AI Literacy Across Disciplines: Education

Insights and best practices for teaching AI literacy to education students

Posted on July 20, 2026 in Blog Posts

At a glance

Discipline	Secondary education
Audience	Graduate students
Instruction format	Two one-shots and a research consultation
Primary AI tools featured	Scite, Consensus, Google Scholar Labs, ChatGPT
Core AI literacy themes	1. Locating scholarly literature using AI research tools 2. Planning a research strategy that takes into consideration the limitations of AI research tools 3. Preparing future educators to navigate AI literacy in their own classrooms

Setting the stage

Guided by our school motto “Pro Humanitate” (“for humanity”), Wake Forest University has adopted an approach to generative AI that emphasizes the ethical and responsible use of AI and the importance of retaining human judgment and creativity. Within the Education Department, views on generative AI reflect a variety of perspectives. Regardless of their personal views, most faculty recognize that future K–12 educators will encounter AI in their classrooms and feel a responsibility to prepare them for that future. Most students enrolled in Wake Forest’s graduate Education program come from non-education undergraduate majors and pursue the program as a pathway to initial teacher licensure. Typically, 8–15 students are enrolled each year. To graduate, students complete a field-based action research project culminating in a 40–50-page thesis-style paper.

While the students are interested in using AI to research sources for their action research project, they often can have mixed feelings about the use of AI in the classroom. This concern is at least partly due to their role as future educators. Many are surprised by the sophistication of AI research tools (e.g., Scite, Consensus, and Elicit), particularly their ability to summarize and synthesize scholarly literature, while also expressing concern about how they will teach foundational skills such as reading complex texts, synthesizing information, and critically evaluating AI-generated content.

Students in the graduate program see a librarian two or three times during their 13-month program. The first librarian visit helps students develop strategies for using library and AI research tools ethically and effectively in support of the literature review portion of their action research projects. Following this session, students have the option to schedule a personal research consultation to continue working on their literature review. The second librarian visit focuses on teaching information literacy in the K–12 setting. Because information literacy and AI literacy have significant areas of overlap, a secondary goal of this visit is to consider how AI is likely to influence K–12 classrooms and what it means to teach students to become AI-literate.

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Teaching AI literacy

First Librarian Visit

My first visit to the graduate program is during their Action Research course, where students research and plan for their action research project. At least during this early stage of the AI era, my AI literacy learning outcomes for graduate students are not substantially different from those I use with undergraduates. Although this may change over time, I do not assume that graduate students have greater AI literacy or more experience using AI for library research than undergraduates.

The first outcome for this visit is to help students choose the right AI tool for research tasks. Although tools like ChatGPT are useful for some tasks, they are not well suited for academic literature searches because they can still hallucinate sources and generate inaccurate citations. To begin, I invite students to participate in a think-pair-share about what they already know regarding the use of ChatGPT for research. I then build on their prior knowledge through a conversational discussion, during which the topic of hallucinations typically emerges naturally. I then let them know that we will instead be trying AI research tools that the library licenses, like Scite and Consensus, as well as Google Scholar Labs, that search verified academic indexes.

The second outcome has to do with understanding the limitations of the AI research tools the library licenses. These limitations include:

• Lack of comprehensive coverage due to each tool having different corpuses and licensing agreements. This is especially important for graduate students whose research requires a comprehensive literature search.
  
  
• Oversimplification and/or inaccuracies in AI-generated summaries and synthesis.
  
  
• The potential for bias in the initial prompt or in the tools’ training data that leads to a misrepresentation of available perspectives.

These concepts are presented in a brief 6–8-minute slide deck, with an opportunity for students to ask questions.

Moving forward with the understanding that research conducted solely with AI tools will be incomplete, the third outcome involves locating scholarly literature using both traditional library databases and AI tools designed for academic research. This outcome uses a peer-teaching model that incorporates four or five AI research tools and library databases. Students work in pairs and are assigned two tools to explore with general guiding instructions that encourage hands-on experimentation. When possible, tools are assigned to multiple groups to provide multiple perspectives. Each pair is responsible for demonstrating one of their assigned tools to the class, discussing its strengths and limitations, and comparing it with the other tools they explored.

As AI literacy-related topics come up during these presentations, I pause to provide additional context. These discussions address the accuracy and reliability of AI-generated summaries and syntheses, as well as the potential risks and benefits of filtering non-English and under-cited sources. The instruction session closes with an exit ticket asking students to identify the two most interesting things they learned and one lingering question. Student feedback on the AI content is highly positive, and their questions help shape my instruction for the second library visit.

Following this visit, roughly half the students set up a personal research appointment with me to continue working on their literature reviews. During these consultations, I often ask about their experience using the AI research tools taught in class. Over the years, I’ve gained valuable insight into what they like and dislike about the tools, what worked for their research topic and what did not, and their general feelings about research-specific AI tools. That has informed my own teaching and advocacy on which tools our library should try to license and teach.  

Second Librarian Visit

This second librarian visit occurs during the second half of the students’ graduate program when most of their time is spent student teaching. This session focuses on approaches to teaching information literacy in K–12 classrooms. Over the past three years, this has increasingly included AI-related information literacy content. However, AI is only one component of the session. The first three learning outcomes involve discussing common information literacy models and learning outcomes, incorporating and scaffolding information literacy into high school research assignments, and discussing the pros and cons of vertical reading (e.g., CRAAP Test) and lateral reading evaluation techniques. I emphasize that lateral reading techniques, like those included in the SIFT Method, are among the best ways to evaluate AI-generated claims.  

For the final 30 minutes of the class visit, students submit anonymous questions via notecard on any topic related to AI literacy, information literacy, or library research. Due to the small size of the graduate program, I typically have time to address every question. I intentionally keep this portion of the visit conversational in nature. I approach it as a discussion among future colleagues because by this point in the program, students are already teaching in local schools and navigating many of the same AI-related challenges that I encounter in my own work.

The most common questions I receive concern maintaining the integrity of the research process—for example, how to encourage students to read the articles they find rather than relying on AI-generated summaries. I include a brief slide that goes over maintaining the integrity of research projects in the AI era that includes advice like in-class research checkpoints, requiring page numbers, and having students print out their sources to highlight quotations and other relevant content. Other common questions involve access to AI tools in the K–12 environment. Funding environments will vary across school districts, so I emphasize the importance of teaching transferable skills, such as evaluating information with lateral reading, that work in multiple information environments. Questions about teaching ethical and responsible use of AI tools are also common. For those questions, I invite students to consider what activities or lessons they might plan around issues like copyright and intellectual property, protecting personal data, and environmental concerns, and share ideas with the classroom.

Reflecting on impact

Students preparing to become educators occupy a unique position when learning about AI literacy. On one hand, they are completing substantial original research projects and are eager to explore how AI can support the research process. On the other, they are already thinking like teachers, raising many of the same questions that librarians do about how generative AI might be detrimental to the learning process. Because librarians are increasingly engaged in AI literacy instruction, library liaisons to education programs have a unique opportunity not only to teach AI literacy to future educators, but also to help prepare them to teach AI literacy to the next generation of students. I would encourage other education liaisons to lean into this dual role of both educator and future colleague of fellow educators. Students frequently thank me for showing them research-specific AI tools, even if they have critiques about specific tools or concerns about AI more broadly. They appreciate being better prepared to teach in an increasingly AI-mediated information and educational environment.

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📣 Do you teach AI literacy? We want to hear from you. Check out our call for papers, “AI Literacy Across Disciplines.”

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