Human Learning is an Underestimated Factor in the Success of Human–AI Teams

"The potential of human–AI collaboration is underestimated." That is the central conclusion of the new study, published in the Proceedings of the National Academy of Sciences, and conducted by an international team led by Julian Berger and Stefan Herzog from the Center for Adaptive Rationality at the Max Planck Institute for Human Development. The research team took a closer look at a review study that made waves in 2024. The review analyzed 74 studies on human–AI interaction, spanning a wide range of tasks from classifying images and predicting house prices to writing code.
Its findings were sobering: On average, the synergy effect was negative. In other words, the human–AI team performed worse together than the better of the two working alone. This fueled doubts about whether human–AI collaboration was worth pursuing at all.

At a glance
- Earlier analyses of human–machine interaction mostly showed negative synergy effects. A review of 74 studies suggested that human–AI teams perform less well than the better of the two working alon
- These 74 studies have been reanalyzed by researchers from the Center for Adaptive Rationality at the Max Planck Institute for Human Development. Their results paint a much more positive picture.
- The reason for the difference: The studies systematically underestimated human learning ability; most did not examine it a all. Yet when participants were able to learn from feedback and explanations, human–AI teams tended to outperform the better partner alone.
- Need for a paradigm shift: The researchers call for future work on human–AI interaction to explicitly factor human learning ability into studies and experiments.