The Impact of AI Usage on University Students’ Willingness for Autonomous Learning
Open accessAbstract
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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Prior research has identified satisfaction as one of the strongest predictors of continuance intention.
“Prior research has identified satisfaction as one of the strongest predictors of continuance intention”
From Exploring the Role of Generative AI as a Research Assistant in Undergraduate Sociology Research Methods (2026) · cites this paper as Wang & Li, 2024
The Discussion and Conclusions section (p. unavailable) identifies satisfaction as a significant influence on university students’ intention to continue using generative AI. It identifies positive emotions—not satisfaction—as the most critical factor, and does not establish that satisfaction is one of the strongest predictors. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI tools contribute to increased intrinsic motivation, student autonomy, and psychological resilience.
“Artificial Intelligence tools also contribute to increased intrinsic motivation, student autonomy, and psychological resilience.”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [60]
The 24/7 availability of personalized learning support enhances student confidence while reducing anxiety often associated with traditional instruction.
“The 24/7 availability of personalized learning support enhances student confidence while reducing anxiety often associated with traditional instruction [59,60].”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [60]
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New Ideas for University Students' Career Planning from the Perspective of AI
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AI Dilemma in Schoolwork: Student Anxiety and Fairness Perceptions in AI Schoolwork Accusations
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- 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)
- 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
- gold
- doi.org
- 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
- Physical Sciences › Computer Science › Information Systems › 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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