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Enhancing college students' AI literacy through generative AI use: a mixed-methods investigation.

Jingsheng Wang¹, Bing Bai^2,3, Qi An³

¹School of Journalism and Communication, Northwest University, Xi'an, China.

Frontiers in Psychology

|March 25, 2026

Summary

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Generative artificial intelligence (GenAI) use in higher education can improve AI Literacy through iterative learning cycles. This study reveals how social factors and perceived usability influence AI skill development, highlighting the need for integrated educational approaches.

Area of Science:

Higher Education Studies
Artificial Intelligence Research
Educational Technology

Background:

Generative artificial intelligence (GenAI) integration in higher education presents challenges in AI Literacy cultivation, widening the knowledge-practice gap.
Traditional educational frameworks are insufficient for addressing AI-mediated learning, ethical concerns, and competency development.
A critical void exists in current AI Literacy cultivation models, necessitating new theoretical and practical approaches.

Purpose of the Study:

To explore the mechanisms by which GenAI use enhances students' AI Literacy.
To develop and validate a novel theoretical framework for AI Literacy development in higher education.
To identify key factors influencing AI skill acquisition and adoption among college students.

Main Methods:

Keywords:

AI literacy college students generative artificial intelligence mixed-method approach network literacy structural equation modeling

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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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Published on: December 15, 2023

An exploratory sequential mixed-methods design was employed.
Qualitative data were gathered through interviews with 30 students.
Quantitative data were collected via surveys from 590 students (98.33% response rate), analyzed using structural equation modeling.

Main Results:

A spiral-ascending literacy construction model emerged, involving iterative cognition-practice-evaluation cycles.
82% of participants demonstrated critical awareness of algorithmic biases and privacy risks associated with GenAI.
The social environment indirectly influences AI application practice through perceived impressions, mediated by group needs; perceived ease of use and technological expectations are key drivers of adoption and future literacy.

Conclusions:

Findings challenge linear AI Literacy models, emphasizing ecological dynamics and recursive learning.
Actionable insights are provided for designing AI-integrated curricula and policies.
Multi-dimensional interventions, including cognitive scaffolding, ethical education, and skill training, are crucial for fostering active AI Literacy.