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Related Experiment Video

Updated: Feb 25, 2026

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
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Exploring the Investment Construct for Quantifying AI Utilization.

Ulubilge Ulusoy1, Garrett E Reisman2

  • 1University of Colorado Boulder, USA.

Human Factors
|February 24, 2026
PubMed
Summary

The Investment Framework quantifies human AI utilization by measuring opportunity costs. Lower task load increased AI investment, with respect influencing subjective investment.

Keywords:
artificial intelligencehuman systemsintelligent systemsteam collaborationtrust in automation

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Last Updated: Feb 25, 2026

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
07:08

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

Published on: December 5, 2025

792

Area of Science:

  • Human-AI Interaction
  • Artificial Intelligence
  • Cognitive Science

Background:

  • The Investment construct offers a novel method to quantify human utilization of AI agents.
  • It is based on measurable opportunity costs incurred by human users.
  • An experimental study was designed to operationalize this construct and investigate its influencing factors.

Purpose of the Study:

  • To quantify human utilization of AI through the Investment construct.
  • To examine factors like respect, trust, workload, and self-confidence influencing AI utilization.
  • To validate the Investment Framework in a practical human-AI collaboration scenario.

Main Methods:

  • An experimental study involving 30 participants performing a maintenance task with AI assistance.
  • Participants were divided into three groups with varying task completion times to simulate different task loads.
  • Objective (typing data) and subjective (custom scale) measures of Investment were collected, alongside data on influencing factors.

Main Results:

  • Significant differences in Investment measures were observed across task load groups, with lower task loads leading to higher Investment.
  • Objective and subjective measures of Investment showed a positive correlation.
  • A correlation was found between respect and the subjective measure of Investment.

Conclusions:

  • This study provides the first empirical support for the Investment Framework, conceptualizing AI utilization via opportunity costs.
  • The findings demonstrate that task load influences human AI Investment.
  • Respect emerged as a significant factor related to subjective AI utilization.