Intelligent and Dynamic Rubrics powered by Artificial Intelligence have the potential to transform the assessment of competencies such as teamwork. This paper analyzes the necessary conditions for applying these rubrics within the CTMTC model (Comprehensive Training Model of the Teamwork Competence), which facilitates the development of teamwork skills while generating continuous and diversified evidence throughout the collaborative process. This evidence, derived from documents, conversations, and dynamic records, enables the evaluation of group, socio-emotional, and individual skills. Furthermore, the CTMTC method fosters learning through errors and continuous feedback, creating an ideal environment for technological integration. AI-driven rubrics require structured and traceable evidence aligned with the model’s indicators. These rubrics offer diagnostic, formative, and summative assessment functionalities, supporting pedagogical decision-making and enabling predictive models. Implementing these rubrics, in coordination with the applied teamwork method, provides advantages such as increased efficiency, accuracy, and flexibility compared to traditional methods. However, specific challenges must be addressed, including the standardization of evidence and the analysis of complex skills. This study lays the foundation for future research on artificial intelligence and educational assessment, contributing to the design of innovative approaches in collaborative environments.