As generative AI products could generate code and assist students with programming learning seamlessly, integrating AI into programming education contexts has driven much attention. However, one emerging concern is that students might get answers without learning from the LLM-generated content. In this work, we deployed the LLM-powered personalized Parsons puzzles as scaffolding to write-code practice in a Python learning classroom (PC condition) and conducted an 80-minute randomized between-subjects study. Both conditions received the same practice problems. The only difference was that when requesting help, the control condition showed students a complete solution (CC condition), simulating the most traditional LLM output. Results indicated that students who received personalized Parsons puzzles as scaffolding engaged in practicing significantly longer than those who received complete solutions when struggling.
Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning
The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Carnegie Mellon University（卡内基梅隆大学）；The University of Pittsburgh（匹兹堡大学）；The University of Hong Kong（香港大学）
