Artificial Intelligence in Educational Technology: Transforming Teaching and Learning in the Digital Era
DOI:
https://doi.org/10.29070/w150z355Keywords:
Artificial Intelligence, Educational Technology, Personalized Learning, Adaptive Learning, Digital Education, AI in Education, Smart Learning, Learning Analytics, Intelligent Tutoring SystemsAbstract
A game-changer in educational technology, artificial intelligence (AI) is reshaping classrooms throughout the world. A new age of data-driven decision-making, adaptive learning environments, intelligent tutoring systems, learning analytics, educational chatbots, and personalised learning has begun with its adoption. The use of artificial intelligence (AI) in the classroom is on the rise as a tool to support educators, encourage active participation from students, broaden participation, and provide more personalised lessons.
Focusing on studies published in 2022, this research study investigates the function, uses, advantages, disadvantages, and potential future developments of artificial intelligence (AI) in the field of educational technology. Using secondary sources including scholarly journals, books, reports on education, and foreign policy papers, this study provides a descriptive and analytical account of the topic. A strong theoretical basis in artificial intelligence (AI) for education, intelligent tutoring systems, learning analytics, adaptive learning, and ethical AI is provided, with a heavy emphasis on recent results but also including crucial references prior to 2022.
The results indicate that AI greatly enhances learner engagement, teaching efficiency, accessibility, personalized education, and institutional decision-making. Nonetheless, critical challenges persist in ethical considerations, digital disparity, teacher readiness, algorithmic bias, transparency, technology dependence, and data privacy. Ultimately, the paper concludes that with human-centered pedagogy, ethical governance, teacher training, and inclusive educational practices, AI has the potential to revolutionize education.
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References
1. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice (pp. 61–75). Springer.
2. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278.
3. Chichekian, T., & Benteux, B. (2022). The potential of learning with artificial intelligence in education. Frontiers in Artificial Intelligence.
4. Crompton, H., Jones, M. V., & Burke, D. (2022). Affordances and challenges of artificial intelligence in K–12 education: A systematic review. Journal of Research on Technology in Education.
5. Gillani, N., Eynon, R., Chiabaut, C., & Finkel, K. (2022). Unpacking the black box of AI in education. arXiv.
6. Han, J. W., Park, J., & Lee, H. (2022). Analysis of the effect of an artificial intelligence chatbot educational program on non-face-to-face classes. BMC Medical Education.
7. Haque, A. K. M. B., Islam, A. K. M. N., & Mikalef, P. (2022). Explainable artificial intelligence from a user perspective. arXiv.
8. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
9. Kshirsagar, P. R., et al. (2022). Human intelligence analysis through perception of AI in teaching and learning. Computational Intelligence and Neuroscience.
10. Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(1), 42–78.
11. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
12. Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901–918.
13. Munir, H., Vogel, B., & Jacobsson, A. (2022). Artificial intelligence and machine learning approaches in digital education: A systematic revision. Information.
14. Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26, 582–599.
15. Salas-Pilco, S. Z., & Yang, Y. (2022). Artificial intelligence applications in Latin American higher education: A
16. systematic review. International Journal of Educational Technology in Higher Education.
17. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
18. Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.
19. UNESCO. (2021). AI and education: Guidance for policy-makers. UNESCO Publishing.
20. VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.
21. Woolf, B. P. (2010). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann.
22. Wu, W., Zhang, B., Li, S., & Liu, H. (2022). Exploring factors of willingness to accept AI-assisted learning environments. Frontiers in Psychology.
23. Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education.
24. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16, Article 39.
