AI in teaching and learning
About 25 minutes · plain language
Students already use AI, whether or not courses allow it. Good teaching decides, task by task, how AI may be used, and still makes sure students learn the core skills themselves. This lesson is for teachers, and for students who want to understand the rules.
- Design an assessment that still measures what students know when AI is available.
- Set a fair course policy on AI use.
Key terms
- Academic integrity
- Honesty and fairness in study and assessment.Example: Submitting your own work and acknowledging help.
- Assessment for learning
- Assessment designed to help students learn, not only to grade them.Example: Feedback on a draft care plan.
- Authentic assessment
- Tasks that resemble real professional work.Example: An observed patient-education session.
- AI literacy
- The knowledge and skills to use AI well and critically: how it works, where it fails, and when not to use it.Example: Spotting an error in an AI-written care plan.
- AI tutor
- An AI tool that explains, quizzes or gives feedback to a learner.Example: A chatbot that asks practice questions on cardiac drugs.
- AI detector
- Software that claims to detect AI-written text. Its results are not reliable proof.Example: A tool that labels an essay “80% AI”.
Set a lane for each task
A single course-wide ban is hard to enforce and wastes a useful tool. Instead, decide for each task whether AI is not allowed, allowed with disclosure, or required (see the figure), and explain why to students before they start. For example, closed-book dosage calculation is “not allowed” because students must be able to do it alone at the bedside.
Assess what AI cannot fake
If a take-home essay can be written by AI, it no longer shows what a student knows. Mix in assessments that AI cannot do for the student: in-class work, oral questioning, observed clinical skills, and tasks that ask students to show their reasoning or to critique an AI answer.
AI as a tutor
AI can give quick feedback and endless practice questions. But people should still write and check the learning content, and decide what counts as correct. UNESCO advises validating AI tools before using them in education and building teachers’ skills, and WHO notes that generative AI in health education needs the same care as in practice.
Do not rely on AI detectors
AI-detection tools make mistakes and can wrongly accuse students, particularly those writing in a second language. Use them, if at all, only as a reason for a conversation, never as proof of misconduct.
Teach AI literacy
Students should learn how AI works, where it fails, how to check it and when not to use it. Nursing literacy frameworks such as NAIL-G describe these skills, and the NLN and AAMC have set out principles for AI in nursing and medical education.
Case: one module, three lanes
In a second-year pharmacology module, students may use AI to explain drug mechanisms if they say so (allowed with disclosure). The dosage calculation test is closed-book (not allowed). One assignment requires AI: students ask a chatbot for a care plan for a patient with heart failure, then find and correct its errors, citing the guideline that shows each correction.
Common mistakes
- Banning AI without explaining why.
- Using AI detectors as proof of misconduct.
- Letting AI grade work with no human review.
Check your understanding
1. Why is a single course-wide AI ban often a poor policy?
It is hard to enforce, ignores useful uses of AI, and does not teach students to use AI well.
2. A detector says an essay is “85% AI-written”. Is that proof of misconduct?
No. Detectors are unreliable and can be wrong, especially for second-language writers.
Try it
- Take one assessment you teach, or one you are set, and decide its lane. Write two sentences explaining the rule.
Remember
- Set a lane for each task: not allowed, with disclosure, or required.
- Assess what AI cannot do for the student.
- Teach students to question AI, not only to use it.
- Miao F, Holmes W. Guidance for generative AI in education and research. Paris: UNESCO; 2023. doi.org
- World Health Organization. Ethics and governance of artificial intelligence for health: guidance on large multi-modal models. Geneva: WHO; 2024. www.who.int
- Masters K. Ethical use of artificial intelligence in health professions education: AMEE Guide No. 158. Med Teach. 2023;45(6):574-84. doi.org
- Association of American Medical Colleges. Principles for the responsible use of artificial intelligence in and for medical education. Version 2.0. Washington (DC): AAMC; 2025. www.aamc.org
- National League for Nursing. Vision statement: artificial intelligence (AI) in nursing education. Washington (DC): NLN; 2025. www.nln.org
- Ibrahim FM, Shahrour G, et al. NAIL-G: a conceptual best-fit framework for nursing AI literacy and governance anchored in socio-technical safety. BMC Nurs. 2026;25. doi.org
Lesson text is our own plain-language summary of these sources; it is not endorsed by their authors. Check the original before relying on any detail.