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Reading for all implementing public policies: Quantitative method and process evaluation in early literacy.
(Elsevier, 2025-01-14) Honorato Errázuriz, Jesús; Bastidas Schade, Valentina; Ramirez Montoya, María Soledad; EGADE Business School
Learning to read in the first grade is essential for reducing educational inequalities, highlighting the need to evaluate and enhance reading programs. This study examines "Plan Leo Primero," an innovative national initiative to ensure that all first-grade students in Chile effectively learn to read and comprehend texts. A total of 715 educational stakeholders—including principals, technical leaders, teachers, and guardians—participated in the evaluation, which used validated questionnaires across two regions of the country. The study employed a quantitative and descriptive methodological approach to evaluate the implementation process of the program, fidelity, and impact on literacy outcomes. Despite challenges posed by the COVID-19 pandemic, such as reduced student attendance, shorter implementation periods, and limited resources, the findings indicated successful program implementation with significant improvements in first-grade literacy. Key results included high acceptance and frequent use of the program’s instructional guides and pedagogical tools by teachers, widespread adoption of structured methods aligned with cognitive theory, enhanced teachers’ digital competencies, and strong engagement from guardians in reading activities. Grounded in action theory, the study emphasizes the alignment between program strategies, stakeholder participation, and systemic actions, fostering sustainable literacy progress. The process evaluation identified areas for improving program fidelity, demonstrating that structured evaluation frameworks ensure robust implementation and open avenues for future research. Future projections include assessing the use of technology and communication tools and promoting the social appropriation of knowledge among all stakeholders to drive inclusive and impactful literacy reforms in Chile and the broader Spanish-speaking world.
Challenges of implementing ChatGPT on education: Systematic literature review
(Elsevier, 2025-01-10) García-Lopez, Iván Miguel; González, Carina; Ramírez-Montoya, Maria Soledad; Molina-Espinosa, Jose Martin; EGADE Bussiness School
Since its launch in 2022, ChatGPT has sparked considerable interest in higher education, raising debates about its benefits, challenges, and ethical implications. This systematic literature review, spanning January 2019 to January 2024, analyzes 42 articles from Web of Science and Scopus to identify key opportunities and challenges in its academic integration. Four core issues emerge: (a) technological integration and obsolescence, emphasizing the need for scalable, modular infrastructures; (b) personalization and equity, focusing on the balance between individualized learning and avoiding algorithmic bias; (c) data quality and security, highlighting the importance of transparent data management and robust encryption to protect sensitive information; and (d) ethics and human-AI collaboration, stressing the importance of institutional policies and continuous teacher intervention to ensure responsible and effective use. This study advances the discourse by recommending sustainable strategies for AI adoption, including professional development and fairness audits, while underscoring the critical role of human oversight in maximizing ChatGPT’s educational impact. Ultimately, it offers actionable insights for institutions to align AI use with ethical principles and long-term educational goals.
Robots Teaching Teachers: Acceptanceof Technology in Higher Education
(2025-01-01) López Caudana, Edgar Omar; Suarez Brito, Paloma; Baena Rojas, Jose Jaime; admmserna
This study aimed to investigate teacher perceptions of using advanced technological tools, specifically the NAO robot, in co-teaching settings to enhance class development and promote complex thinking in higher education. Complex thinking is a crucial skill in higher education, enabling students to effectively address and solve multifaceted problems.
Data analytics and artificial neural network framework to profile academic success: case study
(Taylor & Francis, 2024-12-01) Quintero Gámez, Lorena; Tariq, Rasikh; Sánchez Escobedo, Pedro; Sanabria, Jorge
Academic success in higher education has attracted interest from the scientific community because of its implications for personal development and societal progress. Programmes such as Tecnologico de Monterrey’s Leaders of Tomorrow aim to nurture students’ potential and promote academic success. This study examines the attributes from participating students associated with academic success. The focus is on personality and socio-demographic factors that influence academic excellence. The research contribution of this work is data analysis supported by artificial neural networks to establish the relationship between personality tests and background information with academic performance. The findings were: (a) high school GPA predicts university success; (b) first-generation degree status is associated with higher GPA; (c) gender differences in academic performance vary by context; and (d) personality profiles are not associated with academic performance. The role of socio-demographic and personality factors in predicting the academic success of prospective students is discussed.

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