The Transformation of Personalized Teaching Materials Usage in Higher Education: A Critical Literature Review

Authors

  • Nurmida Catherine Sitompul Universitas PGRI Adi Buana https://orcid.org/0000-0001-9838-7036
  • Suhari Adi Buana PGRI University
  • Retno Danu Rusmawati Adi Buana PGRI University
  • Sri Rahmawati Fitriatien Adi Buana PGRI University
  • I Nyoman Sudana Degeng State University of Malang
  • Putri Nur Aziza Sonia Adi Buana PGRI University
  • Erika Dea Pangestika Adi Buana PGRI University
  • Laela Fauziah Adi Buana PGRI University

DOI:

https://doi.org/10.33394/jtp.v11i2.19267

Keywords:

Adaptive learning; higher education; learner needs; personalized teaching materials; research’s gap

Abstract

Personalized teaching materials are an innovation in answering the diversity of student characteristics in higher education. However, its implementation still faces challenges. This study aims to analyse the trends and identify research gaps of personalized teaching materials usages. The method used is a critical literature review of articles from internationally reputable databases. The results of the study show two main findings: (1) there is a transformation in the use of personalized teaching materials in three phases such as: technical and standardization phase; the dynamic pedagogical and adaptive integration phase, and the artificial intelligence and holistic welfare phase; (2) There are four main research gaps such as: inconsistencies in measurement methodology, obstacles to contextual implementation, conceptual ambiguity of personal needs, and limited lecturer readiness. The most valuable finding from  review that the use of PTM where the implementation no longer lies in the "what" is learned but in the "how" the system is intelligently able to empathize with the cognitive and emotional state of learners to create a truly meaningful instructional experience. These are  theoretical contribution in clarifying the evolution of the concept of PTM as well as practical contributions to the development of adaptive learning designs in higher education. 

Author Biography

Nurmida Catherine Sitompul, Universitas PGRI Adi Buana

Magister Teknologi Pendidikan

References

Abu-rasheed, H., Weber, C., & Fathi, M. (20223). Context based learning : a survey of contextual indicators for personalized and adaptive learning recommendations – a pedagogical and technical perspective. Frontiers in Education Educ., July. https://doi.org/10.3389/feduc.2023.1210968

Alamri, H., Lowell, V., Watson, W., & Watson, S. L. (2020). Using personalized learning as an instructional approach to motivate learners in online higher education : Learner self-determination and intrinsic motivation. Journal of Research on Technology in Education, 52(3), 322–352. https://doi.org/10.1080/15391523.2020.1728449

Area‑Moreira, M., Rodríguez‑Rodríguez, Peirats‑Chacón, J., & Santana‑Bonilla, P. (2023). The Digital Transformation of Instructional Materials . Views and Practices of Teachers , Families and Editors. Technology, Knowledge and Learning, 28(4), 1661–1685. https://doi.org/10.1007/s10758-023-09664-8

Benhamdi, S., Babouri, A., & Chiky, R. (2016). Personalized recommender system for e-Learning environment. Education and Information Technologies, 22, 1455–1477. https://doi.org/10.1007/s10639-016-9504-y

Bishop, P. A., Doownes, J. M., Netcoh, S., Farber, K., DeMink-Carthew, J., Brown, T., & Mark, R. (2020). Teacher Roles in Personalized Learning Enviroments. The Elementary School Journal, 121(2), 311–336. https://doi.org/10.1086/711079

Cavanagh, T., Chen, B., Lahcen, R. A. M., & Paradiso, J. R. (2020). Constructing a Design Framework and Pedagogical Approach for Adaptive Learning in Higher Education: A Practitioner ’s Perspective. International Review of Research in Open and Distributed Learning, 21(1). https://doi.org/10.19173/irrodl.v21i1.4557

Chen, C. (2008). Intelligent web-based learning system with personalized learning path guidance. Computer & Education, 51(2), 787–814. https://doi.org/10.1016/j.compedu.2007.08.004

du Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10(21), e39630. https://doi.org/10.1016/j.heliyon.2024.e39630

Duan, C., Yang, J., Cui, Q., Zhang, W., Wan, X., & Zhang, M. (2025). Enhancing the Recommendation of Learning Resources for Learners via an Advanced Knowledge Graph. Applied Science, 15(8), 4204. https://doi.org/10.3390/app15084204

Elshani, L. (2021). Personalized Learning Path Generation Based on Genetic Algorithms. https://doi.org/10.33107/ubt-etd.2021.2649

Fariani, R. I., Junus, K., & Santoso, H. B. (2022). A Systematic Literature Review on Personalised Learning in the Higher Education Context. Technology, Knowledge and Learning, 28(0123456789), 449–476. https://doi.org/10.1007/s10758-022-09628-4

Fitzgerald, E., Kucirkova, N., Jones, A., Cross, S., Ferguson, R., Herodotou, C., Hillaire, G., Fitzgerald, E., Jones, A., Cross, S., Ferguson, R., Herodotou, C., & Hillaire, G. (2017). Dimensions of personalisation in technology-enhanced learning : A framework and implications for design. British Journal of Educational Technology, 49(1), 165–181. https://doi.org/10.1111/bjet.12534

Garrett, L., Guest, K., Shannon, D., Lee, B., Huang, L., Charleton, M. C., Qazi, M., Kim, Y., Garrett, L., Guest, K., Shannon, D., Lee, B., Huang, L., Charleton, M. C., Qazi, M., & Kim, Y. (2020). Personalizing college mathematics with technology. Journal of Research on Technology in Education, 52(3), 403–428. https://doi.org/10.1080/15391523.2020.1734507

Grant, M. J., Booth, A., & Centre, S. (2009). A typology of reviews : an analysis of 14 review types and. Health Information & Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x

Isaeva, R., Karasartova, N., Mokliuk, M., Dznunusnalieva, K., Mirzoeva, K., & Info, A. (2025). Enhancing Learning Effectiveness Through Adaptive Learning Platforms and Emerging Computer Technologies in Education. Jurnal Ilmiah Ilmu Terapan Universitas Jambi, 9(1), 144–160. https://doi.org/10.22437/jiituj.v9i1.37967

Jantakun, T., Jantakun, K., & Jantakoon, T. (2025). Scientometric Analysis of Personalized Learning Research. Journal of Education and Learning, 14(3), 137–154. https://doi.org/10.5539/jel.v14n3p137

Jesson, J., Lacey, F. M., & Matheson, L. (2011). Doing Your Literature Review. Traditional and Systematic Techniques. SAGE publications Ltd.

Jitpaisarnwattana, N. (2025). The effects of a personalized learning plan in a language MOOC on learners’ oral presentation skills. The JALT CALL Journal, 21(2), 1–23. https://doi.org/10.29140/jaltcall.v21n2.102601

Joshi, M., & Vaidya, R. (2013). A review of paradigm shift from Conventional to Personalized e-learning. International Conference on Advances in Computing, Communications and Informatics (ICACCI), 1263–1269. https://doi.org/10.1109/ICACCI.2013.6637359

Kiran, K., Saat, R. M., Demeester, L., Lew, M. D. N., Neo, W. L., Pausawasdi, N., & Nopparatjamjomras, T. R. (2025). Technology-enhanced personalized learning : Lessons from online teaching at three South-East Asian universities. 17(2). https://doi.org/10.30935/cedtech/15946

Kulviwat, S., Guo, C., & Engchanil, N. (2004). Determinants of online information search : a critical review and assessment. Internet Research, 14(3), 245–253. https://doi.org/10.1108/10662240410542670

Lagos-castillo, A. (2025). Mapping the intelligent classroom: Examining the emergence of personalized learning solutions in the digital age. Contemporary Educational Technology, 17(1), 1–18. https://doi.org/10.30935/cedtech/15617

Laksitowening, K. A., Santoso, A. A., & Hasibuan, Z. A. (2017). E-Learning Personalization Using Triple-Factor Approach in Standard-Based Education. Journal of Physics: Conference Series PAPER. https://doi.org/10.1088/1742-6596/755/1/011001

Leight, E. J., Lee, M., Hare, M. K., Lee, M., & Hare, M. K. (2013). A Web-based Learning Portfolio System. Journal of Physical Education, Recreation, October 2014, 37–41. https://doi.org/10.1080/07303084.2007.10598097

Li, J. (2025). Evaluation of Entrepreneurship Education Course Effect and Personalized Learning Path Using CART Decision Tree. International Journal of Knowledge Management, 21(1), 1–23. https://doi.org/10.4018/IJKM.385732

Liu, B., & Yuan, D. (2024). Research on Personalized Teaching Strategies Based on Learner Profiles in a Blended Learning Environment. International Journal of Information and Communication Technology Education (IJICTE), 20(1), 1–25. https://doi.org/10.4018/IJICTE.346823

Mchugh, D., Shaw, S., Moore, T. R., Ye, L. Z., Romero-, P., Halverson, R., Mchugh, D., Shaw, S., Moore, T. R., Ye, L. Z., Romero-, P., Mchugh, D., & Shaw, S. (2020). Uncovering themes in personalized learning: Using natural language processing to analyze school interviews. Journal of Research on Technology in Education, 52(3), 391–402. https://doi.org/10.1080/15391523.2020.1752337

Nandigam, D., Sreenivas Sremath, T., & Baghaei, N. (2014). Personalized Learning: Current Status and Potential. 2014 IEEE Conference on E-Learning, e-Management and e-Services (IC3e), 111–116. https://doi.org/10.1109/IC3e.2014.7081251

Nuci, K. P. (2025). Cloud eLearning - A New Paradigm of eLearning System Using Artificial Intelligence Techniques for Generating Personalized Learning Paths. Educational Process: International Journal (EDUPIJ), 16(3). https://www.ceeol.com/search/article-detail?id=1385089

O’Keeffe, I., Brady, A., Conlan, O., & Wade, V. (2006). Just-in-Time Generation of Pedagogically Sound, Context Sensitive Personalized Learning Experiences. In E. Baumgartner (Ed.), International Jl. on E-Learning (Vol. 5, Issue 1, pp. 113–127). AACE. https://www.scss.tcd.ie/owen.conlan/publications/iClass_Prolearn_2005_final_v1.4.pdf

Panwale, S. B., & Vijayakumar, S. (2025). Evaluating AI-Personalized Learning Interventions in Distance Education. International Review of Research in Open and Distributed Learning, 26(1), 157–174. https://doi.org/10.19173/irrodl.v26i1.7813

Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6(9). https://doi.org/10.1186/s40561-019-0089-y (2019)

Penuel, W. R., & Johnson, R. (2016). C p : p e p l. In Review of Continued Progress: Promising evidenCe on learning.

Plass, J. L., & Pawar, S. (2020). Toward a taxonomy of adaptivity for learning Toward a taxonomy of adaptivity for learning. Journal of Research on Technology in Education, 52(3), 275–300. https://doi.org/10.1080/15391523.2020.1719943

Salim, A., Manubey, J., & Kuswandi, D. (2024). Neoliberalisme dan Komersialisasi Pendidikan di Indonesia: Sebuah Refleksi By: Agus Salim 1 , Johana Manubey 2 , Dedi Kuswandi 3. Jurnal Pendidikan, 24(2), 97–115. https://doi.org/10.52850/jpn.v24i2.12484

Scanion, E., O’Shea, T., & McAndrew, P. (2015). Technology-Enhanced Learning: Evidence-based Improvement. Proceedings of the Second (2015) ACM Conference on Learning @ Scale, ACM, 229–232. https://doi.org/10.1145/2724660.2728664

Semerikov, S. O., Nechypurenko, P. P., Vakaliuk, T. A., & Mintii, I. S. (2025). Differential effects of Moodle course design on student subpopulations: advancing personalized learning in higher education. Smart Learning Environments, 12(46). https://doi.org/10.1186/s40561-025-00400-6

Shemshack, A., & Spector, J. M. (2020). A systematic literature review of personalized learning terms. Smart Learning Environments, 7(33). https://doi.org/https://doi.org/10.1186/s40561-020-00140-9

Sigalla, L. E., & Kimario, H. F. (2025). Customizing Classrooms : How Teachers Can Adapt Education to Fit Student Needs. The European Journal of Contemporary Education and E-Learning, 3(2025), 38–59. https://doi.org/10.59324/ejceel.2025.3(3).04

Simon, P. D., & Zeng, L. M. (2024). Behind the Scenes of Adaptive Learning: A Scoping Review of Teachers’ Perspectives on the Use of Adaptive Learning Technologies. Education Sciences, 14(12), 1413. https://doi.org/10.3390/educsci14121413

Tang, X., Chen, Y., Li, X., Liu, J., & Ying, Z. (2018). A reinforcement learning approach to personalized learning recommendation systems. British Journal of Mathematical and Statistical Psychology, 72(1), 108–135. https://doi.org/10.1111/bmsp.12144

Teo, C. B., & Gay, R. K. L. (2007). A Knowledge-Driven Model to Personalize E-Learning. Journal on Educational Resources in Computing, 6(1), 1–15. https://doi.org/10.1145/1217862.1217865

Tetzlaff, L., Schmiedek, F., & Brod, G. (2021). Developing Personalized Education : A Dynamic Framework. Educational Psychology Review, 33, 863–882. https://doi.org/https://doi.org/10.1007/s10648-020-09570-w

Tong, C., & Ren, C. (2025). Deep knowledge tracing and cognitive load estimation for personalized learning path generation using neural network architecture. Scientific Reports, 15, 1–15. https://doi.org/10.1038/s41598-025-10497-x 1

Troussas, C., Krouska, A., & Sgouropoulou, C. (2021). Learning Material Delivery Using Instructional. Entropy, 23(6), 668. https://doi.org/10.3390/e23060668

Tudor, I., Dlab, M. H., & Hoić-Božić, N. (2025). Personalized Learning in Secondary and Higher Education: A Systematic Literature Review of Technology-Enhanced Approaches. International Journal of Educational Methodology, 11(3), 359–375. https://doi.org/10.12973/ijem.11.3.359

Tuo, P., Bicakci, M., Ziegler, A., & Zhang, B. (2025). Measuring Personalized Learning in the Smart Classroom Learning Environment: Development and Validation of an Instrument. Educatioan Sciences, 15(5), 620. https://doi.org/10.3390/educsci15050620

Waldeck, J. H. (2007). Answering the Question : Student Perceptions of Personalized Education and the Construct ’ s Relationship to Learning Outcomes Answering the Question : Student Perceptions of Personalized Education and the Construct ’ s Relationship to Learning Outcomes. Communication Education, 56(4), 409–432. https://doi.org/10.1080/03634520701400090

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

Wang, Z., Wang, X., & Wang, X. (2008). Research and Implementation of Web-Based E-Learning Course Auto-generating Platform. In Z. Pan, X. Zhang, A. El Rhalibi, W. Woo, & Y. Li (Eds.), Technologies for E-Learning and Digital Entertainment. Edutainment 2008. Lecture Notes in Computer Science, vol 5093. (pp. 70–76). IEEE, Publisher. https://doi.org/10.1007/978-3-540-69736-7_8

Wen, L., Brayshaw, M., & Gordon, N. (2012). Personalized Content Provision for Virtual Learning Environments via the Semantic Web. Innovation in Teaching and Learning in Information and Computer Sciences ISSN:, 11(1), 14–26. https://doi.org/10.11120/ital.2012.11010014

Xiaoyan, W., & Ping, D. (2011). A study of Personalized Learning System Based on XML and WEB Mining. 2011 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, 1200–1203. https://doi.org/10.1109/CSQRWC.2011.6037176

Xie, H., Chu, H.-C., Hwang, G.-J., & PII:, C.-C. W. (2019). Abstract. Computers & Education, 140, 103599. https://doi.org/10.1016/j.compedu.2019.103599

Yang, Q., & Liang, C. (2025). A Second-Classroom Personalized Learning Path Recommendation System Based on Large Language Model Technology. Applied Science, 15(14), 7655. https://doi.org/10.3390/app15147655

Yang, Y. C., Gamble, J. H., Hung, Y., & Lin, T. (2014). An onlie adaptive learning enviroment for critical-thinking-infused English literacy instruction. British Journal of Educational Technology, 45(4), 723–747. https://doi.org/10.1111/bjet.12080

Yi, B., Zhang, D., Wang, Y., Liu, H., Zhang, Z., Shu, J., Lv, Y., & Elements, A. P. L. (2017). Research on Personalized Learning Model under Informatization Environment. 17 International Symposium on Educational Technology Research. https://doi.org/10.1109/ISET.2017.19

Yu, C. (2025). A Needs-Based Model for the Personalized Design of Academic English Teaching Materials in Engineering Education. English Language Teaching, 18(2), 42–52. https://doi.org/10.5539/elt.v18n2p42

Yu, W., Hui, W., Wei, H., & Yongge, J. (2010). Implementing a Web-based Personalized Learning and Assessment System. The 5th International Conference Pn Computer Science & Education, 27–30. https://doi.org/10.1109/ICCSE.2010.5593590

Zhang, L., Basham, J. D., & Yang, S. (2020). Understanding the implementation of personalized learning: A research synthesis. Educational Research Review, 31, 100339. https://doi.org/10.1016/j.edurev.2020.100339

Zhong, L. (2022). A systematic review of personalized learning in higher education : learning content structure , learning materials sequence , and learning readiness support learning readiness support. Interactive Learning Environments, 31(4), 1–21. https://doi.org/10.1080/10494820.2022.2061006

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2026-04-21

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Sitompul, N. C., Suhari, Rusmawati, R. D., Fitriatien, S. R., Degeng, I. N. S., Sonia, P. N. A., … Fauziah, L. (2026). The Transformation of Personalized Teaching Materials Usage in Higher Education: A Critical Literature Review. Jurnal Teknologi Pendidikan : Jurnal Penelitian Dan Pengembangan Pembelajaran, 11(2). https://doi.org/10.33394/jtp.v11i2.19267

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Vol. 11 No. 2 (2026): April

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Copyright (c) 2026 Nurmida Catherine Sitompul, Suhari, Retno Danu Rusmawati, Sri Rusmawati, I Nyoman Sudana Degeng, Putri Nur Aziza Sonia, Erika Dea Pangestika, Laela Fauziah

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