A Call for Book Chapter Proposals

Deadline: May 15, 2026

We are pleased to share this call for book chapter proposals for Teaching AI, to be published open access by EdTech Books. Abstracts (250 words) are due May 15, 2026. Authors will be notified no later than May 29, 2026. Accepted chapters will be due by July 1, 2026. The book will be published in Fall 2026. Full details are below, but please feel free to contact us with questions or to submit your proposal at rferdig@gmail.com.

We recognize this is an ambitious timeline. However, because each chapter follows a structured template and draws directly from courses you are already teaching, we believe the turnaround is manageable. Accepted authors will receive the full template upon notification and can expect chapters to run approximately 4,000-6,000 words.

Best, Richard E. Ferdig (Kent State University), Richard Hartshorne (U. Central Florida), Enrico Gandolfi (KSU), Laurie O. Campbell (UCF), and Jennifer Petit (KSU)

Purpose

Artificial intelligence is not new. Faculty across computer science, cognitive science, information systems, engineering, and related fields have been teaching AI for decades, building courses, developing pedagogical approaches, and preparing students for a world increasingly shaped by intelligent systems. What has changed in recent years is not the existence of AI but its visibility, its accessibility, and its reach. AI is now part of nearly every discipline and nearly every conversation about the future of education, work, and society.

And yet, despite this breadth, we do not always share what we know. Syllabi go unread beyond individual institutions. Pedagogical decisions made through years of trial and error stay locked in one classroom. Faculty building new AI courses, often under significant institutional pressure and with little time, are reinventing wheels that their colleagues across campus or across the world have already built.

The goal of Teaching AI is to fix that. This edited collection brings together faculty who teach AI (in any discipline, at any level, in any context) to share their syllabi, their teaching strategies, their hard-won best practices, and their vision for where AI education is headed. The result will be a single, rich, open-access resource for anyone teaching AI or thinking about doing so.

This is not a collection about AI in the abstract. It is a collection about the concrete, practical, and deeply human work of teaching AI to students. We are as interested in the instructor who has been teaching machine learning since the 1990s as we are in the instructor who launched an AI literacy course last semester. Both have something essential to contribute.

Each chapter follows a shared template and includes multiple components: course purpose and objectives, disciplinary context, pedagogical approach, AI ethics and academic integrity, course texts and technologies, assignments, an expanded course outline, best practices, and future directions. Chapters will be organized by content area, and that organization will emerge from the submissions themselves.

For a sense of what this format looks like in practice, we encourage prospective contributors to review our related collection, Teaching the Game (Volumes 1 and 2), available free of charge at Volume 1 and Volume 2. While that collection focuses on gaming education, the chapter format, voice, and scope are directly analogous to what we are building here.

Areas We Especially Welcome

Any standalone AI course (i.e., discipline-specific or designed for a general audience) is eligible for consideration. We welcome submissions from institutions around the world and across every level of instruction within higher education. 

To ensure the collection reflects the full breadth of how AI is being taught, we are particularly interested in courses that represent the following areas. This is not an exhaustive list but rather an invitation. If your course does not appear here, that is not a reason to hesitate. It may be exactly what this collection needs.

• AI literacy and general education. Courses designed for students across majors that build foundational understanding of what AI is, how it works, and what it means for society. We welcome both introductory survey courses and more advanced treatments of AI for non-specialists.
  
• AI ethics, policy, and governance. Courses centered on the societal, legal, and ethical dimensions of AI (i.e., bias, accountability, transparency, regulation) and the responsibilities of those who build and deploy intelligent systems.
  
• AI and human interaction. Courses exploring how humans and AI systems work together (i.e., human-centered AI design, explainability, trust, accessibility, and the user experience of intelligent systems).
  
• Generative AI and creativity. Courses built around generative tools and their implications for art, music, writing, design, and other creative disciplines (including both technical and critical approaches).
  
• AI for soft skills. Courses that address how AI and generative AI can be used to develop and strengthen competencies such as collaboration, teamwork, self-efficacy, and empathy (etc.) across disciplines and professions.  
  
• AI and the workforce. Courses focused on how AI is transforming professional practice, career preparation, and workplace dynamics across industries.
  
• AI and society. Courses that examine AI's broader cultural, political, and societal impacts, including surveillance, misinformation, democracy, and questions of power and equity.
  
• Discipline-specific AI. Courses that examine what AI means within a particular field (i.e., health, law, education, business, journalism, the arts, and beyond). Teaching AI in nursing looks fundamentally different from teaching AI in computer science or communications, and those differences are exactly what this collection wants to capture.
  
• Technical and applied AI. Courses focused on building, training, and deploying AI systems (i.e., machine learning, deep learning, natural language processing, computer vision, and related areas) with particular interest in how instructors make technical content accessible and pedagogically meaningful.
  
• International and global perspectives. AI is developed, deployed, and experienced differently across cultures, regions, and political contexts. We actively encourage submissions from institutions outside North America and Western Europe, and from courses that engage critically with global dimensions of AI.

One important note: we are specifically seeking standalone AI courses. These are courses in which AI is the primary subject. Courses that include an AI module or unit within a broader curriculum are outside the scope of this collection.

Details

To be considered, please submit a 250-word abstract by May 15, 2026, that includes:

• Author name(s), institutional affiliation(s), and email address(es) 
  
• Title of course 
  
• Course keywords: content area, level (e.g., undergraduate, graduate, professional development), and delivery mode (e.g., online, face-to-face, hybrid) 
  
• Brief description of the course, including its context, how long it has been taught, and any ways it has evolved in response to recent developments in AI

Full chapters will be due July 1, 2026. Accepted authors will receive a complete updated chapter template. The book will be published open access with Creative Commons licensing by EdTech Books.

Please send proposals and any questions to rferdig@gmail.com.