AI-assisted evaluation of problem-solving performance using eye movement and handwriting

With the ability to predict learning behaviors, artificial intelligence (AI) is increasingly involved in assessing the performance of problem solving. This study explored the potential of AI to predict whether mathematics problems could be solved based on eye movements and handwriting in a digital problem-solving environment. Sixty-one students participated in the experiment. The goal is to examine whether types of eye movement features (AOI-based and fixation-based features), levels of information while solving problems (separated and integrated steps), and handwriting could impact the performance of AI. The results indicated that fixation-based features outperform AOI-based features. Furthermore, information in separated steps could provide higher accuracy than that in integrated steps. Inconsistent patterns between human and AI-based assessment of solvers’ answers are discussed.