Usability and Preferences for a Personalized Adaptive Learning System for AI Upskilling - Mark Core; Benjamin Nye; Kayla Carr; Shirley Li; Aaron Shiel; Daniel Auerbach; Andrew Leeds; William Swartout (Proceedings of the International Florida Artificial Intelligence Research Society Conference)
As AI tools become common across jobs and industries, it is critical to broaden education about AI beyond teaching computer scientists how to build AI systems. To expand AI education, we are researching AI for AI learning: a personalized and adaptive learning system that integrates dialog-based tutoring and gamified programming activities. To study this problem, we adapted and expanded an existing smartphone adaptive coach to develop the Game-if-AI system. Using a design-based research approach, Game-if-AI was iteratively tested and improved across four semesters of optional use in a course designed for technician-level understanding of AI: mastering programming skills to apply AI libraries and established models. In this study, we measured the interests and needs of these technical learners, based on both survey data and on how they engaged with topics in the system. Based on this data, new topics were added and the system was refined. In this paper, we report students' usability ratings for system components and student preferences based on completion rates of AI topics available each semester. Students rated the adaptive system positively overall (93% rated as a "good idea"), but more complex learning activities (tutoring dialogs, programming) were rated lower than traditional ones (e.g., multiple choice, reading). Students were most likely to master topics highly aligned to the course materials, as well as self-directed learning toward easier high-interest topics (e.g., LLM Prompting).