ArticleHeliyon2024
Continue using or gathering dust? A mixed method research on the factors influencing the continuous use intention for an AI-powered adaptive learning system for rural middle school students.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Factors influencing students' intentions to continue learning in AI-assisted painting courses.Scientific reports · 2026Article
- The impact of DeepSeek's perceived interactivity on medical students' self-directed learning ability.Scientific reports · 2026Article
- The influence of physical activity self-efficacy on the continuance use of AI learning tools among physical education students: a three-wave longitudinal study.Frontiers in psychology · 2026Article
- Article
- Psychological determinants of GenAI adoption for foreign language education: an extended UTAUT2 model and sentiment analysis approach.Frontiers in psychology · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper investigates the factors influencing the continuous use intention of AI-powered adaptive learning systems among rural middle school students in China. Employing a mixed-method approach, this study integrates Technology Acceptance Model 3 with empirical data collected from rural middle schools in western China. The main contributions of this study include identifying key determinants of usage intention, such as computer self-efficacy, perceived enjoyment, system quality, and the perception of feedback. The findings provide insights into enhancing rural education through AI and suggest strategies for developing more effective and engaging adaptive learning systems. This research not only fills a significant gap in the understanding of AI in education but also offers practical implications for educators and policymakers aiming to improve learning outcomes in rural settings.
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Registered trials
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