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To engage with AI or not: learning engagement among rural junior high school students in an AI-powered adaptive learning environment

Author

Listed:
  • Jining Han

    (Southwest University)

  • Geping Liu

    (Southwest University)

  • Shu Xiang

    (Longquan No. 1 Primary School)

Abstract

The purpose of this study is to explore the effects of certain factors, as defined by self-determination theory, the technology acceptance model, and cognitive load theory, and their relationships on the learning engagement of rural junior high school students within an artificial intelligence (AI)-powered adaptive learning system (ALS). The main research question investigates how these factors not only interact to understand student engagement but also inform the implementation of AI in educational settings. A model is constructed that reflects these theories and is tested using a survey administered across five rural junior high schools by utilizing AI-powered ALS. Path analysis and mediation effect analysis are employed to assess the effects of various factors on student engagement. The results indicate that technological acceptance and perceived ability significantly enhance student learning engagement. In AI-powered ALS, extrinsic cognitive load is more important than intrinsic load. Perceived autonomy, perceived ability, and perceived relatedness are found to positively affect technological acceptance. The mediation effects reveal that technological acceptance partially mediates the relationship between perceived ability and engagement, fully mediates the relationship between perceived relatedness and engagement, and has no mediating effect on the relationship between perceived autonomy and engagement. The study concludes that to improve learning engagement, educational systems should focus on enhancing technology acceptance by improving system usability and learning outcomes. Furthermore, it recommends fostering student competence through effective monitoring and immediate feedback on errors and minimizing extraneous cognitive load through clearer instructions and feedback. These strategies could be pivotal in designing targeted instructional frameworks for AI-powered learning environments.

Suggested Citation

  • Jining Han & Geping Liu & Shu Xiang, 2025. "To engage with AI or not: learning engagement among rural junior high school students in an AI-powered adaptive learning environment," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-17, December.
    • Handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-05676-0
    DOI: 10.1057/s41599-025-05676-0
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    References listed on IDEAS

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    1. Wadim Strielkowski & Veronika Grebennikova & Alexander Lisovskiy & Guzalbegim Rakhimova & Tatiana Vasileva, 2025. "AI‐driven adaptive learning for sustainable educational transformation," Sustainable Development, John Wiley & Sons, Ltd., vol. 33(2), pages 1921-1947, April.
    2. Adil Ellikkal & S. Rajamohan, 2024. "AI-enabled personalized learning: empowering management students for improving engagement and academic performance," Vilakshan - XIMB Journal of Management, Emerald Group Publishing Limited, vol. 22(1), pages 28-44, July.
    3. Hang Yuan, 2025. "Artificial intelligence in language learning: biometric feedback and adaptive reading for improved comprehension and reduced anxiety," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-16, December.
    4. Yuan Gao & Qingsong He & Yaolin Liu & Lingyu Zhang & Haofeng Wang & Enxiang Cai, 2016. "Imbalance in Spatial Accessibility to Primary and Secondary Schools in China: Guidance for Education Sustainability," Sustainability, MDPI, vol. 8(12), pages 1-16, November.
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