Responsible Use of AI

Responsible Use of AI

AI tools like GitHub Copilot, ChatGPT, and Claude are now part of everyday engineering work, but using them well is a matter of personal judgement, not just skill. This subject covers the responsibility side of AI use: academic integrity (attribution, not outsourcing your thinking), critically evaluating AI output for hallucination and unsafe hardware advice, and the legal risks around copyright, GDPR, and the EU AI Act. It is the personal-ethics counterpart to Working with AI in Designing & Realising.

Starting Points

• LinkedIn Learning. Using Generative AI Ethically at Work (Katrina Ingram, 1h 9m).
• LinkedIn Learning. Ethics in the Age of Generative AI (Vilas Dhar, 39m).
• European Commission. (2022). Ethical guidelines on the use of AI and data in teaching and learning.
• UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence.
• LeMaire, C., & Abshire, B. (2025). AI for Everyday IT: Accelerate Workplace Productivity. Manning.

Key Points

• You explain in your own words what academic integrity means when using generative AI tools, including when use is allowed and how to attribute AI assistance.
• You critically evaluate an AI-generated output (code, wiring advice, or datasheet summary) for correctness, bias, and hidden assumptions before using it.
• You describe the key legal risks of using AI tools at work (copyright of AI-generated content, GDPR data residency, organisational policies) with a concrete example.
• You reflect on how your own use of AI during the studio semester affected your learning: what you learned more deeply, and what risks of outsourcing thinking you experienced.
• You apply at least one organisational guideline or framework for responsible AI use in your project documentation (e.g. noting where and how AI was used).