Castro Chuquiana , Ricardo David
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Castro Chuquiana , Ricardo David
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Integrating Automation and Artificial Intelligence into Educational Practice

2026 , Adrián Vargas-M. , Esteban Fabricio Gonzabay-Jiménez , Homero J. Velasteguí , Castro Chuquiana , Ricardo David

Digital transformation has introduced technologies such as artificial intelligence (AI), automation, and learning analytics into education, generating more personalized, efficient, and data-driven learning environments. These innovations support intelligent tutoring systems, automated feedback, performance prediction, and administrative decision making. However, their implementation also presents ethical, social, and technical risks that remain insufficiently understood and regulated. This study presents a narrative review of scientific literature published between 2021 and 2025, aiming to analyze the uses, benefits, and challenges of AI in educational contexts, with a particular focus on learning analytics. Articles were selected from major databases such as Scopus, IEEE, SpringerLink, and Web of Science, prioritizing studies that offered practical applications and a critical lens. The review highlights promising developments, including systems that enhance personalized learning trajectories, reduce grading time significantly, and predict student dropout risk with high accuracy. Nonetheless, it also exposes significant concerns, such as the reinforcement of algorithmic biases, excessive surveillance, and the exacerbation of digital inequalities—especially in Latin America, where a substantial portion of students still lack stable access to quality Internet. These findings underscore the dual nature of AI in education: while it has the potential to improve quality and equity, its effectiveness depends on ethical governance, equitable infrastructure, and meaningful teacher training. The review concludes that AI should be used to complement, rather than replace educators’ roles, placing emphasis on learner needs, human interaction, and contextualized pedagogy. Achieving a more human-centered and inclusive education will require a deliberate balance between technological innovation and professional educational judgment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

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Academic Performance and Inequality: Can Generative AI Widen the Digital Divide in Higher Education?

2026 , Castro Chuquiana , Ricardo David

Higher educationHigher education has undergone a rapid transformation driven by digitalization and the rise of generative artificial intelligenceGenerative artificial intelligence tools, such as Chat Generative Pre-trained Transformer and Copilot. While these technologies offer significant opportunities to support learning processes, they also risk deepening existing inequalities if not implemented under equitable conditions. Many students, particularly those in rural areas or with limited resources, continue to face barriers related to device availability, Internet connectivity, and digital training, which restricts the effective use of generative AIArtificial intelligence and negatively affects academic performance. To analyze this gap and identify possible solutions, a critical narrative review of eighteen academic and technical sources published between 2022 and 2024 was conducted. The review focused on five key areas: access, digital literacy, academic impact, structural inequality, and inclusive strategies. The findings reveal sharp disparities: only 27% of rural students have access to devices compatible with generative AI, compared to 70% in urban settings, and students with strong digital competencies obtain up to 60% greater academic benefit from these tools. These results underscore the urgent need for targeted interventions to avoid exacerbating the digital divide. In response, the study proposes five strategies: ensuring equitable institutional access, strengthening critical digital literacyCritical digital literacy, adopting hybrid pedagogical models, designing inclusive curricula, and promoting dedicated research lines on public policy. The conclusion is that although generative artificial intelligenceGenerative artificial intelligence holds considerable educational potential, its benefits will only be realized if embedded in inclusive ecosystems that guarantee equal conditions, teacher support, and the ethical development of learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

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