Using Artificial Intelligence for Educational Management: Challenges and Opportunities
Document Type : Scientific research
Author
Master of Mathematical Statistics, Tabriz Branch Azad University, Tabriz, Iran
Abstract
Artificial intelligence (AI) holds great promise for transforming educational management, providing opportunities for personalized learning, administrative efficiency, and evidence-based decision-making. However, integrating artificial intelligence into educational environments presents complex challenges, including technical skill gaps, ethical considerations, and differences in digital access. This review examines the challenges and opportunities of applying artificial intelligence to educational management and examines its implications for educators, administrators, and policy makers. Drawing on a synthesis of the literature, this review identifies key challenges such as the need for digital literacy among educators, ethical concerns around data privacy and algorithmic bias, and inequality in digital access. Despite these challenges, AI offers opportunities for personalized learning experiences, administrative optimization, and data-driven decision-making in education. By addressing the identified challenges and using the opportunities presented by artificial intelligence, educational institutions can increase teaching, learning and organizational effectiveness in the digital age.
Keywords
Subjects
Adams, S., Cummins, M., & Davis, A. (2017). Artificial intelligence: Threat or boon to educational equity, accessibility, and democracy? Educational Media International, 54(3), 161-175.
Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107-136.
Anderson, T., & Dron, J. (2011). Three generations of distance education pedagogy. The International Review of Research in Open and Distributed Learning, 12(3), 80-97.
Baker, R. S. (2017). Educational data mining: An advance for intelligent systems in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(3), e1205.
Baker, R. S., Corbett, A. T., & Aleven, V. (2008). More accurate student modeling through contextual estimation of slip and guess probabilities in Bayesian knowledge tracing. In Proceedings of the 9th international conference on intelligent tutoring systems (pp. 406-415). Springer.
Baker, R. S., D'Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2019). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 122, 153-168.
Barghout, A., & Jansen, B. J. (2019). Ethical considerations for educational uses of artificial intelligence. TechTrends, 63(4), 448-457.
Bates, T. (2019). Teaching in a digital age: Guidelines for designing teaching and learning. Tony Bates Associates Ltd.
Bates, T. (2019). The evolution of AI: From science fiction to effective teaching tool. International Journal of Artificial Intelligence in Education, 29(3), 614-631.
Baylor, A. L., & D'Mello, S. K. (2019). Evaluation of an intelligent tutoring system on student learning gains: A meta-analysis. Educational Psychology Review, 31(4), 779-804.
Bulger, M. E., Mayer, R. E., Almeroth, K. C., & Blau, S. D. (2016). Measuring learner engagement in multimedia learning. Journal of Educational Psychology, 108(4), 508-523.
Bulger, M. E., Mayer, R. E., Almeroth, K. C., & Blau, S. D. (2016). Measuring learner engagement in multimedia learning. Journal of Educational Psychology, 108(4), 508-523.
Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2020). A reference model for learning analytics. Educational Technology & Society, 23(2), 27-49.
Chen, F., & Chen, Z. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.
Crawford, K., & Paglen, T. (2019). Excavating AI: The politics of images in machine learning training sets. Environment and Planning D: Society and Space, 37(6), 945-965.
Crawford, K., & Paglen, T. (2019). Excavating AI: The politics of images in machine learning training sets. Environment and Planning D: Society and Space, 37(6), 945-965.
Daniel, S. J. (2020). Education and the COVID-19 pandemic. Prospects, 49(1), 91-96.
Dawes, L., & Tai, S. J. (2019). Digital technologies and learning in higher education. The International Review of Research in Open and Distributed Learning, 20(3), 127-142.
Dawson, C. W., & Gu, M. (2018). Ethical considerations of using student data for predictive analytics in higher education: A scoping review. Educational Technology Research and Development, 66(5), 1263-1286.
Dawson, C. W., & Gu, M. (2018). Ethical considerations of using student data for predictive analytics in higher education: A scoping review. Educational Technology Research and Development, 66(5), 1263-1286.
Dede, C. (2016). The role of emerging technologies for 21st century schools. In Handbook of research on educational communications and technology (pp. 611-623). Springer.
Delen, D., & Demirkan, H. (2013). Data mining for the internet of things: Literature review and challenges. International Journal of Information Management, 34(2), 202-210.
Green, S. P. (2018). Ethical considerations for education in the era of big data. Educational Technology Research and Development, 66(4), 861-878.
Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. Educause Review, 27.
Holmes, B., & Gardner, J. (2006). E-learning: Concepts and practice. SAGE Publications Ltd.
Holstein, K., & McLaren, B. M. (2018). Towards a theory of personalized dynamic learning analytics in massive open online courses. International Journal of Artificial Intelligence in Education, 28(4), 431-469.
Holstein, K., & McLaren, B. M. (2018). Towards a theory of personalized dynamic learning analytics in massive open online courses. International Journal of Artificial Intelligence in Education, 28(4), 431-469.
Huang, D., Joseph, R., & Swart, E. (2020). The influence of student demographic factors and academic performance on student engagement with online learning platforms: A longitudinal study. Computers & Education, 150, 103838.
Huang, D., Joseph, R., & Swart, E. (2020). The influence of student demographic factors and academic performance on student engagement with online learning platforms: A longitudinal study. Computers & Education, 150, 103838.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
Koedinger, K. R., & Corbett, A. T. (2012). Cognitive tutors: Technology bringing learning science to the classroom. In The Oxford handbook of the learning sciences (pp. 537-554). Oxford University Press.
Koedinger, K. R., & Corbett, A. T. (2012). Cognitive tutors: Technology bringing learning science to the classroom. In The Oxford handbook of the learning sciences (pp. 537-554). Oxford University Press.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
Lane, H. C. (2020). Artificial intelligence and human learning: A new alliance. MIT Press.
Liu, D. Y. (2020). Learning analytics: The next frontier for computer-assisted language learning in big data. Computer Assisted Language Learning, 33(5-6), 511-536.
Loizzo, J., Ertmer, P. A., Watson, W. R., & Watson, S. L. (2021). Moving forward with AI in education: Adaptive learning systems as a case in point. Educational Technology Research and Development, 69(1), 13-36.
Molnar, A. (2020). Artificial intelligence in education: Promises and implications for teaching and learning. OECD Education Working Papers, No. 200, OECD Publishing, Paris.
Molnar, A. (2020). Artificial intelligence in education: Promises and implications for teaching and learning. OECD Education Working Papers, No. 200, OECD Publishing, Paris.
Olson, J. S., & Miller, B. K. (2018). Data-driven decision making in education: Challenges and opportunities. Policy Insights from the Behavioral and Brain Sciences, 5(2), 196-203.
Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438-450.
Picciano, A. G. (2017). Theories and frameworks for online education: Seeking an integrated model. Online Learning, 21(3), 166-190.
Reeves, T. C. (2019). 20 years of change in education. Educause Review, 54(2), 16-28.
Rodriguez-Medina, D. A., & Martínez-Cruz, C. (2021). Social inequality in the digital age: A multidimensional approach to the digital divide. Information, Communication & Society, 24(7), 1011-1031.
Rodriguez-Medina, D. A., & Martínez-Cruz, C. (2021). Social inequality in the digital age: A multidimensional approach to the digital divide. Information, Communication & Society, 24(7), 1011-1031.
Rose, C. P., & Beale, P. E. (2019). A tutorial on Bayesian knowledge tracing. Journal of Educational Data Mining, 11(3), 1-38.
Rothan, H. A. (2018). Artificial intelligence and the future of education. Journal of Taibah University Medical Sciences, 13(5), 400-403.
Rothan, H. A. (2018). Artificial intelligence and the future of education. Journal of Taibah University Medical Sciences, 13(5), 400-403.
Selwyn, N. (2020). What’s wrong with digital education … and how to fix it. Polity.
Selwyn, N. (2020). What’s wrong with digital education … and how to fix it. Polity.
Siemens, G., & Gašević, D. (2019). Learning analytics: Leveraging big data to support learning in higher education. In Handbook of learning analytics (pp. 23-40). Society for Learning Analytics Research.
Siemens, G., & Gašević, D. (2019). Learning analytics: Leveraging big data to support learning in higher education. In Handbook of learning analytics (pp. 23-40). Society for Learning Analytics Research.
Stewart, T., St. Clair, R., & Mastel-Smith, B. (2020). A qualitative case study of the barriers and enablers to educational inclusivity in technology-facilitated health sciences education programs. Journal of Educational Computing Research, 58(6), 1125-1148.
Stewart, T., St. Clair, R., & Mastel-Smith, B. (2020). A qualitative case study of the barriers and enablers to educational inclusivity in technology-facilitated health sciences education programs. Journal of Educational Computing Research, 58(6), 1125-1148.
VanderArk, T. (2020). Redefining readiness: A new foundation for New World Workforce. Getting Smart, 10.
VanLehn, K. (2019). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 54(4), 215-234.
VanLehn, K. (2019). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 54(4), 215-234.
Viberg, O., Hatakka, M., Bälter, O., Mavroudi, A., & Zdrahal, Z. (2018). Overcoming barriers to teaching analytics: Implementation factors to consider at an institutional level. Computers & Education, 122, 20-33.
Wang, Y., & Zhang, T. (2018). Understanding deep learning requires rethinking generalization. In Proceedings of the 6th International Conference on Learning Representations (ICLR).
West, D. M., Whittaker, M., & Winkler, V. (2016). Artificial intelligence and education. Future of Children, 26(2), 141-158.
West, D. M., Whittaker, M., & Winkler, V. (2016). Artificial intelligence and education. Future of Children, 26(2), 141-158.
West, D. M., Whittaker, M., & Winkler, V. (2016). Artificial intelligence and education. Future of Children, 26(2), 141-158.
Williamson, B. (2017). Coding/learning: Software and digital data in education. Learning, Media and Technology, 42(3), 251-253.
Williamson, B. (2019). Governing by metrics: The contours of data-driven education. In C. Lubienski & I. West (Eds.), The Routledge Handbook of International Education and Development (pp. 313-324). Routledge.
Williamson, B. (2019). Governing by metrics: The contours of data-driven education. In C. Lubienski & I. West (Eds.), The Routledge Handbook of International Education and Development (pp. 313-324). Routledge.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (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.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (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.
Volume 1, Issue 4 - Serial Number 4
Spring 2024Pages 122-138
- Receive Date 24 April 2024
- Revise Date 19 May 2024
- Accept Date 13 June 2024