Evidence map›Paper›PMID 40723650›Full record

ArticleBehavioral sciences (Basel, Switzerland)2025

Will Teacher-AI Collaboration Enhance Teaching Engagement?

Lai-Jian Ding, Jia-Min Li, Bei-He Hui

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

3 authors.

Lai-Jian DingDepartment of Physical Education, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Jia-Min LiSchool of Business Administration, Northeastern University, Shenyang 110819, China.ORCID 0000-0001-7371-2605
Bei-He HuiDepartment of Physical Education, Zhejiang Sci-Tech University, Hangzhou 310018, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Against the backdrop of the widespread integration of Artificial Intelligence (AI) into educational practices, collaboration between teachers and AI is profoundly influencing teaching behavior. Drawing on the Conservation of Resources Theory, this study constructs and tests a model examining the impact of teacher-AI collaboration on teaching engagement, with a focus on the mediating role of technological self-efficacy and the moderating role of perceived organizational support. Based on empirical data collected through a survey in China, the results reveal that teacher-AI collaboration significantly and positively predicts teaching engagement. Furthermore, technological self-efficacy mediates this relationship, suggesting that AI collaboration enhances teaching engagement by boosting teachers' confidence in using technology. In addition, perceived organizational support positively moderates the effect of teacher-AI collaboration on technological self-efficacy, forming a moderated mediation model. This research enriches the understanding of teacher behavior in the context of AI integration and offers practical implications for educational institutions seeking to optimize AI adoption strategies and enhance teacher motivation.

Indexed as

conservation of resources theoryperceived organizational supportteacher-AI collaborationteaching engagementtechnological self-efficacy

Identifiers

PMID40723650
PMCPMC12292687

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.