AI-Assisting Technology and Social Support in Enhancing Deep Learning and Self-Efficacy among Primary School Students in Mathematics in China

Yaping Qiu, Nor Asniza Ishak

Abstract


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.

https://doi.org/10.26803/ijlter.24.2.2


Keywords


Artificial intelligence assistant; deep learning; learning self-efficacy; social support

Full Text:

PDF

References


Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M. K., Irshad, M., Arrano-Munoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(311). https://doi.org/10.1057/s41599-023-01787-8

Ahmed, V., & Opoku, A. (2022). Technology-supported learning and pedagogy in times of crisis: The case of the COVID-19 pandemic. Education and Information Technologies, 27, 365–405. https://doi.org/10.1007/s10639-021-10706-w

Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2, 431–440. https://doi.org/10.1007/s43681-021-00096-7

Alreshidi, N. A. K. (2023). Enhancing topic-specific prior knowledge of students impacts their outcomes in mathematics. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1050468

Asghar, M. Z., Arif, S., Barbera, E., Seitamaa-Hakkarainen, P., & Kocayoruk, E. (2021). Support through social media and online class participation to enhance psychological resilience. International Journal of Environmental Research and Public Health, 18(22), 11962. https://doi.org/10.3390/ijerph182211962

Baker, T., Smith, L., & Anissa, N. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta. https://www.nesta.org.uk/report/education-rebooted/

Bekker, C. I., Rothmann, S., & Kloppers, M. M. (2023). The happy learner: Effects of academic boredom, burnout, and engagement. Frontiers in Psychology, 13, Article 974486. https://doi.org/10.3389/fpsyg.2022.974486

Cai, J., & Lian, R. (2022). Social support and a sense of purpose: The role of personal growth initiative and academic self-efficacy. Frontiers in Psychology, 12, 788841. https://doi.org/10.3389/fpsyg.2021.788841

Chang, C. S., Liu, E. Z. F., Sung, H. Y., Lin, C. H., & Chen, N. S. (2014). Effects of online college students' internet self-efficacy on learning motivation and performance. Innovations in Education and Teaching International, 51, 366–377. https://doi.org/10.1080/14703297.2013.771429

Collier, J. E. (2020). Applied structural equation modeling using AMOS. Routledge.

Cortes, B. S., & Ooi, Z. (2023). Cultural intelligence, firm capabilities, and performance: The case of German subsidiaries in Malaysia. Businesses, 3, 460–474. https://doi.org/10.3390/businesses3030028

El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students' engagement. International Journal of Educational Technology in Higher Education, 18(53). https://doi.org/10.1186/s41239-021-00289-4

Essel, H. B., Vlachopoulos, D., Tachie-Menson, A., Johnson, E. E., & Baah, P. K. (2022). The impact of a virtual teaching assistant (chatbot) on students' learning in Ghanaian higher education. International Journal of Educational Technology in Higher Education, 19, Article 57. https://doi.org/10.1186/s41239-022-00362-6

Fakhrou, A., & Habib, L. H. (2022). The relationship between academic self-efficacy and academic achievements in students of the Department of Special Education. International Journal of Higher Education, 11(2). https://doi.org/10.5430/ijhe.v11n2p

Gao, X. (2023). Academic stress and academic burnout in adolescents: A moderated mediating model. Frontiers in Psychology, 14, Article 1133706. https://doi.org/10.3389/fpsyg.2023.1133706

Gligorea, I., Cioca, M., Oancea, R., Gorski, A., Gorski, H., & Tudorache, P. (2023). Adaptive learning using artificial intelligence in e-learning: A literature review. Education Sciences, 13(12), Article 1216. https://doi.org/10.3390/educsci13121216

Hayat, A. A., Shateri, K., Amini, M., & Shokrpour, N. (2020). Relationships between academic self-efficacy, learning-related emotions, and metacognitive learning strategies with academic performance in medical students: A structural equation model. BMC Medical Education, 20, Article 76. https://doi.org/10.1186/s12909-020-01995-9

Huang, G., & Tu, Y. (2021). Roles and research trends of artificial intelligence in. mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), Article 584. https://doi.org/10.3390/math9060584

Huawei Technologies. (2023). A general introduction to artificial intelligence. In Artificial intelligence technology (pp. 1–23). Springer. https://doi.org/10.1007/978-981-19-2879-6_1

Igbokwe, I. C. (2023). Application of artificial intelligence (AI) in educational. Management. International Journal of Scientific and Research Publications, 13(3), 300. https://doi.org/10.29322/IJSRP.13.03.2023.p13536

Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, Article 100017. https://doi.org/10.1016/j.caeai.2021.100017

Kaitera, S., & Harmoinen, S. (2022). Developing mathematical problem-solving skills in primary school by using visual representations on heuristics. LUMAT: International Journal on Math, Science and Technology Education, 10(2), 111–146. https://doi.org/10.31129/LUMAT.10.2.1696

Kova?, V. B., Nome, D. Ø. Jensen, A. R., & Skreland, L. L. (2023). The why, what and how of deep learning: Critical analysis and additional concerns. Education Inquiry. Advance online publication. https://doi.org/10.1080/20004508.2023.2194502

Lai, T., Xie, C., Ruan, M., Wang, Z., Lu, H., & Fu, S. (2023). Influence of artificial intelligence in education on adolescents' social adaptability: The mediatory role of social support. PLoS ONE, 18(3), Article e0283170. https://doi.org/10.1371/journal.pone.0283170

Geitz, G., Van den Brinke, D. J. T., & Kirschner, P. A. (2015). Goal orientation, deep learning, and sustainable feedback in higher business education. Journal of Teaching in International Business, 26(4), 273–292. https://doi.org/10.1080/08975930.2015.1128375

Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: An emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6, Article 9. https://doi.org/10.1186/s40561-019-0089-y

Rodway, P., & Schepman, A. (2023). The impact of adopting AI educational technologies. on projected course satisfaction in university students. Computers and Education: Artificial Intelligence, 5, Article 100150. https://doi.org/10.1016/j.caeai.2023.100150

Salido, V. (2023). Impact of AI-powered learning tools on student understanding and academic performance. BAPS 85: Introduction to Political Analysis and Research. https://doi.org/10.13140/RG.2.2.17259.31521

Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18, Article 54. https://doi.org/10.1186/s41239-021-00292-9

Sheikh, H., Prins, C., & Schrijvers, E. (2023). AI as a system technology. In Mission AI: Research for Policy (pp. 57–77). Springer. https://doi.org/10.1007/978-3-031-21448-6_4

Shi, L., Muhammad Umer, A., & Shi, Y. (2023). Utilizing AI models to optimize blended teaching effectiveness in college-level English education. Cogent Education, 10(2), Article 2282804. https://doi.org/10.1080/2331186X.2023.2282804

Taye, M. M. (2023). Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers, 12(5), Article 91. https://doi.org/10.3390/computers12050091

Thanheiser, E. (2023). What is the mathematics in mathematics education? The Journal of. Mathematical Behavior, 70, Article 101033. https://doi.org/10.1016/j.jmathb.2023.101033

Tsai, C. L., Cho, M. H., & Marra, R. (2020). The self-efficacy questionnaire for online learning (SeQoL). Distance Education, 41(4), 472–489. https://doi.org/10.1080/01587919.2020.1821604

U.S. Department of Education (ED), Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. Washington, DC. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf

Wardat, Y., Tashtoush, M. A., Saleh, S., & Alali, R. (2024). Artificial intelligence in education: Mathematics teachers’ perspectives, practices and challenges. Iraqi Journal for Computer Science and Mathematics, 5(1), 60–77. https://doi.org/10.52866/ijcsm.2024.05.01.004

Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research and future directions. Computers and Education: Artificial Intelligence, 2, Article 100025. https://doi.org/10.1016/j.caeai.2021.100025

Zhao, J., & Liu, E. (2022). What factors can support students' deep learning in the online environment: The mediating role of learning self-efficacy and positive academic emotions? Frontiers in Psychology, 12(13), Article 1031615. https://doi.org/10.3389/fpsyg.2022.1031615


Refbacks

  • There are currently no refbacks.


e-ISSN: 1694-2116

p-ISSN: 1694-2493