education-research-question-generation

# Education Research Question Generation ## Goal Produce focused, answerable, evidence-aware research questions for education papers. ## Inputs - Topic or selected direction - Study type: qualitative, quantitative, mixed methods, review, intervention, design-based research - Target population and context - Available data or planned data - Expected paper level: course paper, master's thesis, PhD dissertation, journal article ## Question Frameworks Choose the framework that fits: - Quantitative: PICOT, PICO, PECO, variable relationship model - Qualitative: phenomenon + participants + context + meaning/process - Mixed methods: quantitative relationship + qualitative explanation - Intervention: population + intervention + comparison + outcome + time - Review: population/context + concept/intervention + evidence scope ## Workflow 1. Classify the topic by method type and likely evidence base. 2. Generate 8-12 candidate research questions: - 3 descriptive/exploratory - 3 explanatory/relationship - 2 intervention/effectiveness if applicable - 2 qualitative mechanism/process questions if applicable 3. Search scholarly databases for each candidate or keyword cluster. 4. Score questions on: - clarity - answerability - novelty - data feasibility - theoretical contribution - method fit 5. Rewrite top questions into publishable form. 6. Generate aligned hypotheses or subquestions. ## Tool Calls ### Elicit Use Elicit manually or through API if available. Best for evidence-grounded research reports and extraction tables. - Web: https://elicit.com/ - Query pattern: "What is known about [topic] in [population/context]?" - Extraction fields: `research question`, `population`, `intervention`, `variables`, `outcomes`, `method`, `limitations`. ### Semantic Scholar Search ```bash curl "https://api.semanticscholar.org/graph/v1/paper/search?query=student%20engagement%20AI%20feedback%20education&limit=20&fields=title,year,abstract,citationCount,authors,venue,fieldsOfStudy" ``` ### Semantic Scholar Recommendations Use after selecting a seed paper: ```bash curl "https://api.semanticscholar.org/recommendations/v1/papers/forpaper/PAPER_ID?fields=title,year,abstract,citationCount,authors" ``` ### OpenAlex Query ```bash curl "https://api.openalex.org/works?search=student%20engagement%20AI%20feedback%20education&filter=from_publication_date:2020-01-01&sort=cited_by_count:desc&per-page=20&mailto=YOUR_EMAIL" ``` ## Output Format Return: | Candidate RQ | Type | Variables/Phenomenon | Population/Context | Evidence Base | Feasibility | Risk | Improved Version | |---|---|---|---|---|---|---|---| Then provide: - Recommended main research question - 2-4 subquestions - Optional hypotheses - Required data - Suggested analysis method - Why this RQ is stronger than alternatives ## Quality Rules - Avoid questions that are too broad, e.g. "How does AI affect education?" - Use measurable verbs for quantitative questions: predict, mediate, moderate, influence, compare. - Use meaning/process verbs for qualitative questions: experience, perceive, negotiate, construct, adapt. - Ensure each question implies a feasible method.