Computer Engineering faces an ongoing challenge: preventing academic content from becoming obsolete in an environment where new technologies and methodologies are constantly emerging. While adapting curricula is vital to remain relevant, administrative and academic procedures are often slow, causing certain courses or concepts to become outdated before they can be revised. Moreover, it is not only about integrating technological innovations, but also about rigorously identifying and replacing curricular elements that have lost their relevance, all while avoiding hasty changes that could undermine the quality of education.

In this context, artificial intelligence (AI) emerges as a promising tool for detecting obsolete content and recommending more up-to-date alternatives. By leveraging natural language processing (NLP) and machine learning techniques, it becomes feasible to systematically compare a course curriculum with the equivalent course at other prestigious universities, both regionally and internationally, as well as with cutting-edge findings from recent scientific literature. This approach not only surpasses the mere identification of fleeting trends but also provides a thorough analysis to pinpoint innovations backed by robust academic and industrial support.

The proof of concept presented in this article focuses on the “Computer Networks” course at the Universitat Politècnica de València (UPV) in Spain. To achieve this, AI algorithms were applied to compare the course’s current content with equivalent offerings at various universities, both national and international. Additionally, the primary technological competencies required by the industrial sector were examined in order to facilitate their integration, and an extensive literature review was carried out to ensure that any proposed innovations are grounded in solid scientific and professional foundations. The results clearly indicate which modules and units require immediate updates, as well as those that can be maintained or even expanded based on their emerging relevance.

The results of this proof of concept confirm the feasibility and effectiveness of AI in evaluating curricula, identifying outdated content, and proposing replacements aligned with scientific and industrial requirements. This approach paves the way for implementing tools that can automatically analyze any course’s curriculum, compare it with equivalent programs at prestigious universities, and subsequently align it with the needs of the technology sector and cutting-edge research.

Adopting these solutions not only reinforces the relevance of academic content but also enhances the quality of teaching by ensuring regular, data-driven curriculum updates. As a result, graduate employability is strengthened, and the competitiveness of educational institutions is elevated. In short, experience shows that AI is a strategic resource for curriculum renewal in Computer Engineering.

Keywords: Artificial Intelligence, Curricular Innovation, Machine Learning.