Role-Biased Chatbots as Pedagogical Provocations for Collaborative Learning

This study examined how undergraduate students engage with biased AI perspectives during management learning tasks. Sixty-two students individually interacted with chatbots intentionally configured to reflect differing corporate roles and associated biases in decision-making and strategic management. They later collaborated with human peers, with reflective checkpoints embedded throughout. We investigated how students integrated insights from their initial human–AI interactions, how these shaped their reasoning during group discussions, and how learning was consolidated following human–human collaboration. Findings indicate that students primarily recognized and interrogated the chatbots’ role-driven biases, using them as a basis for critical comparison during groupwork. Collaborative dialogue further deepened their awareness of decision-making biases and the varied strategic priorities embedded within each role, resulting in more nuanced, evidence-informed perspectives on managerial decision-making and strategy formulation. The design promoted reflection, perspective-taking, and collective sense-making. However, some participants remained reluctant to revise established views, primarily due to the complex, context-dependent trade-offs involved in management. Collectively, the results illustrate the value of structured human–AI engagement in cultivating critical thinking. When students are prompted to interrogate decisions through “why” and “how” questions, reflection becomes a tool for sense-making, thereby elevating the quality of learning in management education.