Published June 22, 2024 | Version v2
Journal article Open

IMPLEMENTING AI IN EDUCATIONAL SETTINGS: CHALLENGES AND OPPORTUNITIES

Creators

  • 1. Lecturer of Kokand university

Description

The integration of Artificial Intelligence (AI) in educational settings presents a transformative potential to enhance learning experiences, personalize education, and improve administrative efficiency. However, the adoption of AI technologies in education also poses significant challenges, including ethical considerations, data privacy concerns, and the need for substantial infrastructure investment. This paper explores the opportunities and challenges associated with implementing AI in educational environments. Through a comprehensive review of current literature and case studies, we highlight key areas where AI can make a substantial impact, as well as the barriers that must be addressed to ensure its successful integration.

Files

445-451 Askarov E new MJST.pdf

Files (613.0 kB)

Name Size Download all
md5:970ad1b57ae320a26143fca2fbbdf4d6
613.0 kB Preview Download

Additional details

References

  • 1. hen, Y., Wang, Y., Kinshuk, & Chen, N. S. (2020). Personalized learning: A review of the literature. Educational Technology Research and Development, 68(3), 1175-1201. doi:10.1007/s11423-020-09800-5
  • 2. Holmes, B., Meehan, M., & Petrosino, A. (2019). Innovations in interactive learning: Engaging students in the classroom with AI. Journal of Educational Technology & Society, 22(2), 21-30. Retrieved from https://www.jstor.org/stable/10.2307/jeductechsoci.22.2.21
  • 3. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Journal of Computer Assisted Learning, 32(6), 574-591. doi:10.1111/jcal.12156
  • 4. Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30-32. Retrieved from https://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-and-education
  • 5. Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 417-422. doi:10.1145/3278721.3278731
  • 6. Williamson, B. (2016). Big data in education: The digital future of learning, policy, and practice. SAGE Publications Ltd. Retrieved from https://uk.sagepub.com/en-gb/eur/big-data-in-education/book243177
  • 7. Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529. doi:10.1177/0002764213479366
  • 8. Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2013). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. U.S. Department of Education, Office of Planning, Evaluation, and Policy Development. Retrieved from https://www2.ed.gov/rschstat/eval/tech/evidence-based-practices/finalreport.pdf
  • 9. Koper, R. (2014). Use of artificial intelligence in education: A critical assessment. International Journal of Artificial Intelligence in Education, 24(1), 1-26. doi:10.1007/s40593-013-0018-x
  • 10. Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press. Retrieved from https://nyupress.org/9781479837243/algorithms-of-oppression