Srivastava, Namrata; Humburg, Megan; Burriss, Sarah; Jain, Shruti; Cohn, Clayton; Kim, Yeojin; Timalsina, Umesh; Danish, Joshua; Hmelo-Silver, Cindy E.; Glazewski, Krista; Lester, James; Biswas, Gautam. (2026).听.听In 16th International Learning Analytics and Knowledge Conference, LAK 2026聽(pp. 947鈥�954).听
This study looked at how students use AI chat agents in problem-based learning, or PBL, where learners solve open-ended problems by exploring information and ideas. The researchers focused on conversational agents, or CAs, powered by large language models (LLMs), which are AI systems that can generate human-like text. These agents were built to support different parts of inquiry in a story-based learning environment: one provided content knowledge, one gave feedback on arguments, and one evaluated arguments. Using log data from 15 student groups and Pedaste et al.鈥檚 inquiry cycle, a framework that describes the main stages of inquiry learning, the researchers examined how students interacted with each agent over time. They found that the agents played different but complementary roles: they could help students search for information, revise their ideas, and reflect on their thinking. At the same time, the agents sometimes steered students in ways that limited exploration. Overall, the study shows that learning analytics, which uses digital data to study learning behavior, can help educators understand how students work with AI support and design more flexible tools for classroom use.
Figure 1:
Example of game characters and conversational agent interface.