Tai-Ping Hsu
Papers
2
Total Citations
30
H-Index
2
About
Tai-Ping Hsu is a leading researcher at the intersection of artificial intelligence education, computational thinking, and teacher professional development. His work critically examines how in-service educators and students adapt to rapidly evolving AI technologies, with a particular focus on the psychological and pedagogical dimensions of AI integration in K-12 settings. Hsu’s most cited study, “The Artificial Intelligence Learning Anxiety and Self-Efficacy of In-Service Teachers Taking AI Training Courses” (2023, 20 citations), investigates how machine learning experience and AI learning anxiety shape the self-efficacy of elementary and junior high school technology teachers, employing the innovative AI2 Robot framework. More recently, his 2025 paper “Teaching AI with games: the impact of generative AI drawing on computational thinking skills” (10 citations) pioneers the use of Generative AI Drawing (GAID) as an intuitive, game-based method to teach complex AI concepts—addressing critical barriers like restrictive access for younger learners. By bridging teacher anxiety, self-efficacy, and student computational thinking, Hsu’s work provides actionable insights for designing inclusive, effective AI curricula. His research is essential reading for educators, instructional designers, and policymakers striving to prepare both teachers and students for an AI-driven future.
Research Focus
Key Achievements
Top Papers
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