Teaching and Learning with AI Conference

This session will discuss and demonstrate my development of custom AI Agents and Content Libraries using different platforms, such as Gemini and NotebookLM. I have created Agents (Gems) that assist with syllabi content, guided learning, practice and feedback, and ethical use of AI. I have also created AI Content Libraries that include infographics, podcasts, and videos and reels posted on social media, catering to different learning styles and preferences. This session will help you understand how these platforms increase engagement and knowledge by promoting students' autonomy, content learning and precision, and ethics. #Custom AI Agents1 #AI-Enhanced Content Libraries#Ethical AI Use in Education

AI has quietly rewritten the rules of research, yet many research methods courses still operate as if it hasn’t. In this session, we share the redesign of an interdisciplinary research methods course that proposes to teach with and about AI by design, not by accident. Moving beyond AI policing or unstructured use, we introduce a practical pedagogical framework that scaffolds student thinking, makes learning visible, and integrates critical AI literacy into core research practices. Using an online research methods course redesign as a case study, we'll explore how to structure AI as a "thinking partner," "practice partner," and "support partner." These are roles that scaffold learning rather than replace it. We'll share specific strategies for designing AI-transparent assessments that make student thinking visible and demonstrate authentic research competency. Leave with actionable approaches for redesigning your own courses to build both disciplinary skills and critical AI literacy simultaneously.

This session examines the development of a student-centered AI Teaching and Learning Lab at a four-year urban university. Participants will learn the rationale for the lab’s creation, the peer-tutor staffing model, and the PEER framework (Permission, Evaluate, Engage, Reflect) used to train student tutors. Drawing on reflections from the lab’s Spring 2026 launch, the session highlights key challenges and successes. Attendees will leave with adaptable materials and practical strategies for building a student-focused AI lab and integrating AI frameworks across classroom contexts.

As artificial intelligence reshapes education, instructors face the critical challenge of transforming existing curricula into AI-native learning experiences. This session explores practical strategies for adapting traditional course materials to BoodleBox's collaborative AI platform, where students and educators engage with AI as an integrated learning partner. Participants will learn how to redesign assignments that leverage AI tools—including AI-assisted research, iterative drafting with AI feedback, and collaborative problem-solving—while preserving academic rigor and learning outcomes. We examine frameworks for scaffolding AI literacy, building assessments that evaluate critical thinking alongside AI fluency, and fostering authentic student engagement in an AI-enabled environment. Practical templates and examples included.

Grab your lunch and join FALCON members for a relaxed conversation on AI in higher education, followed by a short presentation on what's ahead for 2026-2027. Learn about FALCON and connect with colleagues from across the state. All are welcome.

As artificial intelligence transforms higher education, community colleges face the dual challenge of innovation and inclusion. This interactive forum explores how Middlesex Community College (Massachusetts) developed a comprehensive plan for AI integration across teaching, learning, and operations. Assistant Dean for AI Integration Peter Shea will share lessons learned from his first year leading institutional efforts—balancing experimentation with governance, aligning AI literacy across departments, and building faculty trust through training and ethical frameworks. Participants will discuss common barriers, exchange institutional strategies, and co-develop actionable approaches to managing AI adoption within their own colleges. The forum emphasizes practical leadership models, cross-campus collaboration, and strategies for fostering a culture of responsible innovation. (#AI Integration, #change management)

While Generative AI is transforming the workforce, Humanities students often lack technical portfolios to prove relevance in a tech-centric economy. This session presents a tri-divisional model—Fredrickson Family Innovation Lab, Fairfield Meditz Career and Professional Development, and University Advancement—using “Vibe Coding” (natural language programming) to expand career pathways. Career Development translates workforce demands into training competencies, and students learn to manage AI agents to build functional prototypes without traditional coding, thus becoming AI-literate innovators. The training concludes with a vibe coding competition with alumni as mentors, judges, and speakers, strengthening advancement through innovation-focused engagement.

In this presentation, two academic librarians and a student worker will share concrete ideas for introducing hesitant undergraduates to AI tools and building their confidence in ethical and appropriate use of them. Designed for a neurodiverse population, our approach emphasizes explicit, step-by-step instruction and hands-on practice to reduce anxiety and build confidence with emerging technologies. We will review workshop ideas and teaching strategies we have found beneficial for our population and also provide practical suggestions for incorporating AI into assignments. While developed for neurodiverse learners, these methods benefit all college students and are easily adaptable. Attendees will leave with ideas to incorporate at their own institutions.

In 2026, John set out to find the most challenging AI assessment scenarios in higher education and approach them with the latest research on how students learn to determine where AI can enhance today's assessments and where it harms or falls short of traditional assessment.
What resulted was a fascinating look at the state of AI assessment today, and what faculty should know if they are considering or already using AI assessment in their classroom. Topics include:

• The four kinds of AI Assessment and principles for each.
• Breaking down assessments to avoid attention failure.
• Building AI-appropriate rubrics.
• Setting the appropriate "friction" level for students.
• Using the right evidence in the design and continuous improvement of your assessments.

The goal of this session is to share widely applicable principles regardless of what tools one is using in their classroom.