Evidence map›Paper›PMID 40150223›Full record

ArticleBehavioral sciences (Basel, Switzerland)2025

The Role of Individual Capabilities in Maximizing the Benefits for Students Using GenAI Tools in Higher Education.

Jia Qi, Ji'an Liu, Yanru Xu

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 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 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

3 authors.

Jia QiUniversity of Chinese Academy of Sciences, Beijing 101408, China.
Ji'an LiuUniversity of Chinese Academy of Sciences, Beijing 101408, China.
Yanru XuUniversity of Chinese Academy of Sciences, Beijing 101408, China.ORCID 0000-0002-1760-7525

Funding

the National Social Sciences Fund of China on Educational Sciences Planning Project of the Year 2022 for National Youth Scholars CIA220288
6 · The paper itself

Abstract

Although the adoption and benefits of GenAI (Generative Artificial Intelligence) tools among higher education students have been widely explored in existing studies, less is known about how individual capabilities influence the use of these tools. Drawing on the Information System Success Model (ISSM) and the Expectation-Confirmation Model (ECM), this study examines how students' capabilities, including critical thinking, self-directed learning ability, and AI literacy, impact the quality of information obtained from GenAI tools. Additionally, it explores the relationships among information quality, student satisfaction, and the intention to continue using GenAI tools in higher education. Survey data from 1448 GenAI tools users in Chinese universities reveal that students with stronger capabilities tend to extract higher-quality information, which in turn fosters their satisfaction with GenAI tools and the intention to continue using these tools. The findings highlight the crucial role of individual capabilities in maximizing the potential of GenAI tools, and it emphasizes the need to cultivate students' critical thinking, self-directed learning ability, and AI literacy to achieve sustainable success in the GenAI era. Theoretically, this study extends the ISSM and ECM by exploring the impact of students' capabilities and the mediating role of user satisfaction between information quality and the intention to continue using GenAI tools. Practically, this study provides implications for educators and policymakers to enhance students' capabilities, thus maximizing the potential benefits of GenAI tools in higher education.

Indexed as

artificial intelligence literacycritical thinkingGenAI toolshigher education studentsself-directed learning abilityusage behavior

Identifiers

PMID40150223
PMCPMC11939294

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.