This article formalizes AI-assisted assessment as a discrete-time policy-level design for iterative feedback and evaluates it in a digitally transformed higher-education setting. We integrate an agentic retrieval-augmented generation (RAG) feedback engine—operationalized through planning (rubric-aligned task decomposition), tool use beyond retrieval (tests, static/dynamic analyzers, rubric checker), and self-critique (checklist-based verification)—into a six-iteration dynamic evaluation cycle. Learning trajectories are modeled with three complementary formulations: (i) an interpretable update rule with explicit parameters $\eta$ and $\lambda$ that links next-step gains to feedback quality and the gap-to-target and yields iteration-complexity and stability conditions; (ii) a logistic-convergence model capturing diminishing returns near ceiling; and (iii) a relative-gain regression quantifying the marginal effect of feedback quality on the fraction of the gap closed per iteration. In a Concurrent Programming course ($n=35$), the cohort mean increased from 58.4 to 91.2 (0–100), while dispersion decreased from 9.7 to 5.8 across six iterations; a Greenhouse–Geisser corrected repeated-measures ANOVA indicated significant within-student change. Parameter estimates show that higher-quality, evidence-grounded feedback is associated with larger next-step gains and faster convergence. Beyond performance, we engage the broader pedagogical question of what to value and how to assess in AI-rich settings: we elevate process and provenance—planning artifacts, tool-usage traces, test outcomes, and evidence citations—to first-class assessment signals, and outline defensible formats (trace-based walkthroughs and oral/code defenses) that our controller can instrument. We position this as a design model for feedback policy, complementary to state-estimation approaches such as knowledge tracing. We discuss implications for instrumentation, equity-aware metrics, reproducibility, and epistemically aligned rubrics. Limitations include the observational, single-course design; future work should test causal variants (e.g., stepped-wedge trials) and cross-domain generalization.

A neuromethodologia é um conceito sobre o qual dificilmente encontramos informações em bancos de dados científicos e literatura especializada. Portanto, o objetivo geral do trabalho apresentado é analisar a relação entre metodologia de ensino, ensino de neuromethodologia, inclusão educacional, tecnologia e formação de professores. O projeto da pesquisa é não-experimental, descritivo, explicativo, correlacional e regressivo. A amostra utilizada foi uma amostra de conveniência de professores universitários de universidades espanholas, brasileiras, colombianas e paraguaias, com um total de 815 participantes. O instrumento de pesquisa foi um questionário ad hoc à escala Likert com excelente confiabilidade (Cronbach's Alpha, .969), que foi validado em termos de conteúdo e construído através de uma análise exploratória de fatores. A análise de correlação e a modelagem linear automática fornecem uma primeira conclusão mostrando que a neuromethodologia dá cientificidade às tecnologias utilizadas pelo professor, sendo fundamental para a inclusão educacional. Os exemplos de neuroimagem dão uma ideia da pesquisa que estamos desenvolvendo em neuromethodologia.

El presente artículo tiene como objetivo analizar la relación entre las prácticas docentes desde el enfoque de la pluriculturalidad y la inclusión educativa. El diseño de investigación es no experimental, explicativo y correlacional. Se ha utilizado una escala Likert validada en constructo y contenido. La muestra es de 1575 participantes (estudiantes universitarios y docentes). Fue realizado un Análisis Factorial Exploratorio para validar la construcción de la escala y una correlación P (Pearson). El modelaje a través de ecuaciones estructurales nos permite algunas conclusiones, entre ellas que las prácticas del profesorado deben tener carácter pluricultural e inclusivo, siendo dos aspectos que influyen en las prácticas, sin que sea relevante su vinculación.

Industry 5.0 challenges higher education to adopt human-centred and sustainable uses of artificial intelligence, yet many current deployments still treat generative AI as a stand-alone tool, neurophysiological sensing as largely laboratory-bound, and governance as an external add-on rather than a design constraint. This article introduces Nested Learning as a neuro-adaptive ecosystem design in which generative-AI agents, IoT infrastructures and multimodal deep learning orchestrate instructional support while preserving student agency and a “pedagogy of hope”. We report an exploratory two-phase mixed-methods study as an initial empirical illustration. First, a neuro-experimental calibration with 18 undergraduate students used mobile EEG while they interacted with ChatGPT in problem-solving tasks structured as challenge–support–reflection micro-cycles. Second, a field implementation at a university in Madrid involved 380 participants (300 students and 80 lecturers), embedding the Nested Learning ecosystem into regular courses. Data sources included EEG (P300) signals, interaction logs, self-report measures of engagement, self-regulated learning and cognitive safety (with strong internal consistency; $\alpha /\omega \ge 0.82$), and open-ended responses capturing emotional experience and ethical concerns. In Phase 1, P300 dynamics aligned with key instructional micro-events, providing feasibility evidence that low-cost neuro-adaptive pipelines can be sensitive to pedagogical flow in ecologically relevant tasks. In Phase 2, participants reported high levels of perceived nested support and cognitive safety, and observed associations between perceived Nested Learning, perceived neuro-adaptive adjustments, engagement and self-regulation were moderate to strong ($r=0.41$–$0.63$, $p<0.001$). Qualitative data converged on themes of clarity, adaptive support and non-punitive error culture, alongside recurring concerns about privacy and cognitive sovereignty. We argue that, under robust ethical, data-protection and sustainability-by-design constraints, Nested Learning can strengthen academic resilience, learner autonomy and human-centred uses of AI in higher education.

INTRODUCCIÓN. Este estudio se ha basado en el objetivo general de identificar los factores que predicen el riesgo de convertirse en adicto a Internet o a las redes sociales. MÉTODO. Para la investigación se ha utilizado un diseño descriptivo, utilizando la media, asimetría y curtosis, con una regresión binomial logística. Para la investigación se utilizó una muestra de 217 estudiantes universitarios, todos ellos de primer curso de la Facultad de Ciencias de la Educación de la Universidad de Granada. En este estudio se consideraron las variables demográficas de edad y género dentro de los estudiantes investigados, se observa que los estudiantes tenían una edad media de 19.37 años y una mediana de 18 años. En el cual, podemos destacar que el género mayoritario de la muestra es el femenino con un (66.8%) y el 33.2% restante es masculino. Por otro lado, para la recogida de datos se utilizó la escala de riesgo de adicción a las redes sociales e internet para adolescentes (ERA-RSI). RESULTADOS. Los factores que predicen con mayor precisión el riesgo de adicción a las redes sociales y a Internet en los estudiantes universitarios de primer año son la normalización, las dificultades personales y el ego. La soledad resultó ser predictiva, pero en menor grado, y, por último, la desinhibición resultó no tener influencia predictiva. DISCUSIÓN. Se ha comprobado que las aplicaciones telefónicas que se lanzan constantemente en Internet tienen una gran influencia en los predictores de la adicción.

The present work aims to establish the links that may exist between technological training and multicultural training of teachers with educational inclusion. The research design is non-experimental, explanatory and correlational, with a quantitative method. A reliable and content-validated Likert-type scale has been used. The sample used is 594 subjects, formed by university students in their last year of Primary Education and graduate students (Master of Teacher Training) (University of Jaen, Spain). An exploratory factorial analysis was carried out which validated the scale's construct and which allows us to conclude that greater value is given to foreign training than to national training, and furthermore, little value is given to teacher training as a key to an educational system in accordance with the 21st century. Spearman's Rho was applied, where the significant correlation between the training provided by non-university institutions and pluricultural training can be observed. Structural equation modeling (SEM) concludes by demonstrating the strong relationship between inclusive and pluricultural teacher education, and between the latter and technology, with a weak relationship between technology and general teacher education.