Emerging Technologies Faculty Fellows: Capstone Conversations on generative AI from the 2024-2025 cohort

We asked our Fellows, How have your views on teaching with generative AI changed since starting the Faculty Fellows exploration? Their responses touched on the following themes:

Experiment and Adapt: The Fellows describe trial-and-error approaches as they explore how generative AI integrates with their teaching. Their views and practices are evolving as they uncover strengths and limitations of the tools.

Balance Opportunities and Risks: The Fellows recognize the transformative potential of generative AI in enhancing productivity and creativity, but they are equally concerned about students bypassing foundational skills. This tension creates ongoing challenges.

Promote Generative AI Literacy: The Fellows realize they have a role and responsibility in preparing students to critically and ethically engage with generative AI. This includes developing generative AI literacy, understanding its implications for academic integrity, and fostering thoughtful decision-making in academic contexts.

Embrace a Collaborative Approach to Generative AI: The Fellows emphasize partnering with their students to explore boundaries of generative AI work in their discipline. Fellows, like students, are learning to use these tools, and they emphasize building a collaborative learning environment where both teachers and students learn together.

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All of us have foundational skills in our disciplines and classes. We asked our fellows, With the rise of generative AI, what kinds of adjustments have you made to your teaching and assessment of those critical foundational skills? Their responses touched on the following themes:

Shift from Product to Process: Emphasize the process that students use to create course artifacts and shift from traditional summative assessment. Reintroduce in-class, hands-on tasks to ensure foundational skills like writing and critical thinking are built without over-reliance on AI. 

Evaluate AI Outputs: Focus on developing students' abilities to critically assess the accuracy, relevance, and appropriateness of AI-generated content to enhance problem-solving and analytical skills.

Promote Reflection and Metacognition:  Encourage students to use AI for assignments, but require them to reflect on the process and evaluate the tool’s contributions critically. Incorporating reflective exercises and metacognitive activities motivates students and encourages them to think critically about their learning processes and interactions with AI technologies. 

Encourage student goal setting and intrinsic motivation: Encourage students to reflect on their own goals and motivations. What kind of an opportunity does this represent and what do they want from the experience?

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We asked our Fellows, What advice do you have for other faculty and instructors exploring generative AI? Their responses touched on the following themes:

Start with Learning Outcomes and a Conversation: Align use of generative AI with clear goals to ensure the technology supports rather than undermines essential skills. Establish policies around use of generative AI, both at the course and assignment level, and communicate those to students in the course syllabus and verbally.

Model Learning with Generative AI: Foster a collaborative learning environment by openly sharing your own learning process and inviting students to reflect on and share their own experiences.

Take Small, Purposeful Steps: Take incremental steps to experiment with and test generative AI tools, ensuring manageable integration and effective learning as opposed to overhauling curricula.

Develop Support Networks: Identify key ways to inspire and sustain innovation such as participating in communities of practice including within professional organizations, sharing successful AI pedagogical practices and strategies, and fostering peer discussions. 

Consider Risks: Acknowledge the risks of experimenting with generative AI, particularly for non-tenured professors who might see lower SRT scores. Advocate for institutional recognition of innovative teaching approaches.

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We asked our Fellows, What advice do you have for students who are using generative AI? Their responses touched on the following themes:

Ask Questions: Ask clear questions about the generative AI policies in each course and be open about the varying policies. Don’t be afraid to talk with professors and TAs about their positions on AI use.

Practice Critical Evaluation: Critically analyze generative AI outputs and reflect on your own understanding to build deeper learning skills.

Use Generative AI to Support (Not Replace) Learning: Use generative AI tools as supportive resources, rather than shortcuts. Use your own subject knowledge and experiences to write a prompt and to ask probing questions. You will be surprised at how much you already know.

Reflect and Behave with Integrity: Ask yourself “What’s the purpose of learning?” Reflect on generative AI’s role in your work and the importance of academic integrity. Don’t lose sight of your goals and aspirations.

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We asked our fellows, How could the University of Minnesota, individual colleges, or departments better support our understanding and use of generative AI? Their responses touched on thefollowing themes:

Provide Training and Professional Development: Offer Workshops and other learning opportunities to enhance generative AI literacy, explore practical applications of the tools, and consider the ethical implications of generative AI use. Increased local engagement, such as department-specific workshops, in addition to interdisciplinary collaborations, would benefit all faculty.

Connect Faculty with Experts: Create educational innovation teams to provide faculty and students with the technical expertise and pedagogical guidance needed to experiment with and implement generative AI.

Develop Institutional Priorities and Policies, and Provide a Path to Access generative AI Tools: Provide clear guidelines and institutional support for generative AI tool access, licensing, and usage policies. Unclear messaging and slow approval processes of emerging generative AI tools create confusion and frustration.

Recognize and Reward Innovation: Encouraging and rewarding innovation in teaching with generative AI is critical. Fellows expressed concerns about potential penalties from low student evaluations and the lack of recognition for faculty experimenting with new approaches to learning. This underscores the need for institutional backing and incentives. 

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The name of each Fellow below links to a biography page summarizing their project overview and linking to their final reflections.  Go to the Extra Points: Gen AI Explorations series to see interviews with all Fellows.

• Al Fattal Anas, Assistant Professor of Marketing, UMN Crookston
• Fernando Burga, Assistant Professor of Urban & Regional Planning, UMN Twin Cities
• Mark Collier, Professor of Philosophy, UMN Morris
• Kris Cory, Associate Director of First Year Writing, Kris Cory, Associate Director of First Year Writing
• Nicole Dillard, Assistant Professor of Human Resource Development, UMN Twin Cities
• Tim Doherty, Senior Lecturer of Chemistry, UMN Rochester
• Rob Erdmann, Assistant Professor of Bioinformatics/Data Science, UMN Rochester
• Darren LaScotte, Teaching Specialist, Minnesota English Language Program, UMN Twin Cities
• Jonathan Lee, Assistant Professor of Organizational Behavior, UMN Duluth
• Karin Quick, Associate Professor & Director of Division of Dental Public Health, Director of Global Programs, UMN Twin Cities
• Mary Rogers, Associate Professor of Horticulture Science, UMN Twin Cities
• Kristin Shingler, Teaching Assistant Professor of Dentistry, Director of Assessment and Curricular Integration, UMN Twin Cities
• Ezgi Tiryaki, Professor of Neurology, UMN Twin Cities
• Molly Vasich, Associate Director of First Year Writing, UMN Twin Cities
• Junhua Wang, Associate Professor of Business Communication, UMN Duluth