Evidence map›Paper›PMID 42352765›Full record

ArticleBehavioral sciences (Basel, Switzerland)2026

A Double-Edged Algorithm Attitude: How Appreciation and Aversion Shape Students' AI Learning Anxiety in Higher Education.

Zhaolin Lu, Jiayuan Guo, Tian Yuan, Yue Zhang, Jiajie Yang, Yuxuan Du, Minghua Chen, Mingyi Xie, Liangyu Xian, Hui Cao and 1 more

Abstract read
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In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

11 authors.

Zhaolin LuSchool of Design and Arts, Beijing Institute of Technology, No. 5 South Street, Zhongguancun, Haidian District, Beijing 100081, China.ORCID 0009-0008-7775-5352
Jiayuan GuoSchool of Design and Arts, Beijing Institute of Technology, No. 5 South Street, Zhongguancun, Haidian District, Beijing 100081, China.ORCID 0009-0004-8853-0942
Tian YuanSchool of Design and Arts, Beijing Institute of Technology, No. 5 South Street, Zhongguancun, Haidian District, Beijing 100081, China.
Yue ZhangSchool of Architecture and Art, Hefei University of Technology, No. 193 Tunxi Road, Hefei 230009, China.
Jiajie YangSchool of Design and Arts, Beijing Institute of Technology, No. 5 South Street, Zhongguancun, Haidian District, Beijing 100081, China.
Yuxuan DuSchool of Design and Arts, Beijing Institute of Technology, No. 5 South Street, Zhongguancun, Haidian District, Beijing 100081, China.
Minghua ChenSchool of Design and Arts, Beijing Institute of Technology, No. 5 South Street, Zhongguancun, Haidian District, Beijing 100081, China.
Mingyi XieSchool of Design and Arts, Beijing Institute of Technology, No. 5 South Street, Zhongguancun, Haidian District, Beijing 100081, China.
Liangyu XianSchool of Design and Arts, Beijing Institute of Technology, No. 5 South Street, Zhongguancun, Haidian District, Beijing 100081, China.
Hui CaoSchool of Design and Arts, Beijing Institute of Technology, No. 5 South Street, Zhongguancun, Haidian District, Beijing 100081, China.
Kexin ZhangSchool of Design and Arts, Beijing Institute of Technology, No. 5 South Street, Zhongguancun, Haidian District, Beijing 100081, China.

Funding

Key Research Project under the Beijing Municipal '14th Five-Year Plan' for Education Science CDAA21040National Natural Science Foundation of China 52275234Science and Technology Innovation Program Project of Beijing Institute of Technology 2024CX01023
6 · The paper itself

Abstract

Artificial intelligence is rapidly entering higher education, yet many students experience anxiety when learning to use it. This study examines how performance expectations, perceived explainability, and perceived ethical risks shape two algorithm attitudes, algorithm aversion and algorithm appreciation, and how these attitudes influence artificial intelligence learning anxiety. Using a hybrid partial least squares structural equation modeling-artificial neural network (PLS-SEM-ANN) approach, this study analyzed survey data from 409 university students. Results show that both algorithm aversion and algorithm appreciation significantly increase artificial intelligence learning anxiety, although the effect of algorithm aversion is much stronger, supporting an approach-avoidance account. Perceived ethical risk is the strongest predictor of algorithm aversion but has no significant effect on algorithm appreciation. By contrast, performance expectations and perceived explainability strengthen algorithm appreciation while also showing weaker positive effects on algorithm aversion. These findings suggest that, in educational settings, stronger performance value and greater explainability do not simply reassure students; they can also increase pressure by making errors, responsibility, and the need to use artificial intelligence effectively more salient. The artificial neural network results corroborate these patterns. This study extends research on algorithm attitudes and offers guidance for creating more supportive artificial intelligence learning environments.

Indexed as

algorithm attitudeartificial intelligenceartificial neural networkhigher educationlearning anxietystructural equation modeling

Identifiers

PMID42352765
PMCPMC13295411

What OpenQuestion holds

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LicenceCC BY
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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.