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.
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How to Cite: Juárez Cádiz, R. (2026). PathRAG application in adaptive learning with generative AI for inclusive and sustainable education. RIED-Revista Iberoamericana de Educación a Distancia, 29(1), 267–297. https://doi.org/10.5944/ried.45378
