AI Agent Guidelines for CS336 at Stanford
Stanford's CS336 course has unveiled strict guidelines for AI coding assistants, aiming to make them teaching aids rather than solution generators. These rules forbid direct code writing and problem-solving, pushing students toward genuine learning. The Hacker News community is split, debating the practical enforceability of these guidelines and questioning if they truly foster understanding or merely create an easily circumvented hurdle.
The Lowdown
Stanford's CS336 course, "Language Modeling from Scratch," has published detailed guidelines for students using AI coding assistants like ChatGPT, Claude Code, or GitHub Copilot. The core philosophy is to leverage AI as a teaching assistant that guides and explains, rather than an agent that directly solves assignments.<ul><li>AI agents are intended to clarify concepts, direct students to course materials, review student-written code for improvements, and assist with debugging through guiding questions.</li><li>Crucially, AI agents are explicitly forbidden from writing Python or pseudocode, providing direct solutions, completing "TODO" sections in assignments, editing student code, or implementing core assignment components.</li><li>The recommended teaching approach for AI involves asking clarifying questions, referencing course concepts, suggesting next steps without implementing them, and explaining the "why" behind suggestions.</li><li>Academic integrity is a central theme, with the document emphasizing that the goal is student learning through active participation, not passive reception of AI-generated solutions. Agents are instructed to pivot to explanation or guidance when direct implementation is requested.</li></ul>These guidelines represent a notable effort by a leading educational institution to navigate the challenges and opportunities presented by advanced AI tools within a rigorous technical curriculum.
The Gossip
Evasion Expectations & Enforcement Doubts
Many commentators express skepticism about the practical enforceability of these guidelines, arguing that students can easily bypass them by using other models or simply modifying the CLAUDE.md file. Some believe it's a "good intention but useless" effort, comparing it to asking students not to copy from readily available model answers. Others suggest that while direct enforcement is difficult, the guidelines still serve value by setting expectations and promoting academic integrity, perhaps indirectly reinforced through methods like oral examinations.
Learning & Leveraging LLMs
A significant portion of the discussion revolves around the pedagogical effectiveness of limiting AI use. Some argue that preventing full AI use hinders students from learning to leverage modern tools, suggesting that assignments should instead be made more difficult to encourage advanced AI use. Others emphasize that genuine learning requires struggle and deep understanding, which can be undermined by AI providing instant solutions, potentially leading to a "cognitive debt" or "getting stupid." There's a debate about whether merely "looking at the answer" truly fosters learning, with some advocating for active struggle and others seeing value in reverse-engineering solutions provided by AI.
Claude Configurations & Credit Conundrums
Comments also delved into the technical aspects and origins of the CLAUDE.md file. Several users pointed out that these guidelines closely resemble or are directly inspired by a widely shared agent.md from "1cg" (Carson Gross of HTMX fame), with Stanford confirming they referenced it. There was also a discussion on why Claude might not automatically check a generic AGENTS.md file, with theories ranging from marketing choices to the need for specific tool integration, and practical workarounds like symlinking or including other markdown files.