Assessment for learning has become increasingly important in university teaching, particularly regarding the feedback process. However, there is still a perception of student dissatisfaction with the quality of feedback provided by faculty, highlighting the need to innovate in feedback strategies.
This study aimed to explore the pedagogical and technological relevance of integrating Wilson's Feedback Ladder with generative artificial intelligence, specifically GPT-4o, to strengthen formative feedback in university students. The study was conducted using a qualitative and exploratory approach in two phases.
First, a prompt was designed and validated using the Delphi method with the participation of eight experts in assessment and artificial intelligence, applying it to seven state-of-the-art language models. In the second phase, the validated prompt was implemented in two university courses of different nature, Assessment for Learning and Data Structures, integrating automatic feedback into the Moodle platform.
The results showed that the experts agreed on the suitability of AI-mediated Wilson’s Ladder and highlighted the superior performance of GPT-4o.
At the classroom level, students valued the clarity, usefulness, and immediacy of the feedback, although they identified limitations in the tool's lack of contextualization and impersonal tone.
It is concluded that the integration of Wilson’s Ladder with generative artificial intelligence represents a promising innovation, but one that requires disciplinary adjustments, teacher supervision, and careful attention to the human dimension of feedback in e-learning contexts.
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How to Cite: Leiva-Guerrero, M. V., Araya Zamorano, I., Escobar Collins, R., & Silva Castro, F. (2026). Feedback on learning with generative artificial intelligence in university students. RIED-Revista Iberoamericana de Educación a Distancia, 29(1), 241–267. https://doi.org/10.5944/ried.45547
