Toward a Better Buddy: A Value-Sensitive Use Case Study of an Educational AI Chatbot

AI chatbots are rapidly entering higher education as “study buddies” that promise personalized guidance and efficiency. Yet course recommendation is not value neutral: design choices can privilege certain educational goals and constrain others. This paper presents a Value Sensitive Design case study of a university course recommendation chatbot, illustrating how stakeholder values can be elicited and translated into implementable requirements. We combine a conceptual investigation to map stakeholders and define value trade-offs, an empirical study to capture value priorities and preferences, and a technical investigation that operationalizes prioritized values into system norms and design constraints. Initial findings indicate broad alignment across stakeholders and highlight the opportunity to such value-explicit systems. We illustrate this values-to-implementation translation by deriving candidate norms and design requirements from prioritized values.