Problem-based learning (PBL) environments increasingly embed LLM-based conversational agents (CAs) to scaffold inquiry, yet little is known about how learners actually respond to these agents in authentic classroom settings. Learning analytics offers powerful opportunities to capture and interpret how students engage with these agents, enabling deeper understanding of their inquiry processes and informing more adaptive instructional support in PBL settings. In this paper, we examine students’ interactions with three types of LLM-powered CAs — Content Knowledge, Argument Feedback, Argument Evaluation — designed to provide distinct forms of inquiry support within a narrative-centered learning environment. Using Pedaste et al.’s inquiry cycle as a lens, we used contextualized log data from 15 student groups to analyze how these agents shaped inquiry via sequence analysis of students’ coded actions. Our results revealed distinct trajectories of agent episodes and suggest LLM-powered CAs can play complementary pedagogical roles — supporting information seeking, guiding revision, and prompting reflection — but may also channel inquiry in ways that constrain exploration. We discuss the implications of using learning analytics to design adaptive scaffolds and using contextualized log analysis to capture how learners navigate inquiry with AI support in authentic classroom settings.

Multimodal data analysis provides profound insights into behaviors and interactions within various settings. However, the collection and analysis of this data in real-world scenarios are intricate and resource-intensive. To streamline these processes, we introduce ChimeraPy: an open-source, distributed streaming platform optimized for high-throughput data transfer across processing nodes within a computer cluster. The utility and performance of ChimeraPy are showcased through two benchmark applications, highlighting its capability to handle complex data environments.