A new study has found that people are less likely to correct mistakes when they believe they are made by artificial intelligence rather than by humans.
The study, published in PNAS Nexus, involved a randomised experiment with more than 1,300 teachers in Greece.
Researchers measured how teachers responded to unfairly harsh grades given to students’ work by an AI system compared with another teacher. They found teachers were more likely to accept an unfair grade when it had been assigned by AI.
The research was conducted by Dr Sofoklis Goulas (Yale University), Professor Rigissa Megalokonomou (Monash University) and Dr Panagiotis Sotirakopoulos (Curtin University).
Professor Rigissa Megalokonomou, from Monash Business School, said the findings challenged the assumption that human oversight is enough to prevent AI mistakes.
“Teachers are deferring to AI outputs without sufficiently questioning them, and errors are quietly going uncorrected,” Professor Megalokonomou said.
“Multiply that across thousands of students and thousands of classrooms, and you start to see how quickly this becomes a serious problem.”
Professor Megalokonomou said the findings presented challenges for schools, with AI tools increasingly used for lesson planning, feedback, identifying struggling students and administrative tasks.
According to the research, nearly half of teachers (48 per cent) used AI tools at least weekly for lesson preparation. However, only 16 per cent actively urged fellow teachers to adopt the technology.
The study also found that younger teachers, those with postgraduate qualifications, and those who described themselves as technologically confident were the least likely to challenge a harsh AI-generated grade.
“The very people we might expect to be the most capable and critical users of AI tools turned out to be the most likely to defer to a harsh AI grade,” Professor Megalokonomou said.
“That’s counterintuitive and concerning because the people pushing AI integration forward in schools may be the least likely to catch its errors.”
Researchers are now developing a training program to help teachers use AI critically.
“It’s not enough to just tell people AI can be wrong,” Professor Megalokonomou said.
“You need to show them specifically how and when their judgment is likely to go astray and build the habits to push back on AI.”
The team is exploring future projects to investigate whether AI helps reduce or amplify the biases teachers bring to grading, whether AI training improves teachers’ day-to-day productivity, and whether simple, low-cost interventions can change how educators engage with AI tools.
“I hope this research reaches the people who are making decisions right now about AI in schools, including policymakers, school leaders and education departments,” Professor Megalokonomou said.
