Acosta-Enriquez Benicio Gonzalo, Huamaní-Jordan Olger, Morales-Angaspilco Jahaira Eulalia, Heredia-Pérez Oscar, Ruiz-Carrillo Jonathan Ruiz, Blanco-García Luz Elvira, Veliz Palacios de Villalobos Sonia Mercedes
Universidad Nacional de Trujillo, Trujillo, Perú.
Universidad Tecnológica del Perú, San Juan de Lurigancho, Lima, Perú.
BMC Psychol. 2025 Sep 26;13(1):1026. doi: 10.1186/s40359-025-03367-8.
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.
人工智能（AI）融入高等教育给面临巨大学业压力的职前教师带来了复杂挑战。本研究考察了学业工作量如何影响秘鲁职前教师对人工智能的采用情况，并探究了工作压力和绩效期望在这种关系中的中介作用。在此背景下的人工智能模型包括用于内容创作的生成工具、自动评估系统以及教育环境中常用的个性化学习平台。
对来自秘鲁12所大学的876名职前教师进行了一项横断面研究。通过在线问卷收集数据，问卷测量了工作量、工作压力、绩效期望和人工智能使用模式。采用偏最小二乘结构方程模型（PLS - SEM）分析假设关系。
研究结果证实，工作量对人工智能模型的使用有显著的直接正向影响。工作压力显著中介了工作量与人工智能使用之间的关系。此外，工作压力和绩效期望在工作量与人工智能采用之间起连续中介作用，表明心理机制如何将学业压力与技术行为联系起来。
本研究为秘鲁职前教师对人工智能的采用提供了新的见解，这是教育技术研究中一个未被充分探索的群体。研究结果揭示了学业工作量与教师培养中人工智能整合之间的多方面关系。教育机构应实施压力管理干预措施，包括正念训练和时间管理工作坊，同时通过实践人工智能素养项目建立现实的绩效期望，该项目要兼顾技术能力和技术采用中的伦理考量。
