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Background <p>AI literacy is increasingly important in college students’ academic achievement, daily life, and future employability. However, current research predominantly overlooks the heterogeneity in students’ AI literacy, especially how individual psychological characteristics and features of AI technology contribute to this variation. This oversight limits the formulation of tailored strategies to meet the students’ various demands in an era shaped by rapid AI advancement.</p> Objectives <p>This study aims to adopt an individual-centered approach to identify distinct AI literacy profiles among college students. In addition, it investigates, based on affordance theory, how positive emotions, instrumental motivation, perceived ease of use, and psychological anthropomorphism predict assignment to different profiles.</p> Methods <p>A total of 808 Chinese college students participated in this survey. Latent profile analysis (LPA) was employed to classify students into distinct AI literacy profiles. Multinomial logistic regression was conducted to examine how psychological and technological factors predict profile classification.</p> Findings <p>This study identified four distinct AI literacy profiles among college students: preliminary contact type, ethical orientation type, balanced development type, and behavioral conservatism type. These profiles showed significant differences in positive emotions, instrumental motivation, perceived ease of use, and psychological anthropomorphism, highlighting diverse psychological and technological characteristics inherent to each group.</p> Conclusions <p>This study underscores the heterogeneity of AI literacy within the college student population and detects four distinct AI literacy profiles with unique psychological and technological traits. The findings indicate that students’ AI literacy is profoundly affected by emotional tendencies, motivational drives, and technological variables, highlighting the need for tailored educational strategies that address the distinct psychological and technological drivers of each literacy profile.</p>

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Psychological and technological predictors of AI literacy profiles: a latent profile analysis among Chinese college students

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月26日

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  • Yuanyuan Zhang,
  • Bochun Kang

摘要

Background

AI literacy is increasingly important in college students’ academic achievement, daily life, and future employability. However, current research predominantly overlooks the heterogeneity in students’ AI literacy, especially how individual psychological characteristics and features of AI technology contribute to this variation. This oversight limits the formulation of tailored strategies to meet the students’ various demands in an era shaped by rapid AI advancement.

Objectives

This study aims to adopt an individual-centered approach to identify distinct AI literacy profiles among college students. In addition, it investigates, based on affordance theory, how positive emotions, instrumental motivation, perceived ease of use, and psychological anthropomorphism predict assignment to different profiles.

Methods

A total of 808 Chinese college students participated in this survey. Latent profile analysis (LPA) was employed to classify students into distinct AI literacy profiles. Multinomial logistic regression was conducted to examine how psychological and technological factors predict profile classification.

Findings

This study identified four distinct AI literacy profiles among college students: preliminary contact type, ethical orientation type, balanced development type, and behavioral conservatism type. These profiles showed significant differences in positive emotions, instrumental motivation, perceived ease of use, and psychological anthropomorphism, highlighting diverse psychological and technological characteristics inherent to each group.

Conclusions

This study underscores the heterogeneity of AI literacy within the college student population and detects four distinct AI literacy profiles with unique psychological and technological traits. The findings indicate that students’ AI literacy is profoundly affected by emotional tendencies, motivational drives, and technological variables, highlighting the need for tailored educational strategies that address the distinct psychological and technological drivers of each literacy profile.