Evidence map›Paper›PMID 41039317›Full record

ArticleBMC medical education2025

The impact of artificial intelligence-assisted teaching on medical students' learning outcomes: an integrated model based on the ARCS model and constructivist theory.

Xinyu Pang, Jinyan Zou, Xiaopeng Zhang, Yingying Li, Hao Zhang, Fudong Wang, Yuanyuan Zhang, Xiyi Chen

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Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 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

8 authors.

Xinyu Pang *School of Public Health, Dalian Medical University, Dalian, China.
Jinyan Zou *School of Stomatology, Dalian Medical University, Dalian, China.
Xiaopeng Zhang *The Second Hospital of Dalian Medical University, Dalian, China.
Yingying LiSchool of Public Health, Dalian Medical University, Dalian, China.
Hao ZhangSchool of Public Health, Dalian Medical University, Dalian, China.
Fudong WangPeriodical Office, Dalian Medical University, Dalian, China.
Yuanyuan ZhangSchool of Public Health, Dalian Medical University, Dalian, China. zhangyuan@dmu.edu.cn.
Xiyi ChenSchool of Public Health, Dalian Medical University, Dalian, China. xychen@dmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence-assisted teaching, as an innovative model that combines intelligent technology and personalized education, is increasingly being emphasized in higher medical education.

methodsThis study included 523 participants, with a valid response rate of 87.2%. An integrated model based on the ARCS motivation model and constructivist theory was developed to explore the factors influencing medical students' learning outcomes in the context of AI-assisted instruction. Descriptive statistics were conducted using SPSS 23.0, and a structural equation model was constructed and validated using Amos 23.0. Mediation analysis was performed with Process (version 3.3.1).

resultsThe study confirmed that teaching quality had a positive effect on learning motivation (β = 0.645, P < 0.001) and learning outcomes (β = 0.128, P = 0.032). Learning motivation positively influenced learning attitude (β = 0.822, P < 0.001) and learning satisfaction (β = 0.350, P < 0.001). Learning attitude had a positive impact on both learning satisfaction (β = 0.530, P < 0.001) and learning outcomes (β = 0.232, P = 0.020). Learning satisfaction was also positively associated with learning outcomes (β = 0.415, P < 0.001). The external environment had a positive effect on learning motivation (β = 0.449, P < 0.001) and learning outcomes (β = 0.101, P = 0.033). Moreover, learning motivation played a significant mediating role in the relationships between teaching quality and learning outcomes (β_inmedia = 0.343, 95% CI [0.273, 0.414]), as well as between the external environment and learning outcomes (β_inmedia = 0.287, 95% CI [0.218, 0.355]).

conclusionTeaching quality and external environment indirectly enhance medical learning outcomes by strengthening learning motivation. Learning motivation plays a key role in shaping learning attitude, satisfaction, and outcomes, confirming the positive value of AI-assisted teaching in optimizing learning mechanisms. This study contributes to the application of AI-assisted teaching in medical education and provides empirical support for improving medical students' learning performance.

Indexed as

Artificial IntelligenceEducation, Medical, UndergraduateLearningStudents, MedicalAdultFemaleHumansMaleModels, EducationalMotivationSurveys and QuestionnairesTeachingYoung AdultAI-assistedARCS modelConstructivist theoryLearning outcomesMedical education

Identifiers

PMID41039317
PMCPMC12492703

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.