AI in education risks teacher alienation without ethical safeguards

The study finds that alienation is already present across four dimensions: separation from the product of labor, loss of control over the teaching process, weakening of professional identity, and strained interpersonal relationships.

Artificial intelligence is advancing rapidly across global education systems, but new research highlights the risks of alienation for educators if governance frameworks fail to keep pace. In a study published in Sustainability, the researchers analyze how AI integration affects teachers’ professional autonomy, identity, and relationships.

Drawing on Marx’s theory of alienation and contemporary critiques of algorithmic governance in education, the study "Teaching in the AI Era: Sustainable Digital Education Through Ethical Integration and Teacher Empowerment," examines survey responses from nearly 400 educators in Northern Cyprus, where AI adoption in education is still emerging. The findings warn that while AI has the potential to enhance efficiency and teaching effectiveness, its unchecked use risks deepening feelings of disconnection, disempowerment, and estrangement among educators.

How AI reshapes the teacher’s role

The study finds that alienation is already present across four dimensions: separation from the product of labor, loss of control over the teaching process, weakening of professional identity, and strained interpersonal relationships.

Teachers report diminished authorship over their own outputs as algorithmic tools take on core tasks, from grading to content recommendation. AI-driven platforms can streamline administrative work, but they also reshape the process of teaching, often forcing educators to follow rigid protocols that erode flexibility. The study highlights that alienation from professional identity is particularly concerning, as teachers feel that their role is being reduced to facilitators of machine-driven processes rather than autonomous knowledge creators.

Interpersonal relations are not immune either. The integration of AI into communication and evaluation structures sometimes weakens the human dimension of teaching, reducing opportunities for direct, relational engagement between teachers and students. While some educators remain optimistic about AI’s promise, the structural changes in classroom dynamics point to deeper systemic challenges.

Why attitudes alone cannot offset structural risks

Further, the research explores whether positive attitudes toward AI can mitigate alienation. The study finds a partial link: educators with more favorable views of AI report slightly lower alienation, particularly when it comes to the product of their work. However, this effect is modest and limited by the reliability of the measurement tools used.

What stands out, according to the authors, is that attitudes are not enough to counteract the structural forces driving alienation. The problem is less about whether teachers like or dislike AI and more about how AI is implemented. Algorithmic governance and platform logics introduce systemic risks that cannot be solved simply through educator enthusiasm. Without human oversight, accountability, and a focus on preserving professional autonomy, AI adoption may reinforce disempowerment, regardless of individual optimism.

The study notes that this structural reality places responsibility on policymakers and institutions rather than individual educators. It warns against treating alienation as a matter of teacher mindset alone, emphasizing that governance and systemic safeguards are essential to ensure AI strengthens rather than weakens the teaching profession.

Building ethical and sustainable AI integration

The study assesses what kind of governance and policy measures can ensure AI integration supports sustainable digital education. The authors propose a set of practical strategies aligned with global debates on AI ethics and regulatory frameworks.

First, the authors argue for human oversight by default. They recommend a “human-in-command” model where teachers retain final authority over high-stakes decisions. This echoes principles in emerging regulations such as the European Union’s AI Act, which emphasize transparency, explainability, and human oversight.

Next up, the study calls for accountability mechanisms. These include auditable processes, clear opt-out provisions, and rights for educators to contest algorithmic decisions that affect their work. Such safeguards would protect teachers from opaque forms of digital management and restore a sense of agency.

Third, the researchers stress the importance of participatory design. AI systems should be co-created with educators rather than imposed from the top down. Involving teachers in design and deployment can help align technologies with pedagogical values and reduce alienation.

The study urges continuous evaluation and adjustment. AI integration is not a one-off event but an ongoing process that requires regular monitoring to assess impacts on teaching and learning. Policymakers and institutions must treat AI as a tool to be constantly refined rather than a finished solution.