Raquel Hijón-Neira

Basic Vocational Training (FPB) students face substantial learning difficulties in electronics, which leads to a high dependency on the instructor and creates bottlenecks in practical workshops. This study aims to evaluate the effectiveness of generative artificial intelligence (AI) as a support tutor in practical electronics workshops by comparing its effects on conceptual learning, technical performance, motivation, and learner autonomy against a traditional instructional approach. To this end, a quasi-experimental design was implemented with a pilot group of eight Basic Vocational Training students, divided into an AI-assisted group (n = 4) and a traditional control group (n = 4), using pre-test and post-test assessments to measure cognitive progress. The results reveal that the use of AI allows for maintaining performance in conceptual learning despite tasks of increasing difficulty, while significantly increasing students’ motivation and perceived autonomy. Although a drastic reduction in the frequency of technical assembly errors was not recorded, AI proved to be an effective support for resolving procedural questions in real time. It is concluded that the integration of generative AI offers positive implications for Vocational Training, functioning as a supplementary tutor that fosters student independence and optimizes technical classroom dynamics.

This study presents the design, implementation, and exploratory classroom evaluation of a course-constrained educational assistant combining Retrieval-Augmented Generation (RAG) with large language models (LLMs) in Vocational Education and Training (VET). The platform retrieves teacher-provided materials, generates course-aligned responses, displays source references, and returns a predefined abstention message when evidence appears insufficient. It was deployed during a practical session in the Spanish Higher Vocational Training programme Administración de Sistemas Informáticos en Red (ASIR; Network and Information Systems Administration). Nineteen students and one instructor completed a post-session questionnaire. Students reported positive perceptions of usability, clarity, perceived reliability, and learning support, while restricted corpus coverage and the absence of conversational memory emerged as limitations. To complement the perception-based classroom study, a separate post hoc technical assessment used 30 predefined queries with the same prototype and corpus. All 16 fully answerable prompts received substantive responses; 13 were rated fully correct and three partially correct by the first author. Seven of eight deliberately out-of-corpus prompts triggered abstention, whereas one OpenShift Route query produced an unsupported answer from semantically adjacent but non-supporting Kubernetes evidence. The contribution is empirical and design-oriented rather than algorithmic. The findings are context-specific and show both the potential and limitations of bounded cognitive support grounded in instructor-selected materials.

Basic Vocational Training (FPB) students face significant learning difficulties in electronics, which leads to a high dependency on the instructor and creates bottlenecks in practical workshops. This study aims to evaluate whether generative artificial intelligence (AI) can act as an effective support tutor during workshop practice to mitigate these issues. To this end, a quasi-experimental design was implemented with Basic Vocational Training students divided into an AI-assisted group and a traditional control group, using pre-test and post-test assessments to measure cognitive progress. The results reveal that the use of AI allows for maintaining performance in conceptual learning despite tasks of increasing difficulty, while significantly increasing students' motivation and perceived autonomy. Although a drastic reduction in the frequency of technical assembly errors was not recorded, AI proved to be an effective support for resolving procedural questions in real-time. It is concluded that the integration of generative AI offers positive implications for Vocational Training, functioning as a supplementary tutor that fosters student independence and optimizes technical classroom dynamics.

This study presents the design, implementation, and exploratory classroom evaluation of a web-based educational assistant built on Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) for Vocational Education and Training (VET). The platform was designed to generate responses based on teacher-provided course materials, preserve source traceability, and return an abstention message when the retrieved evidence is insufficient. The assistant was deployed in an authentic classroom setting within the Higher Vocational Training programme in Network and Information Systems Administration (ASIR). Nineteen students and one instructor used the system during a practical session and completed a post-session questionnaire combining Likert-scale items with open-ended questions. The findings indicate positive student perceptions of usability, response clarity, perceived reliability, and learning support. Participants particularly valued the ability to obtain focused answers aligned with the instructional materials. The evaluation also revealed a relevant trade-off: restricting the assistant to a controlled corpus reinforced curricular consistency and perceived trustworthiness but limited its capacity to address questions insufficiently covered by the available resources. The absence of conversational memory emerged as the most frequently requested improvement. These preliminary findings suggest that course-constrained RAG assistants may constitute valuable complementary tools for transparent and pedagogically supervised AI-supported learning in technical VET contexts.

Children with autism spectrum disorder (ASD) often experience difficulties in maintaining engagement and completing structured tasks, particularly when instructional complexity and sensory load are not appropriately adapted. Digital interventions, including serious games, offer potential support; however, limited research has examined how specific design features influence usability and task completion in real-world contexts. This pilot study evaluates a sensory-adapted serious game designed to support engagement, comprehension, and task completion in children with ASD aged 5 to 12 years. Thirty-three participants from five educational settings interacted with six game levels that varied in instructional clarity and sensory complexity. Performance was assessed using structured observational measures alongside qualitative feedback from educators. Findings revealed a non-linear pattern of performance, with a marked decline at one level characterized by increased sensory and instructional demands. Higher performance was consistently associated with greater independence in task completion. Qualitative analysis highlighted the importance of clear instructions, reduced visual complexity, and predictable feedback in supporting engagement. These findings provide preliminary evidence that small increases in task complexity may disproportionately affect performance in children with ASD. The study offers practical recommendations for clinicians and educators using digital tools to support engagement and autonomy in structured learning and therapeutic activities.

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving medical image analysis, enabling collaborative model training across distributed institutions without sharing sensitive patient data. However, two key challenges remain: instability of model optimization under non-independent and identically distributed(non-IID)data, and the need for client-specific adaptation. In this paper, we propose a hybrid federated learning framework that integrates proximal regularization (FedProx) with personalized model adaptation(FedPer) to address these challenges jointly. Unlike prior work that treats these mechanisms independently, we investigate their interaction and demonstrate how their combination improves both convergence stability and generalization. We evaluate the proposed framework on the MURA dataset under simulated heterogeneous client distributions. Experimental results show that the hybrid approach consistently outperforms standard federated baselines, achieving up to 96. These findings highlight the importance of jointly considering optimization stability and personalization in federated learning systems, particularly for real-world medical imaging applications.

This study presents SAIL-Y (Sailing Artificial Intelligence for Learning in Youth), a novel gender-focused recommender system designed to promote female participation in STEM careers through data-driven guidance. Drawing inspiration from the metaphor of an academic journey as a voyage, SAIL-Y functions as a digital compass—leveraging socioeconomic profiles and standardised test results (Saber 11, Colombia) to help students navigate career decisions in high-impact academic fields. SAIL-Y integrates multiple machine learning strategies, including collaborative filtering, bootstrapped data augmentation to rebalance gender representation, and socioeconomic-aware conditioning, to generate personalised and bias-controlled career recommendations. The system is explicitly designed to skew recommendations toward STEM disciplines for female students, countering systemic underrepresentation in these fields. Using a dataset of 332,933 Colombian students (2010–2021), we evaluate the performance of different recommendation architectures under the SAIL-Y framework. The results show that a gender-oriented recommender design increases the proportion of STEM career recommendations for female students by up to 25% compared to reference models. Beyond technical contributions, this work proposes an ethically aligned paradigm for educational recommender systems—one that empowers rather than merely predicts. SAIL-Y is thus envisioned as both a methodological tool and a socio-educational intervention, supporting more equitable academic journeys for future generations.

The rapid integration of generative artificial intelligence (AI) into creative workflows is transforming design from a human-driven activity into a synergistic process between humans and AI systems. Yet, most current tools still operate as linear “executors” of user commands, which fundamentally clashes with the non-linear, iterative, and ambiguous nature of human creativity. Addressing this gap, this article introduces a conceptual framework of five irreducible paradoxes—ambiguity vs. precision, control vs. serendipity, speed vs. reflection, individual vs. collective, and originality vs. remix—as core design tensions that shape human–AI co-creative systems. Rather than treating these tensions as problems to solve, we argue they should be understood as design drivers that can guide the creation of next-generation co-creative environments. Through a critical synthesis of existing literature, we show how current executor-based AI tools (e.g., Microsoft 365 Copilot, Midjourney) fail to support non-linear exploration, refinement, and human creative agency. This study contributes a novel theoretical lens for critically analyzing existing systems and a generative framework for designing human–AI collaboration environments that augment, rather than replace, human creative agency.

The rapid integration of generative Artificial Intelligence (AI) into creative workflows is transforming design from a human-driven activity into a synergistic process between humans and AI systems. Yet, most current tools still operate as linear “executors” of user commands, which fundamentally clashes with the non-linear, iterative, and ambiguous nature of human creativity. Addressing this gap, this article introduces a conceptual framework of five irreducible paradoxes—Ambiguity vs. Precision, Control vs. Serendipity, Speed vs. Reflection, Individual vs. Collective, and Originality vs. Remix—as core design tensions that shape Human–AI co-creative systems. Rather than treating these tensions as problems to solve, we argue they should be understood as design drivers that can guide the creation of next-generation co-creative environments. Through a critical synthesis of existing literature, we show how current executor-based AI tools (e.g., Microsoft 365 Copilot, Midjourney) fail to support non-linear exploration, refinement, and human creative agency. This study contributes a theoretical lens for analyzing existing systems and a generative framework for designing Human–AI collaboration environments that augment, rather than replace, human creativity.

This study presents SAIL-Y (Sailing Artificial Intelligence for Learning in Youth), a novel gender-focused recommender system designed to promote female participation in STEM careers through data-driven guidance. Drawing inspiration from the metaphor of an aca-demic journey as a voyage, SAIL-Y functions as a digital compass—leveraging socioeco-nomic profiles and standardised test results (Saber 11, Colombia) to help students navi-gate career decisions in high-impact academic fields. SAIL-Y integrates multiple machine learning strategies, including collaborative filtering, bootstrapped data augmentation to rebalance gender representation, and socioeconomic-aware conditioning, to generate per-sonalised and bias-controlled career recommendations. The system is explicitly designed to skew recommendations toward STEM disciplines for female students, countering sys-temic underrepresentation in these fields. Using a dataset of over 300,000 Colombian stu-dents (2010–2021), we evaluate the performance of different recommendation architec-tures under the SAIL-Y framework. The results show that a gender-oriented recommender design increases the proportion of STEM career recommendations for female students by up to 25% compared to reference models. Beyond technical contributions, this work pro-poses an ethically aligned paradigm for educational recommender systems—one that empowers rather than merely predicts. SAIL-Y is thus envisioned as both a methodologi-cal tool and a socio-educational intervention, supporting more equitable academic jour-neys for future generations.