Machine learning systems as mentors in human learning: A user study on machine bias transmission in medical training

While accurate AI systems can enhance human performance, exerting both an augmentation and good mentoring effect, imperfect systems may act as poor mentors, transmitting biases and systematic errors to users. However, there is still limited research on the potential for AI to transmit biases to humans, an effect that could be even more pronounced for less experienced users, such as novices or trainees, making decisions supported by AI-based systems. To investigate the bias transmission effect and the potential of AI to serve as a mentor, we involved eighty-six medical students, dividing them into an AI-assisted group and a control group. We tasked them with classifying simulated tissue samples for a fictitious disease. In the first phase of the task, the AI group received diagnostic advice from a simulated AI system that made systematic errors for a specific type of case, while being accurate for all other types. The control group did not receive any assistance. In the second phase, participants in both groups classified new tissue samples, including ambiguous cases, without any support to test the residual impact of AI bias. The results showed that the AI-assisted group exhibited a higher error rate when classifying cases where the AI provided systematically erroneous advice, both in the AI-assisted and the subsequent unassisted phase, suggesting the persistence of AI-induced bias. Our study emphasizes the need for careful implementation and continuous evaluation of AI systems in education and training to mitigate potential negative impacts on trainee learning outcomes. • Machine-human knowledge transmission received little attention from prior research. • Our study explored AI-driven upskilling and bias transmission in medical trainees. • Students relied on the AI and mimicked its bias even time after the AI was removed. • Trainees learnt from the AI not only biased but also correct patterns of response. • The research highlights AI’s potential to be both a good and a bad mentor.

Authors

Institutions

Publication Details

Journal
International Journal of Human-Computer Studies
Published
2025-02-20
DOI
https://doi.org/10.1016/j.ijhcs.2025.103474
Citations
1
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.65
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Machine learning systems as mentors in human learning: A user study on machine bias transmission in medical training

Federico Cabitza, Lucía Gómez Vicente, Helena Matute, Caterina Fregosi
1 citations
International Journal of Human-Computer Studies
Artificial Intelligence in Healthcare and Education
0.65
article

Machine learning systems as mentors in human learning: A user study on machine bias transmission in medical training

Federico Cabitza, Lucía Gómez Vicente, Helena Matute, Caterina Fregosi
article en
1 citations

Abstract

While accurate AI systems can enhance human performance, exerting both an augmentation and good mentoring effect, imperfect systems may act as poor mentors, transmitting biases and systematic errors to users. However, there is still limited research on the potential for AI to transmit biases to humans, an effect that could be even more pronounced for less experienced users, such as novices or trainees, making decisions supported by AI-based systems. To investigate the bias transmission effect and the potential of AI to serve as a mentor, we involved eighty-six medical students, dividing them into an AI-assisted group and a control group. We tasked them with classifying simulated tissue samples for a fictitious disease. In the first phase of the task, the AI group received diagnostic advice from a simulated AI system that made systematic errors for a specific type of case, while being accurate for all other types. The control group did not receive any assistance. In the second phase, participants in both groups classified new tissue samples, including ambiguous cases, without any support to test the residual impact of AI bias. The results showed that the AI-assisted group exhibited a higher error rate when classifying cases where the AI provided systematically erroneous advice, both in the AI-assisted and the subsequent unassisted phase, suggesting the persistence of AI-induced bias. Our study emphasizes the need for careful implementation and continuous evaluation of AI systems in education and training to mitigate potential negative impacts on trainee learning outcomes. • Machine-human knowledge transmission received little attention from prior research. • Our study explored AI-driven upskilling and bias transmission in medical trainees. • Students relied on the AI and mimicked its bias even time after the AI was removed. • Trainees learnt from the AI not only biased but also correct patterns of response. • The research highlights AI’s potential to be both a good and a bad mentor.

International Journal of Human-Computer StudiesVol. 198
Universidad de Deusto (ES), Istituto Clinico Sant'Ambrogio (IT), University of Milano-Bicocca (IT)
Openalex Percentile: Top 27%
Artificial Intelligence in Healthcare and Education
0.65
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.