The Digital Educator's Toolkit: Leveraging AI for Effective Learning Analytics
Submitted: 2025-01-05
|Accepted: 2025-06-07
|Published: 2025-10-20
Copyright (c) 2025 Alvaro Ruiz Rodriguez, Andrés Chiappe, Eric Ortega González

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Keywords:
Learning analytics, artificial intelligence, 21st-century education, educational innovation
Supporting agencies:
Abstract:
Amid the fourth industrial revolution, education faces complex challenges, particularly related to harnessing the vast amount of data and digital information generated by students as part of their interactions in digital environments. In the context of 21st-century education, the importance of both Learning Analytics and Artificial Intelligence is emphasized as key technologies to address these challenges and foster educational innovation. To identify the role of AI in enhancing the strengths and addressing the limitations of learning analytics, a literature review of 173 research articles published in peer-reviewed journals indexed in Scopus was conducted. The results highlight strengths in terms of personalization, prediction, and fostering peer-based working, as well as limitations in acquiring relevant qualitative data and engaging the entire educational community in these processes. A critical reflection on the relationship between AI, learning analytics, and 21st-century education is presented in the conclusion.
References:
Abdaljaleel, M., Barakat, M., Alsanafi, M., Salim, N.A., Abazid, H., Malaeb, D., Mohammed, A.H., Hassan, B.A.R., Wayyes, A.M., Farhan, S.S., Khatib, S.E., Rahal, M., Sahban, A., Abdelaziz, D.H., Mansour, N.O., AlZayer, R., Khalil, R., Fekih-Romdhane, F., Hallit, R., ... Sallam, M. (2024). A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT. Scientific Reports, 14(1), 1983. https://doi.org/10.1038/s41598-024-52549-8
Ahmad, S.F., Alam, M.M., Rahmat, M.K., Mubarik, M.S., & Hyder, S.I. (2022). Academic and administrative role of artificial intelligence in education. Sustainability, 14(3), 1101. https://doi.org/10.3390/su14031101
Airaj, M. (2024). Ethical artificial intelligence for teaching-learning in higher education. Educ. Inf. Technol., 29, 17145–17167. https://doi.org/10.1007/s10639-024-12545-x
Alenezi, A. (2024). The effect of emotional intelligence on higher education: A pilot study on the interplay between artificial intelligence, emotional intelligence, and e-learning. Multidisciplinary Journal for Education, Social and Technological Sciences, 11(2), 51–77. https://doi.org/10.4995/muse.2024.21367
Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6. https://doi.org/10.1016/j.caeai.2024.100215
Ayuso Del Puerto, D., & Gutiérrez Esteban, P. (2022). La inteligencia artificial como recurso educativo durante la formación inicial del profesorado. RIED. Revista Iberoamericana de Educación a Distancia, 25(2). https://doi.org/10.5944/ried.25.2.32332
Banihashem, S.K., Farrokhnia, M., Badali, M., & Noroozi, O. (2022). The impacts of constructivist learning design and learning analytics on students’ engagement and self-regulation. Innovations in Education and Teaching International, 59(4), 442–452. https://doi.org/10.1080/14703297.2021.1890634
Bankins, S., & Formosa, P. (2023). The ethical implications of artificial intelligence (AI) for meaningful work. Journal of Business Ethics, 185(4), 725–740. https://doi.org/10.1007/s10551-023-05339-7
Barn, B., Barat, S., & Clark, T. (2017). Conducting systematic literature reviews and systematic mapping studies. Proceedings of the 10th Innovations in Software Engineering Conference, 212–213. https://doi.org/10.1145/3021460.3021489
Baydullaev, A., Tayrov, K., Narzullaev, D., Shadmanov, K., & Yomgirov, O. (2023). The effectiveness of digital technologies use in higher education: A modern approach to training. In A. Gibadullin & G. Khalmatjanova (Eds.), International Conference on Digital Transformation: Informatics, Economics, and Education (DTIEE2023) (p. 52). SPIE. https://doi.org/10.1117/12.2681862
Carrera-Rivera, A., Ochoa, W., Larrinaga, F., & Lasa, G. (2022). How-to conduct a systematic literature review: A quick guide for computer science research. MethodsX, 9, 101895. https://doi.org/10.1016/j.mex.2022.101895
Cerratto Pargman, T., & McGrath, C. (2021). Mapping the ethics of learning analytics in higher education: A systematic literature review of empirical research. Journal of Learning Analytics, 8(2), 123–139. https://doi.org/10.18608/jla.2021.1
Chatti, M.A., & Muslim, A. (2019). The PERLA framework: Blending personalization and learning analytics. The International Review of Research in Open and Distributed Learning, 20(1). https://doi.org/10.19173/irrodl.v20i1.3936
Chen, X., Xie, H., Zou, D., & Hwang, G.J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002
De Andrade, T.L., Rigo, S.J., & Barbosa, J.L.V. (2021). Active methodology, educational data mining and learning analytics: A systematic mapping study. Informatics in Education. https://doi.org/10.15388/infedu.2021.09
De Barba, P.G., Malekian, D., Oliveira, E.A., Bailey, J., Ryan, T., & Kennedy, G. (2020). The importance and meaning of session behaviour in a MOOC. Computers & Education, 146, 103772. https://doi.org/10.1016/j.compedu.2019.103772
Demartini, C.G., Sciascia, L., Bosso, A., & Manuri, F. (2024). Artificial Intelligence Bringing Improvements to Adaptive Learning in Education: A Case Study. Sustainability, 16(3), 1347. https://doi.org/10.3390/su16031347
Duin, A.H., & Tham, J. (2020). The current state of analytics: Implications for learning management system (LMS) use in writing pedagogy. Computers and Composition, 55, 102544. https://doi.org/10.1016/j.compcom.2020.102544
Fincham, E., Gašević, D., Jovanović, J., & Pardo, A. (2019). From study tactics to learning strategies: An analytical method for extracting interpretable representations. IEEE Transactions on Learning Technologies, 12(1), 59–72. https://doi.org/10.1109/TLT.2018.2823317
Flores, A., Alfaro, L., Herrera, J., & Hinojosa, E. (2019). Proposal models for personalization of e-learning based on flow theory and artificial intelligence. International Journal of Advanced Computer Science and Applications, 10(7). https://doi.org/10.14569/IJACSA.2019.0100752
Gan, B., & Zhang, C. (2020). Research on design of personalized learning experience based on intelligent internet technology. 2020 International Conference on E-Commerce and Internet Technology (ECIT), 306–309. https://doi.org/10.1109/ECIT50008.2020.00077
Geçer, E., & Bağci, H. (2022). Examining students’ attitudes towards online education during COVID-19: Evidence from Turkey (Análisis de las actitudes de los estudiantes hacia la educación en línea durante la pandemia de COVID-19. Evidencia de un estudio realizado en Turquía). Culture and Education, 34(2), 297–324. https://doi.org/10.1080/11356405.2022.2031785
Heath, D., West, D., & Huijser, H. (2019). Let’s talk learning analytics and student retention. 631–632. https://eprints.qut.edu.au/199538/1/58569924.pdf
Ifenthaler, D., & Schumacher, C. (2016). Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development, 64(5), 923–938. https://doi.org/10.1007/s11423-016-9477-y
Jin, S.-H., Im, K., Yoo, M., Roll, I., & Seo, K. (2023). Supporting students’ self-regulated learning in online learning using artificial intelligence applications. International Journal of Educational Technology in Higher Education, 20(1), 37. https://doi.org/10.1186/s41239-023-00406-5
Kaliisa, R., Jivet, I., & Prinsloo, P. (2023). A checklist to guide the planning, designing, implementation, and evaluation of learning analytics dashboards. International Journal of Educational Technology in Higher Education, 20(1), 28. https://doi.org/10.1186/s41239-023-00394-6
Karaoglan Yilmaz, F.G., & Yilmaz, R. (2022). Learning analytics intervention improves students’ engagement in online learning. Technology, Knowledge and Learning, 27(2), 449–460. https://doi.org/10.1007/s10758-021-09547-w
Karunaratne, T. (2021). For learning analytics to be sustainable under GDPR—Consequences and way forward. Sustainability, 13(20), 11524. https://doi.org/10.3390/su132011524
Kaswan, K.S., Dhatterwal, J.S., & Ojha, R.P. (2024). AI in personalized learning. In Advances in Technological Innovations in Higher Education (pp. 103–117). CRC Press.
Kaufman, K. (2019). What skills do 21st century high school graduates need to have to be successful in college and life? In A. Sahin & M.J. Mohr-Schroeder (Eds.), STEM education 2.0 (pp. 337–349). BRILL. https://doi.org/10.1163/9789004405400_018
Kew, S.N., & Tasir, Z. (2022). Learning analytics in online learning environment: A systematic review on the focuses and the types of student-related analytics data. Technology, Knowledge and Learning, 27(2), 405–427. https://doi.org/10.1007/s10758-021-09541-2
Khotimah, K., & Mariono, A. (2024). Enhancing metacognitive and creativity skills through AI-driven meta-learning strategies. International Journal of Interactive Mobile Technologies, 18(5), 18–31. https://doi.org/10.3991/ijim.v18i05.47705
Knight, S., Gibson, A., & Shibani, A. (2020). Implementing learning analytics for learning impact: Taking tools to task. The Internet and Higher Education, 45, 100729. https://doi.org/10.1016/j.iheduc.2020.100729
Kollom, K., Tammets, K., Scheffel, M., Tsai, Y.S., Jivet, I., Muñoz-Merino, P.J., Moreno-Marcos, P.M., Whitelock-Wainwright, A., Calleja, A.R., Gašević, D., Kloos, C.D., Drachsler, H., & Ley, T. (2021). A four-country cross-case analysis of academic staff expectations about learning analytics in higher education. The Internet and Higher Education, 49, 100788. https://doi.org/10.1016/j.iheduc.2020.100788
Kong, S.C., Zhu, J., & Yang, Y.N. (2025). Developing and validating a scale of empowerment in using artificial intelligence for problem-solving for senior secondary and university students. Computers and Education: Artificial Intelligence, 8, 100359. https://doi.org/10.1016/j.caeai.2024.100359
Lagos-Castillo, A., Chiappe, A., Ramirez-Montoya, M.S., & Becerra Rodríguez, D.F. (2025). Mapping the intelligent classroom: Examining the emergence of personalized learning solutions in the digital age. Contemporary Educational Technology, 17(1), ep543. https://doi.org/10.30935/cedtech/15617
Li, S., & Lajoie, S.P. (2022). Cognitive engagement in self-regulated learning: An integrative model. European Journal of Psychology of Education, 37(3), 833–852. https://doi.org/10.1007/s10212-021-00565-x
Li, C., Herbert, N., Yeom, S., & Montgomery, J. (2022). Retention factors in STEM education identified using learning analytics: A systematic review. Education Sciences, 12(11), 781. https://doi.org/10.3390/educsci12110781
Liu, J., Loh, L., Ng, E., Chen, Y., Wood, K.L., & Lim, K.H. (2020). Self-evolving adaptive learning for personalized education. Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing, 317–321. https://doi.org/10.1145/3406865.3418326
Llopis-Albert, C., & Rubio, F. (2021). Application of learning analytics to improve higher education. Multidisciplinary Journal for Education, Social and Technological Sciences, 8(2), 1. https://doi.org/10.4995/muse.2021.16287
Lu, C., & Cutumisu, M. (2022). Online engagement and performance on formative assessments mediate the relationship between attendance and course performance. International Journal of Educational Technology in Higher Education, 19(1), 2. https://doi.org/10.1186/s41239-021-00307-5
Maier, U., & Klotz, C. (2022). Personalized feedback in digital learning environments: Classification framework and literature review. Computers and Education: Artificial Intelligence, 3, 100080. https://doi.org/10.1016/j.caeai.2022.100080
Matcha, W., Uzir, N.A., Gašević, D., & Pardo, A. (2020). A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies, 13(2), 226–245. https://doi.org/10.1109/TLT.2019.2916802
McKenzie, S.P., Osborne, M., Johnson, C., Nixon, G., Graydon, K., Tomlin, D., Van Dam, S., & Jongenelis, M.I. (2022). Expanding online education frontiers—Needs, opportunities and examples. In The future of online education (pp. 33–55). Nova Science Publishers. https://novapublishers.com/shop/the-future-of-online-education/
Molenaar, I. (2022). The concept of hybrid human-AI regulation: Exemplifying how to support young learners’ self-regulated learning. Computers and Education: Artificial Intelligence, 3, 100070. https://doi.org/10.1016/j.caeai.2022.100070
Nguyen, A., Gardner, L., & Sheridan, D. (2020). Data analytics in higher education: An integrated view. Journal of Information Systems Education, 31(1), 61.
Nguyen, A., Lämsä, J., Dwiarie, A., & Järvelä, S. (2024). Lifelong learner needs for human-centered self-regulated learning analytics. Information and Learning Sciences, 125(1/2), 68–108. https://doi.org/10.1108/ILS-07-2023-0091
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
Ozturk, O.T. (2022). Examination of 21st century skills and technological competences of students of fine arts faculty. International Journal of Education in Mathematics, Science and Technology, 11(1), 115–132. https://doi.org/10.46328/ijemst.2931
Pant, V., Bhasin, S., & Jain, S. (2017). Self-learning system for personalized e-learning. 2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT), 1–6. https://doi.org/10.1109/ICETCCT.2017.8280344
Pozdeeva, E., Shipunova, O., Popova, N., Evseev, V., Evseeva, L., Romanenko, I., & Mureyko, L. (2021). Assessment of online environment and digital footprint functions in higher education analytics. Education Sciences, 11(6), 256. https://doi.org/10.3390/educsci11060256
Pozdniakov, S., Martinez-Maldonado, R., Tsai, Y.-S., Echeverria, V., Srivastava, N., & Gašević, D. (2023). How do teachers use dashboards enhanced with data storytelling elements according to their data visualisation literacy skills? LAK23: 13th International Learning Analytics and Knowledge Conference, 89–99. https://doi.org/10.1145/3576050.3576063
Praharaj, S., Scheffel, M., Drachsler, H., & Specht, M. (2021). Literature review on co-located collaboration modeling using multimodal learning analytics—Can we go the whole nine yards? IEEE Transactions on Learning Technologies, 14(3), 367–385. https://doi.org/10.1109/TLT.2021.3097766
Ramaswami, G.S., Susnjak, T., & Mathrani, A. (2019). Capitalizing on learning analytics dashboard for maximizing student outcomes. 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 1–6. https://doi.org/10.1109/CSDE48274.2019.9162357
Rets, I., Herodotou, C., Bayer, V., Hlosta, M., & Rienties, B. (2021). Exploring critical factors of the perceived usefulness of a learning analytics dashboard for distance university students. International Journal of Educational Technology in Higher Education, 18(1), 46. https://doi.org/10.1186/s41239-021-00284-9
Rienties, B., Herodotou, C., Olney, T., Schencks, M., & Boroowa, A. (2018). Making sense of learning analytics dashboards: A technology acceptance perspective of 95 teachers. The International Review of Research in Open and Distributed Learning, 19(5). https://doi.org/10.19173/irrodl.v19i5.3493
Rienties, B., Køhler Simonsen, H., & Herodotou, C. (2020). Defining the boundaries between artificial intelligence in education, computer-supported collaborative learning, educational data mining, and learning analytics: A need for coherence. Frontiers in Education, 5, 128. https://doi.org/10.3389/feduc.2020.00128
Ruipérez-Valiente, J.A., Halawa, S., Slama, R., & Reich, J. (2020). Using multi-platform learning analytics to compare regional and global MOOC learning in the Arab world. Computers & Education, 146, 103776. https://doi.org/10.1016/j.compedu.2019.103776
Saqr, M., & López-Pernas, S. (2021). The longitudinal trajectories of online engagement over a full program. Computers & Education, 175, 104325. https://doi.org/10.1016/j.compedu.2021.104325
Sharma, K., & Giannakos, M. (2020). Multimodal data capabilities for learning: What can multimodal data tell us about learning? British Journal of Educational Technology, 51(5), 1450–1484. https://doi.org/10.1111/bjet.12993
Shazly, H.A., Ferraro, A., & Bennet, K. (2020). Ethical concerns: An overview of artificial intelligence system development and life cycle. 2020 IEEE International Symposium on Technology and Society (ISTAS), 33–42. https://doi.org/10.1109/ISTAS50296.2020.9462201
Spikol, D., Ruffaldi, E., Dabisias, G., & Cukurova, M. (2018). Supervised machine learning in multimodal learning analytics for estimating success in project-based learning. Journal of Computer Assisted Learning, 34(4), 366–377. https://doi.org/10.1111/jcal.12263
Stenalt, M.H., & Lassesen, B. (2022). Does student agency benefit student learning? A systematic review of higher education research. Assessment & Evaluation in Higher Education, 47(5), 653–669. https://doi.org/10.1080/02602938.2021.1967874
Susnjak, T., Ramaswami, G.S., & Mathrani, A. (2022). Learning analytics dashboard: A tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19(1), 12. https://doi.org/10.1186/s41239-021-00313-7
Tan, J.P.-L., Yang, S., Koh, E., & Jonathan, C. (2016). Fostering 21st century literacies through a collaborative critical reading and learning analytics environment: User-perceived benefits and problematics. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK ’16, 430–434. https://doi.org/10.1145/2883851.2883965
Tang, H. (2021). Person-centered analysis of self-regulated learner profiles in MOOCs: A cultural perspective. Educational Technology Research and Development, 69(2), 1247–1269. https://doi.org/10.1007/s11423-021-09939-w
Torner, M.E., Aparicio-Fernández, C., Vivancos, J.L., & Cañada-Soriano, M. (2023). Analysis of the optimization of resources with learning analytics techniques. Multidisciplinary Journal for Education, Social and Technological Sciences, 10(2), 46–58. https://doi.org/10.4995/muse.2023.18545
Tsai, Y., Poquet, O., Gašević, D., Dawson, S., & Pardo, A. (2019). Complexity leadership in learning analytics: Drivers, challenges and opportunities. British Journal of Educational Technology, 50(6), 2839–2854. https://doi.org/10.1111/bjet.12846
Tsai, Y.-S., Rates, D., Moreno-Marcos, P.M., Muñoz-Merino, P.J., Jivet, I., Scheffel, M., Drachsler, H., Delgado Kloos, C., & Gašević, D. (2020). Learning analytics in European higher education—Trends and barriers. Computers & Education, 155, 103933. https://doi.org/10.1016/j.compedu.2020.103933
Tzimas, D., & Demetriadis, S. (2021). Ethical issues in learning analytics: A review of the field. Educational Technology Research and Development, 69(2), 1101–1133. https://doi.org/10.1007/s11423-021-09977-4
Tzimas, D., & Demetriadis, S. (2023). Culture of ethics in adopting learning analytics. In C. Frasson, P. Mylonas, & C. Troussas (Eds.), Augmented Intelligence and Intelligent Tutoring Systems (Vol. 13891, pp. 591–603). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-32883-1_52
Van Niekerk, J., Delport, P.M.J., & Sutherland, I. (2025). Addressing the use of generative AI in academic writing. Computers and Education: Artificial Intelligence, 8, 100342. https://doi.org/10.1016/j.caeai.2024.100342
Voogt, J.M., & Pareja Roblin, N.N. (2023). Curriculum and 21st century skills. In International Encyclopedia of Education (Fourth Edition, pp. 49–55). Elsevier. https://doi.org/10.1016/B978-0-12-818630-5.03007-4
Wei, P., & Li, L. (2019). Online education recommendation model based on user behavior data analysis. Journal of Intelligent & Fuzzy Systems, 37(4), 4725–4733. https://doi.org/10.3233/JIFS-179307-w
Williamson, B. (2019). Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education. British Journal of Educational Technology, 50(6), 2794–2809. https://doi.org/10.1111/bjet.12849
Worsley, M., Martinez-Maldonado, R., & D’Angelo, C. (2021). A new era in multimodal learning analytics: Twelve core commitments to ground and grow MMLA. Journal of Learning Analytics, 8(3), 10–27. https://doi.org/10.18608/jla.2021.7361
Yang, C.C.Y., & Ogata, H. (2023). Personalized learning analytics intervention approach for enhancing student learning achievement and behavioral engagement in blended learning. Education and Information Technologies, 28(3), 2509–2528. https://doi.org/10.1007/s10639-022-11291-2
Yessenova, K., Baltabayeva, Z., Amirbekova, A., Koblanova, A., Sametova, Z., & Ismailova, F. (2023). Investigating competencies and attitudes towards online education in language learning/teaching after COVID-19. International Journal of Education in Mathematics, Science and Technology, 11(4), 862–880. https://doi.org/10.46328/ijemst.3348
Yu, J., & Couldry, N. (2022). Education as a domain of natural data extraction: Analysing corporate discourse about educational tracking. Information, Communication & Society, 25(1), 127–144. https://doi.org/10.1080/1369118X.2020.1764604
Zheng, L., Zhong, L., & Niu, J. (2022). Effects of personalised feedback approach on knowledge building, emotions, co-regulated behavioural patterns and cognitive load in online collaborative learning. Assessment & Evaluation in Higher Education, 47(1), 109–125. https://doi.org/10.1080/02602938.2021.188354



