Research on teaching methods of artificial intelligence courses for cultivating computational thinking

Computational thinking is one of the core competencies for talents in the field of artificial intelligence. However, artificial intelligence courses aimed at cultivating computational thinking face three major predicaments: lack of authentic context, cognitive opacity, and weak motivation. Based on an analysis of scenario–based teaching, scaffolding instruction, and gamification, this paper proposes a collaborative teaching approach characterized by “scenarios continuously present, with scaffolding and games dynamically intervening and promptly returning to the scenario upon goal achievement.” Specifically, scenario‑based teaching serves as the underlying cognitive field that runs throughout the entire course, while scaffolding instruction and gamification are introduced on demand into the scenario–based teaching process, targeting cognitive confusion and insufficient motivation respectively. When both cognitive confusion and insufficient motivation occur simultaneously, the principle of “cognitive priority” is followed. Furthermore, taking recommender systems as an example, this paper elaborates on key design points including scenario design covering seven elements, scaffold fading mechanisms, and gamification level design, providing operable methods for cultivating computational thinking in artificial intelligence courses.