Knowledge, Volume 5, Issue 3 (September 2025) – 4 articles

Educational admission systems, particularly in developing countries, often suffer from opaque decision processes, unstructured data, and limited analytic insight. This study proposes a novel methodology that integrates structural causal models (SCMs), ontological modeling, and machine learning to uncover and apply interpretable knowledge from an admission database. Using a dataset of 12,043 records from Benue State Polytechnic, Nigeria, we demonstrate this approach as a proof of concept by constructing a domain-specific SCM ontology, validate it using conditional independence testing (CIT), and extract features for predictive modeling. Five classifiers, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were evaluated using stratified 10-fold cross-validation. SVM and KNN achieved the highest classification accuracy (92%), with precision and recall scores exceeding 95% and 100%, respectively. Feature importance analysis revealed ‘mode of entry’ and ‘current qualification’ as key causal factors influencing admission decisions. This framework provides a reproducible pipeline that combines semantic representation and empirical validation, offering actionable insights for institutional decision-makers. Comparative benchmarking, ethical considerations, and model calibration are integrated to enhance methodological transparency. Limitations, including reliance on single-institution data, are acknowledged, and directions for generalizability and explainable AI are proposed. Full article

This paper shows that artificial intelligence is fundamentally transforming college curricula by enabling data-driven personalization, which enhances student outcomes and better aligns educational programs with evolving workforce demands. Specifically, predictive analytics, machine learning algorithms, and natural language processing were applied here, grounded in constructivist learning theory and Human–Computer Interaction principles, to evaluate student performance and identify at-risk students to propose personalized learning pathways. Results indicated that the AI-based curriculum achieved much higher course completion rates (89.72%) as well as retention (91.44%) and dropout rates (4.98%) compared to the traditional model. Sentiment analysis of learner feedback showed a more positive learning experience, while regression and ANOVA analyses proved the impact of AI on enhancing academic performance to be real. Therefore, the learning content delivery for each student was continuously improved based on individual learner characteristics and industry trends by AI-enabled recommender systems and adaptive learning models. Its advantages notwithstanding, the study emphasizes the need to address ethical concerns, ensure data privacy safeguards, and mitigate algorithmic bias before an equitable outcome can be claimed. These findings can inform institutions aspiring to adopt AI-driven models for curriculum innovation to build a more dynamic, responsive, and learner-centered educational ecosystem. Full article

This paper explores the concept of ‘competency’ in modern organisations. It emphasises the strategic importance of aligning organisational values, strategic goals, and employee competencies. It introduces competency mapping as a framework for ensuring such an alignment, as well as for developing a culture of continuous learning and development, where the emotions and feelings of the interactants are also taken into account based on intrapersonal and interpersonal aspects of human behaviour. The article also elucidates the interconnection among diverse human ‘intelligences’ that are of paramount importance in shaping human knowledge and guiding us in navigating through life more smoothly and efficiently. Thus, through an interdisciplinary scope, we have attempted to analyse the intrinsic value of competency mapping as a knowledge driver in modern organisational and educational settings. Full article

This article presents a comparative analysis of digital manufacturing laboratories (DMLs) in Mexico and Spain. It is argued that DMLs, also known as makerspaces or FabLabs, play a key role in innovation and experimentation, but that their success depends on the relationships they establish with social actors, such as local governments, universities, and firms. Key concepts of the transformative innovation approach such as “protective space” and “embeddedness” are introduced, which allow us to understand how DMLs operate within a complex system. The comparative analysis of a DML in Mexico City (Mexico) and a DML in Valencia (Spain) allows us to identify similarities and differences in their operational contexts. While the Mexican DML faces a lack of government support and dependence on the private sector, the Spanish one benefits from strong institutional support and public policies that facilitate its development. This results in greater stability and capacity for action for the Valencian FabLab VLC compared to the Mexican FabLab Finally, we reflect on how the embeddedness received from different social actors affects the autonomy and transformative capacity of DMLs, suggesting that while both labs have the potential to innovate, their contexts and relationships determine their effectiveness and sustainability in the digital sociotechnical system. Full article