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Neuroeducation and the influence of AI on early childhood education: A systematic review
(2026)
Burbano Enriquez, Betty Paulina
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Moncayo, Hugo
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Andrea Andrade-Zuleta
Neuroeducation integrates knowledge from neuroscience, psychology, and pedagogy to inform evidence-based teaching strategies, especially during early childhood—a stage of heightened brain plasticity and foundational learning. Concurrently, artificial intelligence (AI) offers adaptive and personalized tools that can support neurocognitive development through data-driven educational interventions. This systematic review examines the empirical convergence between neuroeducation and AI in early childhood education, analyzing how AI-enabled tools reflect and apply neuroeducational principles. The review followed the PRISMA protocol to ensure methodological rigor. A total of 735 records were initially identified across five major databases (Scopus, Web of Science, PubMed, PsycINFO, and Elsevier). After applying strict inclusion and exclusion criteria, 18 peer-reviewed studies published between 2020 and 2025 were selected for final analysis. Each study was classified according to a four-part taxonomy of AI interaction modalities: embodied robots, screen-based systems, voice-only interfaces, and multimodal environments. The findings reveal that AI-supported interventions can enhance executive functions, cognitive flexibility, attention regulation, and socioemotional development when designed in alignment with neurodevelopmental needs. Embodied and multimodal AI systems demonstrated effectiveness in fostering engagement, interaction, and social cognition, while screen-based and voice-only systems proved useful for cognitive and linguistic skills. Ethical challenges were also identified, including privacy concerns, emotional dependency, equity of access, and developmental appropriateness. This study highlights that the integration of AI and neuroeducation requires careful interdisciplinary collaboration among educators, technologists, and policymakers. Beyond summarizing current evidence, the review underscores the importance of adopting developmentally appropriate practices, ensuring ethical safeguards, and fostering teacher training in AI-informed pedagogy. By synthesizing empirical research, this work provides a conceptual and practical foundation for advancing early childhood education through a neuroeducational framework enriched by AI technologies.
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Intervention against school bullying through emerging technologies: a literature review
(2025)
Acosta Pérez, Paul Bladimir
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Espinosa Pinos, Carlos Alberto
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Saa, Fernando
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Francisco Sebastián Vaca-Pinto
School bullying remains a persistent issue that negatively affects students' well-being and academic performance. Private educational institutions face unique challenges in addressing this problem due to limited resources and teacher training. This literature review explores the use of emerging technologies - such as virtual reality (VR), mobile applications, and artificial intelligence (AI) - as innovative tools to prevent and mitigate school bullying. Recent studies that implement these technologies in educational settings were analyzed to assess their effectiveness and applicability. The findings suggest that such tools can foster empathy, facilitate anonymous reporting, and enable early detection of incidents, contributing to the development of safer and more supportive school environments. © 2025 IEEE.
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A brief review of robotics and artificial intelligence in education
(2025)
Sebastián Aucapiña
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Manuel Cardona
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Varela Aldas, José
New technologies are consistently integrated into the educational sector to improve its processes and improve learning outcomes. This paper presents a systematic review of the use of robots and artificial intelligence (AI) in education, with a particular focus on systems that incorporate voice recognition or conversational interaction. Educational robots have increasingly supported learning environments, from primary schools to higher education, enhancing motivation, engagement, and participation. Robots with humanoid features and voice capabilities, such as chatbots or speech recognition modules, are especially effective at capturing student attention and fostering interactive learning experiences. The review includes 30 peer-reviewed articles published in the last decade, identified using a PRISMA-based methodology. Studies were selected based on clear methodologies, measurable outcomes, and relevance to general education, excluding medical or highly specialized STEM applications. Mixedmethod analysis combines quantitative assessments of learning gains with qualitative insights into student perceptions. The findings highlight consistent improvements in motivation and classroom dynamics, although cognitive benefits vary depending on educational level, robot design, and integration with pedagogical goals. Limitations include a high reliance on simulations, a strong geographical bias toward English-speaking countries, and a lack of local data sets. In addition, some studies did not find significant learning improvements, highlighting that these technologies should complement rather than replace human teachers. The review identifies future research needs, such as domain adaptation for real-world applications and more extensive studies in underrepresented regions such as Latin America. In general, voice-enabled educational robots demonstrate great potential as interactive learning assistants when thoughtfully implemented, contributing to more engaging, adaptable, and effective educational environments. © 2025 IEEE.
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AI-assisted neurocognitive assessment protocol for older adults with psychiatric disorders
(2025)
Diego D. Díaz-Guerra
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Marena de la C. Hernández-Lugo
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Yunier Broche-Pérez
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Carlos Ramos-Galarza
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Ernesto Iglesias-Serrano
Introduction: Evaluating neurocognitive functions and diagnosing psychiatric disorders in older adults is challenging due to the complexity of symptoms and individual differences. An innovative approach that combines the accuracy of artificial intelligence (AI) with the depth of neuropsychological assessments is needed. Objectives: This paper presents a novel protocol for AI-assisted neurocognitive assessment aimed at addressing the cognitive, emotional, and functional dimensions of older adults with psychiatric disorders. It also explores potential compensatory mechanisms. Methodology: The proposed protocol incorporates a comprehensive, personalized approach to neurocognitive evaluation. It integrates a series of standardized and validated psychometric tests with individualized interpretation tailored to the patient’s specific conditions. The protocol utilizes AI to enhance diagnostic accuracy by analyzing data from these tests and supplementing observations made by researchers. Anticipated results: The AI-assisted protocol offers several advantages, including a thorough and customized evaluation of neurocognitive functions. It employs machine learning algorithms to analyze test results, generating an individualized neurocognitive profile that highlights patterns and trends useful for clinical decision-making. The integration of AI allows for a deeper understanding of the patient’s cognitive and emotional state, as well as potential compensatory strategies. Conclusions: By integrating AI with neuro-psychological evaluation, this protocol aims to significantly improve the quality of neurocognitive assessments. It provides a more precise and individualized analysis, which has the potential to enhance clinical decision-making and overall patient care for older adults with psychiatric disorders
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Predictive Model to Evaluate University Students' Perception and Attitude Towards Artificial Intelligence
(2024)
María Lorena Noboa Torres
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Daniela Alejandra Ribadeneira Pazmiño
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Daniela Paola Avalos Espinoza
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Guevara Maldonado, César Byron
Artificial Intelligence is emerging as a transformative tool impacting various industries, including education As Artificial Intelligence continues to develop and gain prominence in classrooms, understanding how students perceive this integration and how it affects their educational experience becomes crucial. The aim of this research was to develop a model to predict the perception of students at Bolívar State University regarding the use and potentialities of Artificial Intelligence in the educational field. The methodology employed a factorial analysis, which represents the relationships among a set of variables. From this, a logistic regression was performed, generating an equation to identify predictors that allowed understanding student behavior based on specific characteristics such as attitude, perception, and satisfaction. As a technique for information gathering, a questionnaire composed of 25 items on a Likert scale was used, statistically validated with a Cronbach's alpha value of 0.925. The results of the model show that all covariates, except "Insecurity and fear of using artificial intelligence tools", are significant (p < 0.001). This suggests that the remaining variables are related to the dependent variable "Positive Perception of the Usefulness of Artificial Intelligence in Learning". It is concluded that students have limited knowledge about Artificial Intelligence, and this may cause them to have unrealistic expectations. Training can help students learn about AI and how to use it effectively and ethically.
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Validation of a teaching model instrument for university education in Ecuador through an artificial intelligence algorithm
(2025)
Jenniffer Sobeida Moreira-Choez
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Tibisay Milene Lamus de Rodríguez
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Núñez-Naranjo, Aracelly Fernanda
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Ángel Ramón Sabando-García
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María Belén Reinoso-Ávalos
Introduction: In the context of university education in Ecuador, the application of Artificial Intelligence (AI) for the assessment and adaptation of teaching models marks significant progress toward enhancing educational quality. The integration of AI into pedagogical processes is increasingly recognized as a strategic component for fostering innovation and improving instructional outcomes in higher education. Methods: This study focused on the validation of an AI-based instrument, specifically designed for the evaluation and adaptation of pedagogical strategies in the Ecuadorian university environment. A quantitative methodology was adopted, employing multivariate statistical analyses and structural equation modeling (SEM) to examine the internal consistency, construct validity, and interrelations among various didactic dimensions. The instrument was applied to a statistically representative sample of university professors across both undergraduate and graduate levels. Results: The statistical analysis demonstrated high levels of internal consistency and discriminative validity among the constructs representing different teaching models. The confirmatory factor analysis and SEM procedures verified the adequacy of the theoretical structure and the robustness of the proposed measurement model. Coefficients obtained for reliability and model fit met or exceeded established thresholds in educational research. Discussion: The findings confirm the empirical soundness of the AI-based instrument and support the feasibility of using such tools to assess and enhance teaching models in higher education. These results underscore the importance of adopting innovative, data-driven methodologies that respond to the demands of contemporary educational environments. Furthermore, the use of AI in the validation process enables a more precise interpretation of educational information, reinforcing the relevance of AI-supported models in optimizing teaching and learning processes.
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Robotics in higher education and its impact on digital learning
(2025)
Zamora Lascano, Patrick
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Lozada, Alan
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Buele, Jorge
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Avilés-Castillo, Fátima
In recent years, robotics has transformed various industrial processes but has also influenced teaching methodologies. Although there are literature reviews on its application in professional training, many are outdated or lack a current focus on its impact in higher education. Addressing this gap, the present mini review examines the impact, challenges, and opportunities of this technology in the university setting. To this end, a search was conducted in the PubMed, Scopus, IEEE Xplore, APA PsycNet, and Web of Science databases, selecting 11 studies that addressed diverse applications of robotics, including educational robotics, robotic telepresence, human-robot interaction, and artificial intelligence applications. Their effects on teaching, the factors influencing their adoption, and the strategies used to optimize their implementation were analyzed. The findings show that educational robotics enhances student motivation and engagement, with prediction models reaching an accuracy of 98.78% in assessing academic engagement. Additionally, robotic telepresence emerges as an effective alternative for hybrid education, and social robots and AI-based tutors demonstrated their potential to personalize learning. However, methodological and structural challenges persist, such as the need to develop more accurate evaluation metrics and ensure accessibility and educational equity. Future research should focus on improving these aspects, enabling more efficient integration to enhance teaching processes. This study was registered in the Open Science Framework under the code: 10.17605/OSF.IO/KHDTU.
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Can artificial intelligence replace journalists? A theoretical approach
(2025)
Londoño Proaño, Cristián
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Buele, Jorge
In the digital age, journalism is facing significant transformations due to the impact of artificial intelligence, a technology that optimizes processes, but also poses ethical and technical dilemmas. This study addresses whether AI can replace journalists or whether it should be considered as a complementary tool that enhances their capabilities. The problem lies in the increasing automation of journalistic tasks and its impact on the quality, ethics and professional identity of the sector. The research justifies its relevance due to the need to understand the scope and limitations of this technology to guarantee ethical and contextualized journalism. The methodology adopted is qualitative and based on documentary analysis. Academic studies, technical reports, and case studies were reviewed to evaluate the use of AI in newsrooms, highlighting its capabilities in automation, personalization, and data analysis, along with its ethical and operational limitations. Among the main results, it is identified that artificial intelligence is effective for tasks such as automated news generation and massive data analysis, but its inability to perform critical analysis and ethical decisions limits it as a complete substitute for the journalist. Likewise, their dependence on trained data perpetuates biases that can compromise the credibility of information. This study highlights that artificial intelligence should be conceived as a support for the journalist, enhancing creativity and analytical depth without compromising the essential values of the profession. It also underscores the importance of a synergistic collaboration between technology and journalists, including regulation and training to take advantage of it ethically and effectively.
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An adversarial risk analysis framework for software release decision support
(2025)
Refik Soyer
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Fabrizio Ruggeri
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David Rios Insua
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Cason Pierce
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Guevara Maldonado, César Byron
Recent artificial intelligence (AI) risk management frameworks and regulations place stringent quality constraints on AI systems to be deployed in an increasingly competitive environment. Thus, from a software engineering point of view, a major issue is deciding when to release an AI system to the market. This problem is complex due to, among other features, the uncertainty surrounding the AI system's reliability and safety as reflected through its faults, the various cost items involved, and the presence of competitors. A novel general adversarial risk analysis framework with multiple agents of two types (producers and buyers) is proposed to support an AI system developer in deciding when to release a product. The implementation of the proposed framework is illustrated with an example and extensions to cases with multiple producers and multiple buyers are discussed
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