Personalized and Timely Feedback in Online Education: Enhancing Learning with Deep Learning and Large Language Models

This study investigates an Adaptive Feedback System (AFS) that integrates deep learning (a recurrent neural network trained with historical student data) and GPT-4 to provide personalized feedback in a Digital Art course. In a quasi-experimental design, the intervention group (n = 42) received weekly feedback generated from model predictions, while the control group (n = 39) followed the same program without this intervention across four learning blocks or levels. The results revealed (1) a cumulative effect with a significant performance difference in the fourth learning block (+12.63 percentage points); (2) a reduction in performance disparities between students with varying levels of prior knowledge in the experimental group (−56.5%) versus an increase in the control group (+103.3%); (3) an “overcoming effect” where up to 42.9% of students surpassed negative performance predictions; and (4) a positive impact on active participation, especially in live class attendance (+30.21 points) and forum activity (+9.79 points). These findings demonstrate that integrating deep learning with LLMs can significantly improve learning outcomes in online educational environments, particularly for students with limited prior knowledge.