Multimodal Teaching and Cross-Cultural Adaptation of High-Quality Educational Resources: Research on Cognitive Bias Correction ‎Algorithm

  • Authors

    • Yayun Li The Kyrgyz State Technical University Named After I. Kyrgyzstan, Kyrgyz Republic, Bishkek, 66, Ch. Aitmatov Avenue
    • Meng Dai Shinawatra University,99, Bang Toei, Sam Khok District, Pathum Thani 12160, ‎Thailand
    • Pengfei Zhang International Institute of Management and Business,220086, Minsk ‎City, Belarus
    • Qingzheng Liu Belarusian National Technical University, Minsk, 220013, PR Belarus
    • Shuangyan Yan Dai Belarusian state university220071,Belarus, Minsk City
    https://doi.org/10.14419/0baz6468

    Received date: October 19, 2025

    Accepted date: November 18, 2025

    Published date: November 29, 2025

  • Multimodal Teaching; Cross-Cultural Adaptation; High-Quality Educational Resources

  • Abstract

    This paper investigates the intersection of multimodal teaching methods and the cross-cultural ‎adaptation of high-quality educational resources. We propose a conceptual framework for an ‎algorithm designed to correct cognitive biases that arise in diverse learning environments. The core of ‎our research is the premise that effective knowledge dissemination in multicultural settings requires ‎not only adapting teaching materials but also actively mitigating cognitive incongruities that hinder ‎learning. Our quantitative model is designed to identify and rectify these cognitive distortions in real-time. Controlled experiments show that our adaptive system achieved a 97.9% relative improvement ‎in learning outcomes compared to traditional teaching models. These results indicate significant ‎enhancements in learning efficiency, student engagement, and overall academic performance, ‎highlighting the effectiveness of combining multimodal teaching architectures with data-driven bias ‎correction. Collectively, these discernments underscore the latent efficacy of plurimodal pedagogic ‎architectures—particularly when conjoined with statistical punctiliousness—in nurturing autodidactic ‎and reflexive educational enterprises amidst culturally diverse matrices‎.

  • References

    1. Alazmi, H., & Alemtairy, G. M. (2024). The effects of immersive virtual reality field trips upon student academic achievement, cognitive load, and multimodal presence in a social studies educational context. Education and Information Technologies, 29(8), 9875-9903. https://doi.org/10.1007/s10639-024-12682-3.
    2. Dutt, A., Nandi, R., Rao, P. S., Bhargava, P., Goplakrishnan, S., Bose, A., Ghosh, A., & Evans, J. J. (2023). Adaptation of the Addenbrooke's Cogni-tive Examination III for the Bengali-speaking population in India: A systematic approach to reducing cultural and linguistic bias. Journal of the Interna-tional Neuropsychological Society, 29(6), 591-592. https://doi.org/10.1017/S1355617723007555.
    3. Farantika, D., Afrezah, N. N., Salhah, S., Saudah, S., Asiah, A., & Yafie, E. (2024). Enhancing creative thinking in preschoolers: Teacher strategies for creating a multiliteracy-based learning environment. JPUD - Jurnal Pendidikan Usia Dini, 18(1), 230-249. https://doi.org/10.21009/JPUD.181.17.
    4. Godsk, M., & Møller, K. L. (2024). Engaging students in higher education with educational technology. Education and Information Technologies, 29(7), 8234-8267.
    5. Malakul, S., & Park, I. (2023). The effects of using an auto-subtitle system in educational videos to facilitate learning for secondary school students: learning comprehension, cognitive load, and satisfaction. Smart Learning Environments, 10(1), 1-18. https://doi.org/10.1186/s40561-023-00224-2.
    6. Sobocinski, M., Dever, D. A., Wiedbusch, M. D., Mubarak, F., Azevedo, R., & Järvelä, S. (2023). Capturing self-regulated learning processes in vir-tual reality: Causal sequencing of multimodal data. British Journal of Educational Technology, 54(4), 1045-1068. https://doi.org/10.1111/bjet.13393.
    7. Sutherlin, G. (2023). Who is the human in the machine? Releasing the human-machine metaphor from its cultural roots can increase innovation and equity in AI. AI and Ethics, 5(3), 729-736. https://doi.org/10.1007/s43681-023-00382-6.
    8. Tsai, C. Y., & Li, D. C. (2024). Enterprise implementation of educational technology: Exploring employee learning behavior in e-learning environ-ments. Sustainability, 16(4), 1679. https://doi.org/10.3390/su16041679.
    9. Wang, C., Tachimori, H., Yamaguchi, H., Sekiguchi, A., Li, Y., & Yamashita, Y. (2024). A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease. Translational Psychiatry, 14(1), 1-12. https://doi.org/10.1038/s41398-024-02819-w.
    10. Vesudevan, M., Abdullah, Z., Vasudevan, A., & Qin, P. (2024). Integrating sustainable leadership in Malaysian higher education: Effective strategies for implementation and impact. Multidisciplinary Reviews, 8(4), 2025115. https://doi.org/10.31893/multirev.2025115.
    11. Bohari, A., Wider, W., Udang, L. N., Jiang, L., Tanucan, J. C. M., & Lajuma, S. (2024). Transformational leadership's role in shaping Education 4.0 within higher education. Journal of Infrastructure, Policy and Development, 8(8), 4900. https://doi.org/10.24294/jipd.v8i8.4900.

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  • How to Cite

    Li, Y., Dai, M. ., Zhang, P., Liu, Q., & Dai, S. Y. . (2025). Multimodal Teaching and Cross-Cultural Adaptation of High-Quality Educational Resources: Research on Cognitive Bias Correction ‎Algorithm. International Journal of Basic and Applied Sciences, 14(7), 589-594. https://doi.org/10.14419/0baz6468