by Robert Cole, Program Director, Reinert Center
This summer I had time to read a couple new books around the topic of generative AI and higher education. The one I’d like to review for you in this post is The Science of Learning Meets AI: A Practical Faculty Guide to Purposeful Integration, Student Engagement and Ethical Practice (Ludwig & Zakrajsek, 2026). The book is authored by Lewis Ludwig and Todd Zakrajsek. Lewis Ludwig is the Benjamin Barney Chair of Mathematics at Denison University and is also a faculty developer there. He has been thinking, writing, and providing workshops in this space since generative AI came to our attention. Todd Zakrajsek is an adjunct associate professor in the department of family medicine at UNC-Chapel Hill and is the director of the ITLC-Lilly Conferences on Evidence-Based Learning. He has written and presented widely on the topic of learning science, authoring/co-authoring 7 books and multiple articles.
The subject matter of this book is how we can combine the two areas of learning science and generative AI (genAI) to benefit student learning and our design of learning experiences. The authors structure the book in three parts. Part one, Creating Your genAI Roadmap, addresses the fact that few of us saw generative AI coming and changing higher education teaching and learning to the extent that it has. In addition, it speaks to how we might improve our use of generative AI to innovate student learning and our teaching practice. Part two, Building a Foundation for Learning, includes chapters on concepts that may be familiar if you have followed the Reinert Center for some time. Topics include AI and Universal Design for Learning (UDL), Backward Design, Transparency in Learning and Teaching (TiLT), and integrating genAI to create deeper learning opportunities. Finally, Part three, Teaching for Deeper Learning, is about helping students learn how to learn.
One of the things I really like about this book is its practicality. In addition to discussing how we might use generative AI in teaching and learning, the book provides practical examples and prompts for how we can use genAI. There are examples of how we might be able to leverage the power of genAI to help us think more deeply about our pedagogical choices. For example, in one section addressing UDL, there is an activity to prompt genAI to analyze an assignment and point out any instructions that may need to be clarified or may be confusing; then suggest alternative instructions or terminology that we can adopt, adapt, or edit to fit our context. In a section related to backward design, the authors suggest using genAI to analyze the goals of a course, the objectives written for the course, and course assessments and assignments to determine if they are properly aligned. Feedback generated regarding where things are or are not aligned may offer opportunities to more intentionally align these elements.
Another positive has to do with helping the reader really understand the point of the chapter they’re reading. In the “Primary Focus of the Chapter” section for each chapter, there is an explanation of what is being addressed and the role the authors see genAI playing. It’s very helpful for targeting exactly what you may want to learn about. I started by reading the primary focus sections for each chapter before continuing with the rest of the book.
As with so many things, in addition to positives there are some drawbacks as well. For example, in one set of example prompts, we are invited to enter student names, hobbies, major, interests, a hope for the course, etc. While it is certainly understandable that it would take almost no time for a genAI to compile, analyze, and generate results in the form of overlapping student interests, common communication preferences, or collective hopes for the course, the Reinert Center would not recommend putting student names or any other student data into a genAI application. Even if users opt out of being included in training data, unless in very unique circumstances, the security of these applications does not conform to FERPA. In addition, students may not want their names added to a genAI application or system.
Along the same lines, the Center would not recommend putting student work of any kind into genAI. At least one sample prompt asks for student errors to be included to analyze common errors and determine how they overlap. The practice is good, but including student work again may infringe on student wishes to remain out of genAI datasets even if student names are withheld.
After reading The Science of Learning Meets AI: A Practical Faculty Guide to Purposeful Integration, Student Engagement, and Ethical Practice (Ludwig & Zakrajsek, 2026), I would recommend it to instructors interested in using generative AI in a way that may help them be more intentional about their teaching. There are several very good practices, that are not all dependent on using genAI, that can help you think about your pedagogical choices. In this case the authors demonstrate how genAI can be used in that way. That is not to say that exclusive genAI use can help us think through these choices. There are instances where one could read the prompt example and then reflect on pedagogical choices made and why. For example, humans can analyze student errors without inputting their work into genAI. There are many examples of how one might do this. However, for those interested in using genAI more generally to help think through these things, this book may be helpful.
If you would like to talk with someone – rather than read a book – about how you might use genAI to assist in interrogating your teaching practices or otherwise use genAI in your course, consider making a request for a Reinert Center teaching consultation.
The Notebook 