How Students Use AI Feedback Matters: Experimental Evidence on Physics Achievement and Autonomy
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913838113226752 |
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| author | Dai, Xusheng Wen, Zhaochun Jiang, Jianxiao Liu, Huiqin Zhang, Yu |
| author_facet | Dai, Xusheng Wen, Zhaochun Jiang, Jianxiao Liu, Huiqin Zhang, Yu |
| contents | Despite the precision and adaptiveness of generative AI (GAI)-powered feedback provided to students, existing practice and literature might ignore how usage patterns impact student learning. This study examines the heterogeneous effects of GAI-powered personalized feedback on high school students' physics achievement and autonomy through two randomized controlled trials, with a major focus on usage patterns. Each experiment lasted for five weeks, involving a total of 387 students. Experiment 1 (n = 121) assessed compulsory usage of the personalized recommendation system, revealing that low-achieving students significantly improved academic performance (d = 0.673, p < 0.05) when receiving AI-generated heuristic solution hints, whereas medium-achieving students' performance declined (d = -0.539, p < 0.05) with conventional answers provided by workbook. Notably, high-achieving students experienced a significant decline in self-regulated learning (d = -0.477, p < 0.05) without any significant gains in achievement. Experiment 2 (n = 266) investigated the usage pattern of autonomous on-demand help, demonstrating that fully learner-controlled AI feedback significantly enhanced academic performance for high-achieving students (d = 0.378, p < 0.05) without negatively impacting their autonomy. However, autonomy notably declined among lower achievers exposed to on-demand AI interventions (d = -0.383, p < 0.05), particularly in the technical-psychological dimension (d = -0.549, p < 0.05), which has a large overlap with self-regulation. These findings underscore the importance of usage patterns when applying GAI-powered personalized feedback to students. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_08672 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How Students Use AI Feedback Matters: Experimental Evidence on Physics Achievement and Autonomy Dai, Xusheng Wen, Zhaochun Jiang, Jianxiao Liu, Huiqin Zhang, Yu Computers and Society Despite the precision and adaptiveness of generative AI (GAI)-powered feedback provided to students, existing practice and literature might ignore how usage patterns impact student learning. This study examines the heterogeneous effects of GAI-powered personalized feedback on high school students' physics achievement and autonomy through two randomized controlled trials, with a major focus on usage patterns. Each experiment lasted for five weeks, involving a total of 387 students. Experiment 1 (n = 121) assessed compulsory usage of the personalized recommendation system, revealing that low-achieving students significantly improved academic performance (d = 0.673, p < 0.05) when receiving AI-generated heuristic solution hints, whereas medium-achieving students' performance declined (d = -0.539, p < 0.05) with conventional answers provided by workbook. Notably, high-achieving students experienced a significant decline in self-regulated learning (d = -0.477, p < 0.05) without any significant gains in achievement. Experiment 2 (n = 266) investigated the usage pattern of autonomous on-demand help, demonstrating that fully learner-controlled AI feedback significantly enhanced academic performance for high-achieving students (d = 0.378, p < 0.05) without negatively impacting their autonomy. However, autonomy notably declined among lower achievers exposed to on-demand AI interventions (d = -0.383, p < 0.05), particularly in the technical-psychological dimension (d = -0.549, p < 0.05), which has a large overlap with self-regulation. These findings underscore the importance of usage patterns when applying GAI-powered personalized feedback to students. |
| title | How Students Use AI Feedback Matters: Experimental Evidence on Physics Achievement and Autonomy |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2505.08672 |