Evidence map›Paper›PMID 40867368›Full record

ArticleBehavioral sciences (Basel, Switzerland)2025

Whether and When Could Generative AI Improve College Student Learning Engagement?

Fei Guo, Lanwen Zhang, Tianle Shi, Hamish Coates

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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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.

Fei GuoSchool of Education, Tsinghua University, Haidian District, Beijing 100084, China.ORCID 0000-0002-0934-696X
Lanwen ZhangPolicy Research and Planning Office, Tsinghua University, Haidian District, Beijing 100084, China.ORCID 0000-0002-1333-0303
Tianle ShiSchool of Education, Tsinghua University, Haidian District, Beijing 100084, China.ORCID 0009-0004-8457-2758
Hamish CoatesSchool of Education, Tsinghua University, Haidian District, Beijing 100084, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative AI (GenAI) technologies have been widely adopted by college students since the launch of ChatGPT in late 2022. While the debate about GenAI's role in higher education continues, there is a lack of empirical evidence regarding whether and when these technologies can improve the learning experience for college students. This study utilizes data from a survey of 72,615 undergraduate students across 25 universities and colleges in China to explore the relationships between GenAI use and student learning engagement in different learning environments. The findings reveal that over sixty percent of Chinese college students use GenAI technologies in Academic Year 2023-2024, with academic use exceeding daily use. GenAI use in academic tasks is related to more cognitive and emotional engagement, though it may also reduce active learning activities and learning motivation. Furthermore, this study highlights that the role of GenAI varies across learning environments. The positive associations of GenAI and student engagement are most prominent for students in "high-challenge and high-support" learning contexts, while GenAI use is mostly negatively associated with student engagement in "low-challenge, high-support" courses. These findings suggest that while GenAI plays a valuable role in the learning process for college students, its effectiveness is fundamentally conditioned by the instructional design of human teachers.

Indexed as

college student learning engagementeffective teaching practicesgenerative AI

Identifiers

PMID40867368
PMCPMC12382942

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.