Personalized Learning through ChatGPT: A Quasi-Experimental Study on Adaptive Curriculum in High School Settings
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The challenge of catering to diverse learning paces and styles in traditional high school classrooms often limits student potential. The emergence of advanced AI, such as ChatGPT, presents a novel opportunity to create adaptive learning environments that personalize educational content in real-time. This study aimed to investigate the effectiveness of a ChatGPT-driven adaptive curriculum on student academic performance and engagement compared to conventional, non-adaptive teaching methods. A quasi-experimental, pre-test/post-test design was conducted with 110 high school students. The intervention group (n=55) utilized an adaptive curriculum where content was dynamically adjusted by ChatGPT based on performance, while the control group (n=55) received standard instruction. Academic performance was measured via subject-specific tests, and engagement was assessed using the Student Engagement Instrument (SEI). The intervention group demonstrated a statistically significant improvement in academic performance (p < .01) and higher engagement scores (p < .05) compared to the control group. The adaptive curriculum effectively addressed individual learning gaps and maintained student interest. Integrating ChatGPT to facilitate personalized, adaptive curricula is a highly effective strategy for enhancing both academic achievement and student engagement in high school settings. This approach offers a scalable solution to individualized instruction.
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