A practical framework for AI professional development puts teaching goals first, then builds in hands-on review, classroom trials and evidence of learning.
AI training for teachers can produce a stack of sample worksheets and still leave the central question unanswered: should any of them be used with students? The gap between generating an output and making a sound teaching decision is where professional development needs to do its real work.
For school leaders and instructional coaches, that means starting with a classroom need. Teachers might be adapting a reading activity, assessing student work, designing an assessment or helping students scrutinize AI-generated claims. A clear teaching purpose gives participants something concrete to test against, and makes it easier to judge whether AI has helped.
The planning framework below links that purpose to teacher learning, guided practice and classroom follow-up. It draws on published frameworks and research about professional learning, but the template itself has not been formally validated. Treat it as a planning aid, not a proven programme.
Build professional learning around teaching decisions
UNESCO’s AI Competency Framework for Teachers offers a useful set of areas for planning: a human-centred mindset, ethics, AI foundations and applications, pedagogy, and professional learning. That scope helps leaders avoid equating teacher readiness with knowing how to operate a particular product.
The shape of the training matters as much as its subject. A review by Darling-Hammond, Hyler and Gardner identifies features associated with effective teacher professional development: a focus on subject content, active learning, collaboration, modelling, coaching, feedback, reflection and sustained opportunities to learn. Their review addresses teacher development broadly; it does not test the AI planning template presented here.
Learning Forward’s Standards for Professional Learning also point planners toward evidence, implementation and learning design. Taken together, these sources suggest a practical structure: connect an initial session to supported classroom use, then make time to review what happened.
A planning template for school teams
Copy the table into a shared planning document. Fill in the teaching need, learning outcome and evidence of learning before choosing a tool. That order keeps the session anchored in the work teachers need to do.
| Planning area | Questions and fields to complete |
|---|---|
| Session details | Title: ___. Facilitator: ___. Date and duration: ___. Follow-up date: ___. |
| Participants and context | Who is attending? Which subjects and grade levels do they teach? What experience, concerns, access needs, and school expectations should shape the session? |
| Teaching need | What specific problem are teachers addressing? What evidence suggests it deserves attention? |
| Teacher learning outcome | By the end, teachers will be able to ___. Use an observable action such as evaluate, revise, compare, design, or justify. |
| AI competency focus | Which knowledge, judgement, or ethical issue will teachers develop? Identify a relevant UNESCO competency area where useful. |
| Classroom connection | Which curriculum outcome, teaching task, or student need will anchor the work? What thinking must remain with the teacher or student? |
| Tools and materials | Which locally approved tool, sample output, curriculum document, or rubric is needed? Prepare sample outputs for participants who cannot access the tool. |
| Privacy and participation | What information may participants enter? Use fictional or specifically approved materials. Check local requirements, account conditions, and accessibility before the session. |
| Modelled example | What will the facilitator demonstrate? Include the reasoning used to check, revise, or reject an output. |
| Participant practice | What will teachers actually do? Specify the task, materials, and product they will create or evaluate. |
| Feedback and discussion | Who will review the work? Which criteria will guide feedback? How will participants consider different defensible decisions? |
| Evidence of teacher learning | What will show that participants met the outcome? Examples include an annotated output, revised task, or explanation supported by curriculum criteria. |
| Classroom trial | What small use will teachers try? With which class, over what period, and with what support? |
| Follow-up and evaluation | What evidence will teachers bring back? Who will facilitate review? What would justify continuing, revising, or stopping the approach? |
| Responsibilities and resources | Who arranges access, supports participants, answers policy questions, and schedules follow-up? What time or funding is required? |
The privacy and participation prompts reflect UNESCO’s guidance on human agency, data protection, inclusion and educational suitability. Schools should use their local policies and approved procedures to set the specific boundaries.
Related: AI Disclosure Statement Examples for Students
Worked example: reviewing AI-generated reading questions
Consider a session for middle-school teachers who want to create more reading questions but need to check that those questions match both the passage and the intended learning. The example below is illustrative; its activities and timings haven’t been tested as a formal programme.
| Planning area | Example |
|---|---|
| Teaching need | Teachers want additional reading questions but need to judge their alignment with the text and intended learning. |
| Participants | Middle-school teachers with mixed experience using generative AI. |
| Learning outcome | Teachers will evaluate an AI-generated question set, revise unsuitable questions, and explain their decisions using the text and curriculum outcome. |
| Competency focus | AI pedagogy, ethical judgement, and understanding the limitations of generated content. |
| Materials | A permitted reading passage, curriculum outcome, prepared AI question set, and evaluation checklist. |
| Modelled example | The facilitator checks whether a question is answerable from the text and whether it elicits the intended reading skill. |
| Practice | Teachers annotate the question set, identify weaknesses, revise selected questions, and explain what they retained or removed. |
| Feedback | A colleague checks alignment, answerability, clarity, accessibility, and assumptions about students’ background knowledge. |
| Evidence of learning | An annotated original, revised questions, and a short rationale. |
| Classroom trial | Teachers use the reviewed questions in one suitable lesson, if school expectations permit. |
| Follow-up | Teachers discuss where students struggled, which responses showed the intended thinking, and what needs further revision. |
Generating a larger question set is only the starting point. The professional learning comes from inspecting what each question asks students to think about, then deciding what to retain, alter or discard.
A 90-minute session, with follow-up beyond the room
A short workshop can introduce the process and give teachers time to practise. It can’t replace the classroom trial and review, which need their own scheduled time.
| Time | Activity |
|---|---|
| 0–10 minutes | Introduce the teaching need, learning outcome, and participation arrangements. |
| 10–25 minutes | Model the task and explain the decisions involved in evaluating an output. |
| 25–50 minutes | Participants complete a practical task using live tools or prepared examples. |
| 50–65 minutes | Exchange feedback and revise the work against agreed criteria. |
| 65–80 minutes | Plan a small classroom trial and identify evidence to collect. |
| 80–90 minutes | Review learning, identify support needs, and confirm the follow-up date. |
Look for evidence beyond attendance and confidence
An exit survey can show whether teachers found the session useful. To assess whether they learned to make better decisions, examine what they produced and how they justified it.
In the reading-question exercise, can participants spot a question the passage doesn’t answer? Can they revise it while keeping the intended reading skill in view? Can they explain why a polished-sounding question may still be unsuitable for their students? These are more revealing signals than simple tool familiarity.
Classroom follow-up adds another layer. Teachers can bring a revised resource, notes on how they used it, and student response evidence handled appropriately. Review both the gains and the problems. Higher confidence, attendance or tool use alone doesn’t establish an improvement in teaching or student learning.
A sound plan also gives teachers room to decide that a particular use of AI contributes little. That conclusion, when grounded in the task and evidence, is part of professional judgement.
Sources informing the framework
The sources below serve different purposes: the professional development review offers research on teacher learning, while the frameworks and guidance offer planning principles. None validates this exact template.
- Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO.
Useful for selecting competency goals that include ethics, human agency, pedagogy, and professional learning.
Read the framework. - Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017). Effective teacher professional development. Learning Policy Institute.
Supports the emphasis on active learning, modelling, collaboration, feedback, coaching, and sustained opportunities to learn.
Read the report. - Learning Forward. (n.d.). Standards for professional learning.
Useful for planning learning activities, implementation support, and the use of evidence to review professional learning.
Explore the standards. - Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO.
Informs the attention to human agency, privacy, inclusion, and the educational suitability of AI use.
Read the guidance.
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