Abstract
The ongoing evolution of Artificial Intelligence technology is able to influence different fields of human experience, also including communication and professional activities. Artificial Intelligence has been playing a relevant role in education and literature has already addressed the design, impact, and challenges of such technological solutions designed for educational purposes. On the other hand, technology integration, and especially the integration of solutions related to Artificial Intelligence, has started to exert a crucial function in enabling and enhancing Personalized Learning experience. In this regard, the present paper reports the results of a scoping review conducted with the aim of describing the empirical research evaluating the impact of Artificial Intelligence tools and systems as integrated in Personalized Learning approaches designed for K-12 education. The results shows that personalized solutions supported by Artificial Intelligence technology mainly consisted in Intelligent Tutoring Systems designed for online learning platforms and addressed STEM (especially Mathematics and Physics topics) and language learning. Overall, all the studies selected for the purposes of the current reviews reported a positive impact of the Artificial Intelligence-based personalized systems on students’ learning outcomes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mc Carthy, J., Minsky, M.L., Rochester, N., Shannon, C.E.: A proposal for the dartmouth summer research project on artificial intelligence. AI Mag. 27(4), 12–14 (1995)
Xia, Q., Chiu, T.K., Zhou, X., Chai, C.S., Cheng, M.: Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Comp. Edu. Artif. Intell. 4, 100118 (2023)
Zhang, K., Aslan, A.B.: AI technologies for education: recent research & future directions. Comp. Edu. Artif. Intell. 2, 100025 (2021)
Chen, L., Chen, P., Lin, Z.: Artificial intelligence in education: a review. IEEE Access 8, 75264–75278 (2020)
Chen, X., Zou, D., Xie, H., Cheng, G., Liu, C.: Two decades of artificial intelligence in education: contributors, collaborations, research topics, challenges, and future directions. Educ. Technol. Soc. 25(1), 28–47 (2022)
Shemshack, A., Spector, J.M.: A systematic literature review of personalized learning terms. Smart Learning Environments 7(1), 33 (2020)
Walkington, C., Bernacki, M.L.: Appraising research on personalized learning: definitions, theoretical alignment, advancements, and future directions. J. Res. Technol. Educ. 52(3), 235–252 (2020)
Bernacki, M.L., Greene, M.J., Lobczowski, N.G.: A Systematic review of research on personalized learning: personalized by whom, to what, how, and for what purpose(s)? Educ. Psychol. Rev. 33(4), 1675–1715 (2021)
Zhang, L., Basham, J.D., Yang, S.: Understanding the implementation of personalized learning: A research synthesis. Educ. Res. Rev. 31, 100339 (2020)
Xie, H., Chu, H.C., Hwang, G.J., Wang, C.C.: Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Comput. Educ. 140, 103599 (2019)
Maghsudi, S., Lan, A., Xu, J., van der Schaar, M.: Personalized education in the artificial intelligence Era: what to expect next. IEEE Signal Processing Magazine 38(3), 37–50 (2021). https://doi.org/10.1109/MSP.2021.3055032
Bhutoria, A.: Personalized education and Artificial Intelligence in the United States, China, and India: A systematic review using a Human-In-The-Loop model. Comp. Edu. Artif. Intell. 3, 100068 (2022)
Hashim, S., Omar, M., Ab Jalil, H., Sharef, N.: Trends on technologies and artificial intelligence in education for personalized learning: systematic literature review. Int. J. Acad. Res. Progres. Edu. Develop. 11, 884–903 (2022)
Shemshack, A., Kinshuk, Spector, J.M.: A comprehensive analysis of personalized learning components. J. Comp. Edu. 8(4), 485–503 (2021)
Zheng, L., Long, M., Zhong, L., Gyasi, J.F.: The effectiveness of technology-facilitated personalized learning on learning achievements and learning perceptions: a meta-analysis. Educ. Inf. Technol. 27(8), 11807–11830 (2022)
Major, L., Francis, G.A., Tsapali, M.: The effectiveness of technology-supported personalised learning in low-and middle-income countries: A meta-analysis. Br. J. Edu. Technol. 52(5), 1935–1964 (2021)
Zotero software, retrieved from www.zotero.org
Chine, D.R., et al.: Educational Equity Through Combined Human-AI Personalization: A Propensity Matching Evaluation. In: Rodrigo, M.M., Matsuda, N., Cristea, V., Dimitrova, V.A. (eds.) Artificial Intelligence in Education. Lecture Notes in Computer Science, pp. 366–377. Springer International Publishing, Cham (2022)
Cui, W., Xue, Z., Thai, K.P.: Performance comparison of an AI-based adaptive learning system in China. In: 2018 Chinese Automation Congress (CAC), pp. 3170–3175. IEEE (2018)
Pardamean, B., Suparyanto, T., Cenggoro, T.W., Sudigyo, D., Anugrahana, A.: AI-based learning style prediction in online learning for primary education. IEEE Access 10, 35725–35735 (2022)
Rzepka, N., Simbeck, K., Müller, H.G., Pinkwart, N.: Go with the flow: personalized task sequencing improves online language learning. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds.) Artificial Intelligence in Education, Lecture Notes in Computer Science, pp. 90–101. Springer Nature, Cham, Switzerland (2023)
Alharbi, K., Alrajhi, L., Cristea, A.I., Bittencourt, I.I., Isotani, S., James, A.: Data-driven analysis of engagement in gamified learning environments: a methodology for real-time measurement of MOOCs. In: Kumar, V., Troussas, C. (eds.) In Intelligent Tutoring Systems. Lecture Notes in Computer Science, pp. 142–151. Springer International Publishing, Cham (2020)
Ingkavara, T., Panjaburee, P., Srisawasdi, N., Sajjapanroj, S.: The use of a personalized learning approach to implementing self-regulated online learning. Comp. Edu. Artif. Intell. 3, 100086 (2022)
Wongwatkit, C., Panjaburee, P.: A duplex adaptation mechanism in the personalized learning environment. J. Comp. Edu. (2023)
Ma, L., Li, J.: Influence of educational informatization based on machine learning on teaching mode. Int. Trans. Electr. Ener. Sys. e6180113 (2022)
Vainas, O., et al.: Staying in the zone: sequencing content in classrooms based on the zone of proximal development. In: Proceedings of the 12th International Conference on Educational Data Mining EDM, pp. 659–669 (2019)
Gentile, M., Città, G., Perna, S., Allegra, M.: Do we still need teachers? Navigating the paradigm shift of the teacher’s role in the AI era. Frontiers in Education 8 (2023)
Pitrella, V., Gentile, M., Città, G., Re, A., Tosto, C., Perna, S.: La percezione dell’utilizzo dell’intelligenza artificiale nello svolgimento dei compiti a casa in un campione di insegnanti italiani. Annali online della Didattica e della Formazione Docente 15(26), 300–318 (2023)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pitrella, V. et al. (2024). Artificial Intelligence for Personalized Learning in K-12 Education. A Scoping Review. In: Casalino, G., et al. Higher Education Learning Methodologies and Technologies Online. HELMeTO 2023. Communications in Computer and Information Science, vol 2076. Springer, Cham. https://doi.org/10.1007/978-3-031-67351-1_25
Download citation
DOI: https://doi.org/10.1007/978-3-031-67351-1_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-67350-4
Online ISBN: 978-3-031-67351-1
eBook Packages: Computer ScienceComputer Science (R0)