THE SYNERGY OF JUST-IN-TIME LEARNING AND ARTIFICIAL INTELLIGENCE: REVOLUTIONIZING PERSONALIZED EDUCATION
Keywords:
Just-in-Time Learning, Artificial Intelligence In Education, Personalized Learning, Adaptive Learning Platforms, AI-Driven Skill AssessmentAbstract
This article explores the transformative potential of integrating Just-in-Time (JIT) learning with Artificial Intelligence (AI) in educational and professional development contexts. It examines how this synergy addresses the growing demand for flexible, personalized, and immediately applicable learning experiences in our rapidly evolving digital landscape. The article provides a comprehensive overview of AI-enhanced JIT learning mechanisms, including personalized content delivery, real-time skill assessment, and predictive learning needs. It highlights the significant benefits of this approach, such as improved efficiency in information delivery, increased relevance and immediate applicability of learning content, and enhanced scalability across organizations. The article also discusses practical applications across various sectors, including workplace training, healthcare professional development, and customer support enhancement. While acknowledging challenges like data privacy and the need to balance AI assistance with human instruction, the article emphasizes the potential of this combined approach to revolutionize learning methodologies. Looking ahead, it explores future directions, including integration with emerging technologies like Virtual and Augmented Reality, expansion into new industries, and the potential for supporting lifelong learning. This research contributes to the ongoing dialogue about the future of education and professional development in an AI-driven world, offering insights into how these technologies can be leveraged to create more effective, efficient, and personalized learning experiences.
References
Chen, Yunxiao & Li, Xiaoou & Liu, Jingchen & Ying, Zhiliang. (2017). Recommendation System for Adaptive Learning. Applied Psychological Measurement. 42. 014662161769795. 10.1177/0146621617697959. [Online]. Available: https://journals.sagepub.com/doi/10.1177/0146621617697959
Srivastava, Neha & Mandal, Jitendra & Asthana, Pallavi. (2021). Learning Through MOOCs: Self-learning in the Era of Disruptive Technology. 10.1007/978-981-33-4367-2_55. Available: https://link.springer.com/chapter/10.1007/978-981-33-4367-2_55
Moroianu, Nicolae & Iacob, Silvia-Elena & Constantin, Alexandra. (2023). Artificial Intelligence in Education: a Systematic Review. 10.2478/9788367405546-084. [Online]. Available: https://sciendo.com/chapter/9788367405546/10.2478/9788367405546-084
R. S. Baker and A. Hawn, "Algorithmic Bias in Education," International Journal of Artificial Intelligence in Education, vol. 31, no. 4, pp. 650-683, 2021. [Online]. Available: https://doi.org/10.1007/s40593-021-00285-9
Trinh, N., Elbanna, A. (2024). AI and Human Relationship in the Workplace: A Literature Review and Future Research Agenda. In: Sharma, S.K., Dwivedi, Y.K., Metri, B., Lal, B., Elbanna, A. (eds) Transfer, Diffusion and Adoption of Next-Generation Digital Technologies. TDIT 2023. IFIP Advances in Information and Communication Technology, vol 698. Springer, Cham. https://doi.org/10.1007/978-3-031-50192-0_14
M. Mousavinasab et al., "Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods," Interactive Learning Environments, vol. 29, no. 1, pp. 142-163, 2021. [Online]. Available: https://doi.org/10.1080/10494820.2018.1558257
R. A. Calvo and D. Peters, " Design for Wellbeing - Tools for Research, Practice and Ethics," IEEE Transactions on Technology and Society, vol. 2, no. 2, pp. 66-71, 2021. [Online]. Available: https://dl.acm.org/doi/10.1145/3290607.3298800
J. C. Nesbit, O. O. Adesope, Q. Liu, and W. Ma, "How Effective are Intelligent Tutoring Systems in Computer Science Education?," IEEE Transactions on Education, vol. 65, no. 1, pp. 13-22, 2022. [Online]. Available: https://ieeexplore.ieee.org/document/6901409
Yu, H., Miao, C., Leung, C. et al. Towards AI-powered personalization in MOOC learning. npj Science Learn 2, 15 (2017). https://doi.org/10.1038/s41539-017-0016-3
J. Gardner, C. Brooks, and R. Baker, "Evaluating the Fairness of Predictive Student Models Through Slicing Analysis," in Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 2019, pp. 225-234. [Online]. Available: https://doi.org/10.1145/3303772.3303791
W. Holmes, M. Bialik, and C. Fadel, "Artificial Intelligence in Education: Promises and Implications for Teaching and Learning," Center for Curriculum Redesign, 2019. [Online]. Available: https://curriculumredesign.org/our-work/artificial-intelligence-in-education/
M. Radianti, T. A. Majchrzak, J. Fromm, and I. Wohlgenannt, "A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda," Computers & Education, vol. 147, 103778, 2020. [Online]. Available: https://doi.org/10.1016/j.compedu.2019.103778
C. Romero and S. Ventura, "Educational data science in massive open online courses," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 7, no. 1, e1187, 2017. [Online]. Available: https://doi.org/10.1002/widm.1187
