The integration of artificial intelligence (AI) in higher education presents complex challenges for preservice teachers facing significant academic pressures. This study examines how academic workload affects AI adoption among preservice teachers in Peru and investigates the mediating roles of work stress and performance expectations in this relationship. AI models in this context encompass generative tools for content creation, automated assessment systems, and personalized learning platforms commonly utilized in educational settings. A cross-sectional study was conducted with 876 preservice teachers from 12 Peruvian universities. Data were collected through online questionnaires measuring workload, work stress, performance expectations, and AI usage patterns. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the hypothesized relationships. The findings confirmed that workload has a significant direct positive effect on AI model usage. Work stress significantly mediated the relationship between workload and AI usage. Additionally, work stress and performance expectations operate as serial mediators between workload and AI adoption, demonstrating how psychological mechanisms connect academic pressures to technological behaviors. This study provides novel insights into AI adoption among Peruvian preservice teachers, an underexplored population in educational technology research. The findings reveal the multifaceted relationship between academic workload and AI integration in teacher preparation. Educational institutions should implement stress management interventions including mindfulness training and time management workshops, while establishing realistic performance expectations through hands-on AI literacy programs that address both technical competencies and ethical considerations in technology adoption.

This study examines the determinants of the sustainable use of artificial intelligence (AI) among university professors in Peru. This research adopted a quantitative approach through a cross-sectional empirical–explanatory study, employing a structural equation model. Data were collected from 368 professors from eight Peruvian universities using a structured questionnaire that assessed six main constructs: attitude toward AI, prejudice against AI, facilitating conditions, use of AI, teaching concerns, and ethical perception. While the results reveal significant correlational relationships—with attitude toward AI, facilitating conditions, and prejudice against AI showing a significant association with its sustainable use, and the use of AI showing a significant relationship with professors’ ethical perceptions—the cross-sectional nature of this study precludes causal inferences. No significant relationship was found between the use of AI and teaching concerns. Additionally, demographic variables such as gender and age did not exhibit significant moderating effects. These findings contribute to understanding the factors related to the sustainable adoption of AI in higher education and provide valuable insights for the development of effective institutional strategies in the Latin American context.

Purpose: This document seeks to delve into the digital transformation of education during the COVID-19 pandemic, aiming to provide a comprehensive understanding of this evolving phenomenon's purpose and significance. Design/Methodology/Approach: The research approach undertaken is characterized by a non-experimental, documentary, exploratory, and descriptive study methodology, which involves an extensive examination of existing literature and data to gain insights into the digital transformation in education during the pandemic. Findings: The study's key findings revolve around the consensus in existing literature regarding the swift acceleration of the transformation of education from traditional face-to-face classes to virtual learning environments. It also highlights the implications of this transformation, particularly in reshaping teaching models and advocating for a hybrid approach encompassing both face-to-face and virtual learning. Research, Practical & Social implications: The implications of this research extend to informing educational institutions about the need for digital adaptation, guiding policymakers in supporting adaptable learning models, and empowering educators with a deeper understanding of the changing educational landscape. Moreover, it considers the broader societal impact, including equity and access issues in education. Originality/Value: This research is unique in its contribution to understanding the profound impact of the COVID-19 pandemic on education. It emphasizes the significance of adaptability and hybrid learning models while providing a foundation for future educational research and policy development.