Unlocking potential: The impact of AI on education technology

Keerthi Jain; J. Naga Venkata Raghuram

doi:10.31893/multirev.2024049

Keywords:

Artificial Intelligence

education technology

intelligent tutoring system

adaptive learning

Abstract

According to several international journals, artificial intelligence in education (AIET) is a more recent field in the educational sector. Even though it has been there for close to 30 years, educators are still confused about how to utilize it pedagogically on a larger scale and how it may have a substantial impact on teaching and learning as per SDG-4 Indicator 4.4.1, which tracks the proportion of educators and academia with the necessary information technology skills, putting them on the road to better employment and understanding Education 4.0. This article postulates a review of the impacts of AI in education and briefs the number of published studies in the area of AI in education, which has expanded as a result of the growing usage of artificial intelligence (AI) technology in education. However, extensive evaluations have been conducted to fully study the numerous facets of this topic. This study seeks to address this gap by utilizing PRISMA to detect trends and issues relevant to AI applications in education (AIET) based on publications from 2000 to 2022. The review's findings show that the academic community is becoming more interested in applying AI to education. The primary research questions covered in this study are those related to the origin: Rise in AI, Importance, and Impact of AI on Education Technology, as well as related areas such as intelligent tutoring systems for education AI challenges in the education sector, and future scope of AI and ChatGPT-3 in higher education.
References
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23. Hwang, G.-J. (2003). A conceptual map model for developing intelligent tutoring systems. Computers and Education, 40(3), 217–235. https://doi.org/10.1016/S0360-1315(02)00121-5
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25. Improving Quality of Online Teaching Finance and Business Management Using Artificial Intelligence and Backward Design. (2022). Quality – Access to Success, 23(187). https://doi.org/10.47750/QAS/23.187.01
26. Johnson, B. G., Phillips, F., & Chase, L. G. (2009). An intelligent tutoring system for the accounting cycle: Enhancing textbook homework with Artificial Intelligence. Journal of Accounting Education, 27(1), 30–39. https://doi.org/10.1016/j.jaccedu.2009.05.001
27. Lakhal, S., Bateman, D., & Bédard, J. (2017). Blended synchronous delivery modes in graduate programs: A literature review and how it is implemented in the master teacher program. Collected Essays on Learning and Teaching, 10, 47–60. https://doi.org/10.22329/celt.v10i0.4747
28. Leahy, S. M., Holland, C., & Ward, F. (2019). The Digital Frontier: Envisioning Future Technologies Impact on the classroom. Futures, 113, 102422. https://doi.org/10.1016/j.futures.2019.04.009
29. Li, Q., Cao, H., & Lu, Y. (2017). Connecting paper to digitization–a homework data processing system with data labeling and visualization. ACM International Conference Proceeding Series.
30. Liu, M., Ma, J., & Jin, L. (2018). Analysis of military academy smart campus based on big data. Proceedings of the 10th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2018, 2018. https://doi.org/10.1109/IHMSC.2018.00031
31. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
32. McCarthy, J., Feigenbaum, E., & Samuel, A. (1968). Stanford Artificial Intelligence Project – Technical report. https://doi.org/10.21236/ad0677528
33. Meng, T., Jing, X., Yan, Z., & Pedrycz, W. (2020). A survey on machine learning for Data Fusion. Information Fusion, 57, 115–129. https://doi.org/10.1016/j.inffus.2019.12.001
34. Molina-Enriquez, J., Chicaiza-Tamayo, C., Ortiz-Garcés, I., & Luján-Mora, S. (2019). Application of a big data framework for data monitoring on a smart campus. Villegas-Ch, W. Sustainability (Switzerland).
35. 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, 7(1), 95–103. https://doi.org/10.15549/jeecar.v7i1.386
36. 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), 22. https://doi.org/10.1186/s41039-017-0062-8
37. Rahim, T. N. T. A., Aziz, Z. A., Rauf, R. H. A., & Shamsudin, N. (2018). Automated exam question generator using genetic algorithm, 2017. https://doi.org/10.1109/IC3e.2017.8409231
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41. Spolaôr, N., & Benitti, F. B. V. (2017). Robotics applications grounded in learning theories on tertiary education: A systematic review. Computers and Education, 112, 97–107. https://doi.org/10.1016/j.compedu.2017.05.001
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43. The 2005 DARPA grand challenge. (2007). Springer Tracts in Advanced Robotics. https://doi.org/10.1007/978-3-540-73429-1
44. Tripathy, S., & Devarapalli, S. (2021). Emerging trend set by a start-ups on Indian online education system: A case of Byju’s. Journal of Public Affairs, 21(1). https://doi.org/10.1002/pa.2128
45. Tseng, H., Yi, X., & Yeh, H.-T. (2019). Learning-related soft skills among online business students in higher education: Grade level and managerial role differences in self-regulation, motivation, and social skill. Computers in Human Behavior, 95, 179–186. https://doi.org/10.1016/j.chb.2018.11.035
46. Viegas, C. V., Bond, A. J., Vaz, C. R., Borchardt, M., Pereira, G. M., Selig, P. M., & Varvakis, G. (2016). Critical attributes of sustainability in Higher Education: A categorisation from literature Review. Journal of Cleaner Production, 126, 260–276. https://doi.org/10.1016/j.jclepro.2016.02.106
47. Walkington, C., & Bernacki, M. L. (2020). Appraising research on personalized learning: Definitions, theoretical alignment, advancements, and future directions. Journal of Research on Technology in Education, 52(3), 235–252. https://doi.org/10.1080/15391523.2020.1747757
48. Weizenbaum, J. (1983). Eliza — A computer program for the study of natural language communication between man and Machine. Communications of the ACM, 26(1), 23–28. https://doi.org/10.1145/357980.357991
49. Wogu, I. A. P., Katende, J. O., Elegbeleye, A., Roland-Otaru, C. O., Apeh, H. A., Ifeanyi-Reuben, N. J., & Misra, S. (2020). Artificial Intelligence, other minds, and human factor development. In Handbook of Research on the role of human factors in IT project management (pp. 205–220). https://doi.org/10.4018/978-1-7998-1279-1.ch014
50. Yilmaz, B. (2018). Effects of adaptive learning technologies on math achievement: A quantitative study of ALEKS math software. Dissertation Abstracts International, Section A.
51. Yu, P. K. (2020). Legal studies research paper series the algorithmic divide and equality in the age of artificial intelligence. Florida Law Review, 72(19), 331–389.
52. Zhang, M., & Li, J. (2021). A commentary of GPT-3 in MIT Technology Review 2021. In. Fundamental Research, 1(6), 831–833. https://doi.org/10.1016/j.fmre.2021.11.011

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Jain, K., & Raghuram, J. N. V. (2023). Unlocking potential: The impact of AI on education technology. Multidisciplinary Reviews, 7(3), 2024049. https://doi.org/10.31893/multirev.2024049

[1] Beck, J., Stern, M., & Haugsjaa, E. (1996). Applications of AI in education. Crossroads, 3(1), 11–15.

[2] Bellegarda, J. R. (2013). Spoken language understanding for natural interaction: The siri experience. Natural Interaction with Robots, Knowbots and Smartphones, 3–14. https://doi.org/10.1007/978-1-4614-8280-2_1

[3] Benhayoun, L., & Lang, D. (2021). Does Higher Education properly prepare graduates for the growing artificial intelligence market? gaps’ identification using text mining. Human Systems Management, 40(5), 639–651. https://doi.org/10.3233/HSM-211179

[4] Bergamin, P., & Hirt, F. S. (2018). Who’s in charge?—Dealing with the self-regulation dilemma in digital learning environments. In Knowledge management in digital change (pp. 227–245). Springer. https://doi.org/10.1007/978-3-319-73546-7_14

[5] Bodó, B., Helberger, N., Irion, K., Borgesius Zuiderveen, F. J., Moller, J., van der Velde, B., Bol, N. et al. (2017). Tackling the algorithmic control crisis – The technical, legal, and ethical challenges of research into algorithmic agents. Yale Journal of Law and Technology, 19, 133.

[6] Bostrom, N. (2015). Nick Bostrom: What happens when our computers get smarter than we are? https://www.ted.com/talks/nick_bostrom_what_happens_when_our_computers_get_smarter_than_we_are p. 2018. tech trends annual report.

[7] Chan, A. (2023). GPT-3 and InstructGPT: Technological dystopianism, utopianism, and “Contextual” perspectives in AI ethics and industry. AI and Ethics, 3(1), 53–64. https://doi.org/10.1007/s43681-022-00148-6

[8] Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 3443–3463. https://doi.org/10.1007/s10639-020-10159-7

[9] Chen, K., Kvasnicka, V., Kanen, P. C., & Haykin, S. (2001). Multi-valued and universal binary neurons: Theory, learning, and applications [book review]. IEEE Transactions on Neural Networks, 12(3), 647–647. https://doi.org/10.1109/TNN.2001.925572

[10] Crittenden, W. F., Biel, I. K., & Lovely, W. A. (2019). Embracing digitalization: Student learning and new technologies. Journal of Marketing Education, 41(1), 5–14. https://doi.org/10.1177/0273475318820895

[11] Cui, W., Xue, Z., & Thai, K. P. (2019). Performance comparison of an AI-based adaptive learning system in China. In Proceedings of the Chinese Automation Congress, 2018 3170–3175. CAC. https://doi.org/10.1109/CAC.2018.8623327

[12] Future Today Institute. Retrieved May 14, 2018. Retrieved November 17, 2022 from https://futuretodayinstitute.com/2018-tech-trends-annual-report

[13] Fang, Y., Ren, Z., Hu, X., & Graesser, A. C. (2019). A meta-analysis of the effectiveness of ALEKS on learning. Educational Psychology, 39(10), 1278–1292. https://doi.org/10.1080/01443410.2018.1495829

[14] Feigenbaum, E. (1989). Interviewed for expert systems by Kenneth Owen. Expert Systems, 6(2), 112–115. https://doi.org/10.1111/j.1468-0394.1989.tb00084.x

[15] Goel, A. K., & Polepeddi, L. (2016). Jill Watson: A virtual teaching assistant for online education. Georgia Tech.

[16] Grams, D. (2018). A quantitative study of the use of ‘DreamBox learning’ and its effectiveness in improving math achievement of elementary students with math difficulties. ProQuest LLC.

[17] Guan, C., Mou, J., & Jiang, Z. (2020). Artificial Intelligence Innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4), 134–147. https://doi.org/10.1016/j.ijis.2020.09.001

[18] Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of Artificial Intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925

[19] Henderson, L. H., Wersun, A., Wilson, J., Mo-ching Yeung, S., & Zhang, K. (2019). Principles for responsible management education in 2068. Futures, 111, 81–89. https://doi.org/10.1016/j.futures.2019.05.005

[20] Hsieh, Y. Z., Lin, S. S., Luo, Y. C., Jeng, Y. L., Tan, S. W., Chen, C. R., & Chiang, P. Y. (2020). ARCS-assisted teaching robots based on anticipatory computing and emotional Big Data for improving sustainable learning efficiency and motivation. Sustainability, 12(14). https://doi.org/10.3390/su12145605

[21] Hsu, T.-C., & Chen, M.-S. (2022). The engagement of students when learning to use a personal audio classifier to control robot cars in a computational thinking board game. Research and Practice in Technology Enhanced Learning, 17(1). https://doi.org/10.1186/s41039-022-00202-1

[22] Huang, J., Saleh, S., & Liu, Y. (2021). A review on Artificial Intelligence in Education. Academic Journal of Interdisciplinary Studies, 10(3), 206. https://doi.org/10.36941/ajis-2021-0077

[23] Hwang, G.-J. (2003). A conceptual map model for developing intelligent tutoring systems. Computers and Education, 40(3), 217–235. https://doi.org/10.1016/S0360-1315(02)00121-5

[24] İçen, M. (2022). The future of education utilizing artificial intelligence in Turkey. Humanities and Social Sciences Communications, 9(1). https://doi.org/10.1057/s41599-022-01284-4

[25] Improving Quality of Online Teaching Finance and Business Management Using Artificial Intelligence and Backward Design. (2022). Quality – Access to Success, 23(187). https://doi.org/10.47750/QAS/23.187.01

[26] Johnson, B. G., Phillips, F., & Chase, L. G. (2009). An intelligent tutoring system for the accounting cycle: Enhancing textbook homework with Artificial Intelligence. Journal of Accounting Education, 27(1), 30–39. https://doi.org/10.1016/j.jaccedu.2009.05.001

[27] Lakhal, S., Bateman, D., & Bédard, J. (2017). Blended synchronous delivery modes in graduate programs: A literature review and how it is implemented in the master teacher program. Collected Essays on Learning and Teaching, 10, 47–60. https://doi.org/10.22329/celt.v10i0.4747

[28] Leahy, S. M., Holland, C., & Ward, F. (2019). The Digital Frontier: Envisioning Future Technologies Impact on the classroom. Futures, 113, 102422. https://doi.org/10.1016/j.futures.2019.04.009

[29] Li, Q., Cao, H., & Lu, Y. (2017). Connecting paper to digitization–a homework data processing system with data labeling and visualization. ACM International Conference Proceeding Series.

[30] Liu, M., Ma, J., & Jin, L. (2018). Analysis of military academy smart campus based on big data. Proceedings of the 10th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2018, 2018. https://doi.org/10.1109/IHMSC.2018.00031

[31] Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.

[32] McCarthy, J., Feigenbaum, E., & Samuel, A. (1968). Stanford Artificial Intelligence Project – Technical report. https://doi.org/10.21236/ad0677528

[33] Meng, T., Jing, X., Yan, Z., & Pedrycz, W. (2020). A survey on machine learning for Data Fusion. Information Fusion, 57, 115–129. https://doi.org/10.1016/j.inffus.2019.12.001

[34] Molina-Enriquez, J., Chicaiza-Tamayo, C., Ortiz-Garcés, I., & Luján-Mora, S. (2019). Application of a big data framework for data monitoring on a smart campus. Villegas-Ch, W. Sustainability (Switzerland).

[35] 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, 7(1), 95–103. https://doi.org/10.15549/jeecar.v7i1.386

[36] 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), 22. https://doi.org/10.1186/s41039-017-0062-8

[37] Rahim, T. N. T. A., Aziz, Z. A., Rauf, R. H. A., & Shamsudin, N. (2018). Automated exam question generator using genetic algorithm, 2017. https://doi.org/10.1109/IC3e.2017.8409231

[38] Ross, P. (1987). Intelligent tutoring systems. Journal of Computer Assisted Learning, 3(4), 194–203. https://doi.org/10.1111/j.1365-2729.1987.tb00331.x

[39] Southgate, E. (2020). Artificial intelligence, ethics, equity and higher education: A ‘beginning-of-the-discussion’ paper.

[40] Spies, Brothers. (2020). Spies, M. Brothers, P. https://www.holoniq.com/wp-content/uploads/2020/01/HolonIQ-Education-in-2030.pdf. Education in 2030. Five scenarios for the future of learning and talent [Report]. Holoniq.com [Electronic resource]

[41] Spolaôr, N., & Benitti, F. B. V. (2017). Robotics applications grounded in learning theories on tertiary education: A systematic review. Computers and Education, 112, 97–107. https://doi.org/10.1016/j.compedu.2017.05.001

[42] Sruthi, P., & Mukherjee, S. (2020). Byju’s the learning app: An investigative study on the transformation from traditional learning to technology based personalized learning. International Journal of Scientific and Technology Research, 9(30), 5054–5059.

[43] The 2005 DARPA grand challenge. (2007). Springer Tracts in Advanced Robotics. https://doi.org/10.1007/978-3-540-73429-1

[44] Tripathy, S., & Devarapalli, S. (2021). Emerging trend set by a start-ups on Indian online education system: A case of Byju’s. Journal of Public Affairs, 21(1). https://doi.org/10.1002/pa.2128

[45] Tseng, H., Yi, X., & Yeh, H.-T. (2019). Learning-related soft skills among online business students in higher education: Grade level and managerial role differences in self-regulation, motivation, and social skill. Computers in Human Behavior, 95, 179–186. https://doi.org/10.1016/j.chb.2018.11.035

[46] Viegas, C. V., Bond, A. J., Vaz, C. R., Borchardt, M., Pereira, G. M., Selig, P. M., & Varvakis, G. (2016). Critical attributes of sustainability in Higher Education: A categorisation from literature Review. Journal of Cleaner Production, 126, 260–276. https://doi.org/10.1016/j.jclepro.2016.02.106

[47] Walkington, C., & Bernacki, M. L. (2020). Appraising research on personalized learning: Definitions, theoretical alignment, advancements, and future directions. Journal of Research on Technology in Education, 52(3), 235–252. https://doi.org/10.1080/15391523.2020.1747757

[48] Weizenbaum, J. (1983). Eliza — A computer program for the study of natural language communication between man and Machine. Communications of the ACM, 26(1), 23–28. https://doi.org/10.1145/357980.357991

[49] Wogu, I. A. P., Katende, J. O., Elegbeleye, A., Roland-Otaru, C. O., Apeh, H. A., Ifeanyi-Reuben, N. J., & Misra, S. (2020). Artificial Intelligence, other minds, and human factor development. In Handbook of Research on the role of human factors in IT project management (pp. 205–220). https://doi.org/10.4018/978-1-7998-1279-1.ch014

[50] Yilmaz, B. (2018). Effects of adaptive learning technologies on math achievement: A quantitative study of ALEKS math software. Dissertation Abstracts International, Section A.

[51] Yu, P. K. (2020). Legal studies research paper series the algorithmic divide and equality in the age of artificial intelligence. Florida Law Review, 72(19), 331–389.

[52] Zhang, M., & Li, J. (2021). A commentary of GPT-3 in MIT Technology Review 2021. In. Fundamental Research, 1(6), 831–833. https://doi.org/10.1016/j.fmre.2021.11.011

Abstract

References

How to cite