Feedback is an essential component of the teaching–learning process; however, it can vary in quality due to different contexts and students’ and professors’ individual characteristics. This research explores the effect of generative artificial intelligence (GenAI) in strengthening personalized and timely feedback by initially defining an adaptable framework to integrate GenAI into feedback mechanisms defined in theoretical frameworks. We applied a between-subjects analysis in an experimental research design with 263 undergraduate students across multiple disciplines based on an approach consisting of a pretest–post-test process and control and focus groups to evaluate students’ perceptions of artificial intelligence-enhanced feedback versus traditional professor-led feedback. The results show that students who used GenAI declared statistically significantly higher satisfaction levels and a greater sense of ownership in the feedback process. Additionally, GenAI scaffolded continuous improvement and active student participation through a structured and accessible feedback environment, determining that 97% of students are willing to reuse the tool. These findings show that GenAI is a valuable tool to complement professors in the creation of an integrated feedback model. This study draws directions on future research on the combination of artificial intelligence and innovative strategies to produce a long-term impact on education.

This study aims to understand university professors’ perspectives on the learning assessment process, including its importance during teaching and learning, their conceptualization, and their considerations in their practices. The research used a grounded theory approach to recognize evaluation as a dialogical and intersubjective space. The methodology consisted of an open survey and a semi-structured interview with faculty professors from a university in northern Mexico. The findings highlighted the importance of educational institutions, emphasizing that faculties prioritize evaluating quality based on relevance, alignment with learning objectives, continuity throughout the process, and feedback. These aspects align with recent approaches that consider evaluation as a process that promotes learning, as evidenced by the high saturation rate in the theoretical sampling. Furthermore, the study revealed that the institution’s educational model, curricular design, and evaluation policies significantly influence the faculty members’ perspective. As a result, educational institutions must consider these factors when formulating an evaluation model, thereby making the research directly applicable to the work of educational policymakers and university professors.

A methodology for designing holistic rubrics to assess competencies in a Mexican university (Tec21) is presented. It ensures standardized and reliable evaluations, emphasizing collaboration and adaptability in other contexts. The rubrics focus on key behaviors, enabling a comprehensive assessment of student performance. A glossary supports consistent interpretation and effective implementation after faculty training. The holistic and task-specific rubrics offer flexibility and promote fair, meaningful assessment practices.