Artificial intelligence (AI) is changing how we teach and learn, generating excitement and concern about its potential to transform education. To contribute to the debate, this systematic literature review examines current research trends (publication year, country of study, publication journal, education level, education field, and AI type), as well as the benefits and challenges of integrating AI into education. This review analyzed 155 peer-reviewed empirical studies published between 2015 and 2025. The review reveals a significant increase in research activity since 2022, reflecting the impact of generative AI tools, such as ChatGPT. Studies highlight a range of benefits, including enhanced learning outcomes, personalized instruction, and increased student motivation. However, there are challenges to overcome, such as students’ ethical use of AI, teachers’ resistance to using AI systems, and the digital dependency these systems can generate. These findings show AI’s potential to enhance education; however, its success depends on careful implementation and collaboration among educators, researchers, and policymakers to ensure meaningful and equitable outcomes.

Augmented reality (AR) enables an interactive experience with the real world where real-world objects are enhanced with computer-generated perceptual information. Twenty-five years have passed since the first AR application designed exclusively to be used in educational settings. Since then, this technology has been successfully implemented to enrich educational contexts providing learning gains, motivation, enjoyment, autonomy, among other benefits. This study provides an overview of AR technology in education from its origins to the present. Consequently, based on the analysis of its evolution, the study defines three generations of AR in education. Moreover, the study identifies some major challenges from previous AR applications and, finally, it poses some insights to address these challenges to enhance the benefits of AR for education.

Se presenta una metodología para determinar las variables asociadas al rendimiento académico universitario (RA) de una cohorte de estudiantes del año 2014 en una universidad privada del noroeste de Colombia. Se utilizó un discriminante canónico y una regresión logística para establecer la asociación entre variables pedagógicas, institucionales y sociodemográficas con el bajo RA utilizando unicamente información de los sistemas administrativos de la universidad. El análisis discriminante permitió clasificar el 100% de los estudiantes con bajo RA a partir de las variables institucionales y sociodemográficas. Las variables pedagógicas no mostraron discriminar adecuadamente, a excepción de la facultad de derecho. Desde la regresión logística se encontraron asociaciones significativas del bajo RA con la trayectoria del estudiante, el hecho de tener beca, la repitencia y el número de asignaturas canceladas. Se concluye que el análisis discriminante y la regresión logística permiten clasificar y establecer los factores que explican el bajo RA universitario.