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Updated: Jun 15, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Identifying black swans in NextGen: predicting human performance in off-nominal conditions.

Christopher D Wickens¹, Becky L Hooey, Brian F Gore

¹Alion Science Corporation, Micro Analysis and Design Operations, 4949 Pearl East Circle, Suite 300, Boulder, CO 80301, USA. cwickens@alionscience.com

|March 4, 2010

Summary

A computational model accurately predicted pilots' failures to notice unexpected events, validating its use for aviation safety. This model supports predicting risks with new technologies.

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Area of Science:

Aviation psychology
Human factors in aviation
Cognitive modeling

Background:

Pilot failure to notice unexpected events contributes to aircraft accidents.
Change blindness, or failure to notice visual changes, is a key factor.
The N-SEEV (noticing-salience, expectancy, effort, and value) model was developed to predict these failures.

Purpose of the Study:

To validate the N-SEEV computational model of visual attention.
To compare model predictions against empirical data from pilots' failure to notice critical events.

Main Methods:

A meta-analysis pooled miss rate data from 25 high-fidelity cockpit simulation studies.
Data included variables like flight phase, event expectancy, and display types.
N-SEEV model parameters were tailored to match these empirical data dichotomies.

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Last Updated: Jun 15, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Main Results:

The N-SEEV model successfully predicted variance in pilot miss rates (r = .73).
Individual miss rates for six conditions were predicted within 14%, with four within 7% accuracy.

Conclusions:

The N-SEEV model, validated against independent data, accurately predicts pilot responses to abnormal circumstances.
Computational models like N-SEEV offer cost-effective methods for assessing safety risks of future aviation technologies and procedures.