Leveraging artificial intelligence to enhance student feedback: An AHP-based decision-making framework

Purpose Artificial intelligence (AI) in student feedback systems transforms educational assessment and support. AI can improve student learning by providing personalised, timely, and actionable feedback using powerful algorithms and data analytics. These systems analyse student performance, behavioural patterns, and even the natural language used in written work to provide insights that educators may otherwise find difficult to identify. AI may also identify learning gaps, recommend specialised educational resources, and forecast future performance, creating a more proactive and adaptive teaching environment. AI in education has changed many aspects of learning, especially student feedback. Design/methodology/approach Research is separated into three parts: (1) As expected, published studies, project reports, and academic discussions revealed various performance markers. For the Kirkpatrick Model's four tiers, nine performance indicators were chosen. (2) AHP (Analytical Hierarchy Process) calculated the priority weight and rank of selected performance metrics. To ensure the AHP Model stability, a sensitivity analysis was done. (3) The selected performance indicators using the AHP were compared between two groups: traditional feedback and AI-generated feedback. Findings AI-generated feedback in education systems was shown to be practical by analysing the performance of these two groups. The linked research shows that AI-generated feedback provides students with timely, consistent, and personalised support, thereby improving learning. It also helps students to discover their talents and weaknesses. Originality/value AI technology and its careful integration into educational systems will boost learning and student behavioural confidence. Thus, AI-powered feedback mechanisms may shape education in the future.