Eurasia Journal of Mathematics, Science and Technology Education

• Afzal, S., Khan, H. A., Piran, M. J., & Lee, J. W. (2023). A comprehensive survey on affective computing: Challenges, trends, applications, and future directions. IEEE Access, 11, 1-20. https://doi.org/10.1109/ACCESS.2024.3422480
• Al-Saadawi, H. F. T., Das, B., & Das, R. (2024). A systematic review of trimodal affective computing approaches. Expert Systems with Applications, 255, Article 124852. https://doi.org/10.1016/j.eswa.2024.124852
• Azizah, N., Hanafi, I., & Yusro, M. (2025). Artificial intelligence in vocational education: Perspectives and practices from a literature study. Global Synthesis in Education Journal, 3(2), 37-44. https://doi.org/10.61667/w0efrt90
• Calvo, R. A., & D’Mello, S. K. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), 18-37. https://doi.org/10.1109/T-AFFC.2010.1
• Cambria, E., Das, D., Bandyopadhyay, S., & Feraco, A. (2017). Affective computing and sentiment analysis. In E. Cambria, D. Das, S. Bandyopadhyay, & A. Feraco (Eds.), A practical guide to sentiment analysis (pp. 1-10). Springer. https://doi.org/10.1007/978-3-319-55394-8_1
• Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
• D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145-157. https://doi.org/10.1016/j.learninstruc.2011.10.001
• Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
• Li, Y., Schoenfeld, A. H., diSessa, A. A., Graesser, A. C., Benson, L. C., English, L. D., & Duschl, R. A. (2020). On computational thinking and STEM education. Journal for STEM Education Research, 3(2), 147-166. https://doi.org/10.1007/s41979-020-00044-w
• Lin, C.-C., Huang, A. Y. Q., & Lu, O. H. T. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: A systematic review. Smart Learning Environments, 10, Article 41. https://doi.org/10.1186/s40561-023-00260-y
• Marinier, R. P., & Laird, J. E. (2008). Emotion-driven reinforcement learning. In Proceedings of the Cognitive Science Society Conference (pp. 115-120).
• Pei, G., Li, H., Lu, Y., Wang, Y., Hua, S., & Li, T. (2024). Affective computing: Recent advances and future trends. Intelligent Computing, 3, Article 0076. https://doi.org/10.34133/icomputing.0076
• Picard, R. W. (1997). Affective computing. MIT Press. https://doi.org/10.7551/mitpress/1140.001.0001
• Picard, R. W. (2000). Affective computing. MIT Press. https://doi.org/10.1007/978-3-540-45012-2_2
• Picard, R. W. (2003). Affective computing: Challenges. International Journal of Human-Computer Studies, 59(1-2), 55-64. https://doi.org/10.1016/S1071-5819(03)00052-1
• Tao, J., & Tan, T. (2005). Affective computing: A review. In Proceedings of the International Conference on Affective Computing and Intelligent Interaction (pp. 981-995). Springer. https://doi.org/10.1007/11573548_125
• Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2019). Trends and development in technology-enhanced adaptive learning. Computers & Education, 140, Article 103599. https://doi.org/10.1016/j.compedu.2019.103599
• Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009). A survey of affect recognition methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(1), 39-58. https://doi.org/10.1109/TPAMI.2008.52
• Zhai, X., & Tian, L. (2022). Artificial intelligence in STEM education: A systematic review and research agenda. International Journal of STEM Education, 9(1), 1-21. https://doi.org/10.1186/s40594-022-00343-6
• Zhai, X., Chu, H. C., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, J. M., & Liu, J. B. (2021). A review of artificial intelligence in education from 2010 to 2020. Education and Information Technologies, 26(6), 7275-7303. https://doi.org/10.1155/2021/8812542

APA

Zhang, H., & Leong, W. Y. (2026). Validating emotion-driven multimodal AI for STEM vocational education: Evidence from student feedback. Eurasia Journal of Mathematics, Science and Technology Education, 22(3), em2800. https://doi.org/10.29333/ejmste/18075

Vancouver

Zhang H, Leong WY. Validating emotion-driven multimodal AI for STEM vocational education: Evidence from student feedback. EURASIA J Math Sci Tech Ed. 2026;22(3):em2800. https://doi.org/10.29333/ejmste/18075

AMA

Zhang H, Leong WY. Validating emotion-driven multimodal AI for STEM vocational education: Evidence from student feedback. EURASIA J Math Sci Tech Ed. 2026;22(3), em2800. https://doi.org/10.29333/ejmste/18075

Chicago

Zhang, HongLi, and Wai Yie Leong. "Validating emotion-driven multimodal AI for STEM vocational education: Evidence from student feedback". Eurasia Journal of Mathematics, Science and Technology Education 2026 22 no. 3 (2026): em2800. https://doi.org/10.29333/ejmste/18075

Harvard

Zhang, H., and Leong, W. Y. (2026). Validating emotion-driven multimodal AI for STEM vocational education: Evidence from student feedback. Eurasia Journal of Mathematics, Science and Technology Education, 22(3), em2800. https://doi.org/10.29333/ejmste/18075

MLA

Zhang, HongLi et al. "Validating emotion-driven multimodal AI for STEM vocational education: Evidence from student feedback". Eurasia Journal of Mathematics, Science and Technology Education, vol. 22, no. 3, 2026, em2800. https://doi.org/10.29333/ejmste/18075