Original title: Where Artificial Intelligence Enters Teacher Work
Jacob Daniel Holster · September 2026
More teachers are using artificial intelligence, but simple adoption numbers do not explain which specific tasks they assign to the technology. Researchers investigated whether educators turn to artificial intelligence when a particular job feels especially demanding. They examined three major areas of schoolwork: planning lessons, grading assessments, and adapting materials for special-education needs. The study revealed that feeling overburdened does make teachers more likely to adopt the technology. However, how they divide their work among these tools depends strictly on the chore. Teachers who felt higher strain on specific tasks were much more likely to use tools for special-education support and lesson planning. By contrast, when grading felt more demanding, educators actually used the tools less. Work pressure does not create one single pattern of use across teaching duties.
Imagine you have a list of house chores and you can pay someone to help with some of them. You might think you'd pay for the chores you dislike most. But in reality, you pay for vacuuming but not for laundry, even though you hate laundry more. In this paper, the chores are teaching tasks, and paying someone is using AI assistance. Teachers are more likely to use AI for tasks they find relatively demanding, but the connection is not the same for each task—just like your chore preferences are inconsistent.
For school leaders and policymakers, this means that simply encouraging AI adoption is not enough; where teachers actually use AI depends on the task. A teacher might use AI for lesson planning but not for marking, even if marking feels more demanding. Understanding these task-specific patterns can help design better support and training, ensuring AI tools target the tasks where teachers need them most.
The abstract doesn't mention limitations explicitly, but the study only examines three tasks (lesson planning, assessment/marking, special-education support) and relies on self-reported survey data, which may not reflect actual behavior. The findings are descriptive and do not explain why teachers make these choices.
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Where Artificial Intelligence Enters Teacher Work — Jacob Daniel Holster — EdArXiv — doi:10.35542/osf.io/a39rp_v1
Abstract licence: CC BY
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.