to translate AI literacy into sustained, discipline-specific faculty practice. This panel introduces a scalable, role-based model for AI adoption centered on faculty champions.

The panel includes a director of faculty engagement and three faculty members representing different academic disciplines and schools, each serving as a champion in their respective areas of focus. The session is moderated by the director of a future learning lab, who provides an innovation-focused framework for understanding how these roles function together. Panelists will examine how coordinated leadership across these areas enables responsible, innovative AI use while addressing challenges such as uneven faculty engagement, academic integrity, and alignment of AI efforts with the institutional mission.

Drawing on implementation experience, panelists will highlight faculty-centered initiatives such as AI hackathons, course refresh institutes, and just-in-time training models that support timely, practical application. Through facilitated dialogue, panelists will explore areas of alignment and tension in AI implementation, including questions of ownership, scalability, and sustainability. Participants will gain a practical framework for organizing faculty-facing AI initiatives and fostering cross-functional collaboration within their own institutions.

As artificial intelligence reshapes higher education, institutions are grappling not only with tools and policies but with how to build shared understanding across roles, disciplines, and levels of experience. Research on social constructivism and faculty development shows that adoption of new practices is driven less by information alone and more by dialogue, peer sensemaking, and visible norms within a community.

This interactive session presents a replicable model for institution-wide AI sensemaking, grounded in a four-part panel series designed to engage faculty, staff, and industry partners in conversations about ethics, curriculum, assessment, and the future of work. Participants will examine how structured dialogue, real-time polling, and cross-functional perspectives can support more coherent and responsible AI integration.

Participants will engage in a live, Mentimeter-style activity to surface their own institutional perspectives on AI, respond to prompts about responsibility, risk, and practice, and analyze emerging patterns in small groups. Through guided reflection, they will connect these insights to their local contexts and identify opportunities to foster more inclusive, transparent, and actionable AI conversations on their campuses.

Attendees will leave with a practical framework for designing and facilitating AI dialogue that moves beyond awareness toward shared understanding and coordinated action.

Artifical intelligence has intensified concerns about academic integrity, prompting many instituions to focus on detection and restriction. This session reframes the conversation by centering on pedagogy and purpose. Drawing on classroom practice and insights from Who's Cheating: How to stop your students from cheating with AI (Metzler & Dickinson, 2025), participants will explore how AI challenges traditional assumptions about assessment and what it means to demonstrate learning.

At the core of this session is a foundational question, Why do we teach what we teach and how do our assessments reflect that purpose? While most educators are subject-matter experts, fewer have had formal preparation in course design and assessment. In an AI enabled environment, this distinction becomes critical. When the purpose of learning is unclear or misaligned, assessment becomes vulnerable to superficial completion, whether by students or by AI.

Rather than asking how to prevent misuse, this session will asks: What kind of learning experiences make misuse irrevalent? Participants will examine common assessment practices, identify where AI can replicate tasks without meaningful learning, and engage in a guided redesign process.

Through facilitated activities and peer conversation, participants will leave with a practical framework for aligning prupose, pedagogy and assessment in teaching and learning.

AI is rapidly entering higher education through configurable tools that emphasize speed and completion. Yet these defaults often conflict with the aims of liberal education, where learning depends on sustained reasoning and intellectual accountability. This session argues that the issue is not whether to adopt AI, but how institutions design the conditions under which students think with it.

Drawing on a multi-semester implementation in First-Year Writing, this presentation examines structured, AI-mediated modules designed to make student reasoning visible. In an initial pilot, AI-assisted exercises improved skills such as paraphrasing but disrupted higher-order writing, as open-ended feedback led to cognitive overload and deference to AI-generated language. A redesign introduced staged prompts, limited feedback, and requirements that students articulate and revise their reasoning before progressing. This shift led to stronger engagement with evidence, improved claim development, and more consistent transfer to essays.

These findings suggest that AI’s educational impact is not inherent to the technology, but emerges from interaction design. However, institutional support models often prioritize configurable tools rather than structured systems, creating a gap between what AI platforms enable and what learning requires. The session outlines design principles and invites participants to consider how colleges can move from tool adoption to pedagogy-driven implementation.

LLM-based tools to support learning have proliferated across higher education learning environments, producing large text-based datasets of interactions between learners and systems. Educators need reliable methods to make meaning of these datasets and understand how students experience these tools. This session presents a systematic approach to addressing that challenge.

We describe our methodology for analyzing a large dataset of text-based prompts and AI agent responses from health professions students interacting with a multi-agent AI platform. The platform allows students to explore a clinical case study with multiple agentic AI collaborators, each representing a different professional role, in preparation for a face-to-face interprofessional experience. The resulting dataset captures each student's engagement using the system. The challenge we sought to solve was how to analyze this dataset at scale, identify trends across learners, and examine each individual's learning over time.

We employed two parallel approaches: 1) AI-assisted analysis within qualitative data analysis software, and 2) the use of a large language model as a structured, dialogic thinking partner in analysis. Both were guided by a shared epistemological framework, audit trail, and transparency protocols. Comparing the two revealed distinct strengths, limitations, and practical realities, which we share alongside preliminary findings and tools that educators can adapt to their own contexts.

Tilly Norwood, production company Particle6’s “AI Actress” was released to the world in July 2025 to a great deal of criticism but little proof of concept. Decried as a ribald effort to replace human actors with AI generations, the team behind Norwood and even the concept of Norwood itself suffered a barrage of attacks and a vicious turn through the news cycle. Months later, a music video featuring Norwood and its song Take the Lead was released on YouTube to a similar response, with commentators noting the cringeworthy lyrics full of Silicon Valley terminology and the visible misalignment of the vocals and Norwood’s mouth.

However, this criticism is unfounded and results in a clear misunderstanding of what Norwood is and represents. Viewed through the lens of Hollywood, Norwood is an embarrassing and  mediocre simulacrum of human talent. But viewed through the lens of Silicon Valley, where the Least Viable Product is part of the iterative design process and Done is Better than Perfect is a way of life, Norwood represents the future of Hollywood whether Tinseltown likes it or not.

This session, entitled Canaries, Coal Mines, and Tilly Norwood will detail why both faculty of business and film are well-served to teach their students that the condemnation of Tilly Norwood and its current flaws miss the point of the product and that those in the entertainment business dismiss Norwood and its future colleagues at their own peril.

As AI transforms higher education, institutions need coordinated strategies that go beyond individual course changes or one-off workshops. This session presents Collaboratively Harnessing AI in Nursing (CHAIN), a community-of-practice model developed at Vanderbilt University School of Nursing to build Artificial intelligence (AI) competency across students, faculty, staff, and postdoctoral scholars.

CHAIN was designed around the concept of “team lifting,” a practice rooted in healthcare where no one person bears the full weight alone. Applied to AI adoption, this suggests the importance of developing institutional support structures that enable collective learning and growth rather than isolated individual efforts.

The session will cover three areas. First, we introduce the “team lifting” concept and CHAIN's mission, vision, and development. Second, we describe the practical collaborative elements that drive CHAIN, including an advisory council, a Brightspace learning hub, partnerships with campus IT, and governance strategies. Third, we share outcomes from AI coaching activities, literacy quizzes, surveys, video content, and in-person and virtual engagement sessions, highlighting what questions and needs emerged from participants across roles.

We will then open a facilitated discussion inviting participants to share their own strategies for building AI communities of practice, enhancing support structures, and navigating change management in higher education.

When we invite students to use generative AI as a thinking partner, we rarely ask what the AI brought to their thinking; that is, not just the content it generated but what it assumed or imported. The postplagiarism framework (Eaton, 2023) argues that human-AI hybrid writing does not absolve humans of responsibility, but responsibility requires seeing the AI's epistemic contributions, including its biases, cultural defaults, and conceptual limits. When students build on AI output without examining it, those limits get absorbed into learning itself.

In this session, we introduce epistemic auditing--deliberate pedagogical moments where students interrogate what the AI contributed to their thinking. We ground this in two classroom examples. In one, students used generative AI for intersectional identity mapping and discovered the AI could not hold two identity categories simultaneously, replicating the analytical failure Crenshaw (1989) critiques. In another, students ran overwritten cover letter drafts through multiple AI agents and surfaced how each encoded different assumptions about professional voice and credible self-presentation.

In this session, participants will experience a condensed version of the identity-mapping activity, using their own identity dimensions or provided character profiles, then collaboratively examine what the AI surfaced, missed, or flattened to build firsthand understanding of why epistemic auditing matters and how to design for it.

As AI tools become embedded in higher education research workflows, faculty face a defining professional question: not whether to use AI, but how to use it responsibly—and what it means for their role as scholars. This session presents early-stage findings from an exploratory qualitative study examining how higher education faculty perceive an AI-assisted qualitative data analysis tool (Intellectus 2.0) before and after hands-on use.

Using surveys (n=23) and in-depth interviews (n=5) with faculty across roles—professors, program directors, dissertation chairs, deans, and lecturers—the study reveals how faculty AI literacy develops through experiential engagement, how perceptions of AI shift after direct use, and what ethical concerns emerge from the experience. Key findings include the critical importance of foundational methodological knowledge for responsible AI use, the positioning of AI as a complementary but imperfect assistant, and faculty-raised concerns about disclosure and doctoral student dependency on AI outputs.

This session translates findings into implications for how the professoriate is evolving alongside AI: what new competencies faculty need, how institutions must rethink faculty development and scholarly identity, and what responsible AI adoption looks like when faculty are both the adopters and the gatekeepers of research integrity.

Generative artificial intelligence (AI) is becoming increasingly prominent in the higher education, but deciding what skills to teach in the classroom is difficult because of the rapid advancements in this emerging technology. However, despite all the advancements in recent years, a key aspect of these systems remains unchanged: leading AI models use a Python sandbox to execute tasks, from analyzing data in Microsoft Excel to building documents in Microsoft Word. This presentation discusses my experience teaching generative AI to undergraduate students. To build durable skills in the domain of AI, I focus on teaching Python to verify the actions of AI systems after the prompt has been submitted. By teaching students how to skim code written and executed by AI, the resulting output becomes verifiable. I teach students the basics Python packages like python-docx, python-pptx, pandas, and Matplotlib so they understand what is happening inside the AI's sandbox environment whenever uploading a Microsoft Office file to the chat. To test this comprehension, assignments require students to look at the code generated by the AI to identify specific variables or figure out why a graph looked wrong. This approach shows that students do not need to be developers to use AI tools effectively. By focusing on the underlying Python logic rather than just prompt wording, educators can give students a permanent skill that will last longer than the current version of any specific AI model.

As researchers who have led federally-funded initiatives at the intersection of storytelling, student empowerment, and leadership, we have worked with more than 10,000 students and educator across 47 states and 60 cities across America. Project Belonging is a peer-to-peer leadership development program for K-12 and college students across America. First established with an Innovation Grant awarded by the U.S. Department of Homeland Security’s Center for Prevention, Programs, and Partnerships, each student team received a $1,000 stipend to apply the knowledge they gained through our interactive online training to design projects that foster empathy, connection, and belonging in their community.

Within the Project Belonging Training, one of the curricular modules focuses on the responsible navigation of the use of Artificial Intelligence as part of a broader approach to interacting with digital architectures (i.e., social media platforms, smartphone applications). This session will highlight the key principles, practices, and pedagogies underscored in the Project Belonging module to illustrate effective vocabularies and activities to engage students when examining the role of Artificial Intelligence within education. The session will enable participants to understand the design, conceptualization, and implementation of the module in order to take lessons back to their home institutions as they build educational offerings for students in relation to Artificial Intelligence.

AI education has centered on faculty teaching and research, often overlooking how it is transforming the daily work of supervisors and staff who sustain institutional operations. This session highlights an AI‑enabled professional development pathway designed specifically for Facilities supervisors, positioning workforce readiness as a critical dimension of the future of work in higher education. Developed through human resources, instructional design, and information technology cross‑functional collaboration, the initiative embeds AI into real supervisory workflows, including performance and hiring conversations, managing difficult conversations, and routine administrative tasks. The pathway reinforces responsible use, enterprise guardrails, and the importance of human judgment while emphasizing hands‑on, scenario‑based learning that meets supervisors where they are, particularly those with low technology confidence or limited prior exposure to AI tools. Presenters will share the program’s design, learning structure, partnerships, and evaluation findings, including statistically significant gains in participant confidence and understanding of AI’s limitations, risks, and appropriate use cases. Rather than positioning AI as a replacement for expertise, the project frames AI as a support tool within complex, human‑centered supervisory work. Participants will be invited to consider how similar role‑specific, workflow‑embedded approaches could be adapted for other populations.