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AI tools raised English learners' motivation in a review of 16 studies with 1,923 students

Study title: Impact of Artificial intelligence tools on learning motivation in English instruction: a network meta-analysis

Evidence type
Systematic review or meta-analysis
Sample
n = 1,923
Sources
1 sourcePeer-reviewed
Published
2 min read
Truvace record
TRV-2026-2773cite or check it

Study title: Impact of Artificial intelligence tools on learning motivation in English instruction: a network meta-analysis

A network meta-analysis by Liwei Hsu and Yu-Chun Wang pooled 16 experimental and quasi-experimental studies covering 1,923 K-12 and university students learning English as a foreign language. Every AI category beat traditional instruction on learning motivation: language apps (g = 0.907, 2 studies), generative AI chatbots (g = 0.824, 11 studies) and AI writing assistants (g = 0.692, 3 studies).

Read the study abstract

Abstract The rapid expansion of English-Medium Instruction (EMI) programs and the growing demand for English language proficiency have introduced significant motivational challenges for EFL learners who face linguistic barriers. Artificial intelligence (AI) tools offer a potential solution by providing personalized scaffolding and interactive support. This study conducts a network meta-analysis (NMA) of 16 empirical studies (N = 1,923) to compare the effectiveness of three AI interventions, including Generative…

The gain

In a network meta-analysis of 16 controlled studies with 1,923 English-learning students, AI language apps (g = 0.907), chatbots (g = 0.824) and writing assistants (g = 0.692) each raised learning motivation more than traditional instruction.

In brief

The effects are medium to large and exceed the 0.40 benchmark the authors cite for meaningful educational effects. Sensitivity analyses that dropped three higher-risk studies changed the estimates by less than 0.06, and Egger's test found no sign of publication bias.

The ranking of the AI tools against each other is much less certain. No study compared AI categories directly, so those comparisons rest on indirect evidence, and none of the pairwise differences reached statistical significance. The authors call the rankings preliminary.

Main points

  1. The analysis covered 16 studies published 2021 to 2025 and 1,923 students; sample sizes ranged from 50 to 412, with a median of 85.
  2. Eleven studies tested chatbots (N = 1,268), two tested language apps (N = 443) and three tested writing assistants (N = 212).
  3. Nine of the 16 studies were from China; the rest came from Iran (3) and one each from the UAE, Algeria, Nigeria and the United States.
  4. Interventions lasted 6 weeks to one semester, with a median of 8 weeks; 14 studies used traditional instruction as the control.
  5. Heterogeneity was moderate (I2 = 42.3%), and effects were slightly smaller for studies longer than 8 weeks (g = 0.78) than for those of 8 weeks or less (g = 0.86).
  6. University studies (g = 0.81) and K-12 studies (g = 0.89) did not differ significantly, and East Asian and Middle Eastern or African studies gave similar results (g = 0.83 vs 0.82).
  7. The authors propose a three-level framework that pairs apps, writing assistants and chatbots with different support needs, and say AI should complement teacher guidance.

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.

  • Learning motivation of university EFL students using AI tools

    Hedges' g 0.81 (95% CI 0.67 to 0.95)

    14 studies

    The review’s own words
    Studies conducted in university settings (k = 14) yielded a pooled effect of g = 0.81 (95% CI [0.67, 0.95])

  • EFL students' learning motivation with generative AI chatbots compared with traditional instruction

    Hedges' g 0.824 (95% CI 0.690 to 0.959)

    11 studies · 1,268 people

    The review’s own words
    followed by Generative AI Chatbots (g = 0.824, 95% CI [0.690, 0.959]).

  • Learning motivation of K-12 EFL students using AI tools

    Hedges' g 0.89 (95% CI 0.62 to 1.16)

    2 studies

    The review’s own words
    K-12 studies (k = 2) showed a slightly larger pooled effect of g = 0.89 (95% CI [0.62, 1.16])

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, ERIC, PsycINFO and Google Scholar for peer-reviewed studies from January 2015 to December 2025. Of 2,156 records, 138 full texts were assessed and 16 met the criteria, which required a comparison group and quantitative motivation measures. Two reviewers screened independently with high agreement (kappa 0.87 and 0.92).

Study quality was rated with Cochrane's RoB 2 for randomized trials and ROBINS-I for quasi-experimental studies. Blinding was generally not feasible, leaving most studies at moderate risk of bias in performance-related domains. A frequentist random-effects model estimated Hedges' g for each AI category against traditional instruction.

The authors suggest chatbots work partly by reducing speaking anxiety and apps by gamified progress markers that support competence, but these mechanisms are interpretation drawn from other research rather than tested here. They also warn that over-reliance on AI feedback could weaken independent learning, and recommend fading AI support as proficiency grows.

Their recommendations include assessing students' proficiency before choosing a tool and using chatbot practice before oral presentations. They also call for head-to-head randomized trials and multi-semester studies.

Sources

1 on file
  1. Asian-Pacific Journal of Second and Foreign Language Educationdoi:10.1186/s40862-026-00421-9
    Peer-reviewed2026-07-16

Talk it over

Four questions to start onWho gets this benefit, and who was left out of the study or story?

Start with a question

AI tools raised English learners' motivation in a review of 16 studies with 1,923 studentshttps://truvace.com/item/impact-of-artificial-intelligence-tools-on-learning-motivation-in-english-instru

  1. Who gets this benefit, and who was left out of the study or story?

    The gain In a network meta-analysis of 16 controlled studies with 1,923 English-learning students, AI language apps (g = 0.907), chatbots (g = 0.824) and writing assistants (g = 0.692) each raised learning motivation more than traditional instruction.

    Answer in the thread

  2. How 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 1,923, from Asian-Pacific Journal of Second and Foreign Language Education.

    Answer in the thread

  3. What does it leave open? What else would you want to know before acting on it?

    The record’s own caveat All 16 studies compared AI with traditional instruction and none compared AI tools with each other, so rankings among AI types rest on indirect evidence.

    Answer in the thread

  4. Have 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.

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