Abstract:

In the context of problem-based learning (PBL), transparency in learning expectations and assessment criteria plays a crucial role in promoting student engagement, self-regulation, and performance. This contribution presents a research and development project on the systematic use of rubric matrices as tools to support both learning and evaluation processes in higher education. Rubric matrices - structured frameworks that describe performance criteria and quality levels - were implemented as integral components of a series of PBL-based courses. The study explores how these matrices, when introduced, discussed, and co-interpreted with students at the beginning of each case study, shape learning behavior, foster metacognitive awareness, and improve the alignment between teaching intentions and student outcomes.

A central innovation of our approach lies in the AI-supported generation of rubric matrices, which combines pedagogical expertise with large language model (LLM) capabilities. The design process leverages AI to propose initial rubric drafts based on course learning objectives, case descriptions, and desired competencies. These drafts are then refined collaboratively by teaching staff to ensure disciplinary accuracy and contextual appropriateness. This method substantially reduces preparation time while maintaining didactic quality.

The methodological framework combines qualitative and quantitative approaches. Alongside reflective journals and instructor interviews, a two-point quantitative survey was conducted with participating students to capture their perceptions of the rubric matrices, their perceived functionality, and their influence on individual learning processes. Data were collected at two different points in time: first after the introduction of the rubric matrices, and again at the end of the course following the completion of the case study work. The resulting data provide insights into the development of students’ attitudes, their evolving understanding of assessment criteria, and the perceived impact of rubric transparency on motivation and self-directed learning.

Findings indicate that discussing rubric matrices transparently with students not only clarifies assessment expectations but also enhances motivation and ownership of learning. Students report a deeper understanding of task requirements and improved ability to monitor their own progress throughout the PBL cycle. Instructors, in turn, experience greater consistency and fairness in assessment, as well as improved feedback practices aligned with the rubric criteria.

Preliminary results suggest that rubric-based transparency positively transforms the learning culture in PBL environments. The use of AI-assisted design further opens new perspectives for scalable, adaptive rubric creation tailored to specific disciplines and learning outcomes. The contribution concludes with pedagogical and practical implications for integrating rubric matrices as dynamic learning and assessment tools, and outlines future research directions on the sustainable use of AI technologies in higher education.

Keywords:

Problem-based learning, rubric matrices, assessment transparency, AI in education, feedback culture, higher education innovation.