Agentic education · Learning science

Actionable companion · Assessment & reasoning

Learning with agency: Approaches to Identifying STEM Talent in K-12 Gifted Education: A Systematic Review

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Secondary school, K–12 · mixed / unspecified. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

In the era of digital transformation and artificial intelligence, strengthening STEM education is indispensable to technological innovation and social progress. However, the scarcity of effective strategies and empirical evidence for the early identification of STEM-gifted learners constitutes a critical research gap. This study employed a systematic review approach to synthesize existing identification strategies for K-12 STEM-talent learners. Following the PRISMA framework, 26 peer-reviewed empirical and review articles were retrieved and analyzed from nine databases. Findings revealed that the majority of studies originated in the United States, peaked in 2018, and predominantly comprised empirical designs targeting high school students. Identification methods demonstrated an observed shift from holistic to more individualized approaches, encompassing intelligence and spatial ability testing, academic achievement measures, creativity and problem-solving assessments, portfolio evaluations, subject-specific competitions, and teacher or parent recommendations. Cognitive reasoning, creativity, spatial ability, and problem-solving emerged as core indicators, whereas socioeconomic status, cultural background, teacher and parental support, and gender expectations significantly influenced identification processes and talent development. Future research should empirically validate existing identification framework, account for cross-cultural variability, and examine the longitudinal efficacy of emerging artificial intelligence applications. This review synthesizes existing scholarship on K-12 STEM talent identification and delivers evidence-based recommendations to refine identification and assessment frameworks across diverse educational contexts.

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Secondary school; K–12 · mixed / unspecified. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

Given the interdisciplinary nature of STEM talent identification, spanning education, psychology, and cognitive science, a comprehensive search was conducted across nine major bibliographic databases: Web of Science, ERIC, PubMed, PsycINFO, ProQuest, Scopus, EBSCO, SAGE, and Taylor & Francis. The search strategy employed “STEM” and “talent” as core keywords, supplemented by relevant synonyms, to ensure exhaustive coverage of the pertinent literature. Search syntax and indexing conventions were customized for each database to align with its specific search architecture. The initial literature search was executed in August 2024, with an updated search conducted in October 2024 to capture any newly published relevant studies.

Limitations and uncertainty · source passage

This study acknowledges several limitations that influence the scope and applicability of its findings. We excluded 79 non-English articles during literature screening primarily to guarantee consistent coding standards and stable inter-rater reliability among the review team, yet this exclusive reliance on English-language literature inevitably creates prominent Anglo-American/Western-centric linguistic bias. Future research must critically address this linguistic hegemony. A multilingual systematic review approach, incorporating non-English sources from diverse non-Western regions, such as Chinese academic literature indexed in the China National Knowledge Infrastructure (CNKI), is essential to enrich the global discourse on STEM talent identification.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Ask learners to attempt a problem unaided, compare an agent hint, explain a revision and verify the final reasoning against a trusted source. Make the permitted use explicit for each assignment. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Measure unaided transfer and delayed retention, calibration of confidence and the ability to identify an incorrect AI explanation. Record whether help-seeking becomes answer copying. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Jiaqi Tang; Huichao Bi; Jun Wei. Approaches to Identifying STEM Talent in K-12 Gifted Education: A Systematic Review. Journal of Intelligence (2026-07-24). https://doi.org/10.3390/jintelligence14080153

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.