Short answer: Teachers should often begin a lesson-design task without generative AI, then introduce AI to challenge, expand or test their thinking—not automatically to produce the first complete answer. A 2026 study of pre-service teachers offers a useful clue: AI was most effective when it followed initial human-only design work and was paired with collaborative inquiry and reflective learning analytics. The findings are promising, but they do not prove that delaying AI by itself improves teaching in every context.
For schools and teacher-education programmes, the practical question is not simply whether to use AI. It is when to use it, what thinking should happen first, and how educators can tell whether the final design is better.
What the 2026 study found
In August 2026, Yang and colleagues published “Fostering preservice teachers’ design thinking by integrating generative AI and analytics-supported reflective assessment in a collaborative inquiry environment” in Computers & Education. The study followed 110 pre-service teachers enrolled in a semester-long educational technology course at a teacher-education university in central China. Participants worked in a collaborative Knowledge Building environment that combined inquiry, generative AI and analytics-supported reflection.
The design deliberately positioned AI as a cognitive and reflective partner rather than a solution provider. Students first had opportunities to develop ideas through human-led work; AI was introduced strategically later in the process. The researchers examined a design-thinking scale, successive instructional-design artefacts and 1,305 online discourse notes.
The reported pre–post design-thinking score rose from 5.15 to 5.25. The change was statistically significant, but the effect size was small (Cohen’s d = 0.21). The study also found gains in problem discovery and definition, alongside a shift towards more integrated, iterative design practices, particularly prototyping and testing. The authors reported that GenAI was most effective after human-only design efforts.
Source: Yang et al. (2026), Computers & Education, 249, article 105602. A related conference poster is available from the International Society of the Learning Sciences.
Why the timing of AI use may matter
Generative AI can quickly produce lesson objectives, activities, examples and assessment questions. That speed is useful, but it can also hide the thinking that teachers need to practise: identifying the actual learning problem, deciding what evidence would show understanding, anticipating misconceptions and making trade-offs for a particular class.
If a teacher accepts a polished first draft before defining the learning problem, the output can become the frame for every later decision. The teacher may spend time editing an answer whose underlying assumptions were never examined. Starting without AI creates a visible baseline: the teacher can identify what they already understand, where they are uncertain and which decisions need outside challenge.
Introducing AI later changes its role. Instead of asking, “Write my lesson,” the teacher can ask, “What misconception have I missed?”, “Which part of this activity does not align with the learning objective?” or “Offer two alternative explanations for learners who struggle with this concept.” Those prompts support critique and comparison rather than passive acceptance.
This interpretation is consistent with a 2026 critical review of 20 studies on generative AI in teacher professional development. Shenoy and Saarela identify functional, cognitive, social and reflective affordances, while highlighting gaps in explicit training for critical evaluation, ethical reasoning and verification. The review is a synthesis of a still-developing evidence base, not proof that one sequence works for all teachers.
Further reading: Shenoy and Saarela (2026), “A critical review and actionable framework for integrating generative AI into teacher professional development”.
What the study does—and does not—prove
The results should be read carefully. The intervention combined several elements: collaborative inquiry, strategically delayed GenAI use and analytics-supported reflective assessment. It was not a simple experiment in which one group used AI immediately and an otherwise identical group used it later. We therefore cannot attribute the observed change to timing alone.
- It suggests: a structured process that gives teachers room to frame a problem before AI assistance can support design thinking.
- It does not establish: that delaying AI will always improve lesson quality, student attainment or teacher performance.
- It does not mean: teachers should avoid AI during brainstorming or that AI-first work is always harmful.
- It does mean: schools should test the sequence against learning goals and examine the quality of the resulting work, not merely how quickly a draft is produced.
The small effect size is another reason to avoid exaggerated claims. The study is useful as a design signal and a basis for local experimentation, not a universal rulebook.
A four-step human-first workflow teachers can try
1. Frame the learning problem before opening an AI tool
Spend five to ten minutes writing the intended learning outcome, the evidence that would demonstrate it and two likely misconceptions. Identify the learners, prior knowledge, curriculum constraints and any accessibility needs. This short human-only phase gives the teacher a clear point of view against which AI suggestions can be judged.
For example, instead of starting with “Create a lesson on fractions,” write: “Learners should explain why two fractions with different numerators can represent the same quantity, and justify their reasoning using a diagram.” That statement defines a learning problem rather than a content topic.
2. Ask AI to challenge the draft, not replace it
Once a first idea exists, use AI for a bounded purpose. Ask it to identify a misconception, suggest a contrasting example, propose a question that reveals shallow understanding or point out where an activity may not match the objective. Ask for alternatives and reasons, not a single authoritative answer.
A useful prompt is: “Review this lesson idea as a critical teaching colleague. Identify two likely misconceptions, one accessibility barrier and one mismatch between the objective and the assessment. Explain the reasoning for each suggestion. Do not rewrite the lesson yet.”
3. Compare every suggestion with subject knowledge and evidence
Teachers should verify factual claims, curriculum alignment, age appropriateness and the suitability of examples. AI can invent references, oversimplify a concept or recommend an activity that sounds engaging but measures the wrong skill. Reject suggestions that do not fit the learners or the available evidence. Never enter identifiable student information into a tool unless the institution has explicitly approved that use and the relevant privacy requirements are met.
4. Revise, then explain the decisions
After comparing the suggestions, revise the lesson yourself. Record which AI suggestions were accepted, adapted or rejected and why. That brief reflection turns AI use into a professional-learning activity: teachers practise making and defending decisions rather than simply producing a document.
How to check whether the workflow is helping
Do not evaluate the process only by minutes saved or by how polished the lesson plan looks. A school or teacher-education programme can use a short rubric before and after a trial:
| Indicator | Evidence to look for |
|---|---|
| Problem definition | Does the plan name a specific learning difficulty or misconception? |
| Alignment | Do the activities and assessment measure the stated learning objective? |
| Quality of reasoning | Can the teacher explain why key choices were made and which alternatives were rejected? |
| Iteration | Does the final plan improve on the first draft in identifiable ways? |
| Verification and inclusion | Are factual claims checked, student data protected and accessibility needs considered? |
Where feasible, collect a first draft, a revised draft and a short reflection. Compare them using the same rubric. If teachers save time but the reasoning, alignment or accessibility of the lesson deteriorates, the workflow needs redesign. If the quality improves without excessive extra work, the sequence may be worth retaining. A small local trial cannot prove universal effectiveness, but it can inform a better decision than relying on enthusiasm or anxiety alone.
How teacher educators can build this into professional development
Teacher development should teach more than prompt-writing. Participants need opportunities to practise subject-specific judgement, test AI suggestions, discuss privacy and bias, and compare examples of weak and strong instructional alignment. A short workshop can model the four steps, but follow-up is important: teachers should try the process with a real lesson, bring their drafts back and discuss what changed.
Programme leaders can connect this approach with broader AI teacher-training competencies and the practical AI integration framework for educational institutions. Schools can also use our guide to measuring AI literacy beyond prompting to define the competencies teachers should demonstrate. For a wider view of how responsibilities can be divided between educators and tools, see our guide to teacher–AI collaboration in education.
Professional development should also be differentiated. A novice may need support checking outputs and protecting data; an experienced teacher may be ready to use AI to stress-test assessment design or compare alternative explanations. In both cases, the goal is not maximum AI use. It is better professional judgement and more purposeful learning design.
Frequently asked questions
Should teachers always create a lesson plan without AI first?
No. A human-first phase is a useful option when the goal is to practise problem framing, design judgement or independent thinking. For routine administrative tasks or when a teacher needs accessibility support, earlier AI assistance may be appropriate. The sequence should match the learning goal and the risks of the task.
Does delayed AI use improve student learning?
The cited study examined pre-service teachers’ design thinking in a combined collaborative-learning intervention. It does not directly establish that delayed AI use improves school pupils’ learning outcomes. More controlled research is needed to isolate the effect of timing and test it across subjects and learner groups.
What is the difference between using AI as an author and as a critic?
As an author, AI generates a substantial first draft. As a critic, it reviews a draft the teacher has already considered, offering alternatives, identifying possible gaps and raising questions. The second role makes the teacher’s initial reasoning visible and easier to compare with suggestions.
How can a school measure success?
Use a shared rubric to assess problem definition, alignment, reasoning, iteration and verification. Combine those measures with teacher reflections and, where appropriate, evidence from classroom implementation. Track time, but do not treat time saved as a substitute for learning quality.
Conclusion: let AI enter at the point where it adds value
The emerging evidence supports a practical principle: do not let the speed of generative AI decide the order of professional thinking. When the learning goal is to develop teachers’ design judgement, begin by framing the problem, bring AI in to challenge and extend the draft, verify its suggestions and reflect on the choices made. The 2026 study offers a promising example of this sequence, but its small effect and combined intervention call for careful interpretation.
The best question for a teacher is not “Can AI make this lesson for me?” It is “Which part of this task benefits from AI, which part requires my judgement, and what evidence will show that the final lesson is better?”
References
- Yang, Y., Chen, L., Sun, D., Zhu, W., & Pang, S. (2026). Fostering preservice teachers’ design thinking by integrating generative AI and analytics-supported reflective assessment in a collaborative inquiry environment. Computers & Education, 249, 105602. https://doi.org/10.1016/j.compedu.2026.105602
- Shenoy, P., & Saarela, M. (2026). A critical review and actionable framework for integrating generative AI into teacher professional development. Discover Education, 5, 570. https://doi.org/10.1007/s44217-026-01579-7
- International Society of the Learning Sciences. (2026). Fostering preservice teachers’ design thinking… CSCL 2026 conference poster. Repository record