Evidence map›Paper›PMID 41844752›Full record

ArticleScientific reports2026

Artificial intelligence integrated WeChat social media adoption for collaborative learning engagement among university students.

Isaac Kofi Mensah, Muhammad Khalil Khan

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Isaac Kofi MensahSchool of Accounting, Wuhan College, Wuhan, Hubei, People's Republic of China.
Muhammad Khalil KhanDepartment of Journalism and Communication, School of Media and Law, NingboTech University, Ningbo, People's Republic of China. khan@nbt.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has transformed social media into a powerful tool for educational learning engagements. WeChat, a Chinese social media platform, is extensively used by students for academic learning, collaboration, and information acquisition. However, research on the adoption of AI-integrated social media for learning engagement remains limited, especially in the Chinese context. Based on the traditional Technology Acceptance Model (TAM), this study proposes an AI-Integrated Social Media Adoption (AISMA) model for learning engagement by integrating the core TAM constructs-perceived usefulness (PU) and perceived ease of use (PEOU), with the external factors of collaborative learning, social support, resource sharing, and facilitating conditions to investigate their influence on students' intention to adopt AI-integrated WeChat for learning engagement. Data from Chinese university students were analyzed using structural equation modeling (SEM) via Smart-PLS 4. The results confirm that collaborative learning, social support, and resource sharing are significant direct drivers of AI-integrated social media adoption. Furthermore, facilitating conditions significantly influence both PU and PEOU and also directly drive adoption intention. A key finding from the moderating analysis is that PEOU significantly amplifies the effects of all three external factors (collaborative learning, social support, and resource sharing) on adoption intention. While PU significantly strengthens the relationship for social support and resource sharing, it does not moderate the link between collaborative learning and adoption intention. The study concludes by discussing the theoretical contributions and practical implications for educators and platform developers.

Indexed as

Artificial IntelligenceLearningSocial MediaStudentsChinaCooperative BehaviorFemaleHumansMaleSocial SupportUniversitiesYoung AdultArtificial intelligenceCollaborative learningModerating influenceResource sharingSocial mediaTechnology acceptance modelUniversity studentsWeChat

Identifiers

PMID41844752
PMCPMC13128940

What OpenQuestion holds

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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.