Module 2: Pedagogical Road Map for Teaching AI Literacy
AI Literacy Essentials for Academic Libraries: Beyond the Basics
Choice and Clarivate have teamed up once again to expand our eight-week, newsletter-based course on generative AI literacy for academic library workers by creating three new modules. Each module contains bite-sized, self-paced material such as readings, multimedia content, case studies, and interviews with thought leaders.
Make sure you’re registered to receive all three modules in your inbox! You can revisit our original eight-part series here.
In this module, we will discuss pedagogical strategies for AI literacy.
Learning Objectives:
- Develop a strategic approach to AI literacy pedagogy
- Discover practical ways to implement AI literacy in educational settings
- Explore and evaluate different pedagogical frameworks for AI literacy
Table of Contents
Introduction
AI Pedagogy: Teaching With and About AI
by Kimberly Shotick, Associate Professor and Student Success Librarian, Northern Illinois University
Academic librarians are uniquely positioned to teach learners both about and with generative AI tools. This work includes helping students understand what these tools are, how they function, the ethical considerations surrounding their use, and how they can be applied effectively in the research process to enhance engagement and learning.
From Information Literacy to AI Literacy
There are many opportunities to incorporate AI into library instruction, ranging from describing AI-assisted research tools and processes in a research guide to teaching for-credit courses on AI. Regardless of the format, the focus should extend beyond specific products to emphasize the skills and values needed for the effective, efficient, and ethical use of AI. Just as database searching instruction prioritizes skills such as keyword construction and iteration in the search process over “where to click,” teaching learners about and with AI can function as a vehicle for deeper pedagogy.
This connection is evident in the current revision draft of ACRL’s Framework for Information Literacy for Higher Education, which includes specific language related to artificial intelligence. The issues related to AI fit into the Framework’s existing structure because, while technologies change, the core values and skills that comprise information literacy are static. For example, dispositions and skills from the Framework, such as critically examining biases in information and systems, valuing and respecting others’ work, and recognizing unequal access to systems and information, align with many of the key issues involved in teaching about, and with, AI. Understanding how various uses of AI tools fit into the existing Framework can help make the idea of teaching with and about AI less abstract and more familiar.
Through this lens, the goal isn’t to “teach AI,” but to teach these core skills and dispositions in relation to AI. Considering this connection between AI literacy and information literacy, it is no surprise that librarians have been leading AI literacy efforts on many campuses.
Strategic Approaches to AI Literacy Instruction
There are numerous frameworks for AI literacy, many available in the resources below. Generally, these frameworks cover a basic understanding of how AI tools work, ethical considerations around their use, effective use of AI tools (such as prompt engineering), and critical evaluation of AI systems and tools. These frameworks incorporate the cognitive dimensions of understanding, application, and evaluation.
While it may feel overwhelming to begin designing and implementing an AI literacy program, working with campus stakeholders to find opportunities for shared goals, such as collaborating with faculty in programs that have writing and research requirements, is a key to success. To find these opportunities, consider curriculum mapping by identifying which courses incorporate technology, ethics, research, writing, and other forms of content creation, especially general education courses that focus on developing foundational skills. If you have access to course outcomes, connecting them with opportunities for AI literacy can help you be strategic in choosing which courses to collaborate with, as well as how to scaffold instruction across a curriculum. For example, introducing the concept of academic honesty and the use (and misuse) of generative AI in a first-year composition course can pave the way for introducing more advanced, nuanced topics in later courses.
Teaching with AI: Opportunities for Engagement
AI pedagogy involves not just teaching learners about AI but can also include teaching with AI tools to enhance student engagement and learning. For example, librarians can use generative AI to transform learning materials, such as handouts, slides, and video explainers, into multiple formats to support student choice, a principle of Universal Design for Learning. Similarly, generative AI can support scenario-based learning in which students analyze multiple cases involving a student’s misuse of generative AI for a class assignment, evaluate each scenario using an ethical framework, and identify alternative, ethically aligned uses.
More advanced uses of AI tools can be incorporated into instruction to enhance student engagement through personalized and adaptive learning. For example, librarians could create a custom GPT trained on lesson materials that provides real-time feedback to students as they experiment with creating a research question and search strategy. A course’s Open Educational Resource could incorporate AI to transform the examples used in the text to better align with the students’ interests. Additionally, libraries may have institutional access to AI tools that can be incorporated into instruction and assessment.
While AI tools can support library instruction, they should never replace the librarian’s judgment or expertise. Designing meaningful learning experiences, such as scenarios that engage students with the ethics and skills of AI in research, must remain human-led. Generative AI can assist by helping adapt these scenarios to different contexts or student interests, but the pedagogical direction should stay with the librarian. These examples of teaching with AI may seem intimidating, but they become more approachable as librarians engage in ongoing learning about AI technologies
One More Webinar: Keeping Up with Continual Professional Development
While there has always been a need for continuous professional development in librarianship, these technologies underscore that need because of their rapid development and disruption of higher education. Librarians need to stay informed about AI technologies and policies, especially as they shape higher education and the skills students will need in their future careers. Luckily, there is no shortage of professional development opportunities, including this series. Although it can be difficult to find the time and energy to attend one more webinar, keeping up by subscribing to newsletters, participating in professional organizations, and seeking out occasional micro-learning opportunities can help you keep current while avoiding AI burnout. Apply what you learn to create instructional experiences that cultivate ethical, creative, and curious learners who are well-equipped for the future.
Resources and Frameworks
In Practice
AI Literacy Activity Bank
As we develop pedagogies for AI literacy, we need to make an important distinction. AI literacy isn’t simply what a person can do with AI. Indeed, our students may already use AI in very impressive, nuanced, and intelligent ways—ways that expand our own understanding of this technology. Rather, AI literacy emphasizes how students think about AI. That’s where librarians can make their most important and lasting interventions.
To this end, we queried a number of librarians and educators who are actively teaching AI literacy at their universities to ask them to share their favorite activity to use in a classroom, workshop, or curriculum. These activities all prioritize critical thinking and learner engagement, and would make strong additions to your instructional repertoire.
Click here to collapse/expand the AI literacy activity bank.
Bronte Chiang, Digital Literacy Librarian, University of Calgary
“Should We Use AI?”
The “Should We Use AI?” decision activity slows down default automation. Students are given a research or writing scenario and asked to decide whether AI use is appropriate at all. They justify their decision in relation to the task’s goals, expectations, and risks before selecting any tool or generating output. Learning occurs in the rationale and the recognition that nonuse can be an academic choice, not in reaching a correct answer. This activity usually doesn’t include using an AI tool.
Chad Mairn, Professor, St. Petersburg College
Assessing AI-Generated Information
In INFO 200 Information Communities, a graduate-level information science course at San José State University, students are challenged to create a media-based artifact (like an infographic) that visualizes how a specific community uses emerging technology to share information. In this assignment, students have the option to use generative AI to design their artifact. However, the primary goal of the assignment isn’t prompt engineering or copying/pasting ideas; it’s about critical evaluation in the process of using AI.
In addition to the infographic, students must submit a reflective memo analyzing the AI’s output for accuracy and alignment with foundational course theories.
This exercise teaches students that generative AI can be a strong collaborator, not a final publisher. The human has the final say in what is produced. By asking students to cross-reference AI-generated visuals against their curated information, they develop AI literacy skills by learning to critically judge an AI’s capacity for complex, evidence-based data synthesis.
Think Critically. Use AI Wisely. An Interactive Tutorial
Even if you don’t like AI or never plan to use tools like ChatGPT, learning about the technology is vital. AI is already embedded in everyday systems like job applications, search engines, media, and finance; plus, there’s no realistic way to opt out of its societal influence. AI literacy isn’t about becoming a power user; it’s about understanding how these systems affect your life, so you can respond thoughtfully rather than blindly accept or reject them.
While most AI tutorials rely on passive reading, I created a resource that is specifically designed for hands-on student engagement. The tutorial would work well for asynchronous instruction or “flipping the classroom.” Key features include:
- Live Practice Lab: Three interactive modes (Practice, Prompt Critic, and Socratic Tutor) give students instant AI feedback directly inside the tutorial.
- Personalized Assessment: Students receive tailored feedback based on their notes and can generate a “certificate of completion” to submit to an instructor.
- Academic and Professional Guardrails: Includes an AI Use Disclosure Form (inspired by SJSU) and discipline-specific guidance for fields like English Composition, General Science, the Humanities, and Workforce.
- Cutting-Edge Context: Covers emerging topics like agentic and physical AI, with continuous updates planned.
Built with assistance from Anthropic’s Claude, the tutorial requires no login and works seamlessly in any browser. Try it today, or view it on GitHub.
Kenneth Nichols, Full-Time Instructor, SUNY Oswego, and Deborah Bauder, Engineering Research and Instruction Librarian, Cornell University
Refining a Research Topic
College students often struggle when asked to choose a research topic. Generative AI can help refine an overly broad, unwieldy topic to a more specific and useful one.
In class, we have students start with their broad idea and ask the chatbot to list 10 subtopics. Then the student chooses whichever of those is most interesting and asks for 10 more. After a few rounds, the idea narrows and deepens significantly. For example, the freshman writer’s topic evolves thus: social media → influencer culture → parasocial relationships → Twitch streamers and real-time audience intimacy. That last idea is easier to research and offers more opportunity for the student to say something meaningful.
Maxwell Gray, Digital Scholarship Librarian, Marquette University
Imagine AI as an Animal
Here’s an activity I’ve done with faculty, but that I think would work well with students too: ask participants to imagine AI as an animal—”if AI were an animal, then what kind of animal would it be?”—and to draw an image of their “AI animal” on paper. Provide different colored pencils for folks to draw and color with. After 5 to 10 minutes, ask participants to come together in small groups to share their images and why they imagined their “AI animals” the way they did. Next, ask folks to collaborate together in their small groups to imagine AI as a hybrid animal or cryptid—“if AI were a hybrid animal that was a combination of your small group’s ‘AI animals,’ then what kind of hybrid animal would it be?”—and to draw an image of their “AI cryptid” on paper. After 5 to 10 minutes, ask small groups to share their “AI cryptids” and what different ideas or emotions about AI they represent with the rest of the big group.
It’s not a short activity—all told, it probably requires no less than 45 minutes for 25-ish participants—but it’s a fun way for folks to recognize the ambiguity surrounding the potential value and impact of this emerging technology.
Scott Shumate, Assistant Professor and Lead Librarian of Digital Services, Austin Peay State University, and Jenny Harris, Associate Director of Library Services, Austin Peay State University
Developing a Personal Information Literacy Framework
The Personal Information Literacy Framework (hereafter “the Framework”) assignment is designed to encourage students to consider the core principles of information literacy and how they can be applied to AI-generated content, whether their own or in other forms of media. The Framework asks students to reflect on their experiences learning both information and AI literacy in a 16-week credit-bearing course, and then construct a practical document in which they define both the importance and mechanics of information and AI use.
In this assignment, students outline source evaluation strategies, criteria for AI use, and discipline-specific considerations for themselves, and synthesize them into a guiding document. The Framework also asks students to identify their own biases, how those might have changed, and what strategies they can use to keep the Framework current, even after the class is over.
This reflective, student-centered process ensures that students have the opportunity to engage with the topics most relevant to the current information landscape in their field, making the assignment timely for students and requiring little year-to-year alteration by the instructors. Even students who do not follow through and update their Framework after the course can benefit from this process and venture out into the world as information- and AI-literate citizens.
Shannon Pritting, Executive Director of Scholarly Supports, SUNY Empire State University, and Stephanie Maynard-Patrick, Assistant Professor of Human Resource Management, SUNY Empire State University
Using JSTOR’s AI Research Tool to Make Online Discussions More Engaging
Online discussion boards are widely used in online courses to replicate classroom dialogue, usually through an original post and peer responses. A common criticism is that posts are often superficial summaries or unsupported opinions. Students now increasingly use generative AI to produce posts with minimal engagement. Because discussions are intended to demonstrate students’ understanding of scholarly literature, they are ideal for AI literacy instruction.
To encourage reflection on AI’s role in research, we introduced the JSTOR AI research tool, which provides source summaries with supporting links to specific areas of the text and topic-based recommendations for related materials. The revised discussion includes an overview of the JSTOR AI research tool as an example of responsible AI that summarizes source material without supplying analysis or answers. Students use the tool with an assigned JSTOR reading, compare their own interpretation with the AI-generated summary, and identify 2–3 relevant sources to deepen their understanding. Response posts must build on cited articles in the original post to locate new sources.
The use of the JSTOR AI tool enhances online discussions by helping students understand and engage with scholarly material, contribute higher-quality insights, and interact more deeply in discussions with peers.
Trevor Watkins, Teaching and Outreach Librarian, George Mason University
Using an AI Mind Map for AI Literacy and Ipsative Assessment in the Classroom
The following AI literacy activity was conducted during a one-shot session for an English 101 course. The students in the class were First Year Experience (FYE) students. The aim of this activity was to evaluate how a mind-mapping AI exercise could be used as a formative assessment tool to conduct a real-time AI gap analysis with the class to substantiate both the information and AI literacy skills claimed in the pre-assessment survey results.
We collectively created an AI mind map with AI as the core concept, and branched into different AI techniques, such as machine learning, deep learning, expert systems, computer vision, and generative AI, with some simple, intermediary, and advanced prompt engineering techniques that they would use later in an activity in another session. As a class, we discussed the history of AI, how it is used every day in our personal lives and in students’ disciplines, and how they could use it ethically, with a focus on generative AI, in their classroom work. The students were cocreators of the mind map.
Students then individually created their own mind maps based on their research topics, posted them on the whiteboard, and added sticky notes with two comments to their peers’ maps. I asked for three volunteers, who then led the discussion about their mind maps and how they would use AI in their research.
To measure the impact of this activity, we decided to use an ipsative assessment model in which students could compare their prior knowledge of AI to the knowledge gained after the activity. Four of the six frames from the ACRL Framework are rooted in this one-shot session, which includes research as inquiry, searching as strategic exploration, authority is constructed and contextual, and scholarship as conversation. The ACRL AI competencies referenced in the creation of this activity were 2.1, 2.4, and 4.3.
Reading Room
A selection of multimedia content to fit your time commitment.
5 minutes
- “Teaching AI literacy to the next generation.” CBC News Alberta (YouTube). 2025.
- “Beyond the Demo: How Library AI Instruction Can Shift from Literacy to Competency.” LibTech Insights, 2026.
- “How A.I. Killed Student Writing (and Revived It).” New York Times, 2026.
- “My Adventures in Teaching AI Fluency to College Students: A Case for Design Literacy.” LibTech Insights, 2026.
- “When AI Can Do Everything, What Is Left to Learn?” Chronicle of Higher Ed, 2026.
10 minutes
- “How 5 Colleges Are Approaching AI.” Inside Higher Ed, 2026.
- “Refusing (to let go of) AI: An Ignatian Pedagogy for a Real AI Literacy.” ACRLog, 2026.
- “AI Broke College Assessment. One University Believes It’s Got a Fix.” Chronicle of Higher Ed, 2026.
- “Slow AI.” Inside Higher Ed, 2026.
20+ minutes
- “Assigning AI: Seven Ways of Using AI in Class.” One Useful Thing, 2023.
- “Inquiry, AI, and Pedagogy in the Library Classroom.” Virginia Libraries, 2025.
- “Towards a Pedagogy of Self-Determination: Teaching with Generative AI in the Library Classroom.” Georgia Library Quarterly, 2025.
- “Survey on Undergraduate Student Use of Generative AI: Implications for Information Literacy in Academic Libraries.” College & Research Libraries, 2026.
- “Thoughts on Al and Education: Why you should not be concerned!” YouTube, 2026.
Survey
In this brief, seven-question survey, we want to learn more about your experiences teaching and theorizing AI literacy.
Discussion Questions
AI gives us a lot to think about. Share your thoughts in the Leave a Reply section below!
- If you teach AI literacy, what’s your favorite classroom or workshop activity?
- If you aren’t teaching AI literacy, what is the most important lesson that you think librarians as a profession can pass to the rest of the academy?
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23 responses to “Module 2: Pedagogical Road Map for Teaching AI Literacy”
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1. If you teach AI literacy, what’s your favorite classroom or workshop activity?
AI is not yet embedded in our information literacy classes. There’s a task team that has been formed and its role is to come up with a module on how to teach AI literacy. Since I enrolled part 1 of Essential skills of AI literacy last year, I have informally embedded the knowledge of I gained in my information literacy sessions. My favourite classroom activity is prompt engineering (how to prompt effectively and get the exact results). I show the students how to communicate with AI, give it specific instructions on what you really what. By doing so you save time instead of asking questions that will make AI to hallucinate or exceed chat limits because we are still using the free version of Chat-GPT. Secondly, I inform my class about evaluation (compare what they found with other resources and not to rely to AI results only. I also enjoy telling them that they also need look for the disclaimer – that Ai can make mistakes, fabricate information that is not reliable, come up with referencing that it has fabricated. To summarise the latter, I tell them about academic integrity, critical thinking skills, use AI as a study buddy and not entirely rely on it and to avoid plagiarism.2. If you aren’t teaching AI literacy, what is the most important lesson that you think librarians as a profession can pass to the rest of the academy?
Since I have already indicated that through the knowledge gained on Part 1 of this course – I will continue integrating the aspects I have already indicated above and further inform librarians, to use AI responsibly, be able to discern with the results that AI retrieves, use critical thinking skills, check if the information is not bias, check for fairness, privacy, accountability, and that AI will not replace human, formulate AI policies to protect academic integrity, teach about limitations and that as librarians we should work hand in hand with the students. AI is here to stay, is great assisting tool and we should embrace it. -
I know some other responses have stated this, but I agree with them. I think the most important lesson I as a librarian can teach others is to think critically for oneself, check the accuracy of information, and avoid plagiarism. It is also good to know when AI use is and is not required. It should be used as a tool, not a quick and easy way to complete assignments.
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If you aren’t teaching AI literacy, what is the most important lesson that you think librarians as a profession can pass to the rest of the academy? To have AI guidelines to support librarians teaching AI literacy in the classroom. To assist both faculty and students in developing their AI literacy as AI tools become more commonly use across all industries. Students especially need AI literacy skills that they can use on the job to determine if information is accurate and is being used appropriately.
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I am halfway through reading UNESCO’s AI competency framework for students. I am not a teacher so I do not have that insight that comes with teaching but I think it is important that students understand about the other sources which can be used for accessing information and not treat AI tools as a separate topic. It seems that once there is understanding about how those information sources are created and put together, students can appreciate the issues that have been raised with AI tools’ ability to provide a comprehensive and reasoned response.
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I don’t teach AI literacy in detail, due to time constraints. I think the most important aspects when I provide my one-shot library orientations are: the definition AI as a language model, the ethics of using AI, as well as talking about critical thinking in the evaluation of the search results they retrieve from AI.
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We teach a SIFTing through Business Research workshop that focuses on the value of authority, evaluation, and informed judgement of information. We’ve taught this in the classroom with students as a one-shot and as a standalone workshop. Our first-year business students all take this workshop as part of their business communication class. We pair up students so they can discuss what they’re seeing and finding together and then they report out to the larger class.
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For AI literacy, I generally ask the students to use generative AI to summarize a topic they know well. I ask them to identify possible misinformation or entirely incorrect points. I then ask students to respond affirmatively to negative information to demonstrate the way generative AI can have an agreement bias. After this, we search in the library’s catalog for similar information. I think the most important lesson that librarians as a profession can pass is that adaptability does not have to mean implementation. We librarians are good at utilizing promising new technologies, and so many librarians’ hesitations towards AI should be a warning sign. It is possible to educate students on AI literacy without fully embracing generative AI or LLMs.
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We have gone from running an introductory workshop for students on integrated AI tools in academic databases to embedding AI literacy into our other workshops and are now introducing a new workshop – How to Critically Evaluate Ai sources.
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I only teach a one-shot class of an hour’s duration to fourth year and to Masters students, and AI is only a 5-7 minute snippet of that, where I show how to use our institutional subscription to Microsoft Copilot to choose Claude and then, using a research topic, ask Copilot to generate 20 search strings that can be entered into Scopus and Web of Science. My institution is ok with AI being used for brainstorming and planning, and I asked the unit convenors where I do this if they were OK with me adding this, and they agreed it was OK. I consider search string generation part of ‘planning’. Is it my favourite thing? Mo, it’s literally all I’m able to do within the current parameters.
I would rather teach people about Undermind.ai, which I have been experimenting this year as part of my own selected professional development activities. I find it so much more useful than ResearchRabbit. ResearchRabbit seems to be a ‘flavour of the month’ for Masters students and their instructors at my institution – I can see it’s visual learner appeal, but no other appeal (and I am yet to survey them, which is another professional development activity I must do before this calender year ends). I think Undermind would serve them better. I see it as more comprehensive and explanatory. -
I teach 3 credit hour courses on information literacy and include information about AI. I have bits and pieces stuck through out my course modules. A lot of my students do not see that they are shortchanging themselves by using AI; they just want to turn in the work and get the grade. Students need to evaluate, evaluate, evaluate.
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At our university library, we are in the initial stages of our AI literacy journey. We have created a cross-departmental library working group to explore AI integration and are currently developing a dedicated LibGuide for our academic community. Our institution (which uses Clarivate’s Alma/Primo ecosystem) has enterprise agreements with Microsoft Copilot and Google Gemini, alongside an AI Observatory and Turnitin for academic integrity.
Following recent internal staff training on Copilot and Gemini, our current instructional focus is on connecting foundational information literacy with responsible AI use—particularly emphasizing critical evaluation of AI outputs, prompt construction, and academic honesty.
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AI literacy is not about mastering software mechanics; it is about cultivating critical metacognition. While technologies rapidly evolve, the foundational principles of information literacy. This involves strategies usch as evaluating bias, recognizing authority as contextual, and questioning information systems.
As someone who teaches AI literacy, my goals for students are for them to exercise a separation of the medium from the process, algorithmic awareness, and the preservation of human agency.
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I don’t currently teach AI, but I think the most important lesson librarians can pass on to the students and university using AI in research is ethics and integrity and checking the accuracy of the information. These are very important.
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Face-to-face interaction—whether one-on-one or in a group—is my preferred approach for any training activity! I enjoy conversation and seizing the moment to clarify, supplement, and contribute through a two-way exchange. It is fascinating to see what unfolds! For individual sessions, and upon request, we can adopt a more formal institutional framework.
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I don’t teach “AI literacy” specifically in my Introduction to Academic Research (LIB 1000) course, but I do introduce students to AI tools that are built into the Library website (like our new Primo Research Assistant). I also include as part of the Information Literacy module some helpful in-class activities for students to reflect on the ethical uses of AI.
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I do not currently teach AI-specific courses; however, when I conduct information literacy workshops focused on writing, I emphasize the correct and ethical use of AI, as well as the hallucinations it can generate. Students are urged to verify information and rely on trustworthy sources.
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The most important lesson librarians can pass to on is ethics an integrity when it comes to AI. As information professionals, we already know that ethics and integrity is an important part of information literacy.
I think we are in an ideal position to inform others about AI responsibility. With that said, it is imperative that as library professionals, we are well equipped and kept up to date with AI knowledge. -
I don’t teach AI literacy at present, but it’s being incorporated into our library instruction. A top priority to impress upon students is to consider the tool itself before using it. Is this the right tool? Does it carry biases or draw from poor source material? Knowing the flaws, what are the benefits to using the tool and do those outweigh the drawbacks? I agree with what one of the articles in the reading lists stated, that being able to change HOW students think about AI tools and their usage is the critical point where we can create the deepest impact.
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Librarians can help the academic community understand how Kuhlthau’s Information Search Process truly helps students develop their independent learning skills by recognizing that research is an iterative process (the more a student learns, the stronger a knowledge base the student develops, and thus the student realizes that there are additional, and more focused questions to ask. Also, research is supported by ongoing scholarly conversations that test knowledge, include new perspectives and requires both confidence and openminded-ness in students who are becoming scholars. We also, as educators, need to agree on how we communicate the values of academic integrity to our students, not only in formal instruction, but also in everyday conversation, information gathering and retrieval, and how we design assessments that contribute to students’ overall learning while studying at our institutions. I speak as a high school librarian who focuses on research skills and academic integrity awareness in preparation for students bound for higher education.
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The activities I prefer to develop AI literacy are those that favor reflective thinking, both to identify the need for information and to elaborate the prompt. These actions can be included as part of a challenge or rally. All of them were developed and implemented on the virtual campus of my institution.
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If you aren’t teaching AI literacy, what is the most important lesson that you think librarians as a profession can pass to the rest of the academy?
I think the most important lesson that can be passed on is that the objective shouldn’t be to get an answer, but the objective should be to know whether an answer warrants your trust.
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“If you teach AI literacy, what’s your favorite classroom or workshop activity?”
My favorite activity so far is helping students look at a series of images, some human created, some AI generated content, and have discussions on whether or not it’s real or AI generated. I find having students engage directly with a variety of content and have a space to actively discuss if it’s gen AI or not helps boost AI literacy skills and opens up more avenues of conversation about why AI literacy is important and how prevalent it can be in real life.
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The most important lesson from librarians for the academic community is the critical evaluation of information, transparency in citing primary sources, checking sources for accuracy, bias, and reliability in any environment, as well as respect for copyright and the fair use of others’ ideas.
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