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Inclusive higher education for students with special educational needs: review of advances, challenges, and future directions
(2026)
Esparza Yánez, Wilmer
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Lorenzo Hidalgo, Alejandro Ernesto
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Camaño Carball, Lilian
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Israel Vinueza-Fernández
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García Pérez, Dianet
Inclusive education (IE) in Higher Education (HE) has become a global priority, driven by the mandate of the United Nations Convention on the Rights of Persons with Disabilities (CRPD). However, the implementation remains uneven, characterized by fragmented institutional responses and conceptual ambiguities. This article presents an international, critical narrative review of the advances and persistent challenges concerning IE for Students with Special Educational Needs (SEN) and Students with Disabilities (SWDs). A narrative review was conducted on international peer-reviewed literature and policy documents published mainly between 2015 and 2025. The analysis is critically structured around six dimensions: (1) conceptualizations of SEN and disability; (2) characteristics and academic trajectories of SWDs; (3) institutional and pedagogical models; (4) the use of digital and assistive technologies; (5) legislative and policy frameworks; and (6) the economic and financial costs associated with inclusion. The review confirms a necessary paradigm shift from the reactive Medical Model to the proactive, Rights-Based Model. Universal Design for Learning (UDL) and innovative, student-centered pedagogies are crucial tools for systemic change, supported by the transformative potential of emerging technologies like AI and Virtual Reality for personalization. Despite these advances, a critical gap persists between policy and practice. Major challenges include the persistent lack of faculty readiness to implement UDL effectively, fragmented policy management, and inadequate financial models that treat inclusion as a cost rather than an investment. Achieving genuine equity in HE requires moving beyond minimal legal compliance toward an integrated, systemic commitment. Future research must focus on longitudinal studies measuring the impact of UDL on retention, efficacy of faculty training, and developing robust, bifurcated financial models. The ultimate success hinges on redesigning the educational environment—pedagogically, technologically, and financially—to establish diversity as the institutional norm.
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Influence of Artificial Intelligence in the Academic Field Within the Context of Higher Education in Ecuador
(2026)
Javier Guaña-Moya
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Yamileth Arteaga-Alcívar
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Diego Yánez Flores
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Luis Enrique David Tenorio
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David Ramos Galarza
The impact of artificial intelligence (AI) in the academic field and its integration in higher education in Ecuador constitutes an increasingly relevant topic. AI has seen notable advances in various spheres, ranging from agriculture and manufacturing to medicine and education. These technological progresses are generating a growing demand for professionals specialized in AI, causing a substantial transformation in the dynamics of teaching and learning. However, this technological revolution poses significant challenges in social, economic, ethical, and legal terms, which demand reflection and discussion at a global level. The influence of AI in higher education in Ecuador has generated considerable transformation by improving the accessibility and personalization of learning. The automation of administrative tasks and the implementation of AI based recommendation systems have optimized academic efficiency. The introduction of novel assessment methods, such as automatic correction, has streamlined feedback. Despite these benefits, the implementation of AI presents challenges, such as the need for teacher training and ensuring equity in access. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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Advancing University Education: Exploring the Benefits of Education for Sustainable Development
(2024)
Diego Bonilla-Jurado
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Ember Zumba
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Araceli Lucio-Quintana
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Carlos Yerbabuena-Torres
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Andrea Ramírez-Casco
This article addresses the integration of Education for Sustainable Development (ESD) in higher education institutions, exploring its effects on academic performance and students’ ability to address sustainability challenges. Using the PRISMA 2020 methodology for a systematic literature review, 50 relevant articles were selected from 543 records, providing data on the academic impacts of ESD through bibliometric approaches and surveys. The results revealed that ESD improves academic performance, motivation and engagement, as well as enhances students’ ability to solve complex problems sustainably. However, significant barriers, such as a lack of resources and adequate teacher training, hinder effective implementation. Approximately 60% of students in ESD programs show greater motivation and analytical abilities compared to 50% in traditional programs. ESD enriches academic training and equips students with essential practical skills, preparing them to be agents of positive change. Incorporating emerging technologies and participatory learning methods is crucial to enhancing ESD effectiveness. Greater investment in teacher training and standardized educational materials, along with the promotion of international collaboration to share resources and best practices, is required.
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Preparation of Higher Education Students in Ecuador: An Analysis Based on the Knowledge Economy
(2024)
Varela Lascano Darwin Marcelo
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Diego Fernando Salas Heredia
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Silvana Micaela Coloma Gudiño
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Guevara Maldonado, César Byron
The knowledge economy has emerged as a key paradigm in global socioeconomic development, highlighting the importance of higher education in the formation of human capital capable of generating, applying and disseminating innovative knowledge. This study aims to evaluate the preparation of higher education students in Ecuador, considering critical variables such as soft skills, perception of the knowledge economy, university-business linkage and internationalization, and their impact on academic training. The methodology employed was quantitative, using a multiple linear regression model to analyze the relationship between the independent variables and the academic formation of a sample of 205 students from two Ecuadorian universities. Advanced statistical techniques were applied to evaluate the significance and impact of each variable. The results indicate that soft skills (r = 0.713, p < 0.01), perception of the knowledge economy (r = 0.602, p < 0.01) and internationalization (r = 0.594, p < 0.01) have a significant and positive impact on academic training. However, university-business linkage showed a lower and non-significant correlation (r = 0.407, p < 0.01). In conclusion, academic training in Ecuador benefits significantly from the development of soft skills, a positive perception of the knowledge economy and internationalization. However, the lack of significant impact of university-business linkages suggests the need for future studies to explore barriers and improve these collaborations. These findings underscore the importance of educational policies that integrate these factors to improve the preparation of students in a global knowledge economy.
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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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Influence of Higher Education on IoT Acceptance through Hands-On Learning
(2025)
Varela Aldas, José
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Christian Junta
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Elias Choque
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Guillermo Palacios-Navarro
The Internet of Things (IoT) applications are pervasive across various sectors; however, there remains some resistance to its adoption. Education 4.0 promotes the full integration of new technologies, both as tools for learning and instruments for professional development. This work studies the influence of higher education on the willingness towards IoT adoption after hands-on learning experiences. The primary objective is to determine whether a correlation exists between IoT adoption and the education of university students from three distinct professional degrees. The methodology employed involves a practical class where students engage in developing applications for manual data collection. These applications are designed to send data to the Internet, which is then visualized through a web interface. Tailored to each respective degree, three similar applications are developed. For this research, M5 Stack Core2 kits are utilized, along with UIFLOW programming language and the ThingSpeak platform, operating under the MQTT protocol. Following the training, students complete a Technology Acceptance Model (TAM) survey for IoT. The analysis of the influence of higher education on IoT acceptance employs ANOVA to identify differences between group means. The results reveal statistically significant differences in IoT acceptance between students in Industrial and Architecture degrees.
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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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