AI Literacy in Teaching and Learning

Generative AI is “any type of artificial intelligence (AI) that can be used to create new text, images, video, audio, code, or synthetic data” (Rouse, 2023). Although generative AI has existed since the 1960s, most of us only began to experience its impacts on teaching and learning firsthand in the past couple of years (White, 2023). 

Note: This page focuses specifically on generative AI (gen AI). Interested in other types of AI and their role in teaching and learning? Check out this insightful article:

• Artificial Intelligence in Higher Education by Crompton and Burke (2023)

In generative AI, a statistical model is trained–that is, given the opportunity to search for and “learn” patterns–in a large data set. When prompted, it then generates (hence generative AI or gen AI) novel outputs based on what is probable given the patterns detected in the training data. 

For example, imagine a generative AI statistical model is trained on all of the text messages you have ever sent. Would it find that the word “thank” is more likely to be followed by “you” (as in “thank you”) or “me” (as in “thank me”)? Let’s assume “thank” is more likely to be followed by “you.” Now, if you prompt it to compose a text message to let your friend know that you enjoyed coffee with them that morning, it will be more likely to include the phrase “thank you” than “thank me.” 

Want to dig deeper into how generative AI works? Explore these expert resources:

• Caltech – Learn the fundamentals of how generative AI works from leading researchers.
• Wharton School's “Practical AI for Instructors and Students”– Watch this accessible explainer video designed for educators and learners.
• 3Blue1Brown YouTube Channel – Dive into more technical, visual explanations of how specific generative AI models operate.

If you are considering asking students to use gen AI tools in your course, we encourage you to consider the following:

Check Departmental Policies First

Before getting started, check whether your department or program has policies regarding AI use in the classroom.

Start with Course Learning Outcomes

Reflect on your course learning outcomes--that is, what knowledge, skills, behaviors, habits of mind, etc., are students intended to acquire through this course? 

Then, consider the following questions on your own, or if you are co-creating an AI use course policy with your students, discuss these questions with your students:

• Will allowing students to use AI tools support or interfere with them achieving the course learning outcomes?
• What specific uses of AI tools may support or interfere with students achieving the course learning outcomes?

(Co-)Create an AI Use Course Policy, and Communicate this Policy to Students

Now, decide what level of AI use will be allowed in this course--no AI use, limited use, conditional use, or full AI use. Explore this guidance on AI syllabus statements from the BFA (Boulder Faculty Assembly) and CTL.

Make a plan for communicating your AI course policy to your students. This should include an AI use syllabus statement (see above) and may additionally include announcements in Canvas and regular reminders before students begin working on new assignments. Remember to provide opportunities for students to ask questions and get further clarification. 

(Co-)Create Assignment-Specific AI Use Policies, as Needed 

If your course allows limited or conditional AI use, it may be helpful or even necessary to create assignment-specific AI use policies. 

Begin by considering the learning outcomes of this specific assignment--that is, what knowledge, skills, etc., are students intended to practice or acquire through this assignment? We recommend using the CTL's guide to The AI Assessment Scale (Perkins et al., 2024) to explore more granular levels of AI use to inform how you create and communicate with your students about assignment-specific AI use policies.  

Further, consider co-creating assignment-specific AI use policies with your students. Here is an exercise you can complete together:

• Begin by sharing and reflecting on the assignment learning outcomes with your students.
  • Example assignment learning outcome: After completing this literature review, students will be able to synthesize the extant research literature on a topic to identify key findings, as well as discrepancies and gaps.
• Connect the assignment learning outcomes to relevant course learning outcomes.
  • Example course learning outcome: After completing this course, students will be able to critically evaluate and synthesize the peer-reviewed research literature to identify directions for future investigation.
• Ask your students what they will need to do step-by-step to complete this assignment.
  • Example: For a literature review, specific steps might involve...
    • Selecting a research question,
    • Identifying relevant keywords,
    • Using library databases to retrieve relevant peer-reviewed journal articles,
    • Reading and making sense of those articles,
    • Comparing and contrasting methods and findings,
    • Grouping similar studies together,
    • Creating an outline,
    • Writing up a synthesis that includes what has been found, as well as gaps and discrepancies,
    • Revising their writing (e.g., in response to peer or instructor feedback and/or their own self-reflections),
    • Formatting citations in APA style,
    • Proofreading,
    • Etc.
  • Note: Especially if this is the first time your students have completed an assignment of this kind, you will likely need to provide guidance and help clarify the steps. This has the additional benefit of enhancing transparency around the task at hand so that students can dive right in, rather than guess what is expected of them.
• Next, for each step, ask students to discuss whether using AI would support or interfere with them achieving the assignment (and course) learning outcomes.
  • Example: You may determine, through discussion, that using AI to select a research question, identify keywords, use databases to retrieve articles, format citations in APA style, and proofread one's writing will not interfere with students achieving the learning outcomes listed above and may even support them in achieving the learning outcomes by streamlining administrative tasks and buying back time to read and think critically about the articles. In contrast, you may determine that using AI for any other steps would likely interfere with students achieving the learning outcomes.
• Finally, co-create an assignment-specific policy that stipulates for which steps of the assignment students are not allowed to use AI and for which steps, if any, they are allowed to use AI. Further, explicate how students should cite AI-generated content, if this is permitted (learn more about how to cite AI-generated content from this Purdue Lib Guide.
• In sum, co-creating an assignment-specific AI policy with your students should create a sense of shared ownership over the policy, while clarifying their understanding of what is expected of them, and helping them understand how to successfully complete the assignment at hand.

Special thanks to Rebecca Lee, CTL's Student Initiatives Coordinator, for sharing this exercise with us.

Create Multiple Learning Pathways for Students Who Do Not vs. Do Want to Use AI 

If you are requiring or encouraging students to use AI tools, consider building in multiple learning pathways--that is, assignment and assessment options for students who wish to use AI tools and equivalent assignment and assessment options for students who do not wish to use AI tools for ethical or other reasons. This supports student autonomy in deciding whether to "opt in" or "opt out."

If using AI, select Tools with Equity, Accessibility, and Security in Mind

If you decide to use AI tools in your course, consider access and equity, as well as accessibility and data security.

• Do all students have access to this AI tool? Is it free? If it is not free, how will you ensure all students are able to access this tool?
• Has this tool been vetted and approved through OIT’s Information and Communication Technology (ICT) review process to ensure it is accessible and secure? Learn more about the ICT review process, and submit a request for ICT review.

If using AI, Explore Strategies and Sample Assignments

If you decide to use AI in your course, explore the following CTL resources for developing assignments or assessments that purposefully incorporate gen AI:

• The Teaching, Learning, & AI Resource Repository includes sample assignments that incorporate gen AI, as well as other resources for teaching and learning with and about gen AI.
• The AI & Assessment page provides tips and resources for designing assessments that meaningfully incorporate AI use to support student learning.

Foster a Culture of Transparency through Dialogue

Beyond communicating with students about course and assignment-specific AI policies through syllabus statements, Canvas announcements, reminders prior to assignments, etc., consider engaging students in constructive dialogues around AI use in your course. This can be as simple as asking students how they are feeling about gen AI, what concerns they have, what resources they wish they had, etc.

Encourage Metacognition

If students are interacting with AI in your courses, consider incorporating reflection, self-assessment, or other activities to encourage students to engage in metacognitive reflection around how AI use is supporting or interfering with their own learning. 

• See this AI-assisted Learning Template developed by Mark Watkins as a model for how you might engage students in metacognition around their own AI use in your course (see the Reflection on Learning section, specifically).

If you are considering using gen AI to support your own work as an educator, explore this blog post for guidance on choosing an AI system (Mollick, 2025).

Generative AI has several significant limitations. Being aware of these limitations and ensuring that you educate your students on them is an essential component of AI Literacy. These limitations include but are not limited to:

High Risk/High Reward

Although some AI tools show promise for supporting student learning, misapplication of AI tools can have a negative impact (Bastani et al., 2025; Zhang et al., 2025). Initial studies found that when students use gen AI for customized tutoring and resources tailored for their learning style, outcomes were comparable to traditional education. However, when students offloaded too many tasks to the gen AI, they had worse outcomes–even worse than not studying. 

Hallucinations

GenAI can often “hallucinate,” inventing spurious facts and details. This can be especially dangerous to users trying to learn from the gen AI tool, given that they might not have the subject mastery to identify when facts are fabricated (Chelli et al., 2024; Mollick, 2023).

Misinformation

Gen AI tools can be used to create false information in any media format. For example, there is software, such as Hume or ElevenLabs, that can reliably clone voices and generate audio. There is also software, such as Veo and Synesthesia, that can generate photorealistic videos with only a text prompt (note that, although it’s currently limited to short clips or stationary angles, that could change soon). In addition, nearly every LLM (large language model) now has an image generation feature that can generate photorealistic images. Although these AI-powered media generators have safeguards, these safeguards are easy to circumvent. Thus, users acting in bad faith could potentially use gen AI to create media to fake events that never actually occurred. 

Manipulation

LLM chatbots are built to effect a communication style and tone that is engaging and potentially flattering of the user (Mollick, 2025). Moreover, they are programmed such that they can state false information with a tone of confidence. This can make them particularly effective at swaying people’s views (Turner, 2025).

Bias

AI has various issues of bias. Its training data is disproportionately based on WEIRD (Western, Educated, Industrialized, Rich, and Democratic) sources, which creates corresponding cultural biases in its outputs (Bali, 2024). In addition, gen AI has been shown to reproduce non-inclusive language stereotypes along a variety of lines (Sun et al., 2023; Johnson et al., 2022; Abid et al., 2021, Whittaker et al., 2019). Finally, studies have shown that this bias is present covertly in many cases (Hofmann et al., 2024); therefore, any decision being made by gen AI should be scrutinized accordingly. 

Data Privacy

Sam Altman, CEO of OpenAI, has made it clear that user logs are saved and that there is no confidentiality (Perez, 2025). All users should be cautious of what information they share with these chatbots. 

Copyright Concerns

LLMs are trained on libraries of date that leave debts to contributors unacknowledged, which can constitute a form of plagiarism and theft of intellectual property (Reisner, 2025). While some cases have started setting precedents (Bartz v. Anthropic PBC, 2025), there are still many cases pending (BakerHostetler, 2025) in this rapidly evolving space. 

Special thanks to Chris Ostro, Assistant Teaching Professor and Learning and AI Strategist with CE's Learning Design Group, for contributing this section and for providing input on this webpage.

As educators, we can provide students who wish to explore gen AI with guidance on how to use AI responsibly and ethically, as well as opportunities to practice essential AI literacy skills. This includes skills like fact-checking, verifying AI-generated outputs, and writing effective prompts. For examples of how educators can help students develop their AI literacy skill set, see this Times Higher Education (THE) article.  

Other resources: 

• Cornell University Center for Teaching Innovation. (n.d.). Ethical AI for teaching and learning.

• Notre Dame Learning. (n.d.). Teaching with technology: Teaching in the age of AI.