Empowering Education with AI: Insights into Personalization, Adaptive Platforms, and the Future of Learning

Lei Pei, Sarah Qiao, Shiyan Gong

Article ID: 4711
Vol 7, Issue 2, 2024

VIEWS - 133 (Abstract) 150 (PDF)

Abstract


In the era of Industry 4.0, AI has emerged as a transformative force in the educational landscape. This literature review investigates
the profound impact of AI on modern education, with a primary focus on its role in personalized learning, adaptive education, and the advent
of virtual teacher assistants like ChatGPT. Through extensive analysis, we identify that AI systems, by leveraging data, offer personalized
feedback and guidance, thus optimizing the learning experience. While AI holds promising potential for revolutionizing education, it also presents challenges such as ethical considerations of data privacy and the dynamic nature of its technologies. Furthermore, AI’s global influence
promises to reshape traditional teaching methods, emphasizing a more individualized approach, and potentially democratizing high-quality
educational access across the globe.

Keywords


AI; Personalized Learning; ChatGPT; Ethical Considerations

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References


1. [1] Alashoor, T., Keil, M., Smith, H. J., & McConnell, A. R. (2022). Too Tired and in Too Good of a Mood to Worry about Privacy:

2. Explaining the Privacy Paradox through the Lens of Effort Level in Information Processing. Information Systems Research. Retrieved from

3. https://www.researchgate.net/publication/364758936.

4. [2] Asatiani, A., Malo, P., Rådberg Nagbøl, P. R., Penttinen, E., RintaKahila, T., & Salovaara, A. (2021). Sociotechnical Envelopment

5. of AI: An Approach to Organizational Deployment of Inscrutable AI Systems. Journal of the Association for Information Systems, 22(2),

6. 325–352.https://doi.org/10.17705/1jais.00491.

7. [3] Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldán, A. E., & Rodríguez, M. E. (2021). AI and Reflections from Educational

8. Landscape: A Review of AI Studies in Half a Century. Sustainability, 13(800).

9. [4] Dehling, T., & Sunyaev, A. (2023). A Design Theory for Transparency of Information Privacy Practices. Information Systems Research. Institute for Operations Research and the Management Sciences (INFORMS). https://doi.org/10.1287/isre.2019.0239.

10. [5] Fügener, A., Grahl, J., Gupta, A., & Ketter, W. (2021). Will Humans-in-the-Loop Become Borgs? Merits and Pitfalls of Working

11. with AI. MIS Quarterly, 45(3), 1527-1556. https://doi.org/10.25300/MISQ/2021/16553.

12. [6] Jahnke, I., Meinke‑Kroll, M., Todd, M., & Nolte, A. (2022). Exploring Artifact‑Generated Learning with Digital Technologies: Ad_x005fvancing Active Learning with Co‑design in Higher Education Across Disciplines. Technology, Knowledge and Learning, 27, 335-364. https://

13. doi.org/10.1007/s10758-020-09473-3.

14. [7] Leung, A. C. M., Santhanam, R., Kwok, R. C.-W., & Yue, W. T. (2023). Could Gamification Designs Enhance Online Learning Through Personalization? Lessons from a Field Experiment. Information Systems Research, 34(1), 27-49. https://doi.org/10.1287/

15. isre.2022.1123.

16. [8] Liao, G.-Y., Huang, T.-L., Dennis, A. R., & Teng, C.-I. (2023). The Influence of Media Capabilities on Knowledge Contribution in

17. Online Communities. Information Systems Research. https://doi.org/10.1287/isre.2023.1225.

18. [9] Li, J., Li, M., Wang, X., & Thatcher, J.B. (2021). Strategic directions for AI: The role of CIOs and boards of directors. MIS Quarterly, 45(3), 1603–1643.

19. [10] Lou, B., & Wu, L. (2021). AI on drugs: Can AI accelerate drug development? Evidence from a large-scale examination of

20. bio-pharma firms. MIS Quarterly, 45(3), 1451–1482.

21. [11] Nah, F.-F.-H., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: applications, challenges, and AI-human collaboration. Journal of Information Technology. Case Appl. Res., 25(3).

22. [12] Petrović, J., & Jovanović, M. (2021). The Role of Chatbots in Foreign Language Learning: The Present Situation and the Future

23. Outlook. In AI: Theory and Applications (pp.313–330). Springer, Cham.

24. [13] Seeber, I., Bittner, E., Briggs, R. O., de Vreede, T., de Vreede, G. J., Elkins, A., Maier, R., Merz, A. B., Oeste-Reiß, S., Randrup, N.,

25. Schwabe, G., & Söllner, M. (2020). Machines as teammates: A research agenda on AI in team collaboration. Information and Management,

26. 57(2). https://doi.org/10.1016/j.im.2019.103174.

27. [14] Song, X., Xu, B., & Zhao, Z. (2022). Can people experience romantic love for AI? An empirical study of intelligent assistants. In

28. formation and Management, 59(2). https://doi.org/10.1016/j.im.2022.103595.

29. [15] Strich, F., Mayer, A.-S., & Fiedler, M. (2021). What do I do in a world of AI? Investigating the impact of substitutive decision-making AI systems on employees’ professional role identity. Journal of the Association for Information Systems, 22(2), 9.

30. [16] Susarla, A., Gopal, R., Thatcher, J. B., & Sarker, S. (2023). The Janus Effect of Generative AI: Charting the Path for Responsible

31. Conduct of Scholarly Activities in Information Systems. Information Systems Research, 34(2), 399-408. https://doi.org/10.1287/isre.2023.

32. ed.v34.n2.

33. [17] Turja, T., Aaltonen, I., Taipale, S., & Oksanen, A. (2020). Robot acceptance model for care (RAM-care): A principled approach to

34. the intention to use care robots. Information & Management, 57, 103220. https://doi.org/10.1016/j.im.2019.103220.

35. [18] Van den Broek, E., Sergeeva, A., & Huysman, M. (2021). When the machine meets the expert: An ethnography of developing AI

36. for hiring. MIS Quarterly, 45(3), 1557-1.




DOI: https://doi.org/10.18686/ijmss.v7i2.4711

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