Opportunities and challenges for using artificial intelligence in academic continuity: Case of Georgia

Maia Noniashvili; Lela Matchavariani

doi:10.15549/jeecar.v11i4.1817

Authors

Maia Noniashvili Business and Technology Univesity, Tbilisi, Georgia https://orcid.org/0009-0009-2321-0792
Lela Matchavariani Business and Technology University, Tbilisi, Georgia https://orcid.org/0009-0005-7656-1511

DOI:

https://doi.org/10.15549/jeecar.v11i4.1817

Keywords:

EdTech,, Business continuity, Artificial Intelligence, Education

Abstract

This article examined how higher education institutions in the Republic of Georgia responded to the challenges of the COVID-19 pandemic. Focusing on the context of the digital revolution and centering upon the utilization of artificial intelligence (AI), it aimed to discern how these institutions sustained the continuity of the learning process and implemented innovative measures. Based on the research findings, the solutions proposed in this article present AI tools for personalized learning, adaptive assessment, and intelligent tutoring. As institutions navigated the post-pandemic era, the integration of AI into education proved viable. This research provided tangible insights into the digital revolution affecting education and informing strategic decision-making in Georgia's evolving higher education landscape. Recognizing the difficulties caused by the pandemic and the inherent challenges associated with strategic decision-making, a qualitative research approach was used to gain nuanced insights. It relied on in-depth interviews, recognizing the spontaneous and time-sensitive nature of strategic decisions made by universities during the pandemic, often precluding extensive pre-planning. The authors provided critical findings in terms of the pros and cons of distance learning and proposed AI solutions for each challenge that Universities faced during and after this significant disruption, giving real successful examples.

Author Biographies

Maia Noniashvili, Business and Technology Univesity, Tbilisi, Georgia

Maia Noniashvili, PhD, Professor in Management, is Chancellor at Business and Technology University, Tbilisi, Georgia, and Operations Manager at Webster University Campus in Georgia. Her career is full of academic and professional achievements. During 15 years of her practical and academic work in higher education, she has authored ten publications. Her research interests now are EdTech and any field connected with education digitalization and quality enhancement. She is head of the Master's program of “Business Administration and Modern Technologies.”

Lela Matchavariani, Business and Technology University, Tbilisi, Georgia

Lela Matchavariani, MBA, is the chief operations manager at K Group, Tbilisi, Georgia, and training manager at Electronic Academy. Her career is full of different experiences in the management field. Her research interest now is using AI tools in learning and working in any field connected with education and job environment digitalization.

References

Aleven, V., & Koedinger, K. R. (2002). An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26(2), 147-179. https://doi.org/10.1207/s15516709cog2602_1 DOI: https://doi.org/10.1207/s15516709cog2602_1

Day, T. (2015, March). Academic Continuity: Staying True to Teaching Values and Objectives in the Face of Course Interruptions. Teaching & Learning Inquiry the ISSOTL Journal, 3(1), 75–89. https://doi.org/10.20343/teachlearninqu.3.1.75 DOI: https://doi.org/10.20343/teachlearninqu.3.1.75

Dietterich, T. G. (2015). Machine learning for predictive data analytics: A survey. Big data, 3(1), 3-32.

G. (2017, February 10). Benefits and Risks of Artificial Intelligence. AI MAGAZINE, 38(3), 3–24. https://doi.org/10.1609/aimag.v38i3.2756 DOI: https://doi.org/10.1609/aimag.v38i3.2756

Gavrikov, A., Kukhaleishvili, N., & Urushadze, S. (2021). Challenges of Remote Learning During COVID-19: A Case Study of Georgian Higher Education Institutions. International Journal of Learning, Teaching and Educational Research, 20(2), 33-52.

Gillani, Eynon, Chiabaut, & Finkel. (2023, January 26). Unpacking the "Black Box" of AI in Education. Educational Technology & Society, 26(1), 99–111. https://doi.org/10.30191/ETS.202301_26(1).0008

Greenhow, C., & Gleason, B. (2014). Social scholarship: Reconsidering scholarly practices in the age of social media. British Journal of Educational Technology, 45(3), 392-402. https://doi.org/10.1111/bjet.12150 DOI: https://doi.org/10.1111/bjet.12150

Hescock, & Laford. (2014). A Guide to Academic Continuity Planning.

Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. Educause Review, 27.

Junco, R., Heiberger, G., & Loken, E. (2011). The effect of Twitter on college student engagement and grades. Journal of computer assisted learning, 27(2), 119-132. https://doi.org/10.1111/j.1365-2729.2010.00387.x DOI: https://doi.org/10.1111/j.1365-2729.2010.00387.x

Kazybayeva, A., Smykova , M., M. Krueger, T. ., Duchshanova, M., & Sokhatskaya , N. . (2022). Business Student Perspectives Regarding Ways to Enhance the Online Learning Process. Journal of Eastern European and Central Asian Research (JEECAR), 9(2), 284–295. https://doi.org/10.15549/jeecar.v9i2.817 DOI: https://doi.org/10.15549/jeecar.v9i2.817

Khvedelidze, T., Chikovani, M., & Kandelaki, G. (2021). Integrating Artificial Intelligence into Higher Education: Challenges and Opportunities. Journal of Educational Technology, 14(3), 45-62.

Koedinger, K. R., Stamper, J. C., McLaughlin, E. A., & Nixon, T. (2015). Using data-driven discovery of better student models to improve student learning. In The International Conference on Artificial Intelligence in Education (pp. 421-430). Springer, Cham. https://doi.org/10.1007/978-3-642-39112-5_43 DOI: https://doi.org/10.1007/978-3-642-39112-5_43

Kotsiantis, S. B. (2013). Use of machine learning techniques in educational applications: A survey. Educational technology & society, 15(3), 16-28.

Lane, H. C., & VanLehn, K. (2005). Teaching with AI. AI Magazine, 26(3), 25-40.

Mdivnishvili, N., Gagnidze, K., & Berishvili, N. (2020). Challenges of Online Learning During COVID-19: Evidence from Georgian Higher Education. International Journal of E-Learning and Distance Education, 35(2), 87-104.

Means, B., & Neisler, J. (2020). Adaptive learning: Results from early implementations. International Journal of Artificial Intelligence in Education, 30(3), 367-395.

Miao, F., Yuan, Q., & Zhao, Y. (2021). AI and education: Guidance for policymakers. Educational Technology & Society, 24(1), 101-112. https://doi.org/10.55277/researchhub.g6680pdy DOI: https://doi.org/10.55277/ResearchHub.g6680pdy

Miao, Holmes, Ronghuai, & Hiu. (2021). AI and education Guidance for policymakers. In UNESCO https://doi.org/10.54675/PCSP7350 DOI: https://doi.org/10.54675/PCSP7350

Nguwi. (2023). Technologies for Education: From Gamification to AI-enabled Learning. International Journal of Multidisciplinary Perspectives in Higher Education, Volume 8(Issue 1), 111–132.

Noniashvili, M., Dgebuadze , M. ., & Griffin, . G. . (2020). A new tech platform as innovative teaching model at high schools in Georgia . Journal of Eastern European and Central Asian Research (JEECAR), 7(1), 95–103. https://doi.org/10.15549/jeecar.v7i1.386 DOI: https://doi.org/10.15549/jeecar.v7i1.386

Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618. https://doi.org/10.1109/tsmcc.2010.2053532

Romero, C., & Ventura, S. (2010, November). Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601–618. https://doi.org/10.1109/tsmcc.2010.2053532 DOI: https://doi.org/10.1109/TSMCC.2010.2053532

Romero, C., & Ventura, S. (2020, January 13). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3). https://doi.org/10.1002/widm.1355 DOI: https://doi.org/10.1002/widm.1355

Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Pearson.

Siemens, G., & Baker, R. S. (2012). Learning analytics and educational data mining: towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252-254. https://doi.org/10.1145/2330601.2330661 DOI: https://doi.org/10.1145/2330601.2330661

Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), 31-40.

Wang, Liu, & Tu. (2021, July). Factors Affecting the Adoption of AI-Based Applications in Higher Education. Educational Technology & Society, 24(3), 116–129. https://www.jstor.org/stable/27032860

Zhang, W., Wang, X., & Li, X. (2020). Computers & Education, 156, 103961. DOI: https://doi.org/10.1016/j.compedu.2020.103961