Evidence map›Paper›PMID 41888866›Full record

ArticleBMC medical education2026

The relationship between instructional feedback and academic performance: the role of artificial intelligence perception.

Min Jiao, Chenglin Liu, Hongmin Li, Dongxu Li

Abstract read
In one paragraph

Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Min JiaoSchool of Public Health, Jining Medical University, Jining, China.
Chenglin LiuDepartment of Cardiology, Affiliated Hospital of Jining Medical University, Jining, China.
Hongmin LiSchool of Public Health, Jining Medical University, Jining, China. hopebinyi@hotmail.com.
Dongxu LiSchool of Public Health, Jining Medical University, Jining, China. lidongxu0602@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInstructional feedback can effectively improve academic performance of students. This research aims to explore the impact of instructional feedback on academic performance and to assess the indirect association of AI perception in this relationship.

methodsA cross-sectional study design was employed, conducted at Jining Medical University. The independent variable was instructional feedback, the dependent variable was the academic performance, and the mediating variable was the AI perception level. First, univariate and multiple regression analysis were conducted. Then, the bootstrap method was used to test the significance of the mediating effect. Finally, a restricted cubic spline (RCS) model was used to explore potential non-linear associations between AI perception and instructional feedback.

resultsA total of 470 medical undergraduate students were included in the analysis. Among the participants, 349(74.255%) respondents indicated that lecturers used AI to provide timely instructional feedback. Multivariate regression analysis revealed that academic performance was higher among female students (P = 0.017), urban students (P < 0.001), and those reporting instructional feedback (P = 0.002). Mediation analysis showed that instructional feedback was associated with AI perception (β = 0.465, P = 0.041), but AI perception was not significantly associated with academic performance after adjustment (β = 0.227, P = 0.110). RCS analysis suggested an overall association between AI perception and instructional feedback with no evidence of non-linearity (P-overall = 0.044; P-non-linear = 0.648).

conclusionInstructional feedback was associated with academic performance among medical undergraduates, and it was also associated with AI perception. However, mediation analysis did not support a statistically significant indirect association via AI perception. RCS analysis suggested an overall association between AI perception and the odds of reporting receipt of instructional feedback, with no evidence of non-linearity. These findings suggest that integrating AI-enabled instructional feedback into course delivery may be a useful approach to support learning, while the role of AI perception as an explanatory pathway warrants further investigation.

Indexed as

Academic PerformanceArtificial IntelligenceEducation, Medical, UndergraduateFeedbackFormative FeedbackStudents, MedicalCross-Sectional StudiesFemaleHumansMaleYoung AdultAcademic performanceAI perceptionInstructional feedbackMediating effect

Identifiers

PMID41888866
PMCPMC13141253

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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.