NTLSN · Crash Course · Teaching with AI

Teaching with AI — a crash course

Generative AI is already in your students’ hands and your own workflow. Four short lessons on using it deliberately as a teaching and learning aid, then a self-check.

The one thing to remember: AI is a tool for thinking, not a replacement for it. Used deliberately and critically, it can support learning; used by default, it can quietly erode it.

4 lessons~10 min read1 self-checkGrounded in emerging AI-in-education good practice (sector guidance & digital literacy frameworks)

The lessons

Build AI literacy firstKnow what these tools do and don’t do

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Before you teach with generative AI, understand it. These tools predict plausible text; they don’t know what’s true, and confidence is not accuracy.

Expect fabrication — outputs can invent facts, sources and quotes that read convincingly.
Watch for bias and gaps — training data carries the slants and blind spots of its sources.
Treat what you type as potentially retained — don’t paste sensitive student or personal data.
Build your own working sense of where a tool is strong and where it falls down.

Let AI support your teachingDraft and explore — keep your judgement

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AI can take some load off preparation, but the professional decisions stay with you. Use it to get started, not to decide.

Draft first passes — outlines, examples, alternative explanations — then revise to fit your students.
Generate ideas and variations when you’re stuck, and keep the ones that earn their place.
Use it for accessibility — simplifying language, offering alternative formats, summarising.
Check every output against your own knowledge and your context before it reaches students.

Design learning that uses AI criticallyA thinking partner, not a shortcut

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The richest use isn’t hiding AI from students — it’s teaching them to interrogate it. Build tasks where the critique is the learning.

Have students compare and critique AI outputs against criteria and against good sources.
Position AI as a thinking partner — to question, draft and stress-test ideas, not to hand in.
Scaffold the harder thinking so AI supports the work rather than skipping it.
Make the reasoning visible — ask students to show what they kept, changed and rejected.

Grounded in

Digital and AI literacy frameworks
Evaluative judgement and critical-use practice

Equity, ethics & transparencyAccess, data and openness

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AI use sits on uneven ground. Not every student has the same access, and not every tool handles data well. Be open about how and why you use it.

Don’t assume equal access — paid tools, devices and connectivity vary between students.
Mind data and privacy — know what a tool stores and check it against your institution’s policy.
Be transparent with students about where you use AI and what you expect from them.
Set clear, course-level expectations so ‘allowed’ use isn’t left to guesswork.

◇ Bring it together — from the NTLSN commons

Teaching AI prompts Browse “Artificial intelligence”Assessment resources Frameworks & policy

Before you bring AI into your next class — a quick self-check

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I understand these tools can fabricate, and I check outputs for accuracy.

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I don’t paste sensitive student or personal data into AI tools.

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I use AI to draft and explore, but the teaching judgement stays mine.

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My tasks have students critique AI, not just consume it.

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I’ve considered access gaps so AI use doesn’t disadvantage anyone.

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I’m transparent with students about where and why I use AI.

Source & attribution. Curated from emerging AI-in-education good practice and digital-literacy frameworks indexed by the NTLSN commons. Practitioner synthesis, not original research. This is practice guidance in a fast-moving area — treat it as a starting point and check it against current sector and institutional advice.

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