In the rapidly evolving landscape of education, the integration of artificial intelligence (AI) tools has become an emerging focal point, particularly among preservice teachers. A groundbreaking study recently published in BMC Psychology sheds new light on the complex interplay between academic overload, work stress, performance expectations, and the adoption of AI models within this crucial demographic. This research offers pioneering insights into how these factors collectively influence pedagogical approaches and professional development in teacher education.
The study meticulously explores how academic overload—characterized by the cumulative demands of coursework, practical teaching commitments, and institutional expectations—serves as a critical stressor that impacts preservice teachers’ engagement with AI technologies. Given the increasing push for AI literacy in educational settings, understanding the psychological and situational mediators between excessive workload and technology adoption is imperative. The authors underscore the importance of dissecting these psychological processes to foster effective integration of AI in teacher preparation programs.
At the heart of this investigation lies the concept of work stress, a multifaceted psychological strain resulting from the clash between external demands and individual coping resources. The researchers posit that work stress functions as a pivotal mediator, translating the experience of academic overload into either resistance or openness to adopting AI models. This mediation role highlights the necessity of stress management interventions to facilitate smoother transitions towards technology-enhanced teaching methods.
Performance expectations also emerge as a significant variable influencing AI model utilization. These expectations, often externally imposed by academic institutions or internally driven by personal ambition, shape how preservice teachers perceive the utility and feasibility of integrating AI into their instructional repertoire. The study indicates that heightened performance demands can simultaneously motivate and hinder AI adoption, depending on how these expectations interact with stress levels and individual resilience.
Methodologically, the research employs a robust cross-sectional design, enabling the capture of nuanced relationships between variables within a defined temporal snapshot. Utilizing validated psychometric instruments to measure academic overload, work stress, and performance expectations, combined with frequency and proficiency metrics related to AI model usage, provides a comprehensive data set capable of supporting intricate path analyses. This methodological rigor contributes to the study’s validity and generalizability across diverse teacher education contexts.
One of the most striking findings reveals that preservice teachers experiencing moderate levels of work stress are more inclined to actively adopt AI models. This counterintuitive outcome suggests that a certain degree of stress may catalyze adaptive coping strategies, including embracing innovative technologies to manage workload more efficiently. Conversely, excessive stress appears to diminish the likelihood of AI integration, potentially due to cognitive overload and decreased emotional availability.
The research further details the dual-edged nature of academic overload. While traditionally viewed as a barrier to effective learning and technology use, this study nuances this perspective by emphasizing its indirect effects through psychological mediators. Specifically, academic overload’s impact is significantly contingent upon the balance of work stress and the nature of performance expectations, advocating for tailored institutional strategies that address these psychological dimensions.
From a pedagogical standpoint, the implications of this study are profound. As teacher education programs aspire to cultivate digitally competent educators, recognizing and mitigating the deleterious effects of academic overload and work stress becomes paramount. Facilitating realistic and supportive performance expectations can create a conducive environment for preservice teachers to experiment with and master AI tools, ultimately enriching their instructional capabilities and student outcomes.
Moreover, this study contributes to the broader discourse on AI integration in education by emphasizing the human factors that underpin technological adoption. It challenges the simplistic narrative that access and technical proficiency alone guarantee successful AI use, advocating instead for a holistic approach that incorporates emotional and cognitive wellbeing into program design and policy-making.
Another critical dimension unwrapped by this study pertains to the differential responses among preservice teachers to academic and professional pressures. Variability in resilience, self-efficacy, and motivation modulates how academic overload translates into work stress and performance expectations. This heterogeneity suggests that one-size-fits-all interventions may be insufficient, highlighting the need for personalized support strategies within teacher education frameworks.
The longitudinal potential of these findings also invites further exploration. While this study is cross-sectional, the dynamic evolution of stress, expectations, and AI adoption over the course of teacher training and early career phases remains a fertile avenue for research. Understanding these trajectories can inform the timing and nature of interventions to sustain and scale AI integration in educational practice.
Crucially, the study underscores the role of institutional culture in shaping preservice teachers’ experiences. Environments that prioritize realistic workloads, provide accessible mental health resources, and implicitly value innovation and experimentation with emerging technologies create fertile ground for transformative pedagogical shifts. These cultural attributes amplify the positive mediating effects of work stress and performance expectations on AI model adoption.
The findings resonate with current global trends where educational stakeholders are progressively acknowledging AI’s potential to revolutionize teaching and learning processes. By foregrounding the psychological mediators, the study provides actionable insights that transcend mere technical training to embrace the emotional and cognitive ecosystems within which technology use unfolds.
In conclusion, this insightful research not only delineates the intricate pathways between academic overload, work stress, performance expectations, and AI utilization but also paves the way for more empathetic and effective teacher preparation paradigms. Embracing these findings promises to enhance the readiness and resilience of upcoming educators, empowering them to harness AI’s transformative power with confidence and competence.
As education systems worldwide navigate the complexities of incorporating AI, studies such as this are indispensable in charting sustainable, human-centered innovation trajectories. The integration of AI models in teacher training is not simply a technical upgrade but a profound cultural and psychological shift—one that requires nuanced understanding and support reflected in research, policy, and practice.
Subject of Research: The impact of academic overload on preservice teachers’ adoption of AI models, emphasizing the mediating roles of work stress and performance expectations.
Article Title: The mediating role of work stress and the performance expectations in the effect of academic overload on the use of AI models among preservice teachers: a cross-sectional study.
Article References:
Acosta-Enriquez, B.G., HuamanÃ-Jordan, O., Morales-Angaspilco, J.E. et al. The mediating role of work stress and the performance expectations in the effect of academic overload on the use of AI models among preservice teachers: a cross-sectional study. BMC Psychol 13, 1026 (2025). https://doi.org/10.1186/s40359-025-03367-8
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