Gladly We Learn, Teach, and Research with AI
Gladly We Learn, Teach, and Research with AI is an eight-week professional development series designed to help Illinois State University instructors thoughtfully navigate the opportunities and challenges that artificial intelligence presents for teaching and research—whether they are enthusiastic adopters, cautious skeptics, or simply curious. The focus of this series is to equip participants with actionable techniques they can apply with hands-on practice.
Participants can choose between meeting virtually on Microsoft Teams each Monday or in person on Wednesday mornings during the four weeks before and four weeks after spring break. Both sessions each week will cover the same content. Light refreshments will be provided for the in-person sessions.
You may attend each week or just the weeks that interest you. Participants who attend all eight weeks will receive a certificate of completion. Registration is only necessary for the virtual sessions, which you can do up to the start time of the session. Grounded in the Framework for Inclusive Teaching Excellence, the series supports instructors in making informed, values aligned decisions about using (or choosing not to use) AI in ways that sustain equitable, human-centered learning experiences.
This series is presented as a partnership between the Center for Integrated Professional Development and the Adaptive Edge Institute.
Intended Audience: Tenure-Track Faculty, Non-Tenure Track Faculty and Course Instructors, Graduate Students, Staff (AP and Civil Service)
Session 1
Human-Centered AI for Inclusive Teaching
Register for virtual session on Monday, February 9
AI is rapidly transforming higher education, but students still need inclusive, relational learning spaces where their voices and identities matter. This session explores how faculty can integrate AI tools without compromising the human dimensions of teaching—empathy, trust, belonging, and shared meaning-making. Participants will examine practical strategies for balancing automation with interpersonal connection, including approaches to communicating expectations around AI use, designing relational check-ins, and keeping students’ lived experiences at the center.
Participants will leave with:
- Strategies for cultivating community while using AI tools
- Ways to discuss AI use with students transparently and equitably
- Templates for inclusive “AI use norms” that support belonging
Learning Outcome:
Participants will be able to apply strategies that preserve belonging, trust, and student agency when integrating AI into inclusive learning environments.
Session 2
Designing AI-Resilient Learning Activities
Register for virtual session on Monday, February 16
This hands-on session focuses on building assignments that remain meaningful in the age of generative AI. We examine design approaches that encourage cognitive complexity, transparency, iteration, and human judgment—even when students may have access to AI writing, coding, and problem-solving tools.
Participants will leave with:
- A design checklist for AI-resilient assignments
- Examples of assignments that use AI strategically
- Guidance for updating course policies and syllabi
Learning Outcome:
Participants will be able to design learning activities that promote authentic cognitive engagement and remain instructionally meaningful in AI-rich contexts.
Session 3
AI and Cognitive Load: Supporting Student Thinking
AI tools can support students by clarifying complex information, modeling thinking processes, and breaking down tasks—but they can also overwhelm learners. This session examines AI through the lens of cognitive science: working memory, attention, metacognition, and deep vs. shallow processing.
Participants will leave with:
- Techniques for using AI to scaffold complex tasks
- Strategies to support metacognitive reflection when students use AI
- Approaches that prevent cognitive overload in AI-mediated activities
Learning Outcome:
Participants will be able to use AI tools to scaffold student learning in ways that reduce cognitive overload and strengthen metacognitive awareness.
Session 4
Ethical Decision-Making with AI
AI raises new ethical questions—from privacy and academic integrity to bias, transparency, and authorship. This session uses realistic case studies to help instructors navigate ethical dilemmas when students or faculty integrate AI into coursework and scholarly practice.
Participants will leave with:
- Ethical decision making frameworks applicable across disciplines
- Case based practice analyzing real AI dilemmas
- Language for discussing ethical AI use with students and colleagues
Learning Outcome:
Participants will be able to evaluate AI-related teaching scenarios using ethical frameworks that support equitable, transparent, and responsible practice.
Session 5
AI as a Research Partner
AI raises new ethical questions—from privacy and academic integrity to bias, transparency, and authorship. This session uses realistic case studies to help instructors navigate ethical dilemmas when students or faculty integrate AI into coursework and scholarly practice.
Participants will leave with:
- Best practices for incorporating AI into research workflows
- A list of discipline specific risks and safeguards
- Examples of transparent AI-use statements for IRB, journals, and grants
Learning Outcome:
Participants will be able to integrate AI into research workflows in ways that enhance efficiency while maintaining methodological rigor and ethical standards.
Session 6
Data-Informed Reflection with AI
AI tools can help instructors reflect more efficiently by summarizing patterns in student work, analyzing discussion themes, and supporting iterative course improvement. This session examines how AI-supported reflection can help instructors identify equity gaps and improve teaching.
Participants will leave with:
- Techniques for AI-assisted course reflection and improvement
- Methods for identifying patterns in student learning using AI tools
- Guidance on ethical reflection practices that respect student privacy
Learning Outcome:
Participants will be able to interpret AI-supported insights about student learning to inform reflective teaching and iterative course improvement.
Session 7
Collaborative Knowledge-Building with AI
This session explores how generative AI can support teamwork by helping groups brainstorm, structure ideas, visualize problems, and revise drafts together while keeping human creativity and accountability at the forefront.
Participants will leave with:
- Models for group work where AI supports, but does not dominate, collaboration
- Strategies to ensure equitable participation in AI-supported teamwork
- Guidelines for assessing collaborative work in an AI-rich environment
Learning Outcome:
Participants will be able to design collaborative activities where AI enhances group ideation, structure, and revision while preserving human creativity and accountability.
Session 8
AI Futures in Higher Education
This capstone session looks ahead to the evolving landscape of AI in higher education and what it means for inclusive, human-centered teaching. Participants will engage in scenario planning and values-based design to prepare their teaching for an uncertain digital future.
Participants will leave with:
- Ideas for a teaching statement that meets the moment we’re in
- Scenario planning tools for future course design
- A personalized roadmap for continuing AI-related professional development
Learning Outcome:
Participants will be able to formulate a forward looking, values based plan for sustaining inclusive, human centered teaching as AI capabilities evolve.