Teaching with Generative AI

Students need to build knowledge about AI, including:

• How the tools work

• Their social, cultural, environmental and labor impacts

• Potential biase

• Tendencies toward hallucinations and misinformation

• Propensity to center Western European ways of knowing, reasoning and writing

Teaching students about AI aligns with core equity values at our university, and our JEDI framework. To take a JEDI approach to AI, it is important to first understand the ways that AI reinforces systemic biases and harms. "Critical AI: An Overview of Issues" by Jennifer Trainor and Jessica Adams-Gregoriof introduces an array of ethical concerns relevant to higher education that AI scholars have identified.  We encourage you to partner with CEETL as you redesign assignments and activities to teach students about how AI tools work, and to educate them about the issues AI raises, from environmental justice to copyright violations to bias and hallucinations. 

• Leon Furze’s Teaching AI Ethics series
• Adams’ et al (2023) list of ethical concerns for education
• Modern Language Association Guide to AI Literacy for Students
• The People’s Guide to AI for students
• Trent and Lee’s lay-peron’s overview of how LLM’s work
• Autumn Caine’s “Prior to” essay about the conversations to have with students before you introduce or encourage AI
• Maha Bali’s “What I Mean When I Say Critical AI Literacy”
• Ryan Watkins on updating course syllabi to include AI ethics and learning
• Jenka’s (2023) Medium article on AI and the American Smile. 

• View CEETL's Fall 2023 Workshop on Writing with Integrity recording
• Discover how SFSU Computer Science Department is addressing AI
• Watch English Professor Jennifer Trainor’s video on how she developed students’ awareness of AI bias
• Read How I use AI to teach Linguistic Justice by Jennifer Trainor
• Read about SF State philosophy professor Carlos Montemayor’s work on AI
• View the Spring 2024 Critical AI Workshop Slides. In this workshop, Fatima Alaoui and Kira Donnell focused on defining Critical AI and on developing students’ critical AI literacy. Takeaways include:
  • Critical AI literacy should be taught to help students and educators assess AI's ethical implications and understand its potential biases and transparency issues.
  • Critical AI in the classroom involves honing students’ critical thinking, using critical pedagogy classroom methods, and developing students’ critical information literacy.
  • Academic integrity should focus on teaching, not policing, and integrating AI tools as part of professional learning.
• Learn about SFSU's Interdisciplinary Graduate Certificate on Ethical Artificial Intelligence.

What do students need to know to work ethically and equitably with AI, as these tools become:

• increasingly embedded in the platforms and programs we already use

• aggressively marketed

• integrated into the jobs and careers our students hope to enter?

As Kathleen Landy recently wrote, “Now is the time to move toward … collaborative articulation of learning outcomes relative to AI. What do we want the students in our academic programs to be able to do with generative AI? What AI skills do they need to be successful in our classes and beyond?”

Faculty at SFSU are exploring ways to use AI to support existing learning goals such as the development of critical and analytical thinking and rhetorical awareness. 

For example, faculty have created lessons that:

• ask students to use Microsoft Copilot to come up with discussion questions, then rank and rewrite the questions as needed in order to develop students’ analytic thinking

• ask students to debate Microsoft Copilot in order to develop their critical thinking

• engage students in AI-generated scenarios to create engagement and critical thinking

• ask students to evaluate AI solutions in a case study to support critical thinking

• flip the classroom with the aid of AI (see Jason Johnston’s work on AI assignment flipping)

Students are often early adopters of new technology. Your students may already be using AI tools to support them in a variety of academic and non-academic tasks. 

Further reading

• Kathleen Landy, “The Next Step in Higher Ed’s Approach to AI,” Inside Higher Ed, February 28, 2024
• Alex Beall, "Growing AI Education Across the CSU," January 13, 2025
• For more, consider this crowdsourced slide deck of how teachers around use AI with their students.
• To hear how students are using AI tools, view the Fall 23 Workshop on AI Tools for Students recording.
• Ethan Mollick and Lilach Mollick, "How to Use AI to Create Role-Play Scenarios for Your Students," Harvard Business Review, June 12, 2024

• To learn what these tools can do, join AT and ITS's AI Literacy Program
• Petkovic and Chaudhuri's Spring 2025 IAAA presentation on How to smartly use AI in Education
• Cynthia Alby’s AI Prompts for Teaching: A Spellbook
• Antonio Bowen’s and Edward Watson’s book on teaching with AI (please send an email to ceetl@sfsu.edu for a free copy or come and read in the Faculty Studio in LIB 240)
• AI Pedagogy Project
• AI and Effective Teaching with Ethan Mollick
• CSU AI Teaching Commons
• Resources for GWAR instructors
• Lam Family College of Business Webinars and Workshops through the Emerging Technologies Initiative

To see how SFSU faculty are innovating with and exploring AI in their classrooms, check out these recordings from SFSU's Symposium on AI hosted by CEETL in Spring 2024. 

Each recording features an SF faculty member instructor who shared how they use AI in their course as well as lessons learned.

• Watch Professor Wes Bethel share about leveraging AI in the sciences
• Watch Professor Scott Campbell share about AI in the humanities
• Watch Professor Nasser Shahrasbi  share about AI in business
• Watch Faculty Member Casondra Sobieralski share about AI in design
• Watch the whole playlist of faculty AI uses on YouTube

Faculty are also exploring the potential for AI to assist them in curricular design. CEETL’s Fall 23 discussion circle focused on exploring AI tools for faculty use.  Participants shared how they used Generative AI to draft outlines for lessons, generate thank-you notes or recommendation letters, and summarize complex information.

Takeaways included: 

• AI is a Starting Point: Faculty found ChatGPT useful for breaking creative blocks and drafting outlines.
• Customization is Essential: AI-generated content lacks personal voice and context, and AI is prone to hallucinations, requiring instructors to check for misinformation and personalize to their course and students.
• Privacy Concerns: Participants raised concerns about inputting personal information or student data into AI systems.
• Learning vs. Efficiency: Faculty worried that over-reliance on AI might undermine the learning process by prioritizing fast outcomes over deeper engagement with material.
• Ethical Guidelines Needed: Faculty emphasized the need for clear ethical guidelines in AI use, including ensuring students can opt out of AI-based assignments and acknowledging AI contributions in any scholarly or educational context.

CEETL’s Fall 24 workshop on "AI Tools for Faculty" takes a look at the benefits and limitations of using Co-Pilot to enhance teaching materials and increase student engagement. Facilitator Jennifer Trainor shared guiding principles, a demonstration of how to use and critique AI’s output when it comes to course materials, and led a discussion where faculty shared how they use AI in teaching.   

Protecting student learning from AI shortcuts is an essential challenge. As the Washington University Center for Teaching and Learning notes in its guidance on generative AI and foundational learning:

“Sometimes students must first learn the basics of a field in order to achieve long-term success, even if they might later use shortcuts when working on more advanced material. We still teach basic mathematics to children, for example, even though as adults, we all have access to a calculator on our smartphones.

GenAI can also produce ‘hallucinations,’ and often only a user who understands the fundamental concepts at play can recognize this when it happens. … Many assignments are much more about learning how to think and to make arguments than about simply creating a block of text on a particular topic.

Like a runner training for a marathon, the point isn’t just to get from point A to point B, but to transform oneself along the way. A car might be faster, but it won’t build the muscle and endurance we seek. In these cases, it may make sense to adopt teaching strategies that avoid AI and assignments that are more AI-resistant.”

This perspective reminds us that the goal of education is not simply producing an end product, but cultivating intellectual development and disciplinary fluency.

AI tools can produce authoritative-sounding outputs, and because these outputs sound so good, students can feel convinced by them, leading to situations where the output overrides or displaces students' own thinking. AI tools may curtail opportunities for students to develop and practice the kinds of thinking that undergird many learning goals. It is understandable that faculty are clamoring for AI detectors and prohibitive AI policies. But a focus on protecting student learning shifts us away from policing student behavior as a way to ensure learning, and instead invites faculty to think about how they might redesign assignments to provide spaces that protect learning and that encourage students to do their own thinking. 

Providing and protecting such spaces undoubtedly poses new challenges for faculty. But we also know that protecting student learning from easy shortcuts is at the heart of formal education. Consider the planning that goes into determining whether an assessment should be open-book or open-note, take-home or in-class. These decisions are rooted in the third law: what would most protect student learning from shortcuts (textbooks; access to help) that undermine their learning?

Further reading:

• IBM, "What are AI Hallucinations?"

To create student buy-in for learning and review syllabus guidance, see Jennifer Trainor and Jessica Adams-Grigorie, “SF State AI Resource Guide,” CEETL, Fall 2023, updated Spring 2025.

Syllabi policy statements from institutions across the country:

• Zach Justus and Nik Janos, "Your AI Policy Is Already Obsolete," Inside Higher Ed, 2024
• “Syllabi Policies for Generative AI Repository,” a crowdsourced collection of syllabus policy language compiled from instructors across multiple institutions.
• "Syllabi Policies for AI Generative Tools," collaborative document compiling institutional policy language and sample statements.
• Lance Eaton, “Classroom Policies for Generative AI Tools,” collection of instructor-submitted classroom policies designed to support course-level guidance on generative AI use.

Teach, don’t police: Academic integrity resources:

• SFSU Academic Senate: Resolution on the Use of Generative Artificial Intelligence (AI) in Teaching and Learning (RS24-466)
• SFSU Academic Senate: Academic Integrity Policy (#F24-298)
• Jacob Hubbard, "The Pedagogical Dangers of AI Detectors for the Teaching of Writing"
• Liang et al., "GPT detectors are biased against non-native English speakers," Patterns, 2023
• "AI-Detectors Biased Against Non-Native English Writers," Stanford HAI news, 2023
• Jon Stojan, "Undetectable AI Writes Like A Human," 2023
• Revised Bloom’s Taxonomy, framework distinguishing between AI capabilities and distinctly human skills at each level of thinking.

• View the Fall 2023 Workshop on Writing with Integrity recording to see how faculty are working to protect student learning.
• Read Jennifer Trainor’s “How I Use AI to Teach Academic Integrity” for an example from first-year writing.