AI Literacy for HEI Students
A Practical Guide Using the DEC Framework for UP Librarians
Artificial intelligence (AI) is rapidly becoming integral to research and learning in higher education. Academic librarians, as information literacy experts, are uniquely positioned to help students develop AI literacy – the knowledge and skills to understand and use AI effectively and ethically. The Digital Education Council (DEC) [1] AI Literacy Framework defines five key dimensions of AI literacy for all learners. This guide is organized around those five dimensions, illustrating the role librarians can play in each, along with practical teaching strategies, tools, and activities. It concludes with recommendations for a student-facing AI Literacy Workbook aligned to these dimensions. The focus is on actionable approaches in a higher education context, grounded in current best practices in academic libraries.
Call for AI Literacy
Many students and professionals use AI tools to boost productivity, yet uncertainty persists regarding their own proficiency and understanding of responsible AI use. Surveys reveal that this uncertainty arises from a lack of confidence and limited knowledge of best practices and guidelines. As AI becomes more embedded in daily work and study, the ability to use these tools effectively has grown into a critical competency.
Despite widespread recognition of AI’s importance, the number of individuals equipped with strong AI skills remains inadequate. Nearly half of students report that they do not feel prepared for an AI-driven workforce. This gap signals a need for focused educational efforts. Without proper guidance, some individuals risk becoming dependent on AI—relying on it to handle routine tasks without the ability to assess or improve its outputs. Developing AI literacy is essential, not just for using new technologies, but for maintaining critical judgment and agency in an increasingly automated environment.
The DEC Framework
The DEC AI Literacy Framework shown in Figure 1, highlights the need to emphasize both general and specialized dimension of AI literacy. The framework is structured around five central questions, each corresponding to a literacy dimension—from “How does AI work?” to “How do I apply AI in a specific context?” The visual separation between general AI literacy for all and specialized literacy for specific domains underscores the framework’s adaptability. It reflects the need for students to acquire both broad, cross-disciplinary AI knowledge and targeted, field-specific competencies, supporting effective and ethical use of AI in both academic and professional settings.
Figure 2 illustrates the DEC AI Literacy Framework’s structured approach to AI literacy through a competency matrix. It aligns the five literacy dimensions with three levels of mastery, detailing the specific focus at each stage. For example, students move from “AI and Data Awareness” at Level 1 to “AI and Data Optimisation” at Level 3. Similarly, the framework tracks progression from “Question AI Output” to “Challenge AI Output” in critical thinking, and from “Applied AI Awareness” to “Strategic AI Leadership” in domain expertise. This matrix serves as a practical roadmap for educators and librarians, offering a systematic way to assess and build AI literacy across multiple competencies.
Lastly, Figure 3 presents the competency levels defined by the DEC AI Literacy Framework for students across five key dimensions: Understanding AI and Data, Critical Thinking and Judgement, Ethical and Responsible Use, Human-Centricity, Emotional Intelligence and Creativity, and Domain Expertise. Each dimension is described at three progressive stages—Baseline, Expected, and Forward-looking. These levels clarify the progression from foundational awareness of AI concepts to practical application and, ultimately, to advanced, future-ready proficiency. The framework guides both curriculum designers and students in setting clear learning targets, ensuring that students develop not only technical understanding but also critical, ethical, and field-specific AI skills necessary for success in academia and beyond.
1. Understanding AI and Data
What it is: Understanding AI and Data means grasping how AI systems work and how data is collected, processed, and used by AI. Students should learn the fundamentals of machine learning, data quality, and the capabilities and limitations of AI tools. Proficiency in this area enables students to make informed decisions about AI use.
Role of Librarians: Academic librarians can demystify AI for students. They can introduce basic AI concepts in an accessible way, much as they teach information literacy basics. Librarians act as translators of technical AI concepts (algorithms, datasets, models) into lay terms for novices. By building students’ foundational understanding, librarians ensure learners approach AI with knowledge rather than confusion or fear.
Teaching Strategies and Actions:
Offer Introductory AI Workshops: Librarians can develop short workshops or orientation sessions on AI 101 – covering what AI is, common AI applications, and how AI tools use data. For example, a librarian might explain the workflow of a simple AI (input → algorithm → output) and show how data fuels this process. Using analogies (like “AI as a recipe using data ingredients”) and real-life examples (recommendation systems, virtual assistants) makes the concepts relatable.
Integrate Data Literacy into Instruction: Emphasize the connection between data literacy and AI literacy. Librarians can guide students in exploring how data quality affects AI outcomes. For instance, in a workshop students might examine a sample dataset that an AI tool could use – identifying what the data includes and discussing biases or gaps. An activity could involve analyzing a provided dataset for potential biases and predicting how those biases might influence an AI system’s output. This helps students appreciate why “garbage in, garbage out” applies to AI.
Demonstrate AI in Action: During information literacy classes or library tours, librarians can demonstrate simple AI tools to show how they work. For example, using an online AI demo (such as a basic image recognition tool or a chatbot), the librarian can walk through how the AI takes input data and produces results. Observing an AI chatbot answer a question and then examining what data it was trained on can illuminate the AI’s decision-making process. Such hands-on demos let students see AI principles rather than just hear theory.
Curate Learning Resources: Librarians should curate and promote resources for self-paced AI learning. This might include linking to free online courses or tutorials on AI basics, recommending interactive platforms (like Google’s Teachable Machine or Machine Learning for Kids) that let students experiment with training an AI model, or creating a library guide on “Understanding AI.” By pointing students to quality resources, librarians extend learning beyond the one-time workshop.
Example Tools and Resources: Library professionals can leverage a variety of tools to support this dimension. Simple AI platforms (e.g. Teachable Machine for building a tiny image classifier) allow students with no coding background to see how changing training data affects outcomes. Visualization tools can show how algorithms make decisions (such as TensorFlow Playground for visualizing neural networks). Librarians might also use data visualization software with built-in AI (like Tableau or PowerBI with AI features) to illustrate how data is analyzed. Additionally, librarians maintain up-to-date guides with explanatory videos, AI glossaries, and example projects to help students build a mental model of how AI works.
Activities and Instructional Sessions:
Data Bias Exploration: Provide students with a small dataset and an AI demo. Have them modify the dataset (e.g. remove or skew certain entries) and observe changes in the AI’s behavior. For instance, using a sentiment analysis demo – what happens if all the training examples are positive statements? Students can then discuss why the AI’s output changed and what that implies about the importance of balanced, high-quality data.
AI in Daily Life Brainstorm: In a library-led class discussion, ask students to list everyday AI applications they encounter (social media feeds, navigation apps, etc.) and identify the kinds of data those applications use. The librarian can fill in context about each example. This activity raises awareness that AI is already all around us and driven by data, making the learning immediately relevant.
“How AI Works” Mind Map: After a basic introduction, have students collaboratively draw a simple concept map of an AI system, including data input, the algorithm/model, and output. Librarians can prompt considerations like “Where does the data come from? Who builds the algorithm? What is the human’s role in interpreting the output?” By visualizing these connections, students reinforce their understanding of AI components and workflow.
2. Critical Thinking and Judgment
What it is: Critical Thinking and Judgment in AI literacy is the ability to evaluate AI-generated content critically, check for accuracy and bias, and apply logical reasoning instead of blindly trusting AI. Students learn to question AI outputs, verify information against reliable sources, recognize misinformation, and keep human judgment at the center of any AI-assisted process. In short, this dimension ensures AI is used as a tool for augmentation rather than a replacement of human critical thinking.
Role of Librarians: Librarians have long taught students how to evaluate information sources – now they extend that expertise to AI outputs. In the era of ChatGPT and generative AI, students might receive instant answers that sound authoritative. Librarians can coach them to pause and critically appraise those answers just as they would a website or article. Academic librarians serve as a bulwark against complacent acceptance of AI content. They foster healthy skepticism, encouraging students to ask: Where did this information come from? Is it correct or biased? Librarians model a thoughtful approach to using AI, showing students how to verify AI-provided information and integrate it responsibly into research.
Teaching Strategies and Actions:
Embed AI Evaluation in Information Literacy Sessions: When teaching research skills, librarians can include activities focused on AI-generated information. For example, a librarian might present a paragraph answer from an AI chatbot on a scholarly topic and have the class assess it. Students could be tasked with finding supporting or refuting evidence from library databases or other trusted sources for each claim the AI made. This mirrors traditional source evaluation, but applied to an AI’s output. It trains students not to take AI responses at face value.
Teach Verification Techniques: Librarians should share strategies for fact-checking AI content. This can include cross-checking facts in an AI’s answer against reference works or academic sources, using reverse image searches for AI-generated images, or employing AI output detection tools as a form of investigation. For instance, if an AI-generated essay provides a statistic, the librarian might guide students to trace that statistic back to an original source (or discover that the AI fabricated a source). By practicing these techniques, students learn to systematically verify AI outputs for accuracy and bias.
Discuss AI Bias and Misinformation: Incorporate real-world examples where AI got it wrong. Librarians can present cases such as an AI translation service that produced biased translations, or a known incident of AI-generated “fake news.” In a workshop or discussion, students can analyze why the AI made those mistakes – was it biased training data? a flawed algorithm? – and discuss how to spot similar issues in the future. Librarians should also cover deepfakes and misleading AI-generated media, teaching students basic skills to detect when an image, video, or text might have been created by AI. This not only sharpens critical thinking but also digital media literacy in an AI age.
Promote a Mindset of Healthy Skepticism: Librarians must explicitly encourage students to question AI outputs. One way is highlighting the concept of “AI Idiots”, described as individuals who rely on AI uncritically, without the ability to evaluate or supplement AI-provided information. By warning against this over-reliance, librarians reinforce that students should always apply their own judgment. For example, a librarian might say, “ChatGPT can be a helpful starting point, but you are the scholar – it’s your job to check its work.” This message, consistently delivered, helps students internalize that AI is a tool, not an infallible oracle. They should use AI to augment their thinking, not replace it.
Example Tools and Resources: To teach critical evaluation of AI, librarians might use side-by-side comparisons of AI systems: e.g. asking the same question in two different AI chatbots (such as ChatGPT and Bing Chat) and analyzing the differences in responses. They can introduce browser plugins or tools that attempt to detect AI-generated text, prompting discussion on their reliability. Librarians may also utilize frameworks like the CRAAP test (Currency, Relevance, Authority, Accuracy, Purpose), adapting it for AI outputs (“Who is the authority behind this AI’s information?”). Providing students with checklists or worksheets to evaluate AI-generated content can give structure to their judgment process.
Activities and Instructional Sessions:
AI Output Fact-Check Exercise: Give students a short piece of text or an image produced by an AI and a list of claims or “facts” from that output. In small groups, have them use library resources (articles, books, data sources) to verify each claim. They should label each claim as true, false, or uncertain based on evidence found, and report back. This activity mimics real-world scenarios where students might use AI for research and then need to confirm the details.
Comparing AI Answers: In a live session, the librarian can project two answers from different AI tools to the same question (for example: “Summarize the causes of the 2008 financial crisis.”). The class discusses which points are similar, which differ, and speculates why. Are there errors or missing context in one of the answers? Which answer seems more reliable and why? This leads to a conversation about how training data and algorithms influence output, and underscores that AI outputs are not uniform or neutral.
Bias and Misinformation Case Study: Present a brief case study of an AI failure – such as an AI-powered hiring tool that was found to discriminate against certain applicants, or an AI chatbot that started giving harmful health advice. Have students break into groups to dissect the case: What went wrong? What bias or flawed assumption led to the outcome? How could it have been prevented or caught? Each group can then share lessons learned. Librarians facilitate by connecting these lessons back to students’ own use of AI (e.g., “This is why you shouldn’t accept an AI’s answer without question – it may have invisible biases.”).
3. Ethical and Responsible AI Use
What it is: Ethical and Responsible AI Use involves understanding the ethical principles and societal impacts of AI, and using AI in a principled way. This dimension covers AI ethics frameworks (fairness, transparency, accountability, privacy) and the ability to recognize and mitigate risks like bias, discrimination, plagiarism, or misinformation. It also means knowing the rules – from academic integrity policies to laws – that govern AI use, and ensuring one’s own AI practices are compliant and ethical.
Role of Librarians: Academic librarians often champion information ethics (privacy, intellectual freedom, citation practice) and can extend this role to AI. They guide students in navigating questions like: When is it appropriate (or not) to use AI in coursework? How do we give credit for AI-assisted work? What are the privacy concerns with AI tools? Librarians serve as ethical mentors in the AI space, helping students understand not just what AI can do, but what it should or should not do in an academic context. Libraries are increasingly expected to lead students and faculty through the ethical use of AI in order to maintain trust and integrity in academic work.
Teaching Strategies and Actions:
Incorporate AI Ethics into Library Instruction: Librarians can design workshops specifically on AI ethics or embed ethical discussions into existing classes. Key topics include data privacy (e.g., what happens to the data you enter into ChatGPT), algorithmic bias (how AI can perpetuate stereotypes or inequality), and academic honesty (the ethics of using AI in assignments). By reviewing core AI ethics principles – fairness, accountability, transparency, etc. – and discussing them with concrete examples, librarians raise student awareness of responsible AI use. For example, a library session might review a university’s honor code in light of AI: is using an AI writing tool allowed or is it considered plagiarism? What are the guidelines for citing AI-generated content? This grounds ethics in students’ real academic responsibilities.
Use Scenario-Based Learning: Present students with real or hypothetical scenarios that involve ethical dilemmas with AI. For instance: “A student uses an AI tool to write parts of a paper without citation – what are the ethical issues?” or “A company’s AI system makes a decision that negatively impacts a group of people – who is accountable?”. Librarians can have students debate these scenarios or role-play different stakeholder perspectives. By grappling with scenarios like AI-driven surveillance on campus or bias in an AI admissions tool, students practice applying ethical frameworks to make judgments. They learn to consider consequences and moral trade-offs, which prepares them to make responsible choices in their own AI use.
Highlight Case Studies of AI Ethics Failures and Successes: Librarians should share well-documented cases where ethical issues in AI became evident. For example, Google’s AI photo tagging misidentifying people of color, or an AI chatbot that produced hateful content, are cautionary tales. On the flip side, librarians can point to positive examples (an AI used to improve accessibility for disabled users, developed with ethical design in mind). In a class discussion, analyze these cases: What went wrong or right? How could bias have been addressed? What ethical principles were at stake? Having students research and present on a contemporary AI ethics case is another effective strategy. This not only informs them about current events but also reinforces an ethical analysis mindset.
Provide Guidelines for Academic Use of AI: Students need clear guidance on how to use AI responsibly for learning and assignments. Librarians can produce tip-sheets or mini-guides covering best practices such as: do not input sensitive personal data into online AI tools (protect privacy), always confirm AI-generated content from authoritative sources (ensure accuracy), never pass AI-generated work off as your own (avoid plagiarism – cite it if allowed), and be mindful of bias in any AI-provided material. Librarians might collaborate with the campus writing center or teaching center to disseminate these guidelines. By proactively teaching how to use AI responsibly, librarians help build a culture of ethical AI use on campus.
Example Tools and Resources: Librarians can utilize resources like AI ethics frameworks or checklists developed by organizations (e.g., OECD or UNESCO guidelines on AI ethics) to inform their teaching. They might use interactive online modules on AI ethics (if available) or videos of experts discussing AI’s societal impacts. Tools such as AI Fairness 360 (an IBM toolkit) can demonstrate how bias detection works in AI models. Additionally, librarians can keep a repository of articles, blog posts, or institutional policies on AI ethics to use as readings or discussion material with students. Leveraging the library’s role, they can also ensure students know about data privacy – for instance, reading the terms of service of AI tools together to spot what data is collected and how it’s used, which can be eye-opening for students.
Activities and Instructional Sessions:
Ethical Dilemma Debate: Split students into small groups and assign each an ethical dilemma involving AI. For example, one group might discuss “Should professors allow AI tools like ChatGPT for writing assignments, and if so, how do we ensure proper attribution?” Another group might tackle “An AI system is used to decide who gets a loan and seems to discriminate – what should be done?” Each group discusses and then shares their conclusions with the class. Librarians facilitate by ensuring key ethical principles are considered in each case (fairness, accountability, etc.) and highlighting insights from each group’s approach.
AI Policy Exploration: In this activity, librarians provide excerpts from emerging AI policies – such as the university’s policy on AI use in coursework or a tech company’s AI ethics guidelines. Students work in pairs to read and summarize one excerpt, then explain how it would affect someone’s behavior (e.g., “Policy says X, so as a student I should….”). This exercise makes abstract policies concrete and gives students a chance to ask questions. It also sends the message that being informed about policy is part of responsible AI use.
Create an AI Use “Code of Conduct”: In a workshop setting, the librarian and students jointly brainstorm a list of do’s and don’ts for AI use in their academic lives. For instance, Do use AI to brainstorm research ideas; Don’t use AI to complete an exam for you. Do verify facts from AI; Don’t share class data with third-party AI tools without permission. The librarian can compile the input from the class into a one-page AI Use Code of Conduct and share it with attendees (or even more broadly). This collaborative activity not only produces a handy guide for students to follow, but it also gives them ownership and clarity on ethical expectations.
4. Human-Centricity, Emotional Intelligence, and Creativity
What it is: Human-Centricity, Emotional Intelligence, and Creativity refers to the human skills and values that remain crucial in an AI-driven world. Even as AI automates tasks, competencies like empathy, communication, adaptability, and creativity become even more important. This dimension emphasizes keeping humans at the core of AI processes – ensuring AI is used to complement human abilities rather than replace them. It also involves understanding how AI impacts human interaction and well-being, and how we can design and use AI in ways that respect human needs and creativity.
Role of Librarians: Librarians can remind students that learning and innovation are human-centered endeavors, with AI as a partner, not a protagonist. In the library context, this might mean teaching students how to use AI as a brainstorming tool or creative aid, while stressing that the originality, emotional insight, and critical judgment must come from the student. Librarians can foster emotional intelligence by discussing the limitations of AI in understanding context or feelings, thereby encouraging students to supply those human elements. They also serve as advocates for users – for example, ensuring that new AI-driven library services are user-friendly and considerate of diverse needs. By modeling adaptability and a growth mindset, librarians show students how to continuously learn (a human skill) alongside evolving AI tools.
Teaching Strategies and Actions:
Emphasize Human-in-the-Loop: Librarians should teach students about the concept of human-in-the-loop – the idea that human oversight and input should remain in AI systems, especially for important decisions. In instruction, a librarian might illustrate this by mapping out the stages of an AI project (data collection, model training, output, decision) and highlighting where human judgment is needed at each step. Students could be asked, “What could go wrong if we remove humans from this step?” (e.g., no human checking an AI’s medical diagnosis could lead to dangerous errors). This drives home the point that human values and intervention are critical to keep AI outcomes aligned with what we truly want.
Encourage Creative Use of AI with Reflection: Librarians can design activities where students use AI creatively – for instance, generating an AI-produced poem, artwork, or design – and then reflect critically on the process and result. The librarian might run a session in the library’s makerspace or computer lab where students experiment with an AI art generator or music composition tool. After the fun of creation, the crucial part is discussion: How did the AI’s output differ from what the student imagined? In what ways did the student’s unique perspective still need to guide or edit the result? By engaging in this, students learn that AI can enhance creativity (sparking ideas, prototyping quickly) but human creativity and taste are still paramount.
Develop Soft Skills in Parallel: In library workshops, librarians can include components that build communication and empathy skills in an AI context. For example, they might simulate an “AI assistant vs human librarian” reference interview: one student asks a complex research question to an AI and another student asks a librarian. The group then compares the experiences – perhaps the AI gave a quick answer, but the librarian understood the nuance and emotions better. This kind of exercise highlights the irreplaceable value of human empathy and listening. Librarians can follow up by discussing how students, as future professionals, can ensure technology is used in a way that enhances human connections rather than detracting from them.
Highlight Lifelong Learning: Librarians should convey that adaptability and continuous learning (parts of emotional intelligence and professional growth) are key in the age of AI. A strategy here is for librarians to share their own experiences learning new AI tools or adapting to changes – modeling a positive attitude toward change. They can facilitate workshops on learning how to learn about emerging technologies, encouraging curiosity and resilience. For instance, a librarian might say, “I didn’t know how this AI tool worked at first, but I attended a webinar, tried it out, asked colleagues – and now I can teach you. You can do the same for new tools in your future career.” This framing empowers students to view AI not as a threat, but as an evolving set of tools they can approach with confidence and a human-centric perspective.
Example Tools and Resources: On the creativity side, librarians can introduce tools like AI drawing or writing assistants (e.g., DALL-E for images, or ChatGPT’s creative writing mode) in a controlled, educational setting. For emotional intelligence aspects, showing clips of AI voice assistants vs human interactions could spark discussion (for example, comparing the empathy in a therapy chatbot to a human counselor – what’s missing?). Resources such as design thinking exercises can be repurposed to consider human-centered AI design: librarians might use worksheets that have students design an imaginary AI tool with ethical and human considerations built in. Additionally, libraries can host events like an “AI and Humanity” panel or book club, using literature (sci-fi, ethics texts) to explore how AI and human values intersect, thereby deepening students’ understanding in a multidisciplinary way.
Activities and Instructional Sessions:
Human vs. AI Decision Mapping: Provide students with a template diagram of an AI lifecycle (from data input to decision output). Working in small teams, students annotate the diagram with where human oversight is present or needed. For each point, they note what uniquely human quality is important (e.g., “At data selection stage – Human judgment needed to ensure diverse, unbiased data”). Then, have teams imagine the diagram without those human touchpoints and predict the consequences. They can present their findings. This visual and analytical activity, facilitated by the librarian, makes abstract concepts of human-centric AI more concrete.
AI Creative Challenge: In a one-hour workshop, challenge students to use an AI tool to create something (an essay outline, a piece of art, a short song) on a fun prompt related to their studies. After creation, each student or group shares their AI-generated product and, importantly, critiques it: Is it good? Where does it fall short of human-created content? What would they change or add as humans? For example, a student uses an AI to outline an essay and then notes which parts of the outline were insightful and which parts missed the nuance that a human writer would include. The librarian leads a reflection on how AI can jump-start tasks but how human creativity and critical thought must refine and finalize the work.
Role-Play: AI Assistant with a “Heart”?: Librarian sets up a role-play where one student acts as a user asking for help (research guidance, or even personal advice), and another student uses an AI assistant to respond. Then a second round: a student (or the librarian) responds as a human would. The group observes differences in emotional tone, understanding, and helpfulness. This playful exercise can generate laughs but also insights – students will likely notice that the AI, while informative, might ignore emotional cues or individual context that a human picks up. The discussion afterward, led by the librarian, centers on why human emotional intelligence is crucial in many interactions and how students can ensure AI is used in a human-centered way rather than a cold automation.
5. Domain Expertise
What it is: Domain Expertise in AI literacy is the ability to apply and understand AI within a specific academic or professional field. It means knowing how AI is used in your discipline, being able to evaluate domain-specific AI tools, and understanding field-specific challenges (ethical, legal, operational) that come with AI. This dimension connects general AI literacy to specialized knowledge – for example, what an English major needs to know about AI (like AI for textual analysis or writing) will differ from what an Engineering student needs (AI for simulation or data analysis in engineering). Ultimately, domain expertise ensures students can critically leverage AI in their chosen field and remain competitive and competent in an AI-infused workforce.
Role of Librarians: Academic librarians, especially subject liaison librarians, can bridge AI literacy with discipline-specific learning. They are well-positioned to understand both the information needs of their subject areas and the emerging AI tools relevant to those areas. Librarians can introduce students to how AI is transforming research and practice in their majors – for instance, a medical librarian might teach about AI in diagnostic tools and how to find literature on it, or a business librarian might cover AI in market analysis. By doing so, librarians ensure that AI literacy isn’t taught in a vacuum but is tied to students’ academic and career contexts. Librarians also collaborate with faculty to embed AI literacy into the curriculum, ensuring that as students build domain knowledge, they simultaneously learn how AI applies to that domain. This dual lens helps future graduates become not just subject-matter experts, but also adept in using cutting-edge AI tools in their field.
Teaching Strategies and Actions:
Tailor Instruction to Each Discipline: Librarians should customize AI literacy materials for different subject areas. For example, during a library instruction session for engineering students, the librarian might introduce AI tools used in engineering research (such as MATLAB’s AI capabilities or engineering-specific datasets for machine learning). For social science students, the librarian might focus on AI tools for data analysis, mapping or qualitative analysis (like NVivo’s AI features). The idea is to speak the language of the discipline: demonstrating AI’s role using relevant examples (AI in legal research for law students, AI in drug discovery for pharmacy students, etc.). This makes AI literacy immediately applicable. Subject liaisons already teach concepts like database searching within a discipline – now they can also point out, “Here’s how AI is being used by professionals in your field,” normalizing AI as part of domain knowledge.
Create Domain-Specific AI Resource Guides: Librarians can develop LibGuides or resource lists for “AI in [Discipline]”. These guides might include links to prominent AI tools, datasets, journals or blogs discussing AI developments in that field, and ethical guidelines or standards specific to that discipline’s use of AI. For instance, a guide for Journalism might link to resources on deepfakes and AI in media verification; a guide for Biology might highlight AI for genome analysis. By curating these resources, the librarian provides a one-stop introduction for students to explore how AI intersects with their major. They can use these guides in instruction sessions or make them available for independent learning.
Collaborate on Course and Assignment Design: Academic librarians can work with faculty to integrate AI literacy into course assignments. For example, a professor in finance might partner with the business librarian to create an assignment where students use an AI-powered financial analytics tool and then evaluate its recommendations. The librarian can support by teaching students how to use the tool and how to find additional information to validate the AI’s output. Another approach is adding an AI-related discussion or reflection component to existing research assignments (e.g., “find a scholarly article about AI in our field and discuss its implications”). By embedding AI into coursework, librarians and faculty together ensure students get hands-on practice with AI in a manner relevant to their domain.
Host Interdisciplinary AI Events: Since AI is a hot topic across fields, librarians can organize events like panels, guest lectures, or hackathons that bring in experts to talk about AI in various disciplines. For instance, invite a data scientist to speak to humanities students about text-mining AI, or a digital humanities professor to demonstrate an AI project in literature analysis. These events raise awareness and show students the frontiers of AI in their area of study. Librarians can moderate these sessions, connecting the discussion to library resources (e.g., “We have books and databases where you can learn more about what our guest discussed”). Such programming positions the library as a hub for interdisciplinary AI knowledge and encourages students to engage with AI beyond their textbooks.
Example Tools and Resources: Depending on the field, the tools will vary widely:
STEM fields: programming libraries like Python’s scikit-learn for data science, Jupyter Notebooks for AI experiments, domain-specific AI software (e.g., bioinformatics tools with AI features). Librarians might maintain datasets relevant to these fields and show students how to access and use them.
Arts and Humanities: tools like Voyant (for text analysis), AI image or music generation tools for creative arts, or archives that use AI for digital collections. Librarians can demonstrate, for example, an AI that analyzes Shakespeare’s texts or an image recognition tool used in art history.
Social Sciences: AI tools for survey analysis or GIS mapping with AI. A librarian could introduce a tool that uses machine learning to analyze social media data for sociology students.
Professional fields: In law, tools like Westlaw Edge’s AI search; in medicine, AI diagnostic apps or PubMed’s AI search filters; in business, AI for data visualization and forecasting. Librarians should choose a couple of representative tools to show in instruction sessions, giving students a taste of what practitioners use. They also emphasize evaluation: for example, discussing the limitations of an AI legal research tool in finding precedents, thereby reinforcing that the student’s legal reasoning is still crucial.
Activities and Instructional Sessions:
Case Study Analysis in the Discipline: The librarian presents a brief case study where AI was applied in the field and asks students to analyze it. For instance, for nursing students: a case where an AI tool assisted in diagnosing patients – how did it help, and what were the concerns (e.g., accuracy, ethics)? Students could break into groups to discuss questions like “Would you trust this AI? How would you double-check its suggestions? What does a nurse or doctor need to know to use this AI effectively?” Each group then shares insights. This activity uses domain context to make AI literacy tangible and prompts students to consider their future professional roles alongside AI.
Guest Speaker Reflection: After hosting a guest lecture from a professional who uses AI (say, a marketing expert discussing AI in advertising), librarians can hold a follow-up session or include an assignment for students to reflect. Students might write a short reflection or discuss in class: “What did you learn about how AI is changing work in your field? What skills did the speaker mention as important?” The librarian can guide the reflection toward identifying literacy skills the DEC framework highlights – understanding the tool, critical evaluation, ethical considerations, etc., all in the specific context of that field. This helps students connect the abstract dimensions of AI literacy to real-world practice.
Domain AI Tool Demo and Hands-on Practice: During a library instruction class, allocate time for students to try a domain-specific AI tool themselves (if resources allow). For example, in an education class, students might try an AI tutoring app; in computer science, play with an open-source machine learning model; in literature, use a text analysis tool on a novel. The librarian prepares a step-by-step activity (possibly a worksheet or guide) for the tool. After trying it, students discuss what the tool did well and where domain knowledge was needed to guide it. They could also evaluate the tool’s output: did it miss context that a human expert would catch? This active learning approach not only builds technical skill but reinforces the idea that domain expertise + AI is more powerful than AI alone.
Recommendations for a Student-Facing AI Literacy Workbook
To complement in-person instruction, academic librarians can create a student-facing AI Literacy Workbook aligned with the five DEC dimensions. This workbook would serve as a practical, interactive resource that students could use in workshops or for self-study, reinforcing each literacy dimension through exercises, prompts, and real-world tool explorations. Below is a recommended structure and content for such a workbook, organized by the five dimensions:
Understanding AI and Data – Foundations Module
Goal: Ensure students grasp how AI works and the role of data.
Content: Clear, jargon-free explanations of key concepts (AI, machine learning, algorithms, training data, etc.), with diagrams or illustrations.
Practical Exercises: For example, an exercise asks students to identify AI applications they use in daily life and list what data those applications likely use. Another exercise might walk students through a simple online AI demo: the workbook could link to a tool like Teachable Machine and instruct, “Try training the image classifier with 10 photos of cats and 10 of dogs, then test a new image. Now add 10 more cat photos and retrain – what changes?” Students would record their observations in the workbook.
Reflection Prompts: Questions that encourage thinking about data and AI, such as: “What surprised you about how the AI made its decision?”, “Why do you think data quality matters for AI?”, “How did the AI’s output change when you gave it different input data?”. These prompts help students articulate their understanding and any misconceptions for later discussion.
Real-World Tool Engagement: The module can include a sidebar with a recommended “Explore More” (e.g., “Interested in learning more? Try Google’s ‘AI for Everyone’ interactive module or watch XYZ video on how Netflix’s recommendation algorithm works.”). This encourages curious students to go deeper using librarian-curated resources.
Critical Thinking and Judgment – Evaluation Module
Goal: Teach students to critically evaluate AI outputs and not accept them uncritically.
Content: A brief overview of the importance of questioning AI (possibly citing the “AI Idiots” cautionary tale that warns against over-reliance, explained in student-friendly terms). Include a simple checklist for evaluating AI-generated information (e.g., “Check accuracy against another source; look for bias; identify the source of the AI’s training data if possible; consider what might be missing from the AI’s answer”).
Practical Exercises: One exercise might present an AI-generated paragraph on a topic with three embedded errors or inconsistencies, and ask the student to find the errors. The workbook could instruct: “Below is an answer about climate change that an AI provided. Mark or highlight any statements that you think might be incorrect or need verification. Use the internet or library resources to check these facts, and write the correct information or a source that confirms/denies the claim.”. Another exercise might have the student compare two AI answers: “AI A says X, AI B says Y – list which answer you find more trustworthy and why.” This builds comparative judgment skills.
Reflection Prompts: Questions like “How did it feel to find out the AI was wrong?”, “What strategies helped you verify the information?”, “In what situations would you trust an AI’s answer, and when would you be skeptical?”. These get students thinking about their own trust and verification process.
Real-World Tool Engagement: The workbook could suggest: “Try using an AI chatbot to get an answer to a homework question, then use the library website to find an article on the same topic. Compare the depth and credibility of the information.” It might provide space for students to jot down differences. By actually engaging with a real AI tool and a real library resource side by side, students practice the habit of cross-verification in a guided manner.
Ethical and Responsible AI Use – Ethics Module
Goal: Instill an understanding of AI ethics and guidelines for responsible use in academics and society.
Content: An accessible summary of key ethical issues (bias, privacy, transparency) and any relevant campus policies on AI use. This section might include a short scenario or comic illustrating an AI ethics dilemma to grab interest. Librarians can also include a checklist “Responsible AI Use Tips for Students” listing do’s and don’ts (e.g., “DO cite AI content you include in assignments, per your instructor’s guidelines; DON’T share personal data with unfamiliar AI tools; DO consider who might be harmed or excluded by an AI’s outcome; DON’T assume AI outputs are unbiased”).
Practical Exercises: Include a case-study exercise: “Read the scenario below and answer the questions.” For example, a scenario: “A student used an AI image generator to create artwork for an assignment but later learned the tool was trained on artists’ work without permission.” Questions might be: “What are the ethical issues here?”, “Should the student credit the AI or the original artists? Why?”, “How could this situation be handled responsibly?”. Another exercise: “Personal AI Use Pledge” – have students draft 3-5 statements as their own guidelines for how they will use AI in their studies (e.g., “I will not use AI to cheat; I will use AI to brainstorm but will do my own writing”; etc.). This personal pledge solidifies their commitment to ethical practices.
Reflection Prompts: Prompts might include: “Describe a time (real or imagined) when using AI might conflict with your sense of academic integrity.”, “How can AI be used for good in your field, and what precautions are needed?”, “What responsibility do you think companies should have when creating AI tools for public use?”. These encourage students to connect ethics to their personal values and future roles.
Real-World Tool Engagement: The module could suggest a guided exploration: “Visit an AI tool’s website (for example, an AI writing assistant) and find its privacy policy or terms of service. What does it say about how your data is used? Note one thing you learned or one concern you have.” This direct engagement teaches students to seek out and understand the fine print – a key responsible practice.
Human-Centricity, Emotional Intelligence, and Creativity – Human-AI Balance Module
Goal: Encourage students to consider the unique value of human abilities in conjunction with AI, and to use AI as a tool for creativity and problem-solving without losing human insight.
Content: This section might start with a short reflection or quote on human creativity – for instance, a statement like “AI can compose a melody, but can it feel joy from the music? That’s where you come in.” It would explain concepts like human-in-the-loop and the importance of empathy and creativity that AI cannot replicate. Librarians can include a small chart or table showing “What AI is good at” vs “What humans are good at,” to clearly delineate complementary strengths.
Practical Exercises: One exercise could be a concept map activity on paper: “Draw a concept map or diagram showing how humans and AI can work together to solve a problem.” The workbook could provide a blank template and an example (e.g., for solving a medical diagnosis – AI analyzes data quickly, human doctors provide empathy and ethical judgment – both pieces are needed). Another creative exercise: “Use any AI tool to help you with a creative task of your choice (such as brainstorming a story idea, generating a graphic, or suggesting solutions to a case study). Document what tool you used and what it produced. Then, describe at least two changes or improvements you made using your own ideas or feelings.”. This exercise pushes students to actively combine AI output with their personal touch and to recognize that interplay.
Reflection Prompts: Ask questions like: “In your experience using AI for a creative or personal task, what did the AI do well, and what was missing that only you could add?”, “How did it feel to collaborate with an AI? Did it spark your creativity or constrain it?”, “Why is it important for humans to remain at the core of decision-making in the age of AI?”. These prompts make students consciously evaluate the human-AI relationship and their comfort with it.
Real-World Tool Engagement: The workbook can include a fun but enlightening activity: “Test an AI chatbot’s emotional intelligence. Ask it something like ‘I’m having a bad day, what should I do?’ and note its response. How does it compare to what a friend might say? Write down your thoughts.” Such an activity directly exposes the student to the difference between AI-generated empathy and real human empathy, reinforcing the lesson that AI lacks true emotional intelligence and that’s where humans must lead.
Domain Expertise – AI in My Field Module
Goal: Help students connect AI literacy to their own academic discipline or future profession, making their AI skills relevant and specific.
Content: This section would likely be more customizable or include examples from multiple fields so that students from any major can relate. It could start with a brief introduction: “No matter your major – be it Finance, Biology, Art, or History – AI is changing how work is done. This module helps you explore AI in your area.” Then it might instruct students to complete tasks specific to their domain. Librarians can provide a list of industries or disciplines with a prominent AI example for each (e.g., Healthcare – AI in diagnostics, Literature – AI for literary analysis, Marketing – AI for consumer behavior predictions, etc.), allowing the student to pick which is closest to their interest.
Practical Exercises: One key exercise is a research task: “Find a recent example of AI being used in your field.* Use library databases or the web to locate a news story, research article, or case study. Summarize what the AI does and what impact it has.”* The workbook can give guidance, like how to search (suggest keywords) and a form to fill in (AI tool name, purpose, benefits, challenges). Another exercise: “Identify a skill or knowledge from your major that you think is important for working with AI in that field. Explain why AI cannot fully replace that human expertise.”* For instance, an education major might write that understanding student emotions is a skill a teacher has that an AI tutor lacks. This exercise personalizes the value of their human expertise. A more hands-on exercise (if resources are available) could be: “Try out one tool or demo related to your discipline from the list below”, followed by a few suggested free tools (like a legal AI research demo, or an AI chemistry molecule builder, etc.). Students would then answer a question: “What did this tool do? How might you use it or critique it as a future professional?”.
Reflection Prompts: “After researching AI in your field, what excites you about these technologies and what concerns you?”, “How do you think AI will change the job you hope to have after graduation?”, “What can you do during your studies to prepare for an AI-enhanced workplace?”. These prompts help students project into their career and see AI literacy as part of their professional development, not just an abstract school exercise.
Real-World Tool Engagement: The workbook might also encourage students to talk to a professional or professor in their field about AI. For example: “Interview a professor, graduate student, or professional in your field: ask if and how they use AI in their work. Jot down what you learn.” While not always possible for every student, this can be a powerful reality check and networking exercise. At minimum, the workbook could include a short Q&A excerpt from an expert (“Librarian spoke with Dr. X in Engineering who said…”) to provide insight. Librarians could also embed links or QR codes to short videos (perhaps a TED talk or an industry clip) showing AI in action in various domains, to enrich the learning experience.
Overall, the student-facing workbook should be highly interactive and reflective – not just reading material. By aligning each section with the DEC framework dimensions, librarians ensure that the workbook is comprehensive. By including practical exercises and real-world AI tool interactions, they ensure the learning is hands-on and relevant. The workbook can be used in library-led workshops, as a course supplement, or as a self-guided tutorial that students complete at their own pace, with librarians available for follow-up questions or discussions. Its development exemplifies how academic librarians can lead in AI literacy instruction: providing structured, scaffolded learning experiences that empower students to thrive with AI in their academic and future professional lives.
Reference
1.
Digital Education Council AI Literacy Framework — digitaleducationcouncil.com.