Inquiry, AI, and Pedagogy in the Library Classroom

Introduction

This paper originated from the researcher’s personal inquiry; specifically, how will AI change the way inquiry-based learning is achieved in the classroom, and how do we prepare students to do inquiry in the real world? As an instructor and former librarian who teaches the Inquiry pedagogy course to future school, public, and academic librarians and our information literacy class to undergraduates, I was curious what I should be teaching my students, especially as this field continues to grow and evolve. Our undergraduate course, designed around the Association of College & Research Libraries (ACRL) framework for freshmen and sophomores, has multiple student learning outcomes, but those relevant to inquiry include: develop and maintain lifelong-learning research skills to access, retrieve, evaluate and use information; effectively and creatively use various forms of technology as they apply to the research process; and distinguish between legal/ethical and illegal/unethical uses of information. Current student learning outcomes for our graduate course for future librarians include: reflect on how information seeking, information behavior theories and learning theories impact how to design inquiry instruction in a practical context; identify and use information tools available in K-12 and college libraries; and design and implement a one-shot lesson plan based on the ACRL framework or American Association of School Libraries (AASL) Standards. These student learning outcomes are still critical in an AI-inquiry age. However, we cannot discount the ways that AI will reframe information seeking, inquiry, and the pedagogical practices we use to prepare students for authentic research and lifelong learning. The engaged reception to the presentation, “Inquiry as a Higher Order Thinking Tool: In a World with AI Does Any Other Pedagogy Matter?” at The Innovative Library Classroom conference, held at William & Mary College in June of 2025, showed that I am not alone in considering how the field of AI will impact instructional librarianship.

What is clear is that AI presents immediate challenges to educators. Education is traditionally understood as a process of gaining knowledge, but what happens when AI can not only answer questions in diverse ways but also create?¹ AI is often described as a “black box” in that we input something, and it returns an output, but the internal process remains opaque.² Education is often its own black box; we may teach facts, skills, frameworks, or ethics, but there is no set amount of knowledge that guarantees the students will emerge as educated individuals who are ready for all life’s future challenges. Ultimately, AI introduces the question of a post-knowledge society.³ Can we bypass foundational learning because AI holds the inputs, and we just need to focus on students who can produce the outputs? What will this mean for student learning and the purpose of assignments? Because inquiry is the bridge between education and information seeking, it offers a way to frame these challenges. Both Guided Inquiry Design and the ACRL framework remind us that inquiry is not just about finding information or learning, but engaging in a reflective, iterative process, not unlike other educational models.⁴

Barr and Tagg argued in 1995 that education needed a paradigm shift from a teacher-centered to a student-centered model, noting, “Our mission is not instruction but rather that of producing learning with every student by whatever means work best.”⁵ Today, with the rise of AI, we have a new paradigm shift in that the means of production have become even more simplified, yet teachers worry that students are producing content that doesn’t seem to provide evidence of learning.⁶ The key question is not whether students are using AI in the classroom, since we know they are, but how to integrate AI into education to supplement and enhance instruction without replacing the learning process. While Barr and Tagg’s article did not consider the role of inquiry-based learning, it emphasized the role of college in making “students [who are] flexible adaptable learners, able to drive in a knowledge society.”⁷ This paper explores how librarians, through an inquiry-based learning model, can guide students at each stage in developing the skills and dispositions to use AI responsibly and participate successfully in a post-knowledge society.

The Inquiry Model in Education

The inquiry model is one approach to pedagogy that has been used in education. Khalaf and Mohammed Zin note that inquiry-based learning as a model has been around since 1911, but has become increasingly popular since the 1960s and 1970s.⁸ However, the exact methodology and approach to inquiry learning is variable, with some instructors equating it with problem-based learning, others with testing hypotheses, and others with social engagement and connecting evidence to knowledge and theories.⁹ In Inquiry-Based Learning: Undergraduate Research, Mieg highlights inquiry-based learning as “a didactic principle in higher education that relies on student independence: learning by conducting their own research. The principle of inquiry-based learning is part of the long tradition of education through scholarship.”¹⁰

Types of Inquiry Models in Library and Information Science

In library science, information search and information behavior models were a key feature of the move to user-centered research and understanding how individuals conduct inquiry within information systems. Wilson explored what is deemed information, what is an information need, and how technology mediates information access. Wilson also considered the social role of information and how individuals often ask other people when they have questions.¹¹ Dervin’s look at Sense-making was another of the moves in library science to user-centered research, by identifying how an individual’s cognitions, attitudes, and feelings interact with how they take in information.¹² Kuhlthau’s research focused on the individual’s experience throughout the Information Search Process (ISP) and how that experience is manifested in the cognitive, affective, and physical realms.¹³

As librarians have been at the forefront of information literacy research, they are also poised to help design frameworks for AI literacy. A wide variety of librarians at both the primary and secondary levels are investigating how individuals use AI in the research process, and they are creating standards and frameworks to help provide structure to future AI literacy pedagogy. In “Integrating Basic Artificial Intelligence Literacy with Media and Information Literacy in Higher Education,” Ndungu offers a useful table mapping an AI literacy framework to the ACRL Framework for Information Literacy for Higher Education.¹⁴ Florida International University also offers a LibGuide, “Artificial Intelligence + ACRL Frameworks,” that provides both basic information about what AI is, links to lesson plans, research articles, and tools.¹⁵ The School Library Systems Association of New York has created LibraryReady.AI, an AI scope and sequence designed to support the education of PreK-12 grade students.¹⁶ Lo provides another example of an AI literacy framework that looks at technical knowledge, ethical awareness, critical thinking, practical use, and societal impact of using AI systems.¹⁷

Just as information literacy is embedded across all stages of inquiry, AI literacy can also be integrated at multiple points in inquiry. The Guided Inquiry Design (GID) Framework emerged from Kuhlthau’s work on the ISP to support teachers and librarians as they guide students through the learning process.¹⁸ GID merged the research-based concepts of information searching and constructivist teaching into a model framework for education. This model empowers learners to follow personal interests in inquiry-based projects, construct meaning based on the information found, and participate socially in an educational process with others. The eight stages include Open, Immerse, Explore, Identify, Gather, Create, Share, and Evaluate, and were modeled from Kuhlthau’s ISP tasks: Initiation, Selection, Exploration, Formulation, Collection, and Presentation.¹⁹ These stages map naturally onto the ways students might use AI tools for questioning, discovery, creation, and reflection. Table 1 highlights different AI platforms that might be used during different GID stages. Kuhlthau’s work, centered on the user, highlights that inquiry enhances learning by focusing on how students engage with information not merely how they access it, a concept which is applicable even in today’s evolving landscape of information access and AI literacy frameworks.

Open and Immerse Stages

In Guided Inquiry Design, Kuhlthau, Maniotes, and Caspari describe the Open and Immerse stages as the first stages of an inquiry process.²⁰ In other educational settings, the opening may be called the hook or anticipatory set of the lesson or unit, and include an attention-grabbing activity that captures the students’ imagination and helps set the stage for the inquiry project.²¹ A lesson hook can be short, such as a probing question or video, or longer, such as a surprising experiment or authentic experience outside the classroom. During the open and immerse stages, librarians can work collaboratively with teachers and faculty to spark curiosity and introduce tools that invite students into deeper exploration and engagement.

The newness of AI has provided its own hook to the learning process through its opportunity to serve as a curiosity both as itself and as a jumping-off point for content.²² AI-generated media hooks might include the Gemini Podcast, which takes a document and transforms it into a podcast-style discussion.²³ AI-generated pictures and memes also offer a chance for engaging conversation starters in the classroom and can be connected with content.²⁴ In addition, AI-generated content can be rife with errors, often called “hallucinations,” although they might more clearly be considered glitches.²⁵ Identifying the “hallucinations” in AI-generated content can spark curiosity and encourage users to engage with AI and critically evaluate the information it produces.²⁶ Since AI-generated content is based on statistical likelihood rather than verified accuracy, several writers and leaders in educational technology have suggested that AI is the new Wikipedia: a helpful starting point for learning, but one that requires deeper, more rigorous research to follow.²⁷ Regardless of the librarian’s official role, the opportunity to share content expertise and technology leadership at the beginning of the inquiry process can help set the stage for the entire project.²⁸

Leading from the initial hook of the lesson, an immersion experience also continues to engage students and may help them relate to the background knowledge they already have about the topic. A successful immersion experience allows students to engage with interesting details they may wish to explore further.²⁹ Immersion experiences take more time as they are designed to teach content as well as engage students. Virtual reality has emerged as one way to engage students in place-based learning and provide engaging content.³⁰ AI offers new opportunities for students to engage, such as utilizing custom chatbots that allow students to virtually interact with writers or thinkers from the past.³¹ Students may even benefit from creating their own custom chatbots to immerse themselves in data. These types of activities, where students design their own AI and teach it by feeding it information and then asking it questioning prompts, puts students in the role of the teacher and gives them new perspective on data.³²

Explore, Identify, and Gather

The Explore, Identify, and Gather stages allow students to dig into the content, discover interesting ideas, and identify a research question.³³ Once they have identified that question, they can continue searching for sources that might provide an answer. The process includes multiple stages because students are not yet ready to identify an actual inquiry question until they know enough about a topic to identify a sensible question that is meaningful to them. The ISP provides some direction on students’ cognitive thinking at this stage, which goes from vague to more focused, and their affect, which changes in predictable ways as they continue throughout the project. During the exploratory phase, students may feel confused and frustrated, but as they identify a question, they gain clarity and begin to feel a sense of direction in their search.³⁴

These three middle stages can be considered the information literacy portion of the research project, and the librarian’s role can be critical.³⁵ Students who are trying to find direction need additional support, and teachers and faculty will benefit from librarians’ collaboration. Students should understand that finding a topic is not a one-stage process, but something that takes immersion in a topic, exploration, and identification before sources are gathered and papers are written. In GID, Kuhlthau, Maniotes, and Caspari suggest a number of activities designed to help students extend and understand the information they find by working collaboratively with others, connecting ideas to their own world, and visualizing the information in charts, pictures, and mind maps.³⁶ These activities are designed to give students time and new insights into content. It is worth noting that many of these activities could be done by AI, so it is essential to provide students with in-class time to complete activities for monitoring and support. Instructional librarians fulfill many roles at this stage, including as instructors for information literacy and inquiry-based courses, embedded librarians within inquiry-based or content-led courses, collaborative course designers, or one-shot teachers.³⁷

The librarian’s role in teaching information literacy in the AI age is similar, even as the tools students use may differ. Librarians may find they are increasingly called to help support students in information evaluation versus information retrieval. One study of 60 university students found that 35% of students who used ChatGPT, among other AI tools for information searching, found that using AI tools reduced the time and anxiety they felt in searching.³⁸ There is an opportunity here to reframe the role of the librarian from one who gives answers to one who helps define the question. Van Scoy highlighted how reference librarians often fulfill multiple roles, but one that helps students manage and learn how the information process unfolds is their role as a “counselor.”³⁹

Students in the Gather stage may continue to search with AI and may need information on effective prompt writing. This is where identifying the actual question and continuing to refine the prompt can lead to better results. Schuetzler provides a figure in their article, “Student Interaction with Generative AI: An Exploration of an Emergent Information-Search Process,” highlighting the cyclical nature of the information search process even when using a generative AI product.⁴⁰ Many researchers have suggested mnemonics to aid in prompt engineering, such as the Pentagram framework: Persona, Constraint, Output, Context, and Task.⁴¹ The focus of such prompts is often less about information retrieval and more about generating content, such as writing a paper or creating an outline for the student. This shift raises important questions about acceptable use and academic integrity that librarians will need to answer along with their faculty and institution.

Librarians supporting students through the Explore, Identify, and Gather stages may suggest ways to find information and connect students with more research-based AI tools, such as Research Rabbit, Connected Papers, or Litmaps.⁴² Dedicated scholars will find that these resources will allow them to make connections in their research, consider it visually, and find new authors and articles, providing an updated version of mind mapping. Newer researchers may find more benefits from the Consensus.app and Elicit.org, which offer more introductory searching based on research questions.⁴³ Librarians can point students to opportunities to use AI in ways that will help students find information from academic sources and compare the results of different types of AI tools. In particular, they will benefit from highlighting the importance of diverse perspectives in research and the challenge of stepping out of the “echo chamber” of traditional and AI-based search.⁴⁴

Create and Share

The Create and Share phases of GID offer a chance for students to organize their learning and present it in a culminating project.⁴⁵ In the university setting, the traditional assignment of choice has been the academic paper, but instructors have been searching for ways to ‘AI-proof’ their assignments. AI-proofing assignments may include having students turn in drafts of their work, submitting assignments with editing features turned on so instructors can see the writing process, and encouraging in-class written assignments and discussions instead of a traditional research paper.⁴⁶ Engaging students with the inquiry process provides an additional opportunity to encourage critical thinking and support higher-order thinking through activities that may rely more on collaboration, instructor feedback, and time with content than AI-generated responses.⁴⁷ Furthermore, students who engage with the whole inquiry process can gain confidence in their learning and may be less reliant on AI to create an assignment for them.⁴⁸

As students move toward completion and submission of their assignment, it can be beneficial to utilize AI to review their work. A wide range of file types can be uploaded into these programs, which can then assess writing quality, bias, and other specific strengths and weaknesses.⁴⁹ Mollick’s book, Co-Intelligence, highlights how he used AI to co-write his book by having it evaluate his writing in three different ways, exemplified by three different personas that he names, Ozymandias, Mnesmosyne, and Steve. He prompted AI with these different personas in order to provide a different outlook or perspective on his writing. Ozymandias was focused on simplifying concepts, Mnesmosyne was to find helpful connections related to the topic, and Steve was to exemplify the everyman reader who holds a basic interest in science and computers. Students may benefit from using these types of tools to evaluate themselves, seeing how an assignment or document might change based on the stated purposes or audience for which it is being written or created.⁵⁰

Evaluate

The Evaluate stage of GID is a chance for instructors and students to assess how well the assignment’s stated learning goals have been met. This is also a good time to incorporate student self-reflection on their learning, their understanding of the inquiry process, and why it matters to them.⁵¹ It also provides instructors and librarians with information to guide future instruction and adapt supports for deeper student learning, and hopefully provides students with some understanding of how to best tackle their next inquiry project.⁵²

While specific AI tools exist to provide teachers and professors with options to evaluate student work and assess student work for evidence of plagiarism, copying, etc, they should be used carefully.⁵³ Uploading documents to AI platforms raises privacy concerns, especially when it is unclear how the data will be stored, used, or shared by the service provider.⁵⁴ In addition, utilizing AI tools to replace teacher or librarian feedback can be considered evidence of shoddy or unprofessional work, as the Hill notes in the NYtimes article, “[Students] are paying, often quite a lot, to be taught by humans, not an algorithm that they, too, could consult for free.”⁵⁵ Maniotes also highlights the importance of the human element of the research and educational process. Ideally, the inquiry topic engages students in an area of interest, which supports student motivation and sustained effort. Providing human-based feedback, rather than AI based feedback, can reiterate to students the importance of their work they are doing in the classroom.⁵⁶

The evaluate stage is also a great time to examine not just the achievement of learning goals but also the process of learning and subsequently the impact of AI tools in the classroom. The Evaluate stage of Guided Inquiry Design aligns strongly with features of both the ACRL Framework and Lo’s Universal AI Framework, particularly around critical judgment and ethical reflection.⁵⁷ The ACRL frame Information Has Value offers a basis for reflecting on the ethical ramifications of information creation and usage, while Research as Inquiry promotes iterative questioning regarding the process of engaging with AI during the research process.⁵⁸ Lo’s model adds depth to this by challenging students to develop solutions for ethical issues.⁵⁹ Librarians can offer engagement sessions with students tailored to discussing and identifying questions relevant to AI and its use, such as “‘How much agency is given to technology versus the human user?’”⁶⁰ By offering opportunities to question the ethical aspects of AI, librarians ensure that evaluation goes beyond determining the usefulness of a tool to include factors like societal implications, environmental impacts, and examination of AI’s role in information ecosystems.

Conclusion

The guiding question of this inquiry was: How can librarians and educators incorporate AI into inquiry-based learning? As generative AI collapses the distance between question and answer, the Guided Inquiry Design framework offers a structure that helps librarians and educators move students beyond quick answers to deeper meaning. Librarians, drawing on their expertise in GID and information literacy, can model how to integrate AI tools thoughtfully depending on the stage of inquiry students are engaged with. While AI offers a method of empowering learners with information retrieval, creation, and evaluative tools, inquiry-based learning offers students a chance to engage with constructive, personally meaningful questions so they can continue to grow and develop as individuals. In this respect, Martin Luther King Jr.’s reminder that “education should furnish them with noble ends rather than means to an end” remains relevant.⁶¹ AI may provide new means of accessing and producing information, but librarians have an opportunity to provide leadership through inquiry-based learning that allows students to pursue their own meaningful ends.

Competing Interests

The author has no competing interests to declare.