Abstract
The Conceive–Design–Implement–Operate (CDIO) Syllabus expresses graduate competencies in natural language developed through project-based learning, yet the link between declared competencies and delivered content is maintained manually. This study proposes the CDIO Competency–Knowledge Ontology (CDIO–CKO): a decidable OWL 2 Description Logic (DL) formalization of the Syllabus connected to programs, projects, courses, learning outcomes, and knowledge-content units, paired with a parameterized transformation that converts educational projects into competency-linked units via decomposition, extraction, semantic annotation, and aggregation. Applied to one 240-ECTS mechatronics and robotics program (42 courses, 18 projects, 156 competencies), the methodology produced 1,287 individuals and 9,540 triples classifying in 2.8 s. On a 240-unit held-out test set against a three-expert gold standard, automated mapping reached precision 0.91, recall 0.86, and F1 0.88 (95% CI 0.84–0.91; system–standard κ = 0.79, expert pre-adjudication κ = 0.74). Traceable coverage rose from 0.59 to 0.84, and 23 prerequisite inconsistencies missed by routine manual review were surfaced (23 of 26 issues in a dedicated expert audit: precision 1.00, recall 0.88). Against five comparator families, the method achieved the highest F1 among the five evaluated baselines while uniquely supporting consistency checking, proficiency propagation, and an auditable evidence trail. Results are a single-program proof-of-concept; broader validation remains future work.