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Review Article

The Role of Artificial Intelligence in Enhancing Educational Outcomes: A Multidisciplinary Review

Authors

  • Peter Gary

Abstract

The integration of Artificial Intelligence (AI) in education has sparked significant interest due to its potential to revolutionize learning experiences, improve educational outcomes, and address challenges such as personalization, access, and scalability. This paper presents a multidisciplinary review of AI's role in enhancing educational outcomes, drawing from fields such as cognitive science, educational technology, and computer science. The review examines the benefits and limitations of AI applications in education, including intelligent tutoring systems, adaptive learning technologies, and AI-driven assessments. It also highlights the ethical considerations, challenges, and future directions for research in this area. By analyzing empirical studies and theoretical models, this review underscores the importance of a comprehensive, interdisciplinary approach to understanding and implementing AI in education.

Article information

Journal

Journal of Multidisciplinary Research and Innovation

Volume (Issue)

3 (1)

Pages

23-34

Published

2024-09-05

How to Cite

Gary, P. (2024). The Role of Artificial Intelligence in Enhancing Educational Outcomes: A Multidisciplinary Review. Journal of Multidisciplinary Research and Innovation, 3(1), 23-34. https://jilpublishers.com/index.php/ijmri/article/view/15

References

Baker, R. S. (2016). Stupid tutoring systems, intelligent humans. *International Journal of Artificial Intelligence in Education*, 26(2), 600-614. DOI: [10.1145/2991537.2991543](https://doi.org/10.1145/2991537.2991543)

Baker, T., & Smith, L. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. *British Journal of Educational Technology*, 50(6), 2212-2225. DOI: [10.1111/bjet.12853](https://doi.org/10.1111/bjet.12853).

Graesser, A. C., Hu, X., & Cai, Z. (2018). Intelligent tutoring systems. *Frontiers in Education*, 3(113), 1-15. DOI: [10.3389/feduc.2018.00113](https://doi.org/10.3389/feduc.2018.00113).

Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial intelligence in education: Promises and implications for teaching and learning. *Educational Technology Research and Development*, 69(3), 899-915. DOI: [10.1007/s11423-021-09954-7](https://doi.org/10.1007/s11423-021-09954-7).

Holstein, K., McLaren, B. M., & Aleven, V. (2020). Designing for fairness in educational technologies: A systematic review. *Proceedings of the ACM on Human-Computer Interaction*, 4(3), 1-25. DOI: [10.1145/3330430.3330438](https://doi.org/10.1145/3330430.3330438).

Jia, J., Feng, M., & Miao, Y. (2020). AI-driven formative assessment: Improving learning outcomes through personalized feedback. *Computers & Education*, 153, 103902. DOI: [10.1016/j.compedu.2020.103902](https://doi.org/10.1016/j.compedu.2020.103902).

Jordan, S. (2019). Artificial intelligence in assessment: An overview of advances and applications. *Educational Technology Research and Development*, 67(3), 469-480. DOI: [10.1007/s11423-019-09706-1](https://doi.org/10.1007/s11423-019-09706-1).

Kumar, V., Singh, A., & Kumar, V. (2020). Predictive analytics in higher education using machine learning: A comprehensive review. *Computers & Education*, 143, 103779. DOI: [10.1016/j.compedu.2019.103779](https://doi.org/10.1016/j.compedu.2019.103779).

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. *Routledge*. DOI: [10.4324/9781315630501](https://doi.org/10.4324/9781315630501).

Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. *Computers & Education*, 82, 53-65. DOI: [10.1016/j.compedu.2014.09.005](https://doi.org/10.1016/j.compedu.2014.09.005).

Mayer, R. E. (2019). The promise and limitations of educational artificial intelligence: A systematic review of meta-analyses. *Computers & Education*, 145, 103702. DOI: [10.1016/j.compedu.2019.04.002](https://doi.org/10.1016/j.compedu.2019.04.002).

Means, B., Toyama, Y., Murphy, R., & Baki, M. (2014). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. *Teachers College Record*, 116(3), 1-47. DOI: [10.1177/016146811411600307](https://doi.org/10.1177/016146811411600307).

Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. *Research and Practice in Technology Enhanced Learning*, 12(1), 1-13. DOI: [10.1186/s41039-017-0062-8](https://doi.org/10.1186/s41039-017-0062-8).

Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. *International Journal of Artificial Intelligence in Education*, 26(2), 582-599. DOI: [10.1007/s40593-016-0108-x](https://doi.org/10.1007/s40593-016-0108-x).

Shute, V. J., & Kim, Y. J. (2014). Formative and stealth assessment. *Computers & Education*, 67, 1-10. DOI: [10.1016/j.compedu.2013.09.020](https://doi.org/10.1016/j.compedu.2013.09.020).

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. *Educational Psychologist*, 46(4), 197-221. DOI: [10.1080/00461520.2011.611369](https://doi.org/10.1080/00461520.2011.611369).

Williamson, B. (2016). Digital education governance: Data visualization, predictive analytics, and ‘real-time’ policy instruments. *Journal of Education Policy*, 31(2), 123-141. DOI: [10.1080/1360080X.2016.1168431](https://doi.org/10.1080/1360080X.2016.1168431).

Woolf, B. P. (2017). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. *Cambridge University Press*. DOI: [10.1017/9781108235146.011](https://doi.org/10.1017/9781108235146.011).

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gerrero-Roldán, A. E. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? *International Journal of Educational Technology in Higher Education*, 16(1), 39. DOI: [10.1186/s41239-019-0177-8](https://doi.org/10.1186/s41239-019-0177-8).

Zheng, L., Kwon, K., & Schmidt, M. (2020). Adaptive learning technologies in higher education: A systematic review. *Computers & Education*, 148, 103735. DOI: [10.1016/j.compedu.2019.103735](https://doi.org/10.1016/j.compedu.2019.103735).

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Keywords:

Artificial Intelligence, Educational outcomes, Adaptive learning, Intelligent tutoring systems, AI in education, Personalized learning