Use when positioning an American Educational Research Journal (AERJ) manuscript in the literature. AERJ readers span the whole field, so the paper must engage the literatures a general education-research audience expects and speak past its own subfield. Builds the positioning argument; it does not write the literature review for you.
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An AERJ paper must locate itself in the field-wide conversation, not just a niche citation cluster. The literature review establishes the gap, the stakes, and why this study advances understanding for a general education-research reader.
aerj-theory-and-framework: the gap
motivates the conceptual frame.Write one sentence a researcher in a different education subfield would underline. If the only people who would care are already in your niche, broaden the framing or the literatures you engage.
Because AERJ readers span the whole field, referees probe whether the review reaches beyond the author's home cluster. Map your citations against the literatures each lens's reviewers treat as table stakes.
| Lens | Literatures referees expect engaged | Engaging-only-your-niche tell |
|---|---|---|
| Policy/institutional | Policy/governance, organizational, stratification/equity, plus the relevant methods literature | Only program-evaluation cites from one district context |
| Teaching/learning/development | Learning sciences, instruction/curriculum, developmental/cognitive, plus measurement literature | Only recent intervention RCTs in one content area |
| Either | The foundational statement of each construct you invoke | Citing textbook glosses instead of the canonical source |
A quasi-experimental evaluation of a dual-language immersion program initially cites only recent bilingual-education impact studies. A general AERJ reader cannot see why the field should care. The positioning fix maps the paper onto two debates — language-as-resource vs language-as-problem framing (a constructs debate) and how schools allocate access to enrichment tracks (a policy/institutional stratification debate) — then names the precise gap: prior work estimates average effects but never tests the access mechanism. With an illustrative effect of 0.22 SD on biliteracy, the contribution now reads as advancing a field-wide conversation about access, not a program report.
【Conversation map】the 2–4 debates/constructs the paper sits within
【Gap】the precise unanswered question / unresolved tension
【Cross-subfield link】the adjacent literature engaged
【So-what-for-the-field】the underline-able sentence
【Hand-off】how the gap motivates the framework
【Next】aerj-theory-and-framework
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Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
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Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.
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Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.
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Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.
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Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.
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Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.
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