Teaching and Generative AI: 

Pedagogical Possibilities and Productive Tensions

Beth Buyserie, Ph.D., & Travis N. Thurston, Ph.D.

Once again, teachers are facing a new pedagogical challenge. The rapid development of generative AI programs like ChatGPT promises to forever change the way we approach teaching and learning. This edited collection seeks to provide teachers with guidance and resources for navigating the possibilities and challenges of teaching in an AI era. We invite teachers, researchers, librarians, instructional designers, and other educational specialists from all ranks and institutional settings to submit chapters that investigate and question how our teaching practices might thoughtfully and productively respond to this new teaching context. Chapters might explore explicit uses of AI in the classroom and/or approaches to emphasizing the human element in teaching.

No Longer Accepting Draft Chapters

Call For Chapters

Chapter Considerations:

Because we seek nuanced discussions of this technology across disciplines, chapters should in some way acknowledge or explore both possibilities
and tensions (e.g., limitations, ethical concerns, disciplinary challenges) of teaching in an AI era. To clarify, we do not intend to publish chapters that only praise or criticize AI, but that acknowledge its complexities for educational settings. That said, individual chapters can certainly focus more on either possibilities or tensions, depending on the author’s goals.

Given that generative AI is so new, we recognize that teachers and scholars may still be in the initial stages of conceptualizing and/or implementing AI into their classes. Our goals for this collection are to provide teachers with resources to respond to AI’s current possibilities and challenges, as well as to prepare themselves to respond to future shifts in teaching with generative AI. We therefore encourage authors to write a chapter that best represents their current thinking or approach to teaching with generative AI (even if that approach is still in process or if the assignment has not yet been taught) and/or teaching within the context of AI. Authors might create one of the following chapter types:

    • A narrative or description of an assignment, lesson, activity, or instructional innovation accompanied by teacher reflection or analysis (1000-2000 words)
    • A narrative/description of an assignment, activity, or instructional innovation accompanied by both teacher analysis and framing scholarship (2000-4000 words)
    • A more theoretical or research-based chapter focused on teaching with generative AI and/or teaching within the broader context of AI, supported by relevant scholarship (3,000-5,000 words)

As you develop your chapter, please consider one or more of the following categories and questions:

Pedagogical Guidance:

    • What do we want students to learn? How might teachers utilize generative AI to help promote student learning?
    •  What in-class and out-of-class learning strategies and activities might teachers design with generative AI? How might teachers ensure that students engage in these activities in ways that promote learning and questioning?
    • How might these teaching approaches translate within disciplines and across disciplines? 
    • What new process-based approaches are we considering for teaching writing across and within disciplines?
    • How might teachers utilize AI to help them design and/or teach their courses? To help guide in-class learning? To help provide student-centered, revision-based feedback? 
    • How might programs and administrators prepare teachers, both new and experienced, to teach in an AI-era?
    • How might teachers respond when students trust AI more than the teacher or the teacher’s experience? How might this challenge particularly affect new teachers or teachers who are already marginalized in the classroom or discipline?
    • What policies surrounding AI for both the classroom and the discipline/future professional context might we create? How do we ensure these policies are complex and nuanced, rather than deficit-based? What thoughtful ways do we have for communicating these policies with students and colleagues?

Student Learning:

    • How might we communicate our learning goals with students?
    • How can we (re)design our courses so that students better understand the role of learning?
    •  How do we design productive in-class learning activities that utilize AI as one pedagogical tool?
    •  In what ways does the existence of AI inspire teachers to design pedagogical activities that do not center on AI? That perhaps center student lived experiences or experiential learning? (not as a reaction against AI, but as a way, e.g., to emphasize the human element of learning)
    •  How do we prepare students to consider and navigate when using AI might not be effective, sanctioned, or ethical in future classes or contexts? (e.g., when considering patient confidentiality, intellectual property)
    • How do we identify or measure student learning when they are using generative AI?
    • What other ways can generative AI be leveraged or combined with other technologies to promote student learning?

Theoretical Framing & Ethical Considerations:

    • What discussions should we be having about AI and marginalized student populations? About AI’s possibilities and challenges for equity and student success?
    • What ethical aspects of teaching and learning do we need to (re)consider and complicate? For example, how might AI help us redefine concepts of plagiarism and academic integrity? About the boundaries of collaboration and co-authorship?
    • As teachers, what ethical considerations do we need to address in our own use of AI? For example, how does AI connect to FERPA? What additional ethical considerations do we need to raise?
    • How might we analyze content produced by AI to help us question language or writing norms that are (re)produced?
    • How might the existence of AI, which can quickly produce standardized writing, help us reconsider or value non-normative or non-traditional ways of writing and learning?
    • How might AI help us reconsider the ways we teach and talk about writing across disciplines? About writing’s role in learning? About product vs. process?

Research Potential:

    • What research studies might we design that allow us to investigate and assess AI’s impact on student learning or on our teaching? What might teacher-researchers need to consider as they are designing their own studies?
    • What initial findings do you have from preliminary studies? How might your initial results be helpful for other teachers?

Publication Scope, Revision Process, and Timeline


Chapters may focus on one discipline or course, but please remember that the audience for this collection will be interdisciplinary. Therefore, please provide enough context so that an outside audience can understand your content and, potentially, apply your approach to their own course/discipline.

To Submit a Chapter:

    • Submit your chapter as a Word/.docx file. 
    • Remove author name(s) and institutional affiliation(s) in the document.
    • Adhere to the APA 7th edition style guide, and the word limit. References are not included in the word limit.
    • Include a title and an abstract (100-150 words)
    • If your chapter includes examples of student work, survey results, or other aspects of human subjects research, please provide evidence of your institution’s IRB approval by including the IRB Protocol # in your chapter.
    • Submit your chapter by August 25th via this GoogleForm: https://forms.gle/wCW8UN19dJmU8ViKA.


Editorial Process:

The editorial team is committed to mentoring new and experienced authors, and we are happy to meet with potential authors to discuss chapter ideas. We are committed to transparency and equity, and our editorial process  We are committed to transparency and equity, and our editorial process draws from the Anti-racist Scholarly Reviewing Practices guidelines.

Chapters for inclusion will be selected by the editors using an anonymous review process. All submissions, regardless of whether or not they will be included in the collection, will receive some form of feedback to provide authors with guidance on revising their work. Authors whose work will be included in the collection will receive more extensive revision-based feedback. After authors revise their chapters, the entire book will go through an anonymous peer review process.


Timeline for Submission and Publication

    • June 12, 2023 - Call for Chapter Drafts Open https://forms.gle/wCW8UN19dJmU8ViKA
    • September 8, 2023 - Chapter Drafts Due to Editors
    • October 16, 2023 – Authors are Notified of Acceptance
    • November 30, 2023 – Revisions Due (Revisions may include updates on classroom practices or initial findings on studies)
    • December 15, 2023 – Peer Review Occurs
    • January 15, 2023 – Revisions (Final Drafts) Due
    • January 30, 2023 – Publication

About Empower Teaching Open-Access Series

The Empower Teaching Open-Access Book Series features a variety of peer-reviewed books focused broadly on the multi-disciplinary work of teaching in higher education. Books in the series align with the mission of Empowering Teaching Excellence (ETE) to bolster the culture of teaching excellence for students, staff, faculty and administrators. The books in this series share insightful and innovative perspectives on teaching and learning, and through a partnership with USU Libraries the books are offered in an online and open-access format to amplify the voices of authors and contributors in the series. View the books in the series:

Resilient Pedagogy: Practical Teaching Strategies to Overcome Distance, Disruption, and Distraction (2021)

Edited by Travis N Thurston, Kacy Lundstrom, and Christopher González

Exploring How We Teach: Lived experiences, lessons, and research about graduate instructors by graduate instructors (2022)

Edited by Sam Clem

Making Connections: A Handbook for Effective Formal Mentoring Programs in Academia (May 2023)

Edited by David Law and Nora Domínguez

Habits fo Mind: Designing Courses for Student Success (August 2023)

Edited by Julia M. Gossard & Chris Babits


Beth Buyserie, Ph.D.

Utah State University

Beth Buyserie

Beth Buyserie is the Director of Composition and Assistant Professor of English at Utah State University. She earned her Ph.D. in Cultural Studies and Social Thought in Education from Washington State University. Her teaching and scholarship focus on composition, writing program administration, professional learning, and critical pedagogies. Dr. Buyserie teaches graduate pedagogy and undergraduate composition courses, mentors all teachers in USU’s Composition Program, and has received multiple awards and recognitions for her commitments to teaching, equity, and diversity. As a proponent of interdisciplinary dialogue, she provides pedagogy workshops to faculty across disciplines, and she chairs USU’s interdisciplinary committee for all communication literacy and communication intensive courses.

Travis N. Thurston, Ph.D.(he/him/his)

Utah State University

Travis Thurston

Travis Thurston is the Director of Teaching Excellence in the Office of the Provost and Chief Academic Officer at Utah State University. He earned his Master of Educational Technology degree from Boise State University, and earned his P.hD. in Curriculum and Instruction with emphasis on Instructional Leadership from Utah State University. His teaching and scholarship focus on digital-age teaching and instructional design, creating autonomy-supportive learning environments, and student-centered engagement strategies. Dr. Thurston has published interdisciplinary studies on the scholarship on teaching and learning, and is the lead editor of Resilient Pedagogy: Practical Teaching Strategies to Overcome Distance, Disruption, and Distraction.