A Meta-Study of the Evolutionary Transformative Academic Landscape by Artificial Intelligence and Machine Learning
DOI:
https://doi.org/10.47747/ijets.v4i1.1626Keywords:
Artificial Intelligence, Machine Learning, Higher Education, Remote Learning, Pedagogical InnovationAbstract
This article titled “The Evolutionary Transformative Academic Landscape by Artificial Intelligence and Machine Learning (Meta-Study)" explores the profound impact of AI and ML on education, particularly in the context of remote learning and the COVID-19 pandemic. The study systematically reviews the literature on AI in higher education, aiming to understand its pedagogical advantages and ethical implications. The objectives include assessing digital transformation in classrooms, evaluating the effectiveness of AI and ML in enhancing learning outcomes, examining their role in personalized learning and identifying areas for improvement. The research questions focus on the contribution of AI and ML in digital classrooms, their effectiveness in enhancing learning outcomes, and their role in supporting individualized learning. The literature review delves into AI and ML's role in academia, their impact on teaching, learning, and research, and the ethical considerations of their application. The study employs a meta-systematic review approach, incorporating statistical analyses and addressing ethical concerns to ensure AI's effective and ethical utilization in education. This study's findings indicate a positive correlation between implementing AI and ML technologies and improving student engagement, academic performance, research productivity, and teacher satisfaction. It highlights the necessity of further research to optimize AI use in education, considering software quality, student learning styles, and teacher integration skills. The study contributes to understanding the transformative role of AI and ML in reshaping education and fostering a more advanced, dynamic academic environment.
References
Almusaed, A., Almssad, A., Yitmen, I. and Homod, R.Z. (2023). Enhancing Student Engagement: Harnessing “AIED”’s Power in Hybrid Education—A Review Analysis. Education Sciences, 13(7), p.632. https://doi.org/10.3390/educsci13070632
Anderson, D. (2023). AI in the classroom: A beginner’s guide to ChatGPT and other AI tools for educators. DSpark.
Ardoin, N. M., Bowers, A. W., & Lumkong, D. (2023). Voices from the field: Exploring connections between design thinking approaches and sustainability challenges. In C. Meinel & L. Leifer (Eds.), Design thinking research: innovation – insight – then and now (pp. 90–106). Springer.
Booth, A., Sutton, A., Clowes, M., & Martyn-St James, M. (2021). Systematic approaches to a successful literature review (3rd ed.). Sage Publications.
Bramer, W. M., Rethlefsen, M. L., Kleijnen, J., and Franco, O. H. (2017). Optimal database combinations for literature searches in systematic reviews: a prospective exploratory study. Systematic Reviews, 6(1), 245.
Butcher, T., Read, M. F., Jensen, A. E., Morel, G., Nagurney, A., & Smith, P. A. (2020). Using an AI-supported online discussion forum to deepen learning. In Wilton, L. & C. Brett (Eds.), Handbook of research on online discussion-based teaching methods (pp. 380–408). IGI Global.
Chen, X., Xie, H., Zou, Di, & Hwang, G.-J. (2020). Application and theory gaps during the rise of artificial intelligence in education. International Journal of Educational Technology in Higher Education, 16(39), 100002. https://doi.org/10.1016/j.caeai.2020.100002
Crossley, S. A., Kim, M., Allen, L., & McNamara, D. (2019). Automated summarized evaluation (ASE) using natural language processing tools. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Artificial intelligence in education: 20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part 1 (pp. 84–95). Springer.
De la Torre-López, J., Ramírez, A., & Romero, J. R. (2023). Artificial intelligence to automate the systematic review of scientific literature. Computing, 105, 2171–2194. https://doi.org/10.1007/s00607-023-01181-x
Drucker, A.M., Fleming, P. and Chan, A.W. (2016). Research techniques made simple: assessing risk of bias in systematic reviews. Journal of Investigative Dermatology, 136(11), pp.e109-e114. https://doi.org/10.1016/j.jid.2016.08.021
Dwivedi, R., Nerur, S., & Balijepally, V. (2023). Exploring artificial intelligence and big data scholarship in information systems: A citation bibliographic coupling and co-word analysis. International Journal of Information Management Data Insights, 3(2), 100185. https://doi.org/10.1016/j.jjimei.2023.100185
Gough, D., Oliver, S., and Thomas, J. (2017). An introduction to systematic reviews. Sage.
Hao, X., & Cukurova, M. (2023). We are exploring the impacts of “AI-generated” discussion summaries on learners’ engagement in online discussions. In N. Wang, G. Rebolledo-Mendez, V. Dimitrova, N. Matsuda, & O. C. Santos (Eds.), Artificial intelligence in education: Posters and late-breaking results, workshops and tutorials, industry and innovation tracks, practitioners, doctoral consortium and blue sky: 24th international conference AIED 2023 Tokyo Japan, July 3-7 2023 proceedings (pp. 155–161). Springer.
Hendrickx, V., & Smuha, N. (2023, February 8). Artificial Intelligence and interdisciplinarity: An evaluation. KU Leuven AI Summer School. https://www.law.kuleuven.be/ai-summer-school/blogpost/Blogposts/evaluating-interdisciplinarity-in-ai#:~:text=Benefits%20of%20interdisciplinary%20AI%20research&text=First%2C%20bringing%20together%20insights%20from,some%20of%20its%20core%20issues.
Higgins, J. P., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., and Welch, V. A. (Eds.). (2019). Cochrane handbook for systematic reviews of interventions. John Wiley & Sons.
Hu, Z., Cui, J., & Lin, A. (2023). Identifying potentially excellent publications using a citation-based machine learning approach. Information Processing and Management, 60(3), 103323. https://doi.org/10.1016/j.ipm.2023.103323
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P., ... and Linares-Espinós, E., Hernández, V., Domínguez-Escrig, J.L., Fernández-Pello, S., Hevia, V., Mayor, J., Padilla-Fernández, B. and Ribal, M.J. (2018). Methodology of a systematic review. Actas Urológicas Españolas (English Edition), 42(8), pp.499-506. https://doi.org/10.1016/j.acuro.2018.01.010
Lin, C.-C., Huang, A. Y. Q., & Lu, O. H. T. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: A systematic review. Smart Learning Environments, 10(41), 1–22. https://doi.org/10.1186/s40561-023-00260-y
Martínez-Comesaña, M., Rigueira-Díaz, X., Larrañaga-Janeiro, A., Martínez-Torres, J., Ocarranza-Prado, I., & Kreibel, D. (2023). Impact of artificial intelligence on primary and secondary education assessment methods: Systematic literature review. Revista De Psicodidáctica, 28, 93–103. https://doi.org/10.1016/j.psicoe.2023.06.002
Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). Ai and education: Guidance for policymakers. United Nations Educational, Scientific, and Cultural Organization.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., and Prisma Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS med, 6(7), e1000097.
Munir, H., Bahtijar, V., & Andreas, J. (2022). Artificial intelligence and machine learning approaches in digital education: A systematic revision. Information, 13, 1–20. 203–203. https://doi.org/10.3390/info13040203
Noyes, J., Booth, A., Cargo, M., Flemming, K., Garside, R., Hannes, K., ... and Pantoja, T. (2019). Cochrane Qualitative and Implementation Methods Group guidance series-paper 1: introduction. Journal of clinical epidemiology, 111, 3-9.
O’Kane, P., Ott, D. L., Smith, A. D., & Brown, T. C. (2023). Understanding computer-assisted qualitative data analysis software as a tool to enhance systematic literature reviews in human resource development. Human Resource Development Review, 22(2), 291–307. https://doi.org/10.1177/15344843221144668
Ouzzani, M., Hammady, H., Fedorowicz, Z., and Elmagarmid, A. (2016). Rayyan—a web and mobile app for systematic reviews. Systematic Reviews, 5(1), 210.
Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E. and Chou, R. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. International journal of surgery, 88, p.105906. https://doi.org/10.1016/j.ijsu.2021.105906
Patino, C.M. and Ferreira, J.C. (2018). Inclusion and exclusion criteria in research studies: definitions and why they matter. Jornal Brasileiro de Pneumologia, 44, pp.84-84. https://www.scielo.br/j/jbpneu/a/LV6rLNpPZsVFZ7mBqnzjkXD/?lang=en
Saadia, G., Turgay, C., & Nadia, E. (2023). Personalized adaptive learning technologies based on machine learning techniques to identify learning styles: A systematic literature review. IEEE Access, 11, 48392–48409. https://doi.org/10.1109/ACCESS.2023.3276439
Sarker, I. H. (2022). AI-based modeling: Techniques applications and research issues towards automation intelligent and smart systems. SN Computer Science, 3(158), 1–20. https://doi.org/10.1007/s42979-022-01043-x
Shaikh, A. A., Kumar, A., Jani, K., Mitra, S., García-Tadeo, D. A., & Devarajan, A. (2022). The role of machine learning and artificial intelligence in making a digital classroom and its sustainable impact on education during COVID-19. Materials Today: Proceedings, 56, 3211–3215. https://doi.org/10.1016/j.matpr.2021.09.368
Siontis, K.C., Hernandez-Boussard, T. and Ioannidis, J.P. (2013). Overlapping meta-analyses on the same topic: survey of published studies. BMJ, vol.347, pp1-11.
Tiwari, R. (2023). Integrating AI and machine learning in education and its potential to personalize and improve student learning experiences. Indian Scientific Journal of Research in Engineering and Management, 7(2), 1–11. http://doi.org/10.55041/IJSREM17645
Tuba, B., & Almila, A. (2022). A bibliometric analysis of the use of artificial intelligence technologies for social sciences. Mathematics, 10(23), 1–17. https://doi.org/10.3390/math10234398
Van Dijk, S. H. B., Brusse-Keizer, M. G. J., Bucsán, C. C., van der Palen, J., Doggen, C. J. M., & Lenferink, A. (2023). Artificial intelligence in systematic reviews: promising when appropriately used. BMJ Open, 13(7), 1–6. https://doi.org/10.1136/bmjopen-2023-072254
Wang, H., Tlili, A., Huang, R., Cai, Z., Li, M., Cheng, Z., Yang, D., Li, M., Zhu, X., & Fei, C. (2023). Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective. Education and Information Technologies, 28, 9113–9148. https://doi.org/10.1007/s10639-022-11555-x
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(39), 1–27. https://doi.org/10.1186/s41239-019-0171-0
Zeller, F., & Dwyer, L. (2022). Systems of collaboration: Challenges and solutions for interdisciplinary research in AI and social robotics. Discover Artificial Intelligence, 2(12), 1–12. https://doi.org/10.1007/s44163-022-00027-3
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