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.

Automatically mapping written Kazakh to dictionary-linked Kazakh Sign Language (KSL) lexical signs is difficult because inflected forms often do not match canonical entries in limited digital lexicons. Surdo Med combines Kazakh natural-language processing, Dimskis notation, reusable avatar animation components, and a web interface for medical-domain lexical learning. This study evaluates morphology-aware lexical concept recovery rather than complete Kazakh-to-KSL translation. The primary benchmark uses naturally occurring inflected tokens from the official UD Kazakh-KTB test split that map uniquely to a frozen medical concept inventory. Across 373 supported inflected tokens, direct lookup recovered 1.61% of target concepts, a rule-based baseline recovered 45.31%, and contextual Stanza lemmatization recovered 97.86% (sentence-cluster bootstrap 95% CI: 96.29–99.19%). Recovery remained 96.85% after deduplicating surface–lemma pairs and 94.80% when macro-averaged across 52 lexical concepts. An external analysis using published KSL lexical material showed 20.0% overlap with the medical inventory; within supported items, Stanza recovered 6/6 root tokens versus 3/6 for direct lookup. These results support morphology-aware normalization for dictionary-linked KSL concept recovery. Avatar linguistic quality and educational effectiveness require separate human evaluation.

This study presents a hybrid model for image captioning using a VGG16 convolutional neural network (CNN) for feature extraction and a long short-term memory (LSTM) network for sequential text generation. The proposed architecture addresses the challenges of producing semantically rich and syntactically accurate signatures, especially in languages with limited training data. The model effectively bridges the semantic gap between visual and textual modalities by utilizing pre-trained weights and a robust encoding-decoding system. Experimental results on a dataset of road signs in Kazakhstan show a significant improvement in inscription quality as measured by BLEU and METEOR metrics. The model achieved a maximum METEOR score of 0.9985, indicating high semantic accuracy, and BLEU-1 and BLEU-2 scores of 0.67 and 0.64, respectively, highlighting the model's ability to generate relevant and coherent captions. These findings underscore the model's potential applications in multimodal systems and assistive technologies. Using a pre-trained CNN model (VGG16), we can efficiently encode visual information by extracting high-level features from images. This approach is particularly useful for tasks that require consideration of the semantics of images, such as road sign recognition. The second LSTM model, as a sequence-oriented architecture, is well-suited for text generation, as it effectively considers the context and previous words in a sequence. These models can be integrated into systems requiring the analysis and description of visual information, such as autonomous vehicles or driver assistance systems. In conclusion, the proposed model demonstrates high potential for image caption generation tasks, especially in resource-constrained environments and for specialized datasets.