Utilising Explainable AI to Enhance Real-Time Student Performance Prediction in Educational Serious Games

Abstract

In recent years, serious games (SGs) have emerged as a powerful tool in education by combining pedagogy and entertainment, facilitating the acquisition of knowledge and skills in engaging environments. SGs enable the collection of valuable interaction data from students, allowing for the analysis of student performance, with artificial intelligence (AI) playing a key role in processing this data to make informed inferences about their knowledge and skills. However, the lack of explainability in AI models represents a significant challenge. This research aims to develop an interpretable model for predicting students' performance in real-time while playing an SG by: (1) calculating the performance of an interpretable prediction model of task completion in an SG and (2) demonstrating the application of the interpretable model for just-in-time (JIT) classroom interventions. Our results show that we are able to predict students' task completion in real-time with a balanced accuracy result of 77.21% after a short playtime has elapsed. In addition, an explainable artificial intelligence (XAI) approach has been applied to ensure the interpretability of the developed models. This approach supports personalised learning experiences, unlocks AI benefits for non-technical users, and maintains transparency in education.