INTERNATIONAL JOURNAL OF CHANGES IN EDUCATION
Research Article

Linkages Among AI Elements Affecting Quality and Value of Education

International Journal of Changes in Education, 2(3), 2025, 172-180, https://doi.org/10.47852/bonviewIJCE52023973
Publication date: Aug 22, 2025
Full Text (PDF)

ABSTRACT

Artificial intelligence (AI) has advanced rapidly in recent years and has become widely integrated across various fields, including education. This paper seeks to provide a comprehensive examination of the current state of AI in education by exploring its potential to revolutionize learning experiences through personalized approaches and data-driven multifaceted tools, while also highlighting important challenges that require consideration to ensure its responsible development and implementation. AI shows great promise to personalize instruction for each student based on assessments of their individual strengths, weaknesses, interests, and learning preferences. However, several challenges still necessitate careful examination of AI’s implications on education. Issues like algorithmic bias, the digital divide between socioeconomic groups, and concerns around reduced critical thinking skills all require addressing. If not developed and applied judiciously with these challenges in mind, AI risks exacerbating rather than alleviating existing inequities and hindering the cultivation of higher-order cognitive abilities. Through a comprehensive review of the relevant literature regarding AI’s current and potential roles in education, this paper identifies several key considerations around learning outcomes, challenges, and implications. Findings from interpretative structural modeling analysis also reveal the importance of balancing AI capabilities with safeguarding against potential downsides like those mentioned above. It is imperative that AI integration in education is approached responsibly with an understanding of both its promise and risks to learning to ensure its successful and equitable implementation for all students.

KEYWORDS

artificial intelligence systems approach quality education value education personalized learning interpretative structural modelling (ISM)

CITATION (APA)

Seth, S., & Kaushik, H. (2025). Linkages Among AI Elements Affecting Quality and Value of Education. International Journal of Changes in Education, 2(3), 172-180. https://doi.org/10.47852/bonviewIJCE52023973
Harvard
Seth, S., and Kaushik, H. (2025). Linkages Among AI Elements Affecting Quality and Value of Education. International Journal of Changes in Education, 2(3), pp. 172-180. https://doi.org/10.47852/bonviewIJCE52023973
Vancouver
Seth S, Kaushik H. Linkages Among AI Elements Affecting Quality and Value of Education. International Journal of Changes in Education. 2025;2(3):172-80. https://doi.org/10.47852/bonviewIJCE52023973
AMA
Seth S, Kaushik H. Linkages Among AI Elements Affecting Quality and Value of Education. International Journal of Changes in Education. 2025;2(3), 172-180. https://doi.org/10.47852/bonviewIJCE52023973
Chicago
Seth, Sneha, and Hans Kaushik. "Linkages Among AI Elements Affecting Quality and Value of Education". International Journal of Changes in Education 2025 2 no. 3 (2025): 172-180. https://doi.org/10.47852/bonviewIJCE52023973
MLA
Seth, Sneha et al. "Linkages Among AI Elements Affecting Quality and Value of Education". International Journal of Changes in Education, vol. 2, no. 3, 2025, pp. 172-180. https://doi.org/10.47852/bonviewIJCE52023973

REFERENCES

  1. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Retrieved from: https://discovery.ucl.ac.uk/id/eprint/1475756
  2. Remian, D. (2019). Augmenting education: Ethical considerations for incorporating artificial intelligence in education. Master’s Thesis, University of Massachusetts.
  3. Naqvi, A. (2020). Artificial intelligence for audit, forensic accounting, and valuation. USA: Wiley.
  4. Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23, 100224. https://doi.org/10.1016/j.jii.2021.100224
  5. Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: An emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6(1), 9. https://doi.org/10.1186/s40561-019-0089-y
  6. Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431–440. https://doi.org/10.1007/s43681-021-00096-7
  7. Edwards, B. I., & Cheok, A. D. (2018). Why not robot teachers: Artificial intelligence for addressing teacher shortage. Applied Artificial Intelligence, 32(4), 345–360. https://doi.org/10.1080/08839514.2018.1464286
  8. Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2022.100118
  9. Tao, B., Díaz, V., & Guerra, Y. (2019). Artificial intelligence and education: Challenges and disadvantages for the teacher. Arctic Journal, 72(12), 30–50.
  10. Mohammed, P. S., & ‘Nell’ Watson, E. (2019). Towards inclusive education in the age of artificial intelligence: Perspectives, challenges, and opportunities. In J. Knox, Y. Wang, & M. Gallagher (Eds.), Artificial intelligence and inclusive education: Speculative futures and emerging practices (pp. 17–37). Springer. https://doi.org/10.1007/978-981-13-8161-4_2
  11. Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022). AI-based personalized e-learning systems: Issues, challenges, and solutions. IEEE Access, 10, 81323–81342. https://doi.org/10.1109/ACCESS.2022.3193938
  12. Checkland, P., & Poulter, J. (2020). Soft systems methodology. In M. Reynolds & S. Holwell (Eds.), Systems approaches to making change: A practical guide (pp. 201–253). Springer. https://doi.org/10.1007/978-1-4471-7472-1_5
  13. Jackson, M. C. (2000). Applied systems thinking. In M. C. Jackson (Ed.), Systems approaches to management (pp. 91–104). Springer. https://doi.org/10.1007/0-306-47465-4_5
  14. Kaushik, H., & Kaushik, S. (2024). A study on the associations among the factors influencing digital education with reference to Indian higher education. Education and Information Technologies, 29(12), 14999–15023. https://doi.org/10.1007/s10639-023-12410-3
  15. Atherton, C. R. (1976). Group techniques for program planning: A guide to nominal group and Delphi processes. Social Work, 21(4), 338. https://doi.org/10.1093/sw/21.4.338
  16. Smith, D., Cartwright, M., Dyson, J., & Aitken, L. M. (2024). Use of nominal group technique methods in the virtual setting: A reflective account and recommendations for practice. Australian Critical Care, 37(1), 158–165. https://doi.org/10.1016/j.aucc.2023.09.004
  17. Vahedian-Shahroodi, M., Mansourzadeh, A., Shariat Moghani, S., & Saeidi, M. (2023). Using the nominal group technique in group decision-making: A review. Medical Education Bulletin, 4(4), 837–845.
  18. Warfield, J. N. (1974). Developing interconnection matrices in structural modeling. IEEE Transactions on Systems, Man, and Cybernetics, 1, 81–87. https://doi.org/10.1109/TSMC.1974.5408524
  19. Kaushik, H., & Rajwanshi, R. (2023). Examining the linkages of technology adoption enablers in context of dairy farming using ISM-MICMAC approach. Research on World Agricultural Economy, 4(4), 68–78. http://doi.org/10.36956/rwae.v4i4.887
  20. Gholami, H., Bachok, M. F., Saman, M. Z. M., Streimikiene, D., Sharif, S., & Zakuan, N. (2020). An ISM approach for the barrier analysis in implementing green campus operations: Towards higher education sustainability. Sustainability, 12(1), 363. https://doi.org/10.3390/su12010363
  21. Ahmad, N., & Qahmash, A. (2021). SmartISM: Implementation and assessment of interpretive structural modeling. Sustainability, 13(16), 8801. https://doi.org/10.3390/su13168801
  22. Shidiq, M. (2023). The use of artificial intelligence-based chatgpt and its challenges for the world of education; from the viewpoint of the development of creative writing skills. In Proceeding of 1st International Conference on Education, Society and Humanity, 353–357.
  23. Baker, R. S., & Hawn, A. (2022). Algorithmic bias in education. International Journal of Artificial Intelligence Education, 32(4), 1052–1092. https://doi.org/10.1007/s40593-021-00285-9
  24. Lythreatis, S., Singh, S. K., & El-Kassar, A.-N. (2022). The digital divide: A review and future research agenda. Technological Forecasting and Social Change, 175, 121359. https://doi.org/10.1016/j.techfore.2021.121359
  25. Kaushik, H., Rajwanshi, R., & Bhadauria, A. (2024). Modeling the challenges of technology adoption in dairy farming. Journal of Science and Technology Policy Management, 15(6), 1455–1480. https://doi.org/10.1108/JSTPM-09-2022-0163

LICENSE

Creative Commons License
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.