Epistemic Cognition and Uncertainty Navigation with a Domain-Specific AI Chatbot in STEM Education

Domain-specific AI chatbots are increasingly embedded in STEM courses, yet little is known about how students judge, interpret, and act on AI-generated feedback during learning. This study examines students’ epistemic cognition when interacting with Cyberbot, a domain-specific AI-powered chatbot integrated into a cloud computing course. Drawing on post-course semi-structured interviews with twelve students, we analyze how learners articulate epistemic aims, evaluate the correctness and reliability of AI feedback, and navigate uncertainty when deciding whether to revise or persist in their problem-solving approaches. Findings indicate that Cyberbot does not eliminate uncertainty for learners; instead, it reorganizes epistemic work by shifting responsibility toward verification, judgment, and decision-making. Students demonstrated calibrated trust, actively cross-checking AI responses with other sources and using the chatbot primarily for epistemic verification rather than open-ended exploration. These results highlight the importance of examining epistemic processes when evaluating AI-supported learning.