The Problem Nobody Talks About in CS Education

Picture this: a student is stuck on a transformer attention mask bug at midnight. They open their AI assistant, paste the code, and ask for a fix. Thirty seconds later, the bug is gone. Assignment submitted. Grade earned. But did they actually learn anything?

This is the quiet crisis playing out in computer science programs right now. AI coding assistants are powerful enough to solve most undergraduate assignments in seconds. That makes them genuinely dangerous in an educational context, not because they are bad tools, but because students and instructors rarely set them up correctly.

The good news is that with the right configuration and habits, these tools can become excellent tutors rather than answer machines. This article covers how to set that up, which tools support it best, and what guardrails actually work in practice.

Why Default AI Behavior Undermines Learning

Out of the box, most AI coding assistants are optimized for one thing: giving you a working answer fast. That is great for professional developers on a deadline. It is terrible for someone trying to build foundational skills.

When you ask OpenAI's models or similar tools to fix a bug, they fix the bug. They do not ask what you tried first. They do not probe whether you understand the root cause. They just hand you a corrected file and move on. That is the default mode, and it is the wrong mode for learners.

The deeper issue is that debugging is where most real learning happens. Sitting with a broken training loop, forming hypotheses, testing them one by one, that process builds the mental models that stick. Skip it often enough and you graduate with a credential but not the underlying competence.

Some of the strongest CS programs have started writing explicit behavioral guidelines for AI tools, treating them like teaching assistants with a specific role rather than search engines. That shift in framing changes everything about how students interact with them.

What Happens When AI Just Writes the Code

Students who rely on AI to complete assignments often hit a wall during technical interviews or real-world projects. They can describe concepts but cannot implement them under pressure. The gap between "I have seen this done" and "I can do this" is exactly what practice is supposed to close.

Developers who work with junior engineers often notice this pattern. Someone can set up a complex ML pipeline using AI-generated scaffolding but struggles to explain why a specific optimizer choice matters or how to debug a memory leak in a training loop. These are not trivial gaps.

3 AI Tools That Support Guided Learning Modes

Not every AI assistant behaves the same way when you configure it carefully. Here are three tools worth knowing about for educational use cases, along with how to get the most out of each.

A student using an AI coding assistant on a laptop while studying Python and PyTorch code

Claude with Custom System Prompts

Anthropic's Claude is one of the most configurable AI assistants available in 2026. Its system prompt support is genuinely flexible. You can instruct it to ask clarifying questions before answering, refuse to write code directly, and redirect students to documentation rather than handing them solutions.

Key Guidelines for AI Coding Assistants

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Set Teaching Mode
Configure AI to probe understanding.

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Behavioral Guidelines
Treat AI as teaching assistants.

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Focus on Debugging
Encourage hypothesis testing for learning.

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Avoid Quick Fixes
Prioritize learning over fast answers.

A well-written system prompt for Claude in a CS course context might look like this:

You are a teaching assistant for an advanced machine learning course.
Your role is to guide students toward understanding, not to solve problems for them.
Never write Python code or pseudocode.
Never complete TODO sections in student code.
When a student asks for help, first ask what they have already tried.
Reference official documentation and course materials rather than giving direct answers.
Explain error messages, but do not provide the fix.
Suggest sanity checks and test cases instead of solutions.

That single configuration changes the entire interaction dynamic. Students still get help, but they have to do the thinking. Claude asks "what have you tried?" before it says anything else, which is exactly the right starting point.

GitHub Copilot with Telemetry Restrictions

GitHub Copilot is trickier to constrain for educational use because its core function is autocomplete. It fills in code as you type, which is almost the opposite of what a learning-focused environment needs.

That said, Copilot's chat mode is more configurable. Instructors running courses on GitHub Classroom can pair Copilot with repository-level instructions that shift its behavior. The key is using the .github/copilot-instructions.md file to set context. You can tell Copilot to only explain concepts, point to Hugging Face documentation or official PyTorch docs, and avoid completing function bodies.

The autocomplete feature itself cannot be fully restricted this way. For courses where writing the implementation is the point, some instructors simply disable Copilot at the repository level and only allow chat access. That is a reasonable tradeoff.

Cursor with Project-Level Rules

Cursor is a code editor with deep AI integration, and it supports a .cursorrules file at the project root. This file lets you define how the AI should behave within that specific project. For a machine learning course, you might write rules like these:

# .cursorrules for CS learning environment
- Do not write implementations for any TODO sections
- Do not refactor student code into working solutions
- When asked about errors, explain the error type and ask guiding questions
- Suggest assertions and shape checks rather than code fixes
- Point to official documentation when relevant
- Ask what the student has already tried before responding

Cursor respects these rules across all AI interactions within the project. That makes it one of the more practical options for instructors who want consistent behavior across a student cohort.

The Socratic Method, Encoded in a Prompt

The most effective AI tutor behavior mirrors the Socratic method: answer questions with questions. This is not a new idea in education, but encoding it into an AI system prompt is still relatively new territory.

Here is what a well-designed Socratic AI interaction looks like for a PyTorch debugging session. Compare the two approaches:

Default AI behavior:

Student: My loss is NaN after the first epoch. What is wrong?

AI: Your loss is NaN likely due to one of these issues:
1. Learning rate is too high
2. You have a log(0) somewhere
3. Gradient explosion

Try: optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
Also add: torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)

Socratic AI behavior (with custom system prompt):

Student: My loss is NaN after the first epoch. What is wrong?

AI: That is a useful symptom to investigate. Before I point you anywhere,
what have you already checked? Have you looked at whether the loss is NaN
from the very first batch, or does it appear partway through the epoch?
That distinction matters a lot for narrowing down the cause.

The second version forces the student to actually look at their training logs. The first version just hands them a checklist to copy. Both responses are technically helpful, but only one builds the debugging instinct that carries over to real-world work.

For students who want to build these skills outside a formal course, tools like TalkBuoy AI assistant offer a different kind of active learning. It is designed for real-time dialogue and can be used in debate practice mode to stress-test your understanding of technical concepts by arguing the opposite position. That kind of adversarial questioning is underrated as a study technique.

Building Your Own Learning-Focused AI Workflow

You do not need to wait for your instructor to configure anything. Here is a practical workflow for using AI tools as a genuine learning aid rather than a shortcut. Try this for your next assignment.

Write the system prompt first. Before opening any AI tool, write a short paragraph telling it to act as a tutor, not a solver. Paste it at the start of every conversation.
Describe your attempt before asking. Always explain what you tried and what you expected. This forces you to articulate your mental model, which often reveals the bug on its own.
Ask for concepts, not code. Instead of "fix this function", ask "can you explain how causal masking should work at the attention layer level?" Then implement it yourself.
Request sanity checks, not solutions. Ask the AI to suggest a small test case you could run to verify your logic. Write the test. Run it. Debug from the output.
Use documentation pointers. Ask the AI to point you to the relevant section of the TensorFlow or PyTorch docs rather than explaining everything inline. Reading primary sources builds better habits.
Review your work with AI after you finish. Once you have a working solution, ask the AI to review it for edge cases, inefficiencies, or missing assertions. That is a legitimate use of AI assistance that does not undermine learning.

This workflow takes more time than just asking for the answer. That is the point. The extra friction is where the learning lives. Checking out a curated list of life-changing AI tools can also help you find the right assistant for each stage of your workflow.

What Instructors Can Do Right Now

If you are teaching a technical course and want to preserve the learning experience without banning AI entirely (which is both unenforceable and counterproductive), here are the most practical steps you can take.

Code editor showing a .cursorrules configuration file for an AI-assisted learning environment

First, write a CLAUDE.md or equivalent guidelines file in your course repository. Spell out exactly what the AI should and should not do. Tools like Claude and Cursor read these files and adjust their behavior. Students see the rules. Everyone is on the same page.

AI Assistants vs. Learning Outcomes

Default Mode Fast answers, no understanding

Configured Mode Encourages learning, builds skills

Debugging Focus Promotes critical thinking

Educational Use Guided tutoring vs. quick fixes

Second, design assignments that are harder to shortcut. Asking students to implement a tokenizer from scratch is fine. Asking them to implement a tokenizer, profile it, explain three specific design decisions in writing, and compare their approach to a published method is much harder to fully outsource. The reflection component is key.

Third, hold short oral check-ins. A five-minute conversation where a student explains one function they wrote is a reliable signal of genuine understanding. It is also a natural deterrent against purely AI-generated submissions. You can even use TalkBuoy AI assistant as a practice tool for students to rehearse explaining their own code before the check-in.

For students who want to go deeper on the research side of ML, Papers With Code is an excellent resource for understanding how published implementations differ from course assignments. Seeing the real thing makes the assignment feel more meaningful.

Skills That AI Cannot Replace (Yet)

Here is the thing about AI coding assistants in 2026: they are genuinely excellent at syntax, boilerplate, and pattern-matching to common solutions. They are much weaker at novel debugging, systems-level reasoning, and understanding why a particular design choice matters in a specific context.

Those weaker areas are exactly what CS education should be developing. A student who leans on AI to write every loop and activation function never builds the intuition for when something is fundamentally wrong versus just syntactically broken. That intuition is what separates a capable engineer from someone who can only work from templates.

Studying foundational research directly from sources like DeepMind Research or working through tutorials on Real Python builds context that AI-generated code simply cannot provide on its own. The goal is to use AI to accelerate learning, not to replace the hard parts of it.

For students exploring computer science fundamentals, spending time with tools like a binary translator tool to understand encoding at a low level reinforces the kind of foundational thinking that makes higher-level concepts click faster.

Wrapping Up: Configure Before You Prompt

The difference between an AI that teaches and an AI that cheats for you comes down to configuration. Default behavior is optimized for speed and completeness. Learning requires friction, questions, and partial answers.

Whether you are a student trying to actually learn or an instructor trying to preserve academic integrity, the tools exist to set this up properly. Write the system prompt. Set the project rules. Ask for explanations instead of code. Use AI to review your work after you finish, not before you start.

Want to go further? Explore the research landscape at Papers With Code, practice explaining your implementations out loud using TalkBuoy AI assistant, and check out more life-changing AI tools that can support your workflow without replacing your thinking. If you are sharing course resources or project links with your cohort, a free QR code generator is a quick way to make those links accessible in person or in printed handouts.

The best AI assistant is one that makes you smarter over time. Set it up that way from the start.