AI instructional agent improves student's perceived learner control and learning outcome: empirical evidence from a randomized controlled trial

TL;DR

This study provides empirical evidence that AI instructional agents can enhance students' perceived learner control, engagement, and academic performance in online learning environments through a randomized controlled trial.

Contribution

It demonstrates that AI instructional agents supporting personalized pacing and real-time interaction improve perceived learner control and learning outcomes.

Findings

AI agents increased perceived learner control

Students in AI group performed better on post-tests

Higher interaction frequency correlated with improved learning

Abstract

This study examines the impact of an AI instructional agent on students' perceived learner control and academic performance in a medium demanding course with lecturing as the main teaching strategy. Based on a randomized controlled trial, three instructional conditions were compared: a traditional human teacher, a self-paced MOOC with chatbot support, and an AI instructional agent capable of delivering lectures and responding to questions in real time. Students in the AI instructional agent group reported significantly higher levels of perceived learner control compared to the other groups. They also completed the learning task more efficiently and engaged in more frequent interactions with the instructional system. Regression analyzes showed that perceived learner control positively predicted post-test performance, with behavioral indicators such as reduced learning time and higher interaction frequency supporting this relationship. These findings suggest that AI instructional agents, when designed to support personalized pace and responsive interaction, can enhance both students' learning experience and learning outcomes.