A Personalized and Adaptive Intelligent Tutoring System for Psychomotor Learning

Abstract

Intelligent tutoring systems (ITSs) have advanced learner motivation and personalization in cognitive domains, but extending these gains to psychomotor tasks remains challenging. Existing psychomotor ITSs rely on learner models built before instruction or updated once per module, leaving open how to use real-time assessments of learner cognitive state for finer-grained personalization. Psychomotor learning involves multiple tutoring objectives, including calibrating self-confidence to performance, calibrating workload per the Yerkes-Dodson Law, and driving skill progression toward higher learning stages. How to balance these objectives for learners, and adjust that balance over time, remains open. This study proposes and evaluates an online personalization algorithm that continuously reprioritizes tutoring objectives to match learners' evolving needs. Rather than weighting objectives equally, the actor-critic-inspired algorithm dynamically updates reward weights using recent measures of self-confidence, workload, and performance. These weights inform a multi-input, multi-output cognitive control framework that synthesizes learner-specific tutoring policies via model-based planning. The approach was evaluated in a human-subject study (N = 40) with a 2D quadrotor landing task. Clustering of the learned reward weights revealed distinct prioritization patterns across learners, indicating the algorithm captures heterogeneity in learner needs. Learners whose weights shifted more substantially tended to reach higher final performance, and those whose weights consistently emphasized workload calibration achieved the strongest outcomes. These findings advance online learner modeling and control-based personalization in psychomotor ITSs, showing how adaptive tutoring can better support learning by aligning instructional decisions with learners' evolving cognitive and performance states.