The impact of quality of information on classroom engagement with GenAI tools: integrating TAM and SOR framework.

Fan Li, Juying Chen, Tongtong Shao, Jian Sun

Abstract read

In one paragraph

Article 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 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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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

1 citing paper in PubMed.

Unpacking the dual-path trust mechanism driving students' continuance intention toward campus GenAI: a hybrid SEM-machine learning approach within the S-O-R framework.Frontiers in psychology · 2026
Article

4 · The record

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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.

Fan LiSchool of Music and Dance, Xihua University, Chengdu, Sichuan, China.

Juying ChenFaculty of Humanities and Social Sciences, City University of Macau, Taipa, Macau SAR, China.

Tongtong ShaoFaculty of Humanities and Social Sciences, City University of Macau, Taipa, Macau SAR, China.

Jian SunSchool of Music and Dance, Xihua University, Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the role of quality of information (QI) in shaping students' engagement with GenAI, specifically focusing on the Doubao chatbot in undergraduate English learning. Integrating the Technology Acceptance Model (TAM) with the Stimulus-Organism-Response (SOR) framework, the research examines how QI affects students' perceived ease of use (PEU), perceived usefulness (PU), and ultimately their behavioral (BE), cognitive (CE), and emotional engagement (EE) with the tool. Data were collected from 110 undergraduate students. The findings revealed that QI significantly influences both PEU and PU, which subsequently affect students' engagement. Notably, PEU exerted a stronger impact on engagement than PU. While PU drives behavioral engagement, it does not directly influence cognitive or emotional engagement. The results underscore the importance of high-quality, easy-to-use GenAI tools in enhancing student engagement. Developers and educators are encouraged to prioritize content quality and usability to foster deeper intrinsic motivation and long-term engagement.

Indexed as

perceived ease of use (PEU)perceived usefulness (PU)qdoubao chatbot technology acceptance model (TAM)quality of information (QI)student engagement

Identifiers

PMID41798005

DOI10.3389/fpsyg.2026.1756708

PMCPMC12963208

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LicenceCC BY

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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.