Personalized Learning through ChatGPT: A Quasi-Experimental Study on Adaptive Curriculum in High School Settings - Muhammad, Muhammad; Nizam, Zain; Razak, Faisal (Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia, 2025)
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.