Cultural Intelligence in AI-Driven Online Learning: Examining Bias and Inclusivity in Virtual Classrooms
DOI:
https://doi.org/10.33394/jo-elt.v13i1.20009Keywords:
Artificial intelligence, Cultural intelligence, Online learning, Cultural inclusivity, AI-mediated education, Online educationAbstract
The integration of artificial intelligence (AI) into online education has transformed teaching and learning practices through personalization, automation, and adaptive learning support. However, the increasing use of AI-driven educational systems raises concerns regarding their ability to accommodate culturally diverse learners. While previous studies have primarily focused on the effectiveness of AI in improving engagement and learning outcomes, limited attention has been given to the role of cultural intelligence (CQ) in AI-mediated learning environments. This study examines how AI-integrated online learning platforms support or constrain cultural inclusivity among diverse student populations. The study employed an explanatory sequential mixed-methods design involving 230 participants, including students, instructors, and AI developers. Quantitative data were collected through surveys and analyzed using descriptive statistics, ANOVA, and regression analysis, while qualitative data from semi-structured interviews and focus groups were analyzed through thematic analysis. The findings indicate that although AI technologies improve accessibility and learning efficiency, many AI-driven systems still reflect dominant Western-oriented educational norms and communication styles. Non-Western learners and multilingual students reported lower engagement levels, particularly when AI-generated feedback and content recommendations lacked cultural representation and contextual sensitivity. The study also found that learners with higher levels of cultural intelligence demonstrated stronger adaptability to AI-mediated learning environments. This study contributes to the growing discussion on AI in education by positioning cultural intelligence as an important consideration in the design and implementation of AI-supported learning systems. The findings highlight the need for culturally responsive AI frameworks and emphasize the continuing importance of human–AI collaboration in creating equitable and inclusive online learning environments.
References
Ang, S., & Van Dyne, L. (Eds.). (2015). Handbook of cultural intelligence: Theory, measurement, and applications. Routledge. https://doi.org/10.4324/9781315703855
Atkinson, D., Mejía‐Laguna, J., Ribeiro, A. C., Cappellini, M., Kayi‐Aydar, H., & Lowie, W. (2025). Relationality, interconnectedness, and identity: A process‐focused approach to second language acquisition and teaching (SLA/T). The Modern Language Journal, 109(S1), 39–63. https://doi.org/10.1111/modl.12982
Banwo, B. O., Khalifa, M., & Seashore Louis, K. (2022). Exploring trust: Culturally responsive and positive school leadership. Journal of Educational Administration, 60(3), 323–339. https://doi.org/10.1108/JEA-03-2021-0065
Bhardwaj, V., Zhang, S., Tan, Y. Q., & Pandey, V. (2025). Redefining learning: Student-centered strategies for academic and personal growth. Frontiers in Education, 10, 1518602. https://doi.org/10.3389/feduc.2025.1518602
Bhavana, S., Jayashree, K., & Rao, T. V. N. (2025). Navigating AI biases in education: A foundation for equitable learning. In K. L. Keeley (Ed.), AI applications and strategies in teacher education (pp. 135–160). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-5443-8.ch005
Bhutoria, A. (2022). Personalized education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3, 100068. https://doi.org/10.1016/j.caeai.2022.100068
Brokensha, S., Kotzé, E., & Senekal, B. A. (2023). AI in and for Africa. Chapman & Hall/CRC. https://doi.org/10.1201/9781003276135
Brown, K. D., & Laihonen, P. (2025). Marginalisation: Non-dominant language learning and teaching from a comparative perspective. Compare: A Journal of Comparative and International Education, 55(2), 155–173. https://doi.org/10.1080/03057925.2023.2234281
Bucea-Manea-Țoniş, R., Martins, O. M. D., Gheorghiță, C., Kuleto, V., Ilić, M. P., & Simion, V.-E. (2021). Blockchain technology enhances sustainable higher education. Sustainability, 13(22), 12347. https://doi.org/10.3390/su132212347
Bulathwela, S., Pérez-Ortiz, M., Holloway, C., Cukurova, M., & Shawe-Taylor, J. (2024). Artificial intelligence alone will not democratise education: On educational inequality, techno-solutionism and inclusive tools. Sustainability, 16(2), 781. https://doi.org/10.3390/su16020781
Carlsson, S. V., Esteves, S. C., Grobet-Jeandin, E., Masone, M. C., Ribal, M. J., & Zhu, Y. (2024). Being a non-native English speaker in science and medicine. Nature Reviews Urology, 21(3), 127–132. https://doi.org/10.1038/s41585-023-00839-7
Dai, K., & Wang, Y. (2024). Enjoyable, anxious, or bored? Investigating Chinese EFL learners’ classroom emotions and their engagement in technology-based EMI classrooms. System, 123, 103339. https://doi.org/10.1016/j.system.2024.103339
Earley, P. C., & Ang, S. (2003). Cultural intelligence: Individual interactions across cultures. Stanford University Press. https://doi.org/10.1515/9780804766005
Eguchi, A., Okada, H., & Muto, Y. (2021). Contextualizing AI education for K–12 students to enhance their learning of AI literacy through culturally responsive approaches. KI - Künstliche Intelligenz, 35(2), 153–161. https://doi.org/10.1007/s13218-021-00737-3
Ellikkal, A., & Rajamohan, S. (2025). AI-enabled personalized learning: Empowering management students for improving engagement and academic performance. Vilakshan - XIMB Journal of Management, 22(1), 28–44. https://doi.org/10.1108/XJM-02-2024-0023
Hadinezhad, S., Garg, S., & Lindgren, R. (2024). Enhancing inclusivity: Exploring AI applications for diverse learners. In D. Kourkoulou, A.-O. Tzirides, B. Cope, & M. Kalantzis (Eds.), Trust and inclusion in AI-mediated education: Where human learning meets learning machines (pp. 163–182). Springer. https://doi.org/10.1007/978-3-031-64487-0_8
Halkiopoulos, C., & Gkintoni, E. (2024). Leveraging AI in e-learning: Personalized learning and adaptive assessment through cognitive neuropsychology—A systematic analysis. Electronics, 13(18), 3762. https://doi.org/10.3390/electronics13183762
Kadam, R., Rao, S. A., Abdul, W. K., & Jabeen, S. S. (2021). Cultural intelligence as an enabler of cross-cultural adjustment in the context of intra-national diversity. International Journal of Cross Cultural Management, 21(1), 31–51. https://doi.org/10.1177/1470595821995857
Kassem, H., Beevi, A., Basheer, S., Lutfi, G., Cheikh Ismail, L., & Papandreou, D. (2025). Investigation and assessment of AI’s role in nutrition—An updated narrative review of the evidence. Nutrients, 17(1), 190. https://doi.org/10.3390/nu17010190
Kristianto, H., & Gandajaya, L. (2023). Offline vs online problem-based learning: A case study of student engagement and learning outcomes. Interactive Technology and Smart Education, 20(1), 106–121. https://doi.org/10.1108/ITSE-09-2021-0166
Kunjumuhammed, S. K. (2024). Artificial intelligence in addressing educational inequality dimensions in higher education institutions (HEIs). https://doi.org/10.4018/979-8-3693-2185-0.ch007
Lin, M. P.-C., Liu, A. L., Poitras, E., Chang, M., & Chang, D. H. (2024). An exploratory study on the efficacy and inclusivity of AI technologies in diverse learning environments. Sustainability, 16(20), 8992. https://doi.org/10.3390/su16208992
Makda, F. (2025). Digital education: Mapping the landscape of virtual teaching in higher education – A bibliometric review. Education and Information Technologies, 30(2), 2547–2575. https://doi.org/10.1007/s10639-024-12899-2
McCarthy, A. M., Maor, D., McConney, A., & Cavanaugh, C. (2023). Digital transformation in education: Critical components for leaders of system change. Social Sciences & Humanities Open, 8(1), 100479. https://doi.org/10.1016/j.ssaho.2023.100479
Mhlongo, S., Mbatha, K., Ramatsetse, B., & Dlamini, R. (2023). Challenges, opportunities, and prospects of adopting and using smart digital technologies in learning environments: An iterative review. Heliyon, 9(6), e16348. https://doi.org/10.1016/j.heliyon.2023.e16348
Mohamed Hashim, M. A., Tlemsani, I., & Matthews, R. (2022). Higher education strategy in digital transformation. Education and Information Technologies, 27(3), 3171–3195. https://doi.org/10.1007/s10639-021-10739-1
Mohebi, L. (2024). Empowering learners with ChatGPT: Insights from a systematic literature exploration. Discover Education, 3(1), 36. https://doi.org/10.1007/s44217-024-00120-y
Mouta, A., Torrecilla-Sánchez, E. M., & Pinto-Llorente, A. M. (2025). Comprehensive professional learning for teacher agency in addressing ethical challenges of AIED: Insights from educational design research. Education and Information Technologies, 30(3), 3343–3387. https://doi.org/10.1007/s10639-024-12946-y
Nazarian, M., Duyar, I., & Alhosani, M. (2024). Diversity management as a new organizational paradigm: Leading with cultural intelligence (CQ). In M. C. Shelley, K. H. Yang, & M. A. M. Mohamed Hashim (Eds.), Cutting-edge innovations in teaching, leadership, technology, and assessment (pp. 149–165). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-0880-6.ch011
Ojha, A., & Gupta, B. (2025). Evolving landscape of wireless sensor networks: A survey of trends, timelines, and future perspectives. Discover Applied Sciences, 7(8), 825. https://doi.org/10.1007/s42452-025-07070-6
Rajaram, K. (2023). Future of learning: Teaching and learning strategies. In Learning intelligence: Innovative and digital transformative learning strategies (pp. 3–53). Springer. https://doi.org/10.1007/978-981-19-9201-8_1
Ranjan, R. (2025). AI-powered data platforms bridging the gap between analytics and action in smart education. In Smart education and sustainable learning environments in smart cities (pp. 107–122). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7723-9.ch007
Roshanaei, M. (2024). Towards best practices for mitigating artificial intelligence implicit bias in shaping diversity, inclusion and equity in higher education. Education and Information Technologies, 29(14), 18959–18984. https://doi.org/10.1007/s10639-024-12605-2
Salas-Pilco, S. Z., Xiao, K., & Oshima, J. (2022). Artificial intelligence and new technologies in inclusive education for minority students: A systematic review. Sustainability, 14(20), 13572. https://doi.org/10.3390/su142013572
Samuel, Y., Brennan-Tonetta, M., Samuel, J., Kashyap, R., Kumar, V., Krishna Kaashyap, S., Chidipothu, N., Anand, I., & Jain, P. (2023). Cultivation of human centered artificial intelligence: Culturally adaptive thinking in education (CATE) for AI. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1198180
Schiff, D. (2022). Education for AI, not AI for education: The role of education and ethics in national AI policy strategies. International Journal of Artificial Intelligence in Education, 32(3), 527–563. https://doi.org/10.1007/s40593-021-00270-2
Srinivasa, K. G., Kurni, M., & Saritha, K. (2022). Harnessing the power of AI to education. In K. G. Srinivasa, M. Kurni, & K. Saritha (Eds.), Learning, teaching, and assessment methods for contemporary learners: Pedagogy for the digital generation (pp. 311–342). Springer. https://doi.org/10.1007/978-981-19-6734-4_13
Takona, J. (2024). AI in education: Shaping the future of teaching and learning. International Journal of Current Educational Studies, 3(2), 1–25. https://doi.org/10.46328/ijces.121
Tate, T., & Warschauer, M. (2022). Equity in online learning. Educational Psychologist, 57(3), 192–206. https://doi.org/10.1080/00461520.2022.2062597
Vashishth, T. K., Sharma, V., Sharma, K. K., Kumar, B., Panwar, R., & Chaudhary, S. (2024). AI-driven learning analytics for personalized feedback and assessment in higher education. In T. V. T. Nguyen & N. T. M. Vo (Eds.), Using traditional design methods to enhance AI-driven decision making (pp. 206–230). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-0639-0.ch009
Williams, R. T. (2024). The ethical implications of using generative chatbots in higher education. Frontiers in Education, 8, 1331607. https://doi.org/10.3389/feduc.2023.1331607
Yambal, S., & Waykar, Y. A. (2025). Future of education using adaptive AI, intelligent systems, and ethical challenges. In Artificial intelligence and machine learning applications in education (pp. 171–202). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-6527-4.ch006
Zajda, J. (2021). Constructivist learning theory and creating effective learning environments. In Globalisation and education reforms (pp. 35–50). Springer. https://doi.org/10.1007/978-3-030-71575-5_3
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