Evidence map›Paper›PMID 41750092›Full record

ArticleBehavioral sciences (Basel, Switzerland)2026

A Latent Profile Analysis of Emotions in AI-Mediated IDLE: Associations with Emotion Regulation Strategies and Perceived AI Affordances.

Zihan Gao, Chenxi Du

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

2 authors.

Zihan GaoSchool of Languages and Communication Studies, Beijing Jiaotong University, Beijing 100044, China.
Chenxi DuSchool of International Studies, University of International Business and Economics, Beijing 100029, China.

Funding

China Postdoctoral Science Foundation 2024M760193
6 · The paper itself

Abstract

The rapid development and easy accessibility of artificial intelligence (AI) technology have led to a significant rise in informal digital learning of English (IDLE). However, the emotional experiences across different cohorts of learners remain underexplored. Contextualized in AI-mediated IDLE, the present study integrated the control-value theory of achievement emotions and the process model of emotion regulation to investigate the latent profiles of emotions and further examine their relations to emotion regulation strategies (cognitive reappraisal and expressive suppression) and perceived AI affordances. Questionnaires were administered to 613 English as a foreign language undergraduates in China. Latent profile analysis revealed three emotion profiles, including moderate positive and moderate negative emotions group (Profile 1, 43%); high positive and low negative emotions group (Profile 2, 21%); and high positive and high negative emotions group (Profile 3, 36%). The Bolck-Croon-Hagenaars (BCH) analysis indicated that students in Profile 2 scored the highest on perceived AI affordances, followed by those in Profile 3 and Profile 1. Additionally, multinomial logistic regression analysis showed that cognitive reappraisal was a stronger predictor of membership in Profiles 2 and 3 compared with Profile 1, while expressive suppression predicted membership in Profile 3 to the greatest extent, followed by Profiles 1 and 2. Pedagogical implications were provided to cultivate learners' optimal emotional state.

Indexed as

achievement emotionsAI-mediated IDLEcognitive reappraisalexpressive suppressionlatent profile analysisperceived AI affordances

Identifiers

PMID41750092
PMCPMC12938404

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.