About the Volume
In 2024, Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions brought together instructors, librarians, instructional designers, and other higher education practitioners to examine the opportunities, limitations, and ethical questions surrounding generative AI. Rather than simply praising or rejecting AI, the collection offered practical resources grounded in the varied ways instructors were responding to a changing teaching environment. This new volume builds on that work by returning to the productive tensions of teaching with, around, and sometimes without generative AI.
The central question of this volume is straightforward but increasingly difficult to answer: How do we know students are learning? Generative AI can produce a polished essay, detailed explanation, computer code, visual product, or other convincing response. A finished product may therefore tell us less than it once did about the thinking that produced it. For this volume, AI-aware teaching means recognizing how AI changes course goals, assignments, students' pathways toward learning, and instructors' own practices, then responding intentionally. It can include learning with AI, learning about AI, and deliberate opportunities to learn without it. This framing builds on Vee, Watkins, and Bruff's description of AI-aware teaching as understanding how AI affects course goals, assignments, and students so instructors can make contextual choices—including embracing AI, establishing boundaries, or finding an appropriate balance.
Sometimes AI may change what students need to learn, such as evaluating AI-generated material or making responsible decisions about its use. In other situations, the learning goal remains the same, but AI changes the route students can take toward it. Instructors must therefore consider the locus of cognition: Where is the essential thinking in an activity taking place? What must students think through, practice, decide, create, or explain for the intended learning to occur? What work might appropriately be shared with peers, instructors, technologies, or AI systems? When AI makes a task faster or easier, which human abilities might students practice less, which abilities remain important enough to protect, and which new abilities should students develop?
The messy middle is the ongoing work of navigating how both student and instructor uses of AI reshape teaching and learning: what to preserve, what to redesign, where AI may support learning, and where it may displace essential thinking, motivation, judgment, or human interaction. This volume is not about surveilling students, policing every use of AI, or making assignments "AI-proof." Trust does not remove the need for meaningful evidence of learning; it changes how that evidence is invited, observed, and understood. We are interested in how instructors can communicate clear expectations, engage students openly and as partners, create meaningful reasons to invest in learning, and design experiences through which students can explain, apply, revise, and demonstrate what they understand.
We welcome chapters from instructors and other higher education practitioners who use AI, establish intentional limits, or decline to use it when refusal better serves their values and learning outcomes. Chapters should grow from actual teaching, course design, library practice, instructional support, or faculty development and should offer insights that readers can adapt in their own settings.
For this collection, teaching inquiry includes formal research, classroom assessment, practitioner inquiry, critical reflection, and other evidence-informed examinations of teaching and student learning.
Questions the Volume Seeks to Address
Prospective authors may address one or more of the following questions. These questions are intended as a guide rather than a checklist.
Purpose, Values, and the Human Work of Learning
- When does generative AI change what students need to learn, and when does it change the pathway through which they reach an existing learning goal?
- Where is the locus of cognition in an assignment, activity, or course, and what intellectual work must students undertake themselves for meaningful learning to occur?
- Which human abilities should instructors protect even when AI can perform the associated task more quickly or efficiently? What new abilities do students need in an AI-aware world?
- How do disciplinary values, learning outcomes, teaching philosophies, and ways of knowing guide decisions to use, limit, redesign around, or refuse AI?
Trust, Agency, Ethics, and Access
- How can instructors build trust and make learning visible without turning AI-aware teaching and assessment into surveillance, policing, or default suspicion of students?
- How can students participate as meaningful partners in decisions about AI use, course expectations, learning activities, and ways of demonstrating understanding?
- How might AI increase access for multilingual, disabled, neurodivergent, working, or time-constrained students? Where might it introduce new barriers, dependencies, inequities, or pressures to conform?
- How should privacy, informed consent, student agency, Indigenous ways of knowing, data sovereignty, and community ownership shape AI use in teaching and learning inquiry?
Making Learning Visible and Meaningful
- When AI can produce a convincing final product, what evidence shows that students can explain, defend, adapt, troubleshoot, collaborate, or apply their understanding?
- How can drafting, dialogue, feedback, reflection, oral explanation, peer response, portfolios, demonstrations, or revision make students' thinking and growth visible?
- How can instructors help students evaluate AI-generated material, explain their choices, respond to errors, recognize the limits of their understanding, and take responsibility for their learning?
- How can instructors design work that students find meaningful enough to engage with and persist in when AI offers faster or easier pathways?
- How can instructors distinguish between documentation of a process and meaningful evidence that the intended learning occurred?
Instructional Design, Libraries, and Faculty Development
- How can instructors and instructional designers identify the locus of cognition and align learning goals, activities, and assessments around the intellectual work students need to do?
- How can course design support learning with AI, learning about AI, and purposeful learning without AI?
- How can librarians, instructional designers, and academic-support professionals help students conduct research, evaluate sources, exercise disciplinary judgment, and work responsibly with AI-generated material?
- How does instructors' own use of AI in course planning, instructional materials, feedback, assessment, or communication affect transparency, responsibility, relationships, and human presence?
- As faculty and instructors continue to use AI more, how can instructional designers and faculty developers ensure the same reflective and cognitive engagement that we expect of students in their use of AI?
- How can faculty developers help instructors rebuild learning experiences rather than simply add AI policies to existing courses? What roles can consultations, mentoring, faculty learning communities, departmental conversations, and institutional partnerships play?
Types of Chapter Proposals
We invite proposals for three related types of chapters of 500-800 words. Accepted chapters are expected to be 3,000–5,000 words, excluding references, and may range in style from academic to narrative.
Option 1: Evidence-Based Study or Assessment of Student Learning
These chapters present systematic evidence about student learning in a course, program, or teaching intervention in which AI was used, limited, or refused.
Contributions may draw from:
- IRB-approved research
- Course-level assessment
- Program evaluation
- Analysis of student work
- Student surveys, interviews, or focus groups
- Another structured examination of teaching and learning
Formal research is welcome but is not required. Authors should explain what evidence informed their conclusions, what that evidence can reasonably establish, and how AI's presence or absence affected the learning process. Chapters reporting research with human participants must follow applicable institutional review requirements.
Option 2: Practitioner Inquiry, Critical Narrative, or Practice-Based Case
These chapters draw from instructors' or practitioners' lived experience, professional judgment, and ways of knowing developed through teaching, course design, library work, instructional support, or faculty development.
Contributions may use:
- Reflective practitioner inquiry
- Critical narrative
- Autoethnographic approaches
- Critical incident analysis
- A bounded practice-based case
Chapters should connect experience to relevant scholarship and offer transferable insights rather than personal reflection alone. We particularly welcome accounts of what instructors have chosen to preserve, rebuild, limit, or refuse because generative AI now exists.
Option 3: Blended Contribution
These chapters combine systematic evidence with practitioner reflection, critical narrative, or practical guidance.
For example, an author might analyze student work, reflections, or feedback while also examining:
- How AI changed the learning goal or students' pathway toward it
- What the instructor initially assumed
- What changed after seeing students use, or decline to use, AI
- Which parts of an activity were redesigned
- Which forms of thinking, judgment, interaction, or productive struggle were intentionally preserved
- How student perspectives informed the redesign
- How the instructor's understanding of learning changed
Intended Audience
This volume is intended for people whose work directly shapes teaching and student learning in higher education, including:
- Full-time faculty members of all ranks
- Adjunct and part-time instructors
- Graduate instructors
- Librarians
- Instructional and learning designers
- Educational and faculty developers
- Academic-support professionals
- Others involved in course design, assessment, or instruction in face-to-face, online, and hybrid environments
We seek contributions grounded in actual practice and written for an interdisciplinary audience. Discipline-specific experiences are encouraged, but chapters should explain what instructors and practitioners in other fields or institutional settings might learn or adapt.
How to Propose a Chapter
Prospective authors should submit the following through the chapter-proposal form:
- Proposed chapter title
- A 500–800-word chapter proposal that identifies:
- The teaching, learning, design, or professional context
- The central problem, question, or productive tension
- The proposed chapter type
- The evidence, experience, or practitioner knowledge informing the chapter
- Whether AI changes the learning goal, students' pathway toward that goal, or both
- Where the essential learning occurs, and how we know students are learning
- What the author has chosen to preserve, rebuild, limit, or refuse because generative AI now exists
- How the chapter supports trust, student agency, and meaningful evidence of learning
- The practical insights readers will be able to apply
A strong proposal should make clear what students will do, what thinking or judgment the learning experience requires, and what evidence will help the instructor determine whether the intended learning occurred.
- References cited in the proposal, formatted in APA 7th edition. Proposals should engage at least three relevant sources.
- A brief AI-use disclosure of approximately 50–100 words explaining whether and how artificial intelligence was used in developing the proposal. Authors should identify the tasks for which AI was used and how they reviewed or verified its contributions. Authors who did not use AI may state that directly.
- A brief author biography of approximately 100 words for each contributor
The editors will review proposals for alignment with the volume, clarity of purpose, grounding in teaching and learning practice, and potential usefulness to readers. Accepted chapters will receive editorial and peer review before publication. Submitting a proposal does not guarantee inclusion in the volume.
Suggested Starting Points
This list is intended to orient prospective authors rather than establish a required canon. Authors should also draw from scholarship relevant to their disciplines, teaching contexts, and chapter questions.
Foundational Volumes
Buyserie, B., & Thurston, T. N. (Eds.). (2024). Teaching and generative AI: Pedagogical possibilities and productive tensions. Utah State University. https://aiteachingbook.usu.edu/
Chick, N. L., Felten, P., & Mårtensson, K. (2025). Collecting “traces of learning.” In The SoTL guide: (Re)orienting the scholarship of teaching and learning (Chapter 8, pp. 135–150). Elon University Center for Engaged Learning. https://www.centerforengagedlearning.org/books/the-sotl-guide/chapter-8/
Schnitzler, C., Vee, A., & Laquintano, T. (Eds.). (n.d.). TextGenEd: Continuing experiments. The WAC Clearinghouse. Retrieved November 1, 2026, from https://wacclearinghouse.org/repository/collections/continuing-experiments/
Vee, A., Laquintano, T., & Schnitzler, C. (Eds.). (2023). TextGenEd: Teaching with text generation technologies. The WAC Clearinghouse. https://wacclearinghouse.org/repository/collections/textgened/
Vee, A., Watkins, M., & Bruff, D. (2026). The Norton guide to AI-aware teaching. W. W. Norton & Company. https://wwnorton.com/books/9781324127673
Selected Chapters from B. Buyserie & T. N. Thurston (Eds.), Teaching and generative AI: Pedagogical possibilities and productive tensions. Utah State University.
The following chapters illustrate the range of critical, practical, disciplinary, and evidence-informed contributions this new volume hopes to extend.
Values, deliberation, and refusal
Denial, C. J. (2024). Wrestling with A.I. https://digitalcommons.usu.edu/teachingai/24/
Contextual decisions and student-developed guidelines
Bali, M. (2024). Cake-making analogy for setting generative AI guidelines/ethics. https://digitalcommons.usu.edu/teachingai/19/
Scaffolding versus cognitive offloading
Watkins, M. (2024). Automated aid or offloading close reading? Student perspectives of AI reading assistants. https://digitalcommons.usu.edu/teachingai/36/
Redesigning assignments around inquiry and process
Campbell, L., Green, J., & Bungert, N. (2024). Revisioning a bibliography assignment to center discovery and critical source engagement. https://digitalcommons.usu.edu/teachingai/18/
Making disciplinary thinking visible through comparison and reflection
Gossard, J. M. (2024). Creating constitutions with ChatGPT. https://digitalcommons.usu.edu/teachingai/14/
Students as partners in AI-related teaching inquiry
Smith, W. P., Alward, C., Morris, J., Peach, L., Pulley, V., Routt, P., Sanders, K., & Vogeler, J. (2024). Uses, benefits, and limitations of AI chatbots: Implementing ChatGPT in the first-year writing classroom. https://digitalcommons.usu.edu/teachingai/34/
Disciplinary knowledge and judgment when evaluating AI output
Christiansen, M. A. (2024). ChatGPT assistance in creating chemistry practice problems: Pitfalls, positives, and possibilities. https://digitalcommons.usu.edu/teachingai/8/
Indigenous participation, representation, and data sovereignty
Cardona-Rivera, R. E., Alladin, J. K., Litts, B. K., & Tehee, M. (2024). Indigenous futures in generative artificial intelligence: The paradox of participation. https://digitalcommons.usu.edu/teachingai/48/
Additional Resources
Colver, M. (2025, January 16). How GenAI is forcing an inevitable ability/efficiency trade-off. The EvoLLLution. https://evolllution.com/how-genai-is-forcing-an-inevitable-ability/efficiency-trade-off
Dawson, P., Bearman, M., Dollinger, M., & Boud, D. (2024). Validity matters more than cheating. Assessment & Evaluation in Higher Education, 49(7), 1005–1016. https://doi.org/10.1080/02602938.2024.2386662
Yan, L., Greiff, S., Lodge, J. M., & Gašević, D. (2025). Distinguishing performance gains from learning when using generative AI. Nature Reviews Psychology, 4, 435–436. https://doi.org/10.1038/s44159-025-00467-5
Timeline
- November 2, 2026: Call for chapter proposals opens
- December 14, 2026, by 11:59 p.m. Mountain Time: Chapter proposals due
- January 22, 2027: Proposal decisions sent to authors
- April 5, 2027: Complete chapter drafts due
- April-May 2027: Peer and editorial review
- June 2027: Final revised chapters due
- August 2027: Open-access book publication
Editors
Beth Buyserie
Associate Professor and Director of Composition
Utah State University
Travis N. Thurston
Executive Director, Center for Empowering Teaching Excellence
Utah State University
travis.thurston@usu.edu
