AI-Assisting Technology and Social Support in Enhancing Deep Learning and Self-Efficacy among Primary School Students in Mathematics in China
The extant literature highlighted the significant relationship between artificial intelligence (AI)-assisting technology and deep learning in mathematics among primary school students in China, with learning self-efficacy emerging as a mediator and social support emerging as a moderator. However, the relationships with and their effects on primary school students' deep learning with AI-assisting technology and social support remain less explored. This study aims to address this gap by (a) examining the differential effects of AI-assisting technology on deep learning and (b) determining whether these relationships and effects operate via mediator and moderator, in this case, learning self-efficacy and social support, respectively. Based on a sample of 387 respondents from four primary schools in China, this study examined the underlying causal links among key variables using structural equation modeling (SEM). The findings demonstrate that AI-assisted technology has a significantly positive influence on primary school students' deep learning. Learning self-efficacy supports deep learning improvements within this study's framework. Social support fosters student participation and academic progress, leading to a more enriching learning process. The results strongly validate the proposed theoretical assumptions, demonstrating a clear correlation between the conceptual model and research findings. This study provides valuable perspectives on AI's role in education, emphasizing the necessity of adopting AI-powered educational tools to enhance deep learning among primary school students.
Authors
- Yaping Qiu (ORCID: https://orcid.org/0000-0002-5990-1482)
- Nor Asniza Ishak (ORCID: https://orcid.org/0000-0001-6049-5779)
Institutions
- Hospital Universiti Sains Malaysia (MY)
Publication Details
- Journal
- International Journal of Learning Teaching and Educational Research
- Published
- 2025-02-28
- DOI
- https://doi.org/10.26803/ijlter.24.2.2
- Citations
- 2
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 3.39