Recommendation (short): Treat Copilot as a pair-programmer and learning tool, not a drop-in answer generator. Configure it so suggestions require your explicit acceptance, use it to produce hints/pseudocode/tests/explanations, and always document and rework any AI-provided code before submission.

Why this setup: automatic or full-function completions make it easy to submit code you don’t understand and increase risk of plagiarism flags. Requiring manual acceptance and using Copilot for scaffolding helps you learn and creates a clear audit trail.

Practical settings to use
- Turn off any “auto-accept” behavior or automatic whole-function inserts so every suggestion is reviewed before it becomes part of your file.
- Keep inline suggestions enabled but limit their size (if an option) to short blocks—one logical step at a time.
- Use the Copilot chat/explain features (or an LLM) to ask for explanations, complexity, and test ideas rather than full solutions.
- If available, enable a “show provenance” or suggestion history so you can record what the assistant suggested.

Prompt techniques (safe and learning-focused)
- Hint-only: “Give me 3 hints to implement X without full code, and list edge cases.”
- Pseudocode-first: “Show high-level pseudocode and complexity, then explain each step.”
- Explain-only: Paste your draft and ask “Explain this code line-by-line and point out bugs.”
- Test-first: “Generate unit tests and edge-case tests for problem X.”
- Compare approaches: “Give two different algorithms for X, pros/cons and complexity.”

Honor-code and plagiarism avoidance
- Always understand every line you submit. If you didn’t write or fully understand it, don’t submit it as your own work.
- Keep a short “AI usage log” (timestamped prompts and AI outputs you used) and attach it to your submission or include it in a README. This both documents and teaches your instructor how you used the tool.
- Cite Copilot in your README or assignment form if your course requires disclosure: e.g., “Used GitHub Copilot for hints and unit tests; final code was written and reviewed by me.”
- When in doubt, ask the instructor whether Copilot-derived work is allowed and what level of citation they expect.

Decision criteria (pick approach based on these)
- Skill level: beginners should use hints/pseudocode/tests; advanced students can use larger suggestions but must heavily review and refactor.
- Workflow stage: use hints/pseudocode during planning, use explanations and tests during development, only accept small suggestions during final polishing.
- Assignment policy: strict no-AI rules → don’t use Copilot. Permissive / allowed with disclosure → follow the logging + citation approach.
- Budget: Copilot is paid; if you can’t access it, use free LLMs for explanations only (e.g., ChatGPT free tier) but apply the same reuse/disclosure rules.

Quick checklist before submitting
- Run all tests and test cases you wrote. Confirm you understand the solution.
- Reformat and refactor suggestions into your style; replace variable names and rewrite comments in your own words.
- Add a short AI-usage note (what prompts you used and which outputs were incorporated or adapted).
- Run a plagiarism/similarity checker if your course provides one; if it flags you, be ready to show your AI log and local edits.

Best-for / Avoid-if
- Best for: brainstorming approaches, generating tests, getting explanations, and filling small boilerplate.
- Avoid if: assignment explicitly forbids AI or you cannot convincingly document and explain the final solution.

Final note: transparency + comprehension = safe use. Use Copilot to learn faster, not to bypass learning. If you want, try the settings above and post one example prompt and Copilot reply — I’ll help rewrite it into a safe, explainable submission.

Compare GitHub Copilot and Cursor