In a reported experiment involving more than 1,300 teachers in Greece, teachers were more likely to accept an unfairly harsh grade when it was attributed to AI than when another teacher was said to have assigned it. That is evidence of deference in one grading scenario—not proof that teachers generally trust AI more than people, or that they accept every automated decision.
What did the experiment find?
Monash University’s 20 August 2026 release describes a randomized experiment in which teachers assessed a harsh grade presented as unfair. Participants were more likely to accept the grade when told AI had assigned it than when it was attributed to another teacher. The finding is about whether teachers challenged a particular output under the scenario tested; it is not a measure of trust in every AI system or every school decision. Monash University’s report does not give the full methods, effect sizes, or a detailed account of how unfairness was operationalized, so the size and precise boundaries of the effect cannot be assessed from the release alone.
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The same release reports that, among the Greek teacher sample, 48 per cent used AI tools at least weekly for lesson preparation, while 16 per cent actively encouraged fellow teachers to adopt the technology. Those figures describe reported use and advocacy, not willingness to rely on AI for grading.
Monash quotes Professor Rigissa Megalokonomou as warning: “Teachers are deferring to AI outputs without sufficiently questioning them, and errors are quietly going uncorrected.” The experiment makes that a consequential concern, but it does not establish why individual teachers accepted the grade.
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Does this mean teachers trust AI more than other teachers?
No broad conclusion follows from one task. Studies of teacher advice-taking have found a different pattern: teachers sometimes prefer human advice or resist algorithmic recommendations. The studies also use different tasks and measures, so their findings are not direct contradictions or interchangeable trust scores.
| Study | Participants and setting | What was measured | Reported result |
|---|---|---|---|
| Monash University, 2026 | More than 1,300 teachers in Greece; randomized experiment, as summarized in the university release | Acceptance of a harsh grade attributed either to AI or to another teacher | Teachers were more likely to accept the unfairly harsh grade when it was attributed to AI. The release does not provide effect sizes or full methods. |
| Kaufmann and Budescu, 2020 | 435 middle- and high-school teachers in three online experiments | Whether participants requested and followed computerized expert-model advice on student profiles | They requested and followed the computerized advice less often than human advice. |
| Nazaretsky and colleagues, 2022 | Six in-service high-school biology teachers in Israel, taking part in a professional-development program | Teachers’ views of, and willingness to use, one automated formative-grading tool | Teachers initially expressed algorithm aversion; after learning about the tool and discussing human grading disagreement, their views became more nuanced and willingness to use the tool increased. |
The 2020 study concerned computerized expert models and judgments about student profiles, not a direct test of generative AI grading. The 2022 work followed a small group using one specific tool; it helps illuminate how views can change, but it cannot estimate how common those changes are among teachers generally.
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Why might an AI grade go unchallenged?
The available evidence supports describing the behavior as acceptance of a particular AI-attributed output despite its unfairness. It does not show that teachers accepted it because they considered AI objective, infallible, or superior. Several explanations are plausible, but the Monash release does not establish which, if any, drove the result.
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →- Trust is situational. The 2022 study uses a definition from Lee and See (2004): trust is “the attitude that an agent will help achieve an individual’s goals in a situation characterized by uncertainty and vulnerability.” In grading, the relevant question is not simply whether a teacher likes AI, but whether they believe a specific tool can help with a specific judgment.
- Advice-taking can shift with the source. A broader behavioral-research summary from HEC Paris describes “algorithm appreciation”: people may follow algorithmic advice more than identical human advice, even when told errors are possible. That is context for a possible pattern, not direct evidence about teachers in the Greek grading experiment. The same summary says complex performance information can reduce appreciation of algorithmic advice. HEC Paris’s 2022 overview discusses the broader research.
- Professional experience may change the comparison. In the Israeli professional-development study, teachers encountered explanations of AI decision-making and practical use, while also confronting disagreement in human grading. Their later discussion was more nuanced than their initial skepticism. This suggests that experience can shape judgment; it does not prove training prevents either overreliance or rejection.
Monash reports that younger teachers, teachers with postgraduate qualifications, and teachers who described themselves as technologically confident were least likely to challenge the harsh AI-generated grade. The release supplies no subgroup effect sizes and does not explain why those differences appeared. It would be unwarranted to infer that confidence, education, or age caused greater deference.
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Can AI grading be trusted in schools?
These studies do not establish that AI grading is reliable or fair in general. Nor do they show that human grading is a dependable alternative in every case. The practical lesson is narrower: an AI recommendation should be treated as evidence to examine, not as a verdict whose source settles whether it is correct.
In the 2022 study, teachers’ initial belief in human grading superiority became more complicated as they learned about the tool and saw that human grading could also differ. That matters because the choice is not necessarily between flawless human judgment and flawed automation. Teachers need enough information and authority to evaluate either one.
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How can teachers use AI without blindly following it?
Professional development should build the ability to interrogate a tool’s output, not merely tell staff that AI can make mistakes. Monash quotes Megalokonomou: “It’s not enough to just tell people AI can be wrong.” The small Israeli study offers a compatible direction: explain how the tool makes decisions and let teachers gain practical experience with its recommendations.
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- Keep a human review step. A teacher should be able to inspect a grade and its rationale before it affects a student.
- Make disagreement actionable. Let teachers review, modify, or override a recommendation rather than requiring passive acceptance. The 2022 study’s authors recommend these forms of agency.
- Check the work, not the label. When an output appears unusually harsh or conflicts with a teacher’s reading of the response, examine the student work and the basis for the grade instead of treating “AI” or “human” as proof.
- Use training to develop judgment. Combine explanations of the tool with hands-on practice and discussion of where human graders also disagree. The six-teacher study supports this as a promising approach, not a proven safeguard.
Schools should distinguish adoption from accountable use. Weekly use for lesson preparation, as reported in the Greek sample, does not by itself show that teachers understand the limits of AI grading or have procedures to catch errors. Oversight has to be designed around the decisions that affect students.
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