Evidence map›Paper›PMID 42466030›Full record

SynthesisFrontiers in psychology2026

The positive psychology of AI: a systematic review of generative AI's impact on EFL learners' motivation, anxiety, and well-being in higher education.

Yitong Dong, Kaiyu Zhang, Siou Li, Xiaoyu Gu

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yitong DongSchool of Foreign Languages, Changchun Institute of Technology, Changchun, China.
Kaiyu ZhangSchool of Foreign Languages, Changchun Institute of Technology, Changchun, China.
Siou LiSchool of Foreign Languages, Changchun Institute of Technology, Changchun, China.
Xiaoyu GuSchool of Foreign Languages, Changchun Institute of Technology, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of Generative AI (GenAI) into English as a Foreign Language (EFL) teaching is rapidly expanding, yet its effects on learners' affective states-key to language acquisition from a positive psychology lens-remain inadequately summarized. This systematic review synthesizes empirical evidence on how GenAI tools influence motivation, anxiety, and well-being among EFL learners in higher education. Following PRISMA guidelines, 1,420 records were identified from four databases (2019-2025), with 29 studies meeting inclusion criteria after screening. Results reveal a generally positive affective impact: GenAI enhances motivation and engagement, while typically reducing anxiety-though one study noted increased speaking anxiety. Benefits also extend to well-being, including emotional support and mindfulness. However, effects are moderated by factors such as gender (higher affective scores and AI self-efficacy in males) and academic level (lower perceived utility among Master's students). The findings affirm that well-designed GenAI aligns with Self-Determination Theory by supporting autonomy, competence, and relatedness. In conclusion, GenAI shows strong potential to foster positive psychological outcomes in EFL learning, yet its implementation must be intentionally tailored and inclusively designed to address contextual and individual differences.

Indexed as

anxietyEFL (English as a foreign language)emotionsGenerative AI (GenAI)higher educationmotivationpositive psychology

Identifiers

PMID42466030
PMCPMC13375186

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.