Evaluating the Potential of Generative Artificial Intelligence to Innovate Feedback Processes

Feedback is an essential component of the teaching–learning process; however, it can vary in quality due to different contexts and students’ and professors’ individual characteristics. This research explores the effect of generative artificial intelligence (GenAI) in strengthening personalized and timely feedback by initially defining an adaptable framework to integrate GenAI into feedback mechanisms defined in theoretical frameworks. We applied a between-subjects analysis in an experimental research design with 263 undergraduate students across multiple disciplines based on an approach consisting of a pretest–post-test process and control and focus groups to evaluate students’ perceptions of artificial intelligence-enhanced feedback versus traditional professor-led feedback. The results show that students who used GenAI declared statistically significantly higher satisfaction levels and a greater sense of ownership in the feedback process. Additionally, GenAI scaffolded continuous improvement and active student participation through a structured and accessible feedback environment, determining that 97% of students are willing to reuse the tool. These findings show that GenAI is a valuable tool to complement professors in the creation of an integrated feedback model. This study draws directions on future research on the combination of artificial intelligence and innovative strategies to produce a long-term impact on education.