AI-Driven Feedback Systems for Formative Assessment: Toward Personalized and Real-Time Pedagogy - Abar, Reza Oktiana; Pong, Ming; Som, Rit (Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia, 2025)
The provision of timely and personalized formative feedback is a cornerstone of effective pedagogy, yet it remains a significant challenge in conventional classroom settings due to large class sizes and time constraints. AI-driven feedback systems offer a scalable solution to this long-standing pedagogical problem. This study aimed to evaluate the impact of a real-time, AI-driven feedback system on students’ academic performance, error correction, and development of self-regulation skills during formative assessment tasks. A quasi-experimental study was conducted with 90 undergraduate students. The intervention group (n=45) received instant, personalized feedback from an AI system on their assignments, while the control group (n=45) received traditional, delayed feedback from instructors. Performance was measured by assignment scores and error reduction rates. The intervention group demonstrated significantly higher improvement in assignment scores and a faster rate of error correction compared to the control group. Furthermore, qualitative analysis of student reflections indicated enhanced self-regulation and metacognitive awareness among students using the AI system. AI-driven feedback systems are powerful tools that enhance formative assessment by providing personalized, real-time pedagogical support. This approach not only improves academic performance but also fosters crucial self-regulation skills for lifelong learning.