AI Fluency Teaching Design Tool

Your Teaching Context

Before applying the AI Fluency Framework, establish the context for your teaching. This will help you make better decisions throughout the guide.

What do you teach and who are your students?

Consider: Subject area, level, cohort size, student characteristics, what brought them to this subject.

What are the three foundational questions for your teaching?

These lenses shape everything else you build. If you can't answer them clearly, your materials may inform students without actually reaching them.

Context — What analogies, metaphors, or frameworks make your subject genuinely accessible? Engagement — What makes this subject compelling? What hooks attention on day one, and what sustains momentum when novelty wears off? Meaning — How do students connect what they're learning to their own sense of identity or purpose?

Before Semester: Preparation

The decisions you make before semester begins shape how AI shows up in your teaching for months to come. Get the strategic foundation right (Delegation-Diligence), and you create space for excellent collaborative work (Description-Discernment).

What is your main preparation challenge?

For example: Creating lecture content, developing course materials, creating accessible materials, updating outdated resources, or another preparation task.

Delegation — Strategic Decision

Before touching any AI system, answer these questions.

What exactly needs creating or changing? Which AI modality suits this work? What should remain human versus what could AI help with?

Description & Discernment — Iterative Execution

Don't start with a generic prompt like "Create a lecture on [topic]". Build context over multiple exchanges. Plan your approach here.

What specific context would you share with AI to get genuinely useful outputs? How will you evaluate AI outputs? What would 'good enough' look like? What refinements do you anticipate needing to make?

Diligence — Final Check

Before using AI-assisted content with students, verify the following.

I have verified the accuracy of all claims and content

I have checked cultural appropriateness and accessibility

I have ensured examples don't reinforce stereotypes

I am being transparent about AI involvement where appropriate

I have not shared sensitive or identifiable student data with AI

During Semester: Teaching

This is where preparation meets reality. Students arrive with different needs than anticipated. Energy dips around Week 6. Your carefully designed plans need adaptation.

What is your main teaching challenge?

For example: Facilitating active learning, supporting struggling students, providing feedback on student work, adapting when things aren't working, teaching practical application without industry experience.

Delegation — What needs support?

Which aspect of your teaching needs most support right now? What must remain human versus what AI can support?

Description & Discernment — Working with AI

What specific, contextual prompt would you use rather than a generic request? How will you evaluate whether AI-generated materials genuinely serve learning or just fill time?

Diligence — Responsible Use

Critical principles for the teaching phase.

How will you ensure you never delegate judgement about student quality or achievement to AI? How will you protect student privacy?

Providing Feedback on Student Work

AI can help you articulate feedback more clearly and efficiently — but you must never upload student work to AI systems. The judgement must remain yours. Read the work yourself, make rough notes, use AI to help articulate YOUR observations, then add your personal response.

What is your current feedback challenge?

Consider: Volume of marking, quality of feedback, timeliness, developmental value, maintaining personal connection.

Your ethical feedback process

The correct process: Read work yourself, make rough notes, use AI to help articulate YOUR observations (without sharing student work), then add your personal response.

How will you structure your marking to separate your judgement (human) from articulation support (AI)? What would your rough notes look like before engaging AI? What personal response will you add that only you as their teacher can provide?

After Semester: Reflection & Improvement

The semester ends. This is the moment when small investments in reflection create exponential improvements for next time.

What worked and what didn't?

Don't just summarise feedback — look for deeper themes about what enabled or blocked learning.

What teaching approaches did students find genuinely helpful versus just enjoyable? Where did students struggle most? What surprised you? Where do different data sources (feedback, grades, your observations) agree or contradict?

Planning improvements

You can't redesign everything. Focus on targeted improvements with genuine impact.

What are your highest-priority changes based on this evaluation? What specific, implementable changes will you make for next time?

Building your practice over time

Each semester you learn. Capture that learning so you're not starting from scratch each time.

What approaches worked consistently that you want to document for future reference? What new insights about student learning will inform your future teaching?

This AI Fluency Teaching Design Tool was designed by David Calum Millar using the AI Fluency Framework (Dakan, Feller & Anthropic, 2025) collaboratively with a custom agent called Morna, running on Claude (Anthropic), who is trained on the Creative Compass & Learning Design Compass frameworks by David Calum Millar, then developed using Claude Code. The final design, all editorial decisions, and responsibility for the tool’s content rest with the author.