Relying on LLMs: Student Practices and Instructor Norms are Changing in Computer Science Education

Prior research has raised concerns about students'over-reliance on large language models (LLMs) in higher education. This paper examines how Computer Science students and instructors engage with LLMs across five scenarios:"Writing","Quiz","Programming","Project-based learning", and"Information retrieval". Through user studies with 16 students and 6 instructors, we identify 7 key intents, including increasingly complex student practices. Findings reveal varying levels of conflict between student practices and instructor norms, ranging from clear conflict in"Writing-generation"and"(Programming) quiz-solving", through partial conflict in"Programming project-implementation"and"Project-based learning", to broad agreement in"Writing-revision&ideation","(Programming) quiz-correction"and"Info-query&summary". We document instructors are shifting from prohibiting to recognizing students'use of LLMs for high-quality work, integrating usage records into assessment grading. Finally, we propose LLM design guidelines: deploying default guardrails with game-like and empathetic interaction to prevent students from"deserting"LLMs, especially for"Writing-generation", while utilizing comprehension checks in low-conflict intents to promote learning.