Analysis of Motivations in Machine Learning Textbooks - Malik, Khushi; Richardson, Amber; Zhu, Tingting; Zhang, Lisa (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Recent work has explored the interests that draw learners to Machine Learning (ML), aiming to support their success and broaden participation in the field. However, whether strategies used in textbooks align with these interests is unexplored. We perform a thematic analysis of the introductions from ten openly available ML textbooks to identify their motivational strategies and compare them with student interests documented in prior research. We find that textbooks frequently motivate learners in their introductions by setting learning goals, previewing core ML topics to be covered, showcasing applications and current successes, and, less often, by using learner-centered strategies such as reassurance or curiosity prompts. We group these motivations into three overarching themes: theoretical, practical, and learner-centered. These motivations largely align with student interests, particularly in theory and applications, even in textbooks published before the recent surge of ML and Artificial Intelligence. These findings reveal how textbooks frame ML’s value and offer evidence-based guidance for developing future materials that better engage and support diverse learners.