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Design a practitioner inquiry cycle from research question through data collection to evidence-informed action. Use when starting action research, teacher-led investigation, or professional inquiry.
After any AI-generated explanation, require the learner to identify one place it could be wrong, one thing to check, and one source to consult. Builds epistemic vigilance — treats AI output as a claim to evaluate, not truth to absorb.
Capture confidence ratings before and after a learning attempt to identify overconfidence and underconfidence patterns. Use when a student wants to understand how well they actually know something versus how well they think they know it.
Require the learner to explain a concept in their own words before the AI evaluates or extends it. Ensures the AI works from the learner's understanding rather than providing an explanation from scratch.
Track performance across sessions and reduce scaffolding as competence grows. Makes fading visible — the learner knows when scaffolds are removed and why. Use for sustained learning engagement where independence is the goal.
Stage exploration before instruction on complex problems. The learner produces two attempted approaches before consolidation — which builds on those attempts, not from scratch. Use for genuinely hard problems where struggle produces deeper learning.
Provide graduated assistance from abstract conceptual nudge to concrete procedural step, with reflection required before each escalation. Teaches help-seeking as a skill and prevents direct-answer shortcuts.
| name | teacher-inquiry-cycle-designer |
| description | Design a practitioner inquiry cycle from research question through data collection to evidence-informed action. Use when starting action research, teacher-led investigation, or professional inquiry. |
| disable-model-invocation | false |
| user-invocable | true |
| effort | medium |
| skill_id | professional-learning/teacher-inquiry-cycle-designer |
| skill_name | Teacher Inquiry Cycle Designer |
| domain | professional-learning |
| version | 1.0 |
| evidence_strength | strong |
| evidence_sources | ["Timperley (2011) — Realizing the Power of Professional Learning","Timperley et al. (2007) — Teacher Professional Learning and Development: Best Evidence Synthesis","Cochran-Smith & Lytle (2009) — Inquiry as Stance: practitioner research for the next generation","Dana & Yendol-Hoppey (2014) — The Reflective Educator's Guide to Classroom Research","Earl & Katz (2006) — Leading Schools in a Data-Rich World"] |
| input_schema | {"required":[{"field":"inquiry_question","type":"string","description":"The question the teacher wants to investigate — about their practice and its impact on student learning"},{"field":"teacher_context","type":"string","description":"The teacher's situation — subject, year group, professional learning goal"}],"optional":[{"field":"student_level","type":"string","description":"Age/year group"},{"field":"available_time","type":"string","description":"How much time the teacher can dedicate to the inquiry — one term, one year"},{"field":"data_available","type":"string","description":"What data the teacher already has or can collect — test scores, student work, surveys, observations"},{"field":"collaboration","type":"string","description":"Whether the teacher is working alone or with a team"},{"field":"school_support","type":"string","description":"Whether the school has a structured inquiry programme or the teacher is working independently"}]} |
| output_schema | {"type":"object","fields":[{"field":"inquiry_design","type":"object","description":"The complete inquiry cycle — question, baseline, intervention, evidence collection, analysis, conclusions"},{"field":"data_plan","type":"object","description":"What data to collect, when, and how — practical and manageable"},{"field":"analysis_framework","type":"object","description":"How to analyse the data — what patterns to look for, how to draw conclusions"},{"field":"sharing_plan","type":"string","description":"How to share findings with colleagues — spreading the learning beyond the individual teacher"}]} |
| chains_well_with | ["reflective-practice-prompt-generator","lesson-study-cycle-designer","instructional-coaching-conversation-guide","professional-development-session-designer","pedagogical-content-knowledge-developer","technological-pedagogical-content-knowledge-developer"] |
| teacher_time | 4 minutes |
| tags | ["inquiry","Timperley","practitioner-research","action-research","data-informed","professional-learning"] |
Designs a structured teacher inquiry cycle — a form of practitioner research where a teacher investigates a specific question about their practice and its impact on student learning, collects evidence, analyses what they find, and draws conclusions that inform their future teaching. The critical principle from Timperley's research is that the most powerful professional learning occurs when teachers inquire into the IMPACT of their practice on student outcomes, not just reflect on what they did. The output includes a complete inquiry design (question, baseline, intervention, evidence collection, analysis, conclusions), a practical data plan (what to collect and when — manageable within teaching workload), an analysis framework, and a plan for sharing findings. AI is specifically valuable here because designing a rigorous but manageable inquiry requires balancing research standards (valid question, appropriate evidence, fair analysis) with practical teaching constraints (limited time, limited research training, need for the inquiry to serve learning, not just investigation).
Timperley (2011) placed teacher inquiry at the centre of effective professional learning, arguing that the most powerful professional learning cycle is: identify a student learning need → identify what the teacher needs to learn → engage in professional learning → apply it in practice → assess the impact on students → refine. This is inquiry — a systematic investigation into the relationship between teaching and learning. Timperley et al. (2007) found that the most effective professional development programmes involved teachers actively investigating the impact of new practices on their students — not just implementing strategies but checking whether they worked. Cochran-Smith & Lytle (2009) developed "inquiry as stance" — the idea that inquiry is not a one-off project but a professional disposition: a habitual orientation toward questioning, investigating, and learning from practice. Dana & Yendol-Hoppey (2014) provided practical guidance for classroom research, emphasising that teacher inquiry need not meet the standards of academic research — it is practical, context-specific, and designed to improve teaching, not to produce generalisable knowledge. Earl & Katz (2006) distinguished between "data-driven" decision-making (letting data dictate) and "data-informed" decision-making (using data as one input alongside professional judgement). Teacher inquiry is data-informed — the data illuminates, but the teacher interprets.
The teacher must provide:
Optional (injected by context engine if available):
You are an expert in teacher inquiry and practitioner research, with deep knowledge of Timperley's (2011) inquiry cycle, Cochran-Smith & Lytle's (2009) inquiry as stance, Dana & Yendol-Hoppey's (2014) practical classroom research methods, and Earl & Katz's (2006) data-informed professional practice. You understand that teacher inquiry is PRACTICAL research — designed to improve teaching, not to produce academic publications. It must be rigorous enough to produce trustworthy findings but manageable enough to fit within a teaching workload.
CRITICAL PRINCIPLES:
- **The question must be about IMPACT, not just practice.** "Am I using retrieval practice?" is about practice. "Does my use of retrieval practice improve student retention?" is about impact. Impact questions are more useful because they focus on what matters: student learning.
- **The inquiry must be MANAGEABLE.** A teacher conducting inquiry alongside a full teaching load cannot collect data with the rigour of a university researcher. Design data collection that fits within existing routines — using student work, existing assessments, and brief observations rather than creating additional workload.
- **Baseline before intervention.** The teacher needs to know the starting point before they can measure change. What does student learning look like BEFORE the new practice is introduced?
- **Multiple sources of evidence.** No single data source is sufficient. Triangulate: student work + teacher observation + student voice. If all three point in the same direction, the finding is trustworthy.
- **Honest conclusions.** The inquiry should report what was FOUND, not what was hoped for. If the intervention didn't work, that's a valuable finding — it prevents the teacher from persisting with an ineffective practice.
Your task is to design a teacher inquiry for:
**Inquiry question:** {{inquiry_question}}
**Teacher context:** {{teacher_context}}
The following optional context may or may not be provided. Use whatever is available; ignore any fields marked "not provided."
**Student level:** {{student_level}} — if not provided, infer from the question.
**Available time:** {{available_time}} — if not provided, design for a one-term inquiry (10–12 weeks).
**Data available:** {{data_available}} — if not provided, identify manageable data sources.
**Collaboration:** {{collaboration}} — if not provided, design for a solo inquiry with optional peer review.
**School support:** {{school_support}} — if not provided, design as an independent inquiry.
Return your output in this exact format:
## Teacher Inquiry: [Question]
**Question:** [The inquiry question]
**Teacher:** [Context]
**Duration:** [How long]
### Inquiry Design
**Phase 1 — Baseline (Weeks 1–2):**
[What data to collect before the intervention starts]
**Phase 2 — Intervention (Weeks 3–8):**
[What the teacher will change in their practice and how]
**Phase 3 — Evidence Collection (Throughout):**
[What data to collect during the intervention — practical, manageable sources]
**Phase 4 — Analysis (Weeks 9–10):**
[How to analyse the evidence — what to look for, how to compare to baseline]
**Phase 5 — Conclusions (Weeks 11–12):**
[How to draw conclusions and decide what to do next]
### Data Plan
| Data Source | What It Shows | When to Collect | Time Required |
|---|---|---|---|
| [Source 1] | [What it reveals] | [Timing] | [Time cost] |
| [Source 2] | | | |
| [Source 3] | | | |
### Analysis Framework
[How to make sense of the data — what patterns to look for, how to compare baseline and post-intervention, how to handle mixed results]
### Sharing Plan
[How to share findings with colleagues — not as a formal presentation but as practical professional knowledge]
### Honest Caveats
[What the inquiry can and cannot prove — the limitations of a small-scale classroom inquiry]
**Self-check before returning output:** Verify that (a) the question is about impact on student learning, (b) baseline data is collected before the intervention, (c) multiple data sources are used, (d) the data plan is manageable within teaching workload, (e) the analysis framework is honest about limitations, and (f) the sharing plan spreads the learning beyond the individual teacher.
Scenario: Inquiry question: "Does using retrieval practice at the start of every lesson improve my Year 9 students' retention of key Science vocabulary?" / Teacher context: "Year 9 Science, 5 years' experience, interested in cognitive science and memory research, noticed that students struggle with topic-specific vocabulary and forget key terms between lessons" / Available time: "One term — 10 weeks" / Data available: "End-of-topic test scores from the previous two topics (baseline comparison), student exercise books, and the teacher's own lesson records"
Question: Does using retrieval practice at the start of every lesson improve Year 9 students' retention of key Science vocabulary? Teacher: Year 9 Science, 5 years' experience Duration: 10 weeks (one term)
Phase 1 — Baseline (Weeks 1–2): Before introducing retrieval practice, collect baseline data on current vocabulary retention:
Phase 2 — Intervention (Weeks 3–8): Introduce a 5-minute retrieval practice routine at the start of every Science lesson:
Phase 3 — Evidence Collection (Throughout):
Phase 4 — Analysis (Weeks 9–10): Compare:
Phase 5 — Conclusions (Weeks 10–11): Answer the inquiry question honestly:
| Data Source | What It Shows | When to Collect | Time Required |
|---|---|---|---|
| Baseline vocabulary quiz (10 terms) | Pre-intervention retention level | Week 1 | 10 min lesson time, 15 min to mark |
| Weekly retrieval log (tally) | Trend in retrieval accuracy | Every lesson (Weeks 3–8) | 30 seconds per lesson |
| Mid-point vocabulary quiz | Mid-intervention retention | Week 6 | 10 min lesson time, 15 min to mark |
| End-of-topic test (vocabulary questions isolated) | Post-intervention performance | Week 8 | Already part of normal assessment |
| Student survey (3 questions) | Student perception of vocabulary confidence | Weeks 1 and 8 | 5 min lesson time |
Total additional time: Approximately 90 minutes over 10 weeks (beyond normal teaching). This is manageable.
Look for convergence. If all four data sources (quizzes, test, weekly log, student voice) point in the same direction, the finding is robust. If they diverge, investigate why.
Use simple comparison. This is not a statistical study. Compare means: "Average vocabulary quiz score was 4.2/10 at baseline and 7.1/10 at mid-point." Compare proportions: "In Week 3, 45% of students answered retrieval questions correctly. By Week 8, this had risen to 78%." Visual representation (a simple line graph of weekly retrieval accuracy) makes trends visible.
Be honest about attribution. You cannot prove that retrieval practice CAUSED the improvement. Other factors changed simultaneously (students learned new content, became more familiar with the teacher, matured over the term). What you CAN say is: "Vocabulary retention improved during the period when retrieval practice was introduced, and students reported feeling more confident. This is consistent with the research on retrieval practice and suggests it is worth continuing."
Informal: Share findings with the Science department at a department meeting (15 minutes). Show the data, describe the routine, invite questions. Offer to share the retrieval question bank.
Structured: Write a one-page summary: Question → What I did → What I found → What I'll do next. Pin it to the staffroom professional learning board (if one exists) or share via email.
Practical: Offer to model the retrieval routine for a colleague who is interested. Better yet, invite a colleague to observe one of your lessons specifically to see the retrieval routine in action.
This is a small-scale inquiry, not a controlled experiment. There is no control group (a parallel class that didn't receive retrieval practice). You cannot definitively attribute the improvement to retrieval practice — other factors may have contributed. This is a common limitation of practitioner inquiry — but the findings are still valuable for informing YOUR practice.
The teacher is both researcher and practitioner. You want the intervention to work (you believe in retrieval practice), which creates a bias. Be honest with yourself: if the data doesn't show improvement, resist the temptation to explain it away. Negative findings are just as valuable as positive ones.
One term may not be enough. Retrieval practice effects are strongest over longer periods (Roediger & Butler, 2011). If the results are modest after one term, consider extending the inquiry for a second term before concluding that retrieval practice doesn't work for your students.
Teacher inquiry is context-specific. Findings from one teacher's inquiry with one class in one school cannot be generalised to all contexts. The value of teacher inquiry is that it produces LOCAL knowledge — knowledge that is directly applicable to the teacher's own practice, even if it cannot be claimed as universal truth.
Inquiry requires a genuine question. If the teacher has already decided that retrieval practice works and is using the inquiry to confirm this belief, the inquiry loses its value. Genuine inquiry requires genuine uncertainty: "I think this might work — let me find out." If the teacher is not genuinely open to the possibility that the intervention doesn't work, the inquiry becomes advocacy, not investigation.
Data collection must not harm teaching. If the inquiry's data demands become so burdensome that they reduce the quality of teaching, the inquiry is counterproductive. The data plan above is designed to be minimal — but teachers should abandon any data collection that feels unsustainable. A slightly less rigorous inquiry that is actually completed is more valuable than a perfectly designed inquiry that is abandoned in Week 4.