by Robert Cole, Program Director, Reinert Center
Now that spring break has passed, it may be time to begin thinking about what to read later in the semester, after finals. I’ve been reading three books over the last few weeks that I’d be happy to recommend.
The first book I’d like to share with you focuses on teaching and learning and is available electronically at Pius Library. The second edition of How Learning Works: 8 Research-Based Principles for Smart Teaching (Lovett, Bridges, DiPietro, Ambrose and Norman, 2023) is about researched principles that lead to student learning. The principles include, but are not limited to: how students’ prior knowledge may affect their learning; how students’ motivation determines, directs, and sustains what they learn; and the ways students organize knowledge influences how they learn and apply what they know. In addition to discussion of the principles and the research that accompanies them, there are practical examples that provide ideas regarding how you may think about each principle in the context of your own teaching experience.
If you’re interested in generative AI and understanding a little more about how it works, ways it can be used and how it is being used by some in higher education, you may be interested in Co-intelligence: Living and Working with AI by Ethan Mollick (2024). Mollick teaches entrepreneurship and innovation at the Warton School of the University of Pennsylvania. He has been an avid user and writer regarding generative AI since the early models of ChatGPT became widely available. His insights are deep and widely positive regarding the role generative AI may play in our lives. In addition to very accessibly talking about how large language models (LLMs) work, he devotes chapters to AI as different persona such as a person, a coworker, or a coach. This is a good book if you are looking for something with a generally positive outlook toward AI.
If you are a little more skeptical, you may be interested in Narayanan and Kapoor’s (2024) AI Snake Oil: What Artificial Intelligence Can Do, Can’t Do and How to Tell the Difference. This is a well written book that provides a more balanced view of what AI can and cannot do. In addition to generative AI, there is content devoted to predictive AI and the issues with allowing an algorithm to make decisions based on data provided by actual humans. They also discuss how transformers – the T in ChatGPT – work, and how the diffusion model for image generation works. The authors delve into topics of whether or not AI is or will be an existential threat – think Skynet – or if it could be harnessed for good as Open AI and Anthropic, among others, intend. Perhaps just as interesting is the final chapter entitled where do we go from here. As this book provides a more balanced approach to AI in our society, it may be good way to reflect on your own feelings of generative AI in our day-to-day lives.
Any one of these books would be a great choice whether you are interested in some ideas related to well researched principles regarding how learning works or if you’re interested in knowing a little more about generative AI and how it works. As we are beginning the second half of the semester, you may not have time to read them now, but perhaps you have a reading list to which you’d like to add one or more of these.
If you’d like to discuss the content of one of these in the context of your teaching and student learning, feel free to request a consultation with the Reinert Center.
The Notebook 