Chapter 10. Education, Research, Copyright, and Academic Integrity
10.1 AI as a learning aid—not a substitute for learning
AI can explain difficult concepts, create practice questions, suggest outlines, translate text, support accessibility, and help teachers develop learning materials. It can also provide incorrect explanations, conceal a learner’s lack of understanding, reproduce stereotypes, or make it easier to submit work that does not reflect the student’s own contribution. UNESCO recommends a human-centred approach to generative AI in education and research, with attention to privacy, inclusion, age appropriateness, pedagogical suitability, and human agency (Miao & Holmes, 2023).
Educational institutions should define permitted uses by task and assessment objective. Brainstorming, language feedback, or practice quizzes may be appropriate in one context; unacknowledged generation of an assessed argument may violate the rules in another. The answer is not simply “AI is always allowed” or “AI is always forbidden.”
10.2 Transparent academic use
- Read the institution’s current policy and ask the instructor when the boundary is unclear.
- Use AI only for permitted purposes and disclose meaningful assistance as required.
- Verify every factual claim and reference; do not submit fabricated citations.
- Keep drafts, notes, source records, and a short description of how AI was used when required.
- Be able to explain, defend, and revise the work independently.
- Never upload confidential student data, unpublished research, examination materials, or participant information to an unapproved service.
10.3 Research integrity
Researchers remain responsible for the accuracy, originality, ethics, and provenance of their work. AI can assist with coding, translation, brainstorming, literature discovery, and editing, but it cannot be treated as an accountable author or a substitute for research judgement. Researchers must check that sources exist, quotations are exact, analyses are reproducible, and methods are reported honestly.
AI use in data analysis raises additional questions: Was the data shared lawfully? Can the analysis be reproduced? Did the model transform or omit observations? Were outputs checked against the raw data? Were participants informed where required? The research record should describe material AI assistance according to institutional, publisher, funder, and disciplinary rules.
10.4 Copyright and creative work
Copyright questions can involve training data, generated output, similarity to existing works, licensing terms, attribution, and rights in a person’s likeness or voice. Laws differ by jurisdiction and continue to develop. Do not assume that every output is free of third-party rights or that a tool’s terms settle all legal questions. For commercial or public use, assess the tool’s licence, provenance, permissions, and applicable law; seek legal advice when exposure is material.