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Predicting Behavioral Reliance on AI-Based Depression Treatment Recommendations Among Engineering Graduate Students:

Yeganeh Shahsavar¹, Avishek Choudhury²

¹Armstrong Institute Center for Health Care Human Factors, Johns Hopkins Medicine, Johns Hopkins University, Baltimore, MD, USA.

IISE Transactions on Occupational Ergonomics and Human Factors

|March 7, 2026

Summary

This summary is machine-generated.

This study shows electroencephalography (EEG) can assess how engineering students rely on AI recommendations. This research is crucial for safe human-AI interaction in demanding professional fields.

Keywords:

Behavioral reliance EEG human–AI interaction mental health decision support trust in AI

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Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

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Published on: December 5, 2025

Area of Science:

Neuroscience
Human-Computer Interaction
Cognitive Science

Background:

Engineering professionals increasingly use AI decision systems in critical tasks.
Inappropriate reliance (over-reliance or rejection) on AI can negatively impact performance and safety.
Understanding human reliance behavior is vital for effective human-AI collaboration.

Purpose of the Study:

To explore the feasibility of using electroencephalography (EEG) to measure behavioral reliance on AI recommendations.
To characterize reliance patterns in engineering graduate students interacting with AI.
To inform the development of neurophysiological models for occupational human-AI interaction.

Main Methods:

Utilized electroencephalography (EEG) to capture brain activity during human-AI interaction.
Collected EEG-derived features to analyze behavioral reliance on AI-labeled recommendations.
Employed participant-wise validation for rigorous evaluation of neurophysiological models.

Main Results:

Demonstrated the feasibility of using EEG features to characterize reliance behavior.
Found that predictive performance of the models was modest.
Highlighted the necessity of conservative validation methods for neurophysiological models.

Conclusions:

EEG shows promise as a tool for studying reliance in professional human-AI interaction.
Rigorous evaluation is essential for developing reliable neurophysiological models.
Future research should consider multimodal and context-sensitive approaches for real-world applications.