Impacts of Artificial Intelligence on Student Learning: A Systematic Literature Review
DOI:
https://doi.org/10.23917/varidika.v36i1.4730Keywords:
Artificial Intelligence, Learning Outcomes, Systematic Literature ReviewAbstract
This research presents a systematic literature review on the impact of artificial intelligence (AI) on student learning outcomes. While previous studies have explored various aspects of AI in education, there has been a lack of comprehensive analysis specifically examining its effect on learning outcomes. The objective of this study is to provide a detailed review of the literature on the effects of AI on student learning outcomes from 2013 to 2023, employing the PRISMA methodology. From an initial pool of 1068 papers identified in the Scopus database using defined search criteria, 39 articles were selected for the final analysis. Descriptive data reveal that most of the research focuses on higher education students and aims to enhance cognitive learning outcomes. Despite being grounded primarily in empirical research, the findings suggest that AI has significant potential to enhance educational processes in both schools and universities. This study aims to elucidate how AI can improve the learning experience, identify associated challenges and risks, and underscore the importance of integrating technology into the educational system to elevate the overall quality of learning.
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Alam, M., Al-Mamun, M., Pramanik, M. N. H., Jahan, I., Khan, M. R., Dishi, T. T., Akter, S. H., Jothi, Y. M., Shanta, T. A., & Hossain, M. J. (2022). Paradigm shifting of education system during COVID-19 pandemic: A qualitative study on education components. Heliyon, 8(12). https://doi.org/10.1016/j.heliyon.2022.e11927
Alshahrani, A. (2023). The impact of ChatGPT on blended learning: Current trends and future research directions. International Journal of Data and Network Science, 7(4), 2029–2040. https://doi.org/10.5267/j.ijdns.2023.6.010
Bai, H., Yu, H., Bantsimba N, R., & Luo, L. (2022). How college experiences impact student learning outcomes: Insights from Chinese undergraduate students. Frontiers in Psychology, 13(November), 1–15. https://doi.org/10.3389/fpsyg.2022.1021591
Bartol, T., Budimir, G., Dekleva-Smrekar, D., Pusnik, M., & Juznic, P. (2014). Assessment of research fields in Scopus and Web of Science in the view of national research evaluation in Slovenia. Scientometrics, 98(2), 1491–1504. https://doi.org/10.1007/s11192-013-1148-8
Bonneton-Botté, N., Fleury, S., Girard, N., Le Magadou, M., Cherbonnier, A., Renault, M., Anquetil, E., & Jamet, E. (2020). Can tablet apps support the learning of handwriting? An investigation of learning outcomes in kindergarten classroom. Computers and Education, 151. https://doi.org/10.1016/j.compedu.2020.103831
Cedefop. (2017). Defining, writing and applying learning outcomes: a European handbook. https://www.cedefop.europa.eu/files/4156_en.pdf
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00408-3
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00411-8
Chan, K. S., & Zary, N. (2019). Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review. JMIR Medical Education, 5(1), e13930. https://doi.org/10.2196/13930
Chaudhry, I. S., Sarwary, S. A. M., El Refae, G. A., & Chabchoub, H. (2023). Time to Revisit Existing Student’s Performance Evaluation Approach in Higher Education Sector in a New Era of ChatGPT — A Case Study. Cogent Education, 10(1). https://doi.org/10.1080/2331186X.2023.2210461
Chien, Y.-C., Wu, T.-T., Lai, C.-H., & Huang, Y.-M. (2022). Investigation of the Influence of Artificial Intelligence Markup Language-Based LINE ChatBot in Contextual English Learning. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.785752
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/https://doi.org/10.1016/j.caeai.2022.100118
Christie, M., & de Graaff, E. (2017). The philosophical and pedagogical underpinnings of Active Learning in Engineering Education. European Journal of Engineering Education, 42(1), 5–16. https://doi.org/10.1080/03043797.2016.1254160
Crawford, J., Cowling, M., & Allen, K.-A. (2023). Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching and Learning Practice, 20(3). https://doi.org/10.53761/1.20.3.02
D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157. https://doi.org/https://doi.org/10.1016/j.learninstruc.2011.10.001
da Costa Santos, C. M., Pimenta, C. A. D. M., & Nobre, M. R. C. (2007). The PICO strategy for the research question construction and evidence search. Revista Latino-Americana de Enfermagem, 15 3, 508–511. https://api.semanticscholar.org/CorpusID:46373317
Eager, B., & Brunton, R. (2023). Prompting Higher Education Towards AI-Augmented Teaching and Learning Practice. Journal of University Teaching and Learning Practice, 20(5). https://doi.org/10.53761/1.20.5.02
Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism: Clinical and Experimental, 69, S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011
Heeg, D. M., & Avraamidou, L. (2023). The use of Artificial intelligence in school science: a systematic literature review. Educational Media International, 60(2), 125–150. https://doi.org/10.1080/09523987.2023.2264990
Hooda, M., Rana, C., Dahiya, O., Rizwan, A., & Hossain, M. S. (2022). Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/5215722
How, M.-L. (2019). Future-ready strategic oversight of multiple artificial superintelligence-enabled adaptive learning systems via human-centric explainable ai-empowered predictive optimizations of educational outcomes. Big Data and Cognitive Computing, 3(3), 1–43. https://doi.org/10.3390/bdcc3030046
Hsu, M.-H., Chan, T.-M., & Yu, C.-S. (2023). Termbot: A Chatbot-Based Crossword Game for Gamified Medical Terminology Learning. International Journal of Environmental Research and Public Health, 20(5). https://doi.org/10.3390/ijerph20054185
Hu, Y.-H. (2022). Effects and acceptance of precision education in an AI-supported smart learning environment. Education and Information Technologies, 27(2), 2013–2037. https://doi.org/10.1007/s10639-021-10664-3
Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023). Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers and Education, 194. https://doi.org/10.1016/j.compedu.2022.104684
Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. In Computers and Education: Artificial Intelligence (Vol. 1, p. 100001). Elsevier.
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023). New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution. Sustainability (Switzerland), 15(16), 1–27. https://doi.org/10.3390/su151612451
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering - A systematic literature review. Information and Software Technology, 51(1), 7–15. https://doi.org/10.1016/j.infsof.2008.09.009
Kumpas-Lenk, K., Eisenschmidt, E., & Veispak, A. (2018). Does the design of learning outcomes matter from students’ perspective? Studies in Educational Evaluation, 59, 179–186. https://doi.org/https://doi.org/10.1016/j.stueduc.2018.07.008
Lasfeto, D. B., & Ulfa, S. (2023). Modeling of Online Learning Strategies Based on Fuzzy Expert Systems and Self-Directed Learning Readiness: The Effect on Learning Outcomes. Journal of Educational Computing Research, 60(8), 2081–2104. https://doi.org/10.1177/07356331221094249
Lee, C.-A., Tzeng, J.-W., Huang, N.-F., & Su, Y.-S. (2021). Prediction of Student Performance in Massive Open Online Courses Using Deep Learning System Based on Learning Behaviors. Educational Technology and Society, 24(3), 130–146. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110549417&partnerID=40&md5=97421b8f03375c5656733f08ae0f39e6
Lee, M. C. M., Scheepers, H., Lui, A. K. H., & Ngai, E. W. T. (2023). The implementation of artificial intelligence in organizations: A systematic literature review. Information and Management, 60(5), 182–197. https://doi.org/10.1016/j.im.2023.103816
Liang, X., Haiping, L., Liu, J., & Lin, L. (2021). Reform of English interactive teaching mode based on cloud computing artificial intelligence - A practice analysis. Journal of Intelligent and Fuzzy Systems, 40(2), 3617–3629. https://doi.org/10.3233/JIFS-189397
Lim, L., Bannert, M., van der Graaf, J., Singh, S., Fan, Y., Surendrannair, S., Rakovic, M., Molenaar, I., Moore, J., & Gašević, D. (2023). Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning. Computers in Human Behavior, 139. https://doi.org/10.1016/j.chb.2022.107547
Lin, X., Liu, H., Sun, Q., Li, X., Qian, H., Sun, Z., & Lam, T. L. (2022). Applying project-based learning in artificial intelligence and marine discipline: An evaluation study on a robotic sailboat platform. IET Cyber-Systems and Robotics, 4(2), 86–96. https://doi.org/10.1049/csy2.12050
Lisée, C., Larivière, V., & Archambault, É. (2008). Conference proceedings as a source of scientific information: A bibliometric analysis. Journal of the American Society for Information Science and Technology, 59(11), 1776–1784.
Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J. H., Ogata, H., Baltes, J., Guerra, R., Li, P., & Tsai, C. C. (2020). Challenges and Future Directions of Big Data and Artificial Intelligence in Education. Frontiers in Psychology, 11(October), 1–11. https://doi.org/10.3389/fpsyg.2020.580820
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An argument for AI in education. In Pearson Education. https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/Intelligence-Unleashed-Publication.pdf
McCarthy, J. (2007). From here to human-level AI. Artificial Intelligence, 171(18), 1174–1182. https://doi.org/10.1016/j.artint.2007.10.009
Morrison, B. B., Quinn, B. A., Bradley, S., Buffardi, K., Harrington, B., Hu, H. H., Kallia, M., McNeill, F., Ola, O., Parker, M., Rosato, J., & Waite, J. (2021). Evidence for Teaching Practices that Broaden Participation for Women in Computing. In Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE. https://doi.org/10.1145/3502870.3506568
Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-10203-6
Nguyen, A., Järvelä, S., Rosé, C., Järvenoja, H., & Malmberg, J. (2023). Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics. British Journal of Educational Technology, 54(1), 293–312. https://doi.org/10.1111/bjet.13280
Owan, V. J., Abang, K. B., Idika, D. O., Etta, E. O., & Bassey, B. A. (2023). Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia Journal of Mathematics, Science and Technology Education, 19(8). https://doi.org/10.29333/ejmste/13428
Salas-Pilco, S. Z. (2020). The impact of AI and robotics on physical, social-emotional and intellectual learning outcomes: An integrated analytical framework. British Journal of Educational Technology, 51(5), 1808–1825. https://doi.org/10.1111/bjet.12984
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021a). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(1). https://doi.org/10.1186/s41239-021-00292-9
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021b). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(1). https://doi.org/10.1186/s41239-021-00292-9
Short, J. (2009). The Art of Writing a Review Article. Journal of Management, 35, 1312–1317. https://doi.org/10.1177/0149206309337489
Shu, X., & Gu, X. (2023). An Empirical Study of A Smart Education Model Enabled by the Edu-Metaverse to Enhance Better Learning Outcomes for Students. Systems, 11(2). https://doi.org/10.3390/systems11020075
Slimi, Z. (2023). The Impact of Artificial Intelligence on Higher Education: An Empirical Study. European Journal of Educational Sciences, 10(1), 17–33. https://doi.org/10.19044/ejes.v10no1a17
Sousa, M. J., Mas, F. D., Pesqueira, A., Lemos, C., Verde, J. M., & Cobianchi, L. (2021). The Potential of AI in Health Higher Education to Increase the Students’ Learning Outcomes. TEM Journal, 10(2), 488–497. https://doi.org/10.18421/TEM102-02
Sperling, K., Stenliden, L., Nissen, J., & Heintz, F. (2022). Still w(AI)ting for the automation of teaching: An exploration of machine learning in Swedish primary education using Actor-Network Theory. European Journal of Education, 57(4), 584–600. https://doi.org/10.1111/ejed.12526
Su, K.-D. (2022). Implementation Of Innovative Artificial Intelligence Cognitions With Problem-Based Learning Guided Tasks To Enhance Students’ Performance In Science. Journal of Baltic Science Education, 21(2), 245–257. https://doi.org/10.33225/jbse/22.21.245
Thinh, N. T., Hai, N. D. X., & Tho, T. P. (2020). The influential role of robot in second language classes based on artificial intelligence. International Journal of Mechanical Engineering and Robotics Research, 9(9), 1306–1311. https://doi.org/10.18178/ijmerr.9.9.1306-1311
Toniolo, K., Masiero, E., Massaro, M., & Bagnoli, C. (2020). Sustainable Business Models and Artificial Intelligence: Opportunities and Challenges. https://api.semanticscholar.org/CorpusID:219096261
Tran, K., Nguyen, T., Tran, Y., Nguyen, A., Luu, K., & Nguyen, Y. (2022). Eco-friendly fashion among generation Z: Mixed-methods study on price value image, customer fulfillment, and pro-environmental behavior. PLoS ONE, 17(8 August). https://doi.org/10.1371/journal.pone.0272789
Tsai, C.-C., Cheng, Y.-M., Tsai, Y.-S., & Lou, S.-J. (2021). Impacts of aiot implementation course on the learning outcomes of senior high school students. Education Sciences, 11(2), 1–30. https://doi.org/10.3390/educsci11020082
Tuomi, I. (2018). The Impact of Artificial Intelligence on Learning, Teaching, and Education Policies. In Science for Policy. https://doi.org/10.2760/12297
Wei, J., Marimuthu, K., & Prathik, A. (2022). College music education and teaching based on AI techniques. Computers and Electrical Engineering, 100. https://doi.org/10.1016/j.compeleceng.2022.107851
Wu, S.-Y., & Yang, K.-K. (2022). The Effectiveness of Teacher Support for Students’ Learning of Artificial Intelligence Popular Science Activities. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.868623
Xia, N., Zou, P. X. W., Griffin, M. A., Wang, X., & Zhong, R. (2018). Towards integrating construction risk management and stakeholder management: A systematic literature review and future research agendas. International Journal of Project Management, 36(5), 701–715. https://doi.org/https://doi.org/10.1016/j.ijproman.2018.03.006
Xu, J. J., & Babaian, T. (2021). Artificial intelligence in business curriculum: The pedagogy and learning outcomes. International Journal of Management Education, 19(3). https://doi.org/10.1016/j.ijme.2021.100550
Yang, C. C. Y., Chen, I. Y. L., & Ogata, H. (2021). Toward Precision Education: Educational Data Mining and Learning Analytics for Identifying Students’ Learning Patterns with Ebook Systems. Educational Technology and Society, 24(1), 152–163. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102826885&partnerID=40&md5=6bd0dc3c3ce8ae3afe5b60f9ad8bf706
Zafari, M., Bazargani, J. S., Sadeghi-Niaraki, A., & Choi, S.-M. (2022). Artificial Intelligence Applications in K-12 Education: A Systematic Literature Review. IEEE Access, 10, 61905–61921. https://doi.org/10.1109/ACCESS.2022.3179356
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. https://doi.org/10.1186/s41239-019-0171-0
Zheng, L., Niu, J., Zhong, L., & Gyasi, J. F. (2023). The effectiveness of artificial intelligence on learning achievement and learning perception: A meta-analysis. Interactive Learning Environments, 31(9), 5650–5664. https://doi.org/10.1080/10494820.2021.2015693
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