Actionable companion · Accessibility & inclusion

Learning with agency: Artificial intelligence-assisted vocabulary teaching: a meta-analysis study

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): Age / stage not established. Verify the participant population and task before transferring the findings.

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

Abstract Introduction The literature reports promising findings on the use of artificial intelligence (AI) to enhance vocabulary learning performance. However, this body of research is still relatively recent, important gaps remain, and findings vary across studies. Therefore, synthesizing the available evidence is essential to reach more generalizable conclusions and to gain deeper insight into AI-supported vocabulary instruction. Methods This study employed a meta-analytic design to examine the effects of AI-supported interventions on individuals’ vocabulary learning performance. Articles published between 2015 and 2025 were retrieved from the Web of Science Core Collection (WOS), Scopus, and ERIC databases. Based on the inclusion criteria, 18 studies yielding 23 effect sizes and involving a total of 1,823 participants were included in the analysis. Results The findings showed that AI-supported interventions had a positive and large overall effect on vocabulary learning performance (g = 1.309). This result indicates that AI applications significantly improve individuals’ vocabulary learning outcomes. Moderator analyses revealed significant differences according to language type, learner educational level, control treatment, and intervention setting. In contrast, teacher involvement level and intervention duration did not produce statistically significant moderating effects. Discussion Overall, the results suggest that AI-supported vocabulary instruction is highly effective in improving vocabulary learning performance. These findings indicate that AI applications can provide context-sensitive and personalized learning opportunities across different educational settings. The study contributes to the growing literature by offering a comprehensive synthesis of current evidence and highlighting the instructional potential of AI in vocabulary teaching. Keywords: artificial intelligence, language learning, meta-analysis, the use of technology in language learning, vocabulary acquisition

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Age / stage not established. 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

In the current meta-analysis, we utilised the Comprehensive Meta-Analysis Software (Version 2.2.064) programme to analyse the data. In the process of systematically collecting, rigorously evaluating, and reporting empirical evidence on the effectiveness of AI-supported interventions in the context of vocabulary teaching, we followed these steps: literature review and study identification, eligibility assessment and exclusion, development of a coding guide, calculation of effect sizes, and moderator analyses. Detailed explanations of these application stages and the methodological flow we followed are presented under the following subheadings.

Limitations and uncertainty · source passage

This study has several methodological limitations that should be considered when interpreting the findings. First, the meta-analysis is based on a relatively small number of studies (18 studies and 23 effect sizes), which may limit the generalizability of the results. Second, the very high level of heterogeneity (I2 = 93.868%) indicates substantial variability across studies that is not fully explained by the selected moderator variables. This suggests that other unexamined factors may influence the effectiveness of AI-supported vocabulary learning. Third, although the literature search was limited to three major databases (Web of Science Core Collection, Scopus, and ERIC), this selection was intentional to ensure the inclusion of high-quality, peer-reviewed studies. These databases are widely recognized for their rigorous indexing criteria and are commonly used in meta-analytic research. However, it is acknowledged that studies indexed in other sources such as Google Scholar, IEEE Xplore, ACM Digital Library, or other specialized repositories may have been excluded. Therefore, potential publication bias and database bias should be considered when interpreting the findings. Furthermore, this study was limited to the years 2015–2025, and future reviews may yield different findings by including broader time frames. In addition, the studies reviewed focused primarily on English language learning contexts and L2 populations, which may limit the generalizability of the findings to other linguistic settings. Therefore, the results of this meta-analysis should be interpreted with caution.

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.

Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.

A 90-day district pilot

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.
Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
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.
Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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

Bilal Şimşek; Bekir Direkci; Betül Koparan; Serdar Akbulut; Emine Ela Şimşek; Mevlüt Gülmez; Damla Nur Ökmen. Artificial intelligence-assisted vocabulary teaching: a meta-analysis study. Frontiers in Artificial Intelligence (2026-04-23). https://doi.org/10.3389/frai.2026.1806700

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.