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Related Experiment Video

Updated: Feb 19, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

Published on: September 26, 2025

The Generative Artificial Intelligence (GenAI) Divide: Exploring the Individual Factors Influencing GenAI Competency,

Christopher Ball¹, Kuo-Ting Huang², Xuan Nie²

¹Department of Journalism and Institute of Communications Research. University of Illinois Urbana-Champaign, Urbana, Illinois, USA.

Cyberpsychology, Behavior and Social Networking

|February 17, 2026

Summary

Generative artificial intelligence (GenAI) adoption faces a digital divide, influenced by individual factors like skills and attitudes. Addressing psychological barriers and promoting AI literacy are crucial for equitable GenAI access and benefits.

Keywords:

AI literacy ChatGPT digital divide digital inequality generative artificial intelligence (GenAI)

Related Experiment Videos

Last Updated: Feb 19, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

Published on: September 26, 2025

Area of Science:

Social Sciences
Computer Science
Human-Computer Interaction

Background:

Generative artificial intelligence (GenAI) offers transformative potential but risks exacerbating digital inequalities.
The 'GenAI divide' highlights uneven distribution of benefits and challenges associated with GenAI adoption.
Understanding individual factors influencing this divide is critical for equitable technology integration.

Purpose of the Study:

To examine individual factors (sociodemographic, competency, psychological) influencing GenAI adoption readiness.
To investigate psychological barriers, GenAI competency, and adoption readiness.
To explore the relationship between GenAI reliance, self-assessed competency, and user anxiety.

Main Methods:

Stratified online survey of 305 participants.
Analysis of sociodemographic variables (age, sex, race, education, income).
Assessment of operational/creative skills, AI attitudes, decision reliance, and self-efficacy.

Main Results:

Operational skills and AI comfort correlate with lower anxiety and higher adoption readiness.
Negative technology attitudes increase psychological barriers to GenAI adoption.
Increased reliance on GenAI for decisions is linked to lower self-assessed competency.

Conclusions:

The GenAI divide is multi-dimensional, shaped by individual factors and leading to inequality.
Targeted interventions like AI literacy programs and digital communities can mitigate disparities.
This research provides insights into digital inequality in the era of generative AI.