Tutor, Not Solver: Designing a Guardrailed AI Assistant for Learning in Higher Education: A Design Case of PeteChat

arXiv:2606.09845v2 Announce Type: replace Abstract: Generative artificial intelligence (AI) tutors hold significant promise for higher education, yet designing systems that scaffold learning without undermining academic integrity remains an open design challenge. This paper presents PeteChat, a course-aligned AI tutor developed and piloted at a large U.S. research university and documented through the lens of design-based research (DBR). Drawing on formative expert evaluation with teaching assistants and user-experience and developer stakeholders, we report eight transferable design principles for assessment-aware AI tutors, ranging from homework guardrails and debugging scaffolds to self-regulated learning support and instructor-facing customization tools. The system is built on a locally hosted large language model from the Llama-3 family, enhanced with retrieval-augmented generation (RAG) grounded in course-specific materials. Rather than reporting controlled experimental outcomes, this design case foregrounds the situated design reasoning, iterative refinement, and principled decision-making that shaped PeteChat across four development phases. The principles and methodological approach offer actionable guidance for institutions seeking to deploy responsible, integrity-preserving AI tutors at scale.