AI tools in education linked to rising technostress among teachers

AI-enabled education systems increasingly support lesson planning, grading, student monitoring, and administrative reporting. While these tools are often marketed as efficiency boosters, the research shows that their introduction has expanded teachers’ responsibilities rather than reduced them.

New research suggests that the rapid introduction of AI into schools is creating a hidden cost for teachers. Many educators are experiencing rising levels of technostress driven not by technology itself, but by unclear roles, uneven support, and growing pressure to adapt.

The study Research on Middle School Teachers’ Technostress Empowered by Artificial Intelligence, published in Frontiers in Artificial Intelligence, examines how AI integration is reshaping teachers’ working conditions, particularly in middle school environments.

AI adoption reshapes the teaching role

AI-enabled education systems increasingly support lesson planning, grading, student monitoring, and administrative reporting. While these tools are often marketed as efficiency boosters, the research shows that their introduction has expanded teachers’ responsibilities rather than reduced them.

One of the key findings is that role conflict is the strongest driver of technostress. Teachers are expected to remain subject experts, mentors, classroom managers, and emotional supporters, while also learning to operate, supervise, and troubleshoot AI systems. These overlapping demands blur professional boundaries and create uncertainty about where human judgment ends and algorithmic decision-making begins.

The pressure is particularly acute in systems where AI adoption is driven from the top down. Schools often mandate the use of AI tools without fully redefining teachers’ roles or adjusting workload expectations. As a result, educators must integrate new technologies into existing routines while still meeting traditional performance metrics, such as exam results and classroom observation standards.

The study finds that technostress levels are not uniform across the teaching workforce. Male teachers report higher stress levels than female teachers, a pattern the authors link to differences in perceived role expectations and adaptation strategies. Teachers with five to eight years of experience, as well as those with more than fifteen years in the profession, also experience elevated stress. Mid-career teachers face rising performance pressure, while senior teachers often struggle with rapid technological change after decades of established practice.

Geography also matters. Teachers in rural schools report significantly higher technostress than their urban counterparts. Limited infrastructure, fewer training opportunities, and weaker institutional support amplify the challenges of AI integration in less-resourced settings. The findings highlight a digital divide not just among students, but among educators themselves.

Technology design and perception matter more than skills

Contrary to common policy responses, the research shows that technical competence alone does not significantly reduce stress. Teachers who possess strong digital skills still experience high technostress if they perceive AI tools as unreliable, poorly designed, or misaligned with classroom needs.

Instead, technological characteristics emerge as the most powerful protective factor. When AI systems are perceived as useful, stable, and easy to integrate into teaching workflows, technostress drops markedly. Teachers respond positively to tools that clearly support instructional goals, reduce redundant tasks, and function consistently without frequent errors or updates.

Closely linked to this is digital awareness, which the study defines as teachers’ understanding of AI’s purpose, limitations, and educational value. Digital awareness has a direct and significant effect on reducing technostress. Teachers who understand why AI tools are being introduced and how they complement human teaching are more likely to view them as allies rather than threats.

By contrast, digital knowledge and application competence only reduce stress indirectly, through their influence on awareness. This distinction has important policy implications. Training programs that focus narrowly on technical operation may fail to address deeper concerns about professional identity, autonomy, and trust in AI systems.

The study also finds that individual competence can act as a double-edged sword. While higher competence improves awareness, it can also increase role conflict by raising expectations. Teachers who are more capable with AI tools are often asked to take on additional responsibilities, such as supporting colleagues or managing digital platforms, further expanding their workload.

These findings suggest that technostress is not simply a transitional issue that will fade as teachers become more skilled. Without changes in system design and role definition, stress may persist or even intensify as AI becomes more embedded in daily teaching practice.

Organizational support shows mixed results

Organizational support is often presented as the solution to technostress, but the study paints a more complex picture. Institutional measures such as training programs, technical assistance, and policy guidance do improve teachers’ application competence and digital awareness. However, they do not directly reduce technostress and can, in some cases, increase it.

The reason lies in how support is implemented. When training and mandates are imposed without adjusting workloads or clarifying expectations, they add to teachers’ sense of role conflict. Teachers may feel pressured to demonstrate AI proficiency while still meeting existing teaching demands, leading to longer working hours and heightened stress.

The study highlights that support focused on compliance rather than empowerment is particularly problematic. Teachers report greater stress when AI adoption is framed as an obligation tied to performance evaluation, rather than as a tool designed to meet their needs. In such cases, organizational support becomes another source of pressure rather than relief.

These findings underscore the importance of aligning institutional policies with teachers’ lived realities. Effective support must go beyond providing tools and training to address workload distribution, role clarity, and professional autonomy. Without these adjustments, investments in AI infrastructure may fail to deliver their intended benefits.

Managing technostress in AI-enabled education requires a shift in perspective. Rather than treating stress as an individual weakness or a temporary adjustment issue, it should be recognized as a systemic outcome of how technology is introduced and governed.

The authors call for demand-driven AI design that prioritizes teachers’ instructional needs, differentiated support strategies based on experience and context, and clearer definitions of teachers’ roles in human–AI collaboration. They also call for greater attention to digital awareness, ensuring that educators understand not just how to use AI tools, but why they are being used and what they are not meant to replace.