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Cited by 127Artificial Intelligence in EducationTechnology Adoption and User BehaviourOnline and Blended Learning

The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.

The Impact of AI Usage on University Students’ Willingness for Autonomous Learning

Open access
Wang, Ling, Li, Wenye·2024·Behavioral Sciences
DOI 10.3390/bs14100956PMC PMC11505466
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Abstract

As artificial intelligence (AI) technology becomes increasingly integrated into education, understanding the theoretical mechanisms that drive university students to adopt new learning behaviors through these tools is essential. This study extends the Expectation-Confirmation Model (ECM) by incorporating both cognitive and affective variables to examine students’ current AI usage and their future expectations. The model includes intrinsic and extrinsic motivations, focusing on three key factors: positive emotions, digital efficacy, and willingness for autonomous learning. A survey of 721 valid responses revealed that positive emotions, digital efficacy, and satisfaction significantly influence continued AI usage, with positive emotions being particularly critical. Digital efficacy and perceived usefulness also impact satisfaction, but long-term usage intentions are more effectively driven by positive emotions. Furthermore, digital efficacy strongly affects the willingness for autonomous learning. Therefore, higher education institutions should promote AI technology, enhance students’ expectation-confirmation levels, and emphasize positive emotional experiences during AI use. Adopting a “human–machine symbiosis” model can foster active learning, personalized learning pathways, and the development of students’ digital efficacy and innovation capabilities.

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Research works printed but not linked: 6

  1. Biggs, J.B. (2011). Teaching for Quality Learning at University: What the Student Does, McGraw-Hill Education.
  2. Lee, 2011, Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees’ Intentions to Use E-Learning Systems, Educ. Technol. Soc., 14, 124
  3. Wang, 2018, Research on the Influencing Factors of Users’ Continuous Contribution Behavior in Network Group Collaboration, Libr. J., 37, 12
  4. Liu, 2022, Artificial Intelligence Support for Language Study: How Anthropomorphic Technology Affects Study, J. Electrochem. Educ. Res., 10, 102
  5. Lv, 2019, Knowledge Purport: The Deep Driving Force Affecting the Quality of Undergraduate Learning Experience in Top Universities: Based on the SERU Survey Data of Eight Universities in China and the United States (2017–2018), High. Educ. Jiangsu, 9, 57
  6. Sun, 2021, Game and Construction of Human-Technology Relationship in Artificial Intelligence Education: From Reverse Domestication to Technical Mediation, Open Educ. Res., 27, 37

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Metadata

Authors
Wang, L., & Li, W.
Affiliations
  • Yili Normal University (CN)
  • Nanjing University (CN)
Corresponding author
Li, Wenye
ORCID
Venue
Behavioral Sciences
Publisher
MDPI AG
Published
2024-10-16
Volume
14
Issue
10
Pages
956
ISSN
2076-328X (ISSN-L)
DOI
10.3390/bs14100956
PMC
PMC11505466
OpenAlex
W4403493263
Cited by
94
References
54
Citations by year
2026: 45 · 2025: 47 · 2024: 2
FWCI
35.26
Type
journal article
Language
en
Open access
Licence
Locations
  • Behavioral Sciences (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Education Planning Topics General Project (BIA240122, KYCX24_0098)
  • Postgraduate Research & Practice Innovation Program of Jiangsu Province (BIA240122, KYCX24_0098)
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Education › Online and Blended Learning
Keywords
artificial intelligence in education, university students, self-directed learning, expectation-confirmation model, positive emotions, perceived usefulness, user satisfaction, intrinsic and extrinsic motivation, human-machine symbiosis, personalized learning

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