PathRAG application in adaptive learning with generative AI for inclusive and sustainable education
DOI:
https://doi.org/10.5944/ried.45378Keywords:
generative artificial intelligence, adaptive learning, educational inclusion, sustainability, hybrid education, personalized learningAbstract
This study presents the implementation of the PathRAG model within an adaptive, hybrid, and inclusive learning environment, supported by generative artificial intelligence. Aligned with the Sustainable Development Goals (SDGs), the proposal aims to personalize university-level learning through dynamic and equitable educational pathways. The objective is to address student diversity while reducing access and participation gaps through advanced educational technology. A quasi-experimental design was applied to a sample of 52 students enrolled in a Master’s program in Inclusive Education at a Spanish university. The intervention was developed in a hybrid format, combining the PathRAG algorithm with generative AI tools (GPT-3.5 turbo). Key indicators such as active participation, competence development, perceived inclusion and equity, and overall student satisfaction were assessed. Findings show significant improvements in active engagement, skill acquisition, and inclusive perception, especially among students with special educational needs or limited technological access. Overall satisfaction was high, particularly regarding the usefulness of personalized learning paths. The study concludes that PathRAG fosters more equitable and adaptive learning processes. Nevertheless, limitations such as the absence of a control group, short duration, and lack of validated instruments are acknowledged. Future research should involve controlled designs, broader samples, and longitudinal approaches. This work highlights the transformative potential of generative AI in promoting sustainable and inclusive educational models.
Downloads
References
Ainscow, M. (2020). Promoting inclusion and equity in education: Lessons from international experiences. UNESCO. https://doi.org/10.1080/20020317.2020.1729587
Anderson, T., Borup, J., & West, R. E. (2020). Learning analytics in higher education: A review of the literature. Journal of Research on Technology in Education, 52(4), 433-451. https://doi.org/10.1080/15391523.2020.1739846
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623). https://doi.org/10.1145/3442188.3445922
Birhane, A. (2023). Algorithmic injustice: A relational ethics approach. Patterns, 4(2), 100693. https://doi.org/10.1016/j.patter.2023.100693
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
Chen, B., Guo, Z., Yang, Z., Chen, Y., Chen, J., Liu, Z., Shi, C., & Yang, C. (2024). PathRAG: Pruning graph-based retrieval augmented generation with relational paths. arXiv. https://doi.org/10.48550/arXiv.2502.14902
Chen, X., Wang, L., & Li, Z. (2024). Generative AI in higher education: Ethical and pedagogical considerations for ChatGPT. AI & Society. https://doi.org/10.1007/s00146-024-01720-9
Cobo, C., Morales, A., & Navarro, C. (2022). Artificial intelligence in education: Equity and ethics in Latin America. International Journal of Educational Technology in Higher Education, 19, 32. https://doi.org/10.1186/s41239-022-00312-3
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Conati, C., Holstein, K., & Roll, I. (2021). The future of intelligent tutoring systems: A survey. IEEE Transactions on Learning Technologies, 14(4), 464-484. https://doi.org/10.1109/TLT.2021.3071740
Conati, C., & Kardan, S. (2013). User modeling in intelligent tutoring systems: A survey. In P. Brusilovsky, G. D. Verbert, & P. De Bra (Eds.), Adaptive and adaptable learning (pp. 45-63). Springer. https://doi.org/10.1007/978-3-642-40866-0_3
Floridi, L. (2019). The ethics of artificial intelligence. Oxford University Press.
García-Peñalvo, F. J. (2022). Tecnología y Educación: Hacia una visión transformadora. Ediciones Universidad de Salamanca.
Holmes, W., Porayska-Pomsta, K., Holstein, K., & Rodrigo, M. M. T. (2021). Ethics of AI in education: Towards a community-wide framework. European Journal of Education, 56(4), 541-558. https://doi.org/10.1111/ejed.12481
Ifenthaler, D., & Yau, J. Y.-K. (2020). Utilising learning analytics to support study success in higher education. Springer. https://doi.org/10.1007/978-3-319-64792-0
Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2021). Awareness is not enough: Pitfalls of learning analytics dashboards in the educational practice. British Journal of Educational Technology, 52(6), 1864-1880. https://doi.org/10.1111/bjet.13168
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Liang, W., Tadesse, G. A., Ho, D., Li, F., Liu, S., Yu, H., & Sun, J. (2023). Personalized education in the era of big data. IEEE Transactions on Knowledge and Data Engineering, 35(3), 2245-2261. https://doi.org/10.1109/TKDE.2021.3117424
Liu, Y., Tamkin, A., Geng, X., & Ganguli, D. (2023). Lost in the middle: How LLMs use long contexts. Transactions of the Association for Computational Linguistics, 11, 1200-1218. https://doi.org/10.1162/tacl_a_00606
Lu, X., van de Sande, B., & Marsh, J. A. (2024). Hybrid learning environments and adaptive support. Computers & Education, 210, 104975. https://doi.org/10.1016/j.compedu.2023.104975
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2022). Artificial intelligence and human learning: Partners in progress. OECD Publishing.
OECD. (2023). The OECD AI principles: An overview. OECD Publishing.
Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2021). Artificial intelligence in education: Challenges and opportunities for sustainable development. UNESCO.
Ruipérez-Valiente, J. A., Cobos, R., & Muñoz-Merino, P. J. (2022). Knowledge graphs in education: A systematic literature review. IEEE Transactions on Learning Technologies, 15(4), 485-500. https://doi.org/10.1109/TLT.2022.3190858
Selwyn, N., Macgilchrist, F., & Williamson, B. (2023). EdTech and equity: Critical perspectives on the promises of digital education. Learning, Media and Technology, 48(1), 1-11. https://doi.org/10.1080/17439884.2022.2156041
Susnjak, T. (2022). ChatGPT: The end of online exam integrity? arXiv. https://doi.org/10.48550/arXiv.2212.09292
UNESCO. (2020). Global education monitoring report 2020: Inclusion and education: All means all. UNESCO. https://doi.org/10.54676/JJNK6989
UNESCO. (2022). Reimagining our futures together: A new social contract for education. UNESCO.
UNESCO. (2023). AI and education: Guidance for policy-makers. https://unesdoc.unesco.org/ark:/48223/pf0000384803
van de Sande, B., Marsh, J. A., & Lu, X. (2023). Large language models in education: Opportunities and challenges. Computers & Education: Artificial Intelligence, 4, 100132. https://doi.org/10.1016/j.caeai.2023.100132
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, 39. https://doi.org/10.1186/s41239-019-0171-0
Zhang, D., & Wang, X. (2025). Adaptive learning systems: A review and future directions. Journal of Educational Technology & Society, 28(1), 1–15.
Zhang, D., Wang, X., & Chen, X. (2021). Adaptive learning technologies in higher education: A meta-analysis of effectiveness. Educational Research Review, 33, 100390. https://doi.org/10.1016/j.edurev.2021.100390
Zhou, M., & Wang, D. (2020). Ethical considerations in AI-supported education: A perspective from responsible innovation. AI & Society, 35(3), 601-611. https://doi.org/10.1007/s00146-019-0910-1
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Rubén Juárez Cádiz
This work is licensed under a Creative Commons Attribution 4.0 International License.
The articles that are published in this journal are subject to the following terms:
1. The authors grant the exploitation rights of the work accepted for publication to RIED, guarantee to the journal the right to be the first publication of research understaken and permit the journal to distribute the work published under the license indicated in point 2.
2. The articles are published in the electronic edition of the journal under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can copy and redistribute the material in any medium or format, adapt, remix, transform, and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
3. Conditions for self-archiving. Authors are encouraged to disseminate electronically the OnlineFirst version (assessed version and accepted for publication) of its articles before publication, always with reference to its publication by RIED, favoring its circulation and dissemination earlier and with this a possible increase in its citation and reach among the academic community.
