The rapid diffusion of artificial intelligence (AI) in education is reshaping teachers’ professional roles in ways that extend beyond the adoption of new instructional tools. Drawing on recent international survey data, policy reports, and emerging experimental evidence, this article analyses how AI transforms key dimensions of teachers’ work, including instructional planning, assessment practices, and professional responsibility. The analysis demonstrates that while AI can reduce workload and support instructional design, it simultaneously amplifies the importance of teacher judgement, ethical mediation, and assessment governance. The article argues that the central challenge is not technological adoption, but the preservation of pedagogical authority and professional accountability in AI-mediated educational environments.
Keywords: artificial intelligence, teacher professionalism, assessment, workload, educational governance
Introduction
Artificial intelligence has moved rapidly from experimental use to routine presence in educational settings. Unlike earlier digital technologies, contemporary AI systems—particularly generative AI—are capable of producing content, analysing learner data, and simulating pedagogical interactions. As a result, AI no longer functions solely as an auxiliary teaching aid, but increasingly intervenes in core pedagogical processes.
This development has direct implications for the professional role of teachers. Rather than focusing on whether AI should be used in education, current evidence requires a more precise question: how does AI redistribute pedagogical responsibilities, and what forms of professional expertise become more, rather than less, essential?
Empirical evidence of change in teachers’ work
Recent international data indicate that AI use among teachers is already substantial. OECD analyses linked to TALIS 2024 show that approximately one third of teachers across participating systems report having used AI in their professional practice, with considerable national variation. Importantly, AI use is concentrated in lesson planning, content summarisation, and resource adaptation, while substantially fewer teachers report using AI for analysing student performance data.
This pattern is corroborated by national-level studies. A 2025 RAND survey in the United States found that over half of secondary teachers reported using AI for school-related tasks, despite limited institutional guidance. Similarly, data from the United Kingdom indicate a rapid increase in teacher use of generative AI between 2023 and 2025, primarily for preparing instructional materials and assessments.
These findings suggest that AI is currently perceived by teachers as a support for instructional preparation, rather than as a substitute for pedagogical judgement in evaluation and grading.
Workload reduction and its limits
One of the strongest empirical claims regarding AI in education concerns workload reduction. Evidence from the Education Endowment Foundation’s Teacher Choices trial indicates that teachers using generative AI for lesson preparation saved measurable amounts of time—approximately 25 minutes per week—without a decline in perceived lesson quality.
While modest in absolute terms, these findings are significant because they are based on experimental rather than self-reported data. However, the evidence remains limited to specific contexts and tasks. There is currently insufficient longitudinal evidence to conclude that AI reduces overall workload once verification, adaptation, and governance responsibilities are taken into account.
Consequently, workload reduction should be interpreted as a conditional benefit, dependent on teachers retaining control over instructional decisions and quality assurance.
Assessment, validity, and professional responsibility
Assessment represents the most sensitive domain of AI integration. Generative AI challenges established assumptions about authorship, originality, and evidence of learning, particularly in written assignments. Survey data consistently show greater teacher reluctance to use AI for grading or performance analysis than for planning tasks, reflecting concerns about validity, bias, and accountability.
Policy guidance from OECD and UNESCO converges on the principle that AI should support, but not replace, teacher judgement in assessment. Teachers are increasingly required to redesign assessment tasks, combine process-based and oral components, and establish clear rules for acceptable AI use. These responsibilities reinforce, rather than diminish, the teacher’s role as the primary guarantor of assessment integrity.
Reconfiguring the teacher’s professional role
Taken together, current evidence suggests that AI transforms teaching through a reallocation of professional labour, rather than through displacement. Five role dimensions become more salient:
- Instructional designer, specifying pedagogical intent and constraints for AI-supported materials.
- Curator and verifier, responsible for checking accuracy, coherence, and developmental appropriateness.
- Assessment governor, ensuring validity, fairness, and transparency in AI-influenced evaluation.
- Ethical and data steward, overseeing privacy, consent, and responsible data use.
- Relational professional, focusing on feedback, motivation, and the social dimensions of learning.
These functions require advanced professional judgement and cannot be fully automated.
Conclusion
Artificial intelligence is transforming the teacher’s role not by reducing professional relevance, but by intensifying the need for pedagogical, ethical, and evaluative expertise. The most robust evidence to date supports limited workload benefits and widespread use for instructional preparation, while highlighting unresolved challenges in assessment and governance. The critical issue for education systems is therefore not the adoption of AI itself, but whether teachers are adequately supported to exercise informed professional authority within AI-mediated environments.
References
OECD. (2025). Teaching for Today’s World: Results from TALIS 2024.
OECD. (2026). Digital Education Outlook 2026: Exploring Effective Uses of Generative AI in Education.
UNESCO. (2023). Guidance for Generative AI in Education and Research.
UNESCO. (2024). AI Competency Framework for Teachers.
RAND. (2025). AI Use in Schools Is Quickly Increasing but Guidance Lags Behind.
Education Endowment Foundation. (2024). Teacher Choices Trial: Generative AI and Lesson Planning.
Selwyn, N. (2019). Should Robots Replace Teachers? Polity Press.