Beyond Code Assistants: A Technical–Ethical–Agentic Partnership Framework for LLM Integration in Project-Based CS Education

Abstract

The rapid emergence of large language models (LLMs) is reshaping Computer Science (CS) education. Yet, little is known about how students engage with these tools as both technical and ethical learning partners in authentic project-based environments. This qualitative study investigates how 29 undergraduate CS students used LLMs while developing open-ended AI applications in a project-based course. Analysis of project documentation, reflection logs, and presentation transcripts revealed three interconnected forms of student–LLM interaction. First, we observed a technical partnership, in which students leveraged LLMs for code generation, debugging, architectural planning, and API integration while working on complex development challenges. Second, there was an ethical-reflective partnership, as students negotiated transparency, originality, bias, and the risks of over-reliance, demonstrating elements of critical AI literacy. Third, students reported perceived shifts in their learning practices, including increased confidence, more intentional problem-solving, and changes in how they sought support from peers and instructors, suggesting a reconfiguration of self-regulatory learning practices in interaction with LLMs. Together, these findings suggest that LLMs can be understood not merely as productivity tools but as multifaceted partners involved in the technical, ethical, and self-regulatory dimensions of learning in CS education. The study offers theoretical and practical implications for designing AI-enabled curricula that cultivate responsible, reflective, and critically engaged use of LLMs.