Papers
6
Total Citations
218
H-Index
5
About
Zehui Zhan is a leading researcher at the intersection of artificial intelligence education, game-based learning, and K–12 robotics. Her work systematically explores how emerging technologies can transform classroom instruction, with a particular focus on making complex AI and programming concepts accessible to young learners. In her highly cited 2022 systematic review (78 citations), Zhan provided the first comprehensive synthesis of game-based learning in AI education, establishing a foundational framework for the field. She has also made significant empirical contributions to collaborative learning design, demonstrating through controlled experiments (45 citations) how group size affects motivation, cognitive load, and problem-solving quality in introductory AI courses. In robotics education, Zhan pioneered the investigation of reverse engineering pedagogy (35 citations) and developed the IRobotQ3D simulation platform (27 citations), enabling K–12 students to experiment with robotics in virtual environments before transitioning to physical hardware. Her innovative work combining virtual and physical robots (28 citations) has informed best practices for staged learning in robotics curricula. Most recently, Zhan has explored human-machine competition through blended board game systems, pushing the boundaries of computational thinking instruction. With over 200 total citations and a consistent focus on evidence-based design, Zhan’s research directly shapes how educators integrate AI and robotics into classrooms worldwide.
Research Focus
Key Achievements
Top Papers
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