An international study led by The Education University of Hong Kong (EdUHK) offers a method for measuring how students learn, not just what they produce, when using AI tools. The research, published June 29, 2026, shows that AI chatbots can drive deeper inquiry and collaboration, but only if assessment shifts to capture the learning process.
Tracking the learning journey with MOVA
The study, published in Computers & Education, introduced Movement Analysis (MOVA), a method that traces how learners move through stages of inquiry, collaboration, and problem-solving. Instead of grading final essays or presentations, MOVA follows the dynamic path of group learning. The researchers analyzed 1,617 online discussion messages from 108 university students working in 16 groups. They identified distinct inquiry states: questioning, exploration, social connection, integration, and resolution.
Chatbots push groups past surface discussion
Groups using a chatbot powered by OpenAI's GPT-4 model showed more frequent transitions between inquiry states and greater engagement in higher-order thinking. They moved beyond idea generation to integration and application. Groups without the chatbot tended to stay in static discussions, focused on social cohesion or basic exploration. The study's findings add to the evidence that AI for Education can support deeper inquiry when integrated thoughtfully.
MOVA also revealed where groups got stuck. Some teams showed sustained, dynamic movement, while others remained confined to surface-level exploration. The analysis pinpointed bottlenecks that prevented students from progressing to higher-order work.
Rethinking assessment in the AI era
Dr. Shen Ba, assistant professor in the Department of Curriculum and Instruction at EdUHK and the study's lead author, said, "As AI changes how students learn and complete academic work, assessment also needs to change. We need to understand the process behind the product. MOVA provides a way to examine how students learn with peers, teachers and AI."
The research suggests that assessment should pay greater attention to the process of learning, not just the final output. By seeing how students think, collaborate, and respond to feedback over time, teachers can offer targeted support and guide students toward becoming reflective, critical learners.
Why this matters for education professionals
For teachers and school leaders, the study makes a clear case: evaluating only the polished product risks missing whether students genuinely understand the material. A process-oriented approach, supported by tools like MOVA, can reveal how students engage with AI and peers. This visibility lets educators intervene early, help students navigate complex thinking, and build collaboration skills that matter long after the assignment is turned in.
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