aceModerate evidence
More
Meta-analysis of 18 studies finds AI helps students learn vocabulary, with wide variation
Study title: Artificial intelligence-assisted vocabulary teaching: a meta-analysis study
- Evidence type
- Systematic review or meta-analysis
- Sample
- n = 1,823
- Sources
- 1 sourcePeer-reviewed
- Published
- 2 min read
- Truvace record
- TRV-2026-2803cite or check it
Study title: Artificial intelligence-assisted vocabulary teaching: a meta-analysis study
A meta-analysis in Frontiers in Artificial Intelligence pooled 18 experimental or quasi-experimental studies, published 2015 to 2025, with 1,823 learners from preschool to university. Learners taught with AI-supported vocabulary tools scored higher than control groups, with a pooled effect of g = 1.309, which the authors call very large.
Read the study 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-supporte…
The gain
A meta-analysis of 18 controlled studies with 1,823 learners found AI-supported vocabulary teaching beat control instruction on vocabulary tests, with a pooled effect of g = 1.309 (95% CI 0.876 to 1.742).
In brief
The size of the benefit depended on context. Effects were larger for second-language learning (g = 1.447) than first-language learning (g = -0.321, not significant), and in schools (g = 1.606) than outside them (g = 0.758). Against traditional teaching the effect was g = 1.633; against multimedia-supported teaching it fell to g = 0.802. Preschoolers showed no clear benefit (g = 0.029).
The studies differed greatly from one another (I² = 93.868%), so the pooled figure is an average rather than a precise estimate. Small samples, short interventions and limited reporting of randomisation were the most common weaknesses in the included studies, and nearly all second-language studies covered English.
Main points
- The analysis pooled 23 effect sizes from 18 studies with 1,823 participants, drawn from Web of Science, Scopus and ERIC for 2015 to 2025.
- Overall effect: g = 1.309 (95% CI 0.876 to 1.742), with very high heterogeneity (I² = 93.868%).
- Second-language studies (k = 2 in the reported subgroup counts) showed g = 1.447, while first-language studies were not significant (g = -0.321); the authors say the small number of first-language studies weakens that comparison.
- By education level, primary school showed g = 1.927, university 1.548, secondary school 1.358 and preschool 0.029 (not significant).
- AI beat traditional instruction by g = 1.633 but beat multimedia-supported instruction by a smaller g = 0.802.
- In-school use (g = 1.606) outperformed out-of-school use (g = 0.758); teacher involvement and intervention length did not differ significantly.
- Leave-one-out checks moved the pooled effect between 1.116 and 1.378; Egger's test was p = 0.070 and the fail-safe N was 2,488.
What the review found when it combined the studies
These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.
Vocabulary learning with AI interventions in second-language learners
Hedges' g 1.447 (95% CI 0.998 to 1.896)
2 studies
The review’s own words
According to the analysis results, the effect of AI-supported interventions is larger in the L2 group [ g = 1.447, 95% CI (0.998, 1.896)].
Vocabulary learning with AI interventions in school settings
Hedges' g 1.606 (95% CI 1.030 to 2.182)
15 studies
The review’s own words
The results show that AI interventions in the school setting [ g = 1.606, 95% CI (1.030, 2.182)] had a greater effect on vocabulary learning success compared to those in the out-of-school setting [ g = 0.758, 95% CI (0.146, 1.369)].
Vocabulary learning with AI interventions in out-of-school settings
Hedges' g 0.758 (95% CI 0.146 to 1.369)
4 studies
The review’s own words
The results show that AI interventions in the school setting [ g = 1.606, 95% CI (1.030, 2.182)] had a greater effect on vocabulary learning success compared to those in the out-of-school setting [ g = 0.758, 95% CI (0.146, 1.369)].
Vocabulary learning with AI interventions compared with traditional teaching
Hedges' g 1.633 (95% CI 0.943 to 2.323)
14 studies
The review’s own words
Experimental procedures supported by AI applications showed a relatively larger effect compared to traditional teaching [ g = 1.633, 95% CI (0.943, 2.323)].
Vocabulary learning with AI interventions compared with multimedia-supported teaching
Hedges' g 0.802 (95% CI 0.548 to 1.117)
9 studies
The review’s own words
However, when compared to multimedia-supported teaching, the effect size was considerably high but relatively smaller [ g = 0.802, 95% CI (0.548, 1.117)].
Vocabulary learning performance with AI-supported interventions compared with control teaching
Hedges' g 1.309 (95% CI 0.876 to 1.742)
18 studies · 1,823 people
The review’s own words
Furthermore, the overall effect size [ g = 1.309, 95% CI (0.876, 1.742), p < 0.05] demonstrates that the effect of AI use in the context of vocabulary teaching is positive and large.
Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results
The full story
The authors searched Web of Science, Scopus and ERIC, screening 3,435 records down to 18 studies. Only experimental or quasi-experimental designs with an AI group and a non-AI control group qualified. Two researchers screened and coded independently (Cohen's kappa 0.92), and effect sizes were calculated as Hedges' g under a random-effects model.
Moderator analyses found significant differences by language type, education level, control treatment and setting. Teacher role (teacher-led g = 1.449, assistant 1.446, no teacher 0.855) and duration (long term 1.623, medium 1.159, short 1.036) did not differ significantly. The authors say the teacher result should not be read as AI replacing teachers.
The authors rate the included studies as low to moderate risk of bias using an adapted ROBINS-I approach, and note that several studies contributed multiple effect sizes, which may create dependency. Funnel plots looked broadly symmetrical, but a mild small-study effect could not be ruled out.
Sources
1 on file- Frontiers in Artificial Intelligencedoi:10.3389/frai.2026.1806700
Talk it over
Who gets this benefit, and who was left out of the study or story?
Meta-analysis of 18 studies finds AI helps students learn vocabulary, with wide variationhttps://truvace.com/item/artificial-intelligence-assisted-vocabulary-teaching-a-meta-analysis-study
Who gets this benefit, and who was left out of the study or story?
The gain A meta-analysis of 18 controlled studies with 1,823 learners found AI-supported vocabulary teaching beat control instruction on vocabulary tests, with a pooled effect of g = 1.309 (95% CI 0.876 to 1.742).
Answer in the threadHow do we know? What can this kind of evidence show, and what can’t it?
The evidence This rests on a systematic review or meta-analysis with a sample of 1,823, from Frontiers in Artificial Intelligence.
Answer in the threadWhat does it leave open? What else would you want to know before acting on it?
The record’s own caveat The analysis rests on only 18 studies, shows very high heterogeneity (I² = 93.868%) not fully explained by the moderators, and drew mainly on English second-language learners and university students.
Answer in the threadHave you seen AI used this way where you study, work or live? Did it go the same way?
Answer in the thread
The questions are the same on every record. The details under them are taken from this record as written.
What readers say