The Effect of Artificial Intelligence-Assisted Personalized Learning on Student Learning Outcomes: A Meta-Analysis Based on 31 Empirical Research Papers
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Published
Sep 30, 2024
Abstract
The application of artificial intelligence in education has garnered more attention in academia, and its role in promoting student personalized learning has sparked a lot of discussion. Many researchers have emphasized the positive effect of intelligent technology in supporting student personalized learning; however, there is a lack of systematic data evidence in this regard. This article seeks to evaluate the effects of artificial intelligence-assisted personalized learning on student learning outcomes based on a meta-analysis of 36 experimental and quasi-experimental studies from 31 published papers. The analysis results show that artificial intelligence-assisted personalized learning has moderately positive effects on student learning outcomes in terms of knowledge, competence, and emotional development. Variables such as the type of Edutech applications, learning scenario, and duration of application can moderate the relationship between artificial intelligence-assisted personalized learning and student learning outcomes, whereas the education phase and disciplinary domain do not exhibit significant moderating effects on this relationship. The purpose of this study is to provide implications and references for further research and practical explorations of artificial intelligence application in education.
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Keywords
Artificial Intelligence, Personalized Learning, Student Learning Outcomes, Meta-Analysis
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No funding sources declared.
References
(The studies included in the meta-analysis are marked with asterisks)
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*Deng, K., & Wang, G. (2024). Online mode development of Korean art learning in the post-epidemic era based on artificial intelligence and deep learning. The Journal of Supercomputing, 80(6):8505-8528. DOI: https://doi.org/10.1007/s11227-023-05776-1
*Divekar*, R. R., Drozdal*, J., Chabot*, S., Zhou, Y., Su, H., Chen, Y., ... & Braasch, J. (2022). Foreign language acquisition via artificial intelligence and extended reality: design and evaluation. Computer Assisted Language Learning, 35(9):2332-2360. DOI: https://doi.org/10.1080/09588221.2021.1879162
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Gutierrez, F., & Atkinson, J. (2011). Adaptive feedback selection for intelligent tutoring systems. Expert Systems with Applications, 38(5):6146-6152. DOI: https://doi.org/10.1016/j.eswa.2010.11.058
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Huang, J., Saleh, S., & Liu, Y. (2021). A review on artificial intelligence in education. Academic Journal of Interdisciplinary Studies, 10(3):206-217. DOI: https://doi.org/10.36941/ajis-2021-0077
*Hwang, G. J., Sung, H. Y., Chang, S. C., & Huang, X. C. (2020). A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors. Computers and Education: Artificial Intelligence, 1, 100003. DOI: https://doi.org/10.1016/j.caeai.2020.100003
*Julià, C., & Antolí, J. Ò. (2016). Spatial ability learning through educational robotics. International Journal of Technology and Design Education, 26:185-203. DOI: https://doi.org/10.1007/s10798-015-9307-2
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*Li, S., & Zheng, J. (2017). The effect of academic motivation on students’ English learning achievement in the eSchoolbag-based learning environment. Smart Learning Environments, 4:1-14. DOI: https://doi.org/10.1186/s40561-017-0042-x
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*Ma, Y. (2023). Design and Practice of Personalized Teaching in Primary Mathematics education Using the “Optimized Learning” Smart Education Platform (master’s thesis). Ningxia University. DOI: https://doi.org/10.27257/d.cnki.gnxhc.2023.000158
Ma, Z. & Liu, Y. (2019). From project-based learning and paired programming to interdisciplinary integrated design: A meta-analysis based on studies of K-12 computational thinking published from 2006 to 2019. Journal of Distance Education, 2019(05):75-84. DOI: https://doi.org/10.15881/j.cnki.cn33-1304/g4.2019.05.009
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*Nugent, G., Barker, B., Grandgenett, N., & Adamchuk, V. I. (2010). Impact of robotics and geospatial technology interventions on youth STEM learning and attitudes. Journal of Research on Technology in Education, 42(4):391-408. DOI: https://doi.org/10.1080/15391523.2010.10782557
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*Qianjing, M., & Lin, T. (2021). An artificial intelligence-based construction and application of English multimodal online reading mode. Journal of Intelligent & Fuzzy Systems, 40(2):3721-3730. DOI: https://doi.org/10.3233/JIFS-189406
Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological bulletin, 86(3):638-640. DOI: https://doi.org/10.1037/0033-2909.86.3.638
*Serrano, M. Á., Vidal‐Abarca, E., & Ferrer, A. (2018). Teaching self‐regulation strategies via an intelligent tutoring system (TuinLECweb): Effects for low‐skilled comprehenders. Journal of Computer Assisted Learning, 34(5):515-525. DOI: https://doi.org/10.1111/jcal.12256
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vX5mnUbllPDzodOhH1x4UW3Gv8A9yrmFp8rrkOeRO1uDwzNGrVN1lnQtLRDvnZiOo1kpXYuSqSYK0dnsz1SosOO80tUoUC4OVzi4j7Dk6c2DVY6cYv9tLqTN0hnkMfRwD2dsssXrbeVZxz5OcYPge34uEBDxIKHqE1g_lqkSBHGvUJI=&uniplatform=NZKPT&language=CHS
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*Alfieri, L., Higashi, R., Shoop, R., & Schunn, C. D. (2015). Case studies of a robot-based game to shape interests and hone proportional reasoning skills. International Journal of STEM Education, 2:1-13. DOI: https://doi.org/10.1186/s40594-015-0017-9
Ayeni, O. O., Al Hamad, N. M., Chisom, O. N., Osawaru, B., & Adewusi, O. E. (2024). AI in education: A review of personalized learning and educational technology. GSC Advanced Research and Reviews, 18(2):261-271. DOI: https://doi.org/10.30574/gscarr.2024.18.2.0062
*Bahçeci, F., & Gürol, M. (2016). The effect of individualized instruction system on the academic achievement scores of students. Education Research International, 2016(1), 7392125. DOI: https://doi.org/10.1155/2016/7392125
*Beal, C. R., Arroyo, I. M., Cohen, P. R., & Woolf, B. P. (2010). Evaluation of AnimalWatch: An intelligent tutoring system for arithmetic and fractions. Journal of Interactive Online Learning, 9(1):64-77.
Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2021). Introduction to meta-analysis. John Wiley & Sons. DOI: https://doi.org/10.1002/9780470743386
*Chen, C. M. (2009). Ontology‐based concept map for planning a personalized learning path. British Journal of Educational Technology, 40(6):1028-1058. DOI: https://doi.org/10.1111/j.1467-8535.2008.00892.x
Chen, C. M., Wang, J. Y., & Hsu, L. C. (2021). An interactive test dashboard with diagnosis and feedback mechanisms to facilitate learning performance. Computers and Education: Artificial Intelligence, 2, 100015. DOI: https://doi.org/10.1016/j.caeai.2021.100015
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8:75264-75278. DOI: https://doi.org/10.1109/ACCESS.2020.2988510
Cohen, J. (2013). Statistical power analysis for the behavioral sciences (2nd Edition). Routledge. DOI: https://doi.org/10.4324/9780203771587
*Dai, X. (2021). Design and Practice of Seventh Grade Mathematics Instruction Based on the Intelligent Adaptive Learning System (master’s thesis). Ningxia University. DOI: https://doi.org/10.27257/d.cnki.gnxhc.2021.001445
*Deng, K., & Wang, G. (2024). Online mode development of Korean art learning in the post-epidemic era based on artificial intelligence and deep learning. The Journal of Supercomputing, 80(6):8505-8528. DOI: https://doi.org/10.1007/s11227-023-05776-1
*Divekar*, R. R., Drozdal*, J., Chabot*, S., Zhou, Y., Su, H., Chen, Y., ... & Braasch, J. (2022). Foreign language acquisition via artificial intelligence and extended reality: design and evaluation. Computer Assisted Language Learning, 35(9):2332-2360. DOI: https://doi.org/10.1080/09588221.2021.1879162
*Dolenc, K., & Aberšek, B. (2015). TECH8 intelligent and adaptive e-learning system: Integration into Technology and Science classrooms in lower secondary schools. Computers & Education, 82:354-365. DOI: https://doi.org/10.1016/j.compedu.2014.12.010
Glass, G. V. (1976). Primary, secondary, and meta-analysis of research. Educational researcher, 5(10):3-8. DOI: https://doi.org/10.3102/0013189X005010003
*Goda, Y., Yamada, M., Matsukawa, H., Hata, K., & Yasunami, S. (2014). Conversation with a chatbot before an online EFL group discussion and the effects on critical thinking. The Journal of Information and Systems in Education, 13(1):1-7. DOI: https://doi.org/10.12937/ejsise.13.1
Gutierrez, F., & Atkinson, J. (2011). Adaptive feedback selection for intelligent tutoring systems. Expert Systems with Applications, 38(5):6146-6152. DOI: https://doi.org/10.1016/j.eswa.2010.11.058
Harry, A., & Sayudin, S. (2023). Role of AI in Education. Interdisciplinary Journal and Humanity (INJURITY), 2(3), 260-268. DOI: https://doi.org/10.58631/injurity.v2i3.52
Higgins, J. P., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses. Bmj, 327(7414), 557-560. DOI: https://doi.org/10.1136/bmj.327.7414.557
*Hooshyar, D., Ahmad, R. B., Yousefi, M., Fathi, M., Abdollahi, A., Horng, S. J., & Lim, H. (2016). A solution-based intelligent tutoring system integrated with an online game-based formative assessment: development and evaluation. Educational Technology Research and Development, 64:787-808. DOI: https://doi.org/10.1007/s11423-016-9433-x
Hu, D. & Wang, Y. (2022). The effect of artificial intelligence technology on student learning outcomes: A meta-analysis based on 40 experimental and quasi-experimental studies from 2010 to 2022. Journal of Tianjin University (Social Sciences Edition), 2022(06):493-502. Available at: https://kns.cnki.net/kcms2/article/abstract?v=v-1aSqfKcSXoNjRT63VN87RRpFiQJDmU7JdKCdgUypaQgGYtPh8XrxGugVym3Z4P4P1dpg0iOHJ-i5fCf9Vpb4bZN7ihrpUiClHwgOscoTjLEWgvHenF4p1ZD5Xn4DuGMmMhdzt3UFBrpROHUMEE0baqZh77XNlL3yZq0WUzXPNz24rQZMXZt2W-6shksxe2iMQDY5VEENI=&uniplatform=NZKPT&language=CHS
Huang, J., Saleh, S., & Liu, Y. (2021). A review on artificial intelligence in education. Academic Journal of Interdisciplinary Studies, 10(3):206-217. DOI: https://doi.org/10.36941/ajis-2021-0077
*Hwang, G. J., Sung, H. Y., Chang, S. C., & Huang, X. C. (2020). A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors. Computers and Education: Artificial Intelligence, 1, 100003. DOI: https://doi.org/10.1016/j.caeai.2020.100003
*Julià, C., & Antolí, J. Ò. (2016). Spatial ability learning through educational robotics. International Journal of Technology and Design Education, 26:185-203. DOI: https://doi.org/10.1007/s10798-015-9307-2
*Kong, W., Han, S., & Zhang, Z. (2020). Paths to artificial intelligence-assisted adaptive learning. Modern Distance Education Research, 2020(03):94-103. Available at: https://kns.cnki.net/kcms2/article/abstract?v=v-1aSqfKcSXF2_j81oMWEonrVXkJVygtcKM1-z5NddZBidg5AiqZ2mOfNTEpmtp5vLflAhB6vgf452llRqfp4B8mREpA80e-dAx_L0FV560McOvWm4f_23vZG1ndc4CzJwahTZrLdMdnXGZAsNfTUrSKxVPw-O0CPapaH6SZu2_uHVaxw1-XfP21s8QoCMbeRwxOtRtevg0=&uniplatform=NZKPT&language=CHS
*Lang, Q., Zhang, C., Qi, H., Du, Y., Zhu, X., Zhang, C., & Li, M. (2023). Mining and utilizing knowledge correlation and learners’ similarity can greatly improve learning efficiency and effect: A case study on Chinese writing stroke correction. Sustainability, 15(3), 2393. DOI: https://doi.org/10.3390/su15032393
Lee, J., & Park, O. (2008). Adaptive instructional systems. In J. M. Spector, M. D. Merrill, J. V. Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed., pp. 469–484). Lawrence Erlbaum Associates. Available at: https://www.taylorfrancis.com/chapters/edit/10.4324/9781410609519-35/adaptive-instructional-systems-ok-choon-park-jung-lee
*Li, S., & Zheng, J. (2017). The effect of academic motivation on students’ English learning achievement in the eSchoolbag-based learning environment. Smart Learning Environments, 4:1-14. DOI: https://doi.org/10.1186/s40561-017-0042-x
*Lim, L., Bannert, M., van der Graaf, J., Fan, Y., Rakovic, M., Singh, S., ... & Gašević, D. (2024). How do students learn with real‐time personalized scaffolds? British Journal of Educational Technology, 55(4):1309-1327. DOI: https://doi.org/10.1111/bjet.13414
*Luo, Y. (2023). An Empirical Study of the Transformation and Innovation of Classroom Teaching with the Assistance of Intelligent Platforms (master’s thesis). Gannan Normal University. DOI: https://doi.org/10.27685/d.cnki.ggnsf.2023.000573
*Ma, Y. (2023). Design and Practice of Personalized Teaching in Primary Mathematics education Using the “Optimized Learning” Smart Education Platform (master’s thesis). Ningxia University. DOI: https://doi.org/10.27257/d.cnki.gnxhc.2023.000158
Ma, Z. & Liu, Y. (2019). From project-based learning and paired programming to interdisciplinary integrated design: A meta-analysis based on studies of K-12 computational thinking published from 2006 to 2019. Journal of Distance Education, 2019(05):75-84. DOI: https://doi.org/10.15881/j.cnki.cn33-1304/g4.2019.05.009
Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022). AI-based personalized e-learning systems: Issues, challenges, and solutions. IEEE access, 10: 81323-81342. DOI: https://doi.org/10.1109/ACCESS.2022.3193938
*Niknam, M. & Thulasiraman, P. (2020). LPR: A bio-inspired intelligent learning path recommendation system based on meaningful learning theory. Education and Information Technologies, 25(5):3797-3819. DOI: https://doi.org/10.1007/s10639-020-10133-3
*Nugent, G., Barker, B., Grandgenett, N., & Adamchuk, V. I. (2010). Impact of robotics and geospatial technology interventions on youth STEM learning and attitudes. Journal of Research on Technology in Education, 42(4):391-408. DOI: https://doi.org/10.1080/15391523.2010.10782557
Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. Unesdoc.unesco.org. DOI: https://unesdoc.unesco.org/ark:/48223/pf0000366994
*Qianjing, M., & Lin, T. (2021). An artificial intelligence-based construction and application of English multimodal online reading mode. Journal of Intelligent & Fuzzy Systems, 40(2):3721-3730. DOI: https://doi.org/10.3233/JIFS-189406
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*Serrano, M. Á., Vidal‐Abarca, E., & Ferrer, A. (2018). Teaching self‐regulation strategies via an intelligent tutoring system (TuinLECweb): Effects for low‐skilled comprehenders. Journal of Computer Assisted Learning, 34(5):515-525. DOI: https://doi.org/10.1111/jcal.12256
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How to Cite
Hu, S. (2024). The Effect of Artificial Intelligence-Assisted Personalized Learning on Student Learning Outcomes: A Meta-Analysis Based on 31 Empirical Research Papers. Science Insights Education Frontiers, 24(1), 3873–3894. https://doi.org/10.15354/sief.24.re395
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