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A Meta-analysis of College Students' Intention to Use Generative Artificial Intelligence

Yifei Diao, Ziyi Li, Jiateng Zhou, Wei Gao, Xin Gong

TL;DR

This study addresses inconsistent findings on college students' intention to use GenAI by conducting a meta-analysis of 27 empirical studies (87 effect sizes, n=33,833) within an integrated theoretical framework combining TPB, TAM/UTAUT, and EVT. It finds attitude (r=0.576) and performance expectancy (r=0.389) as the strongest predictors of behavioural intention, with other factors like habit, social influence, facilitating conditions, effort expectancy, and hedonic motivation showing significant, yet smaller effects, while perceived cost is negatively related. The analysis also reveals regional moderation—effort expectancy and habit vary between developed and developing regions—and gender moderation limited to the attitude–intention link. The findings offer evidence-based guidance for educators, policymakers, and GenAI developers to foster responsible adoption in higher education, highlighting the importance of shaping positive attitudes and realistic performance expectations.

Abstract

It is of critical importance to analyse the factors influencing college students' intention to use generative artificial intelligence (GenAI) to understand and predict learners' learning behaviours and academic outcomes. Nevertheless, a lack of congruity has been shown in extant research results. This study, therefore, conducted a meta-analysis of 27 empirical studies under an integrated theoretical framework, including 87 effect sizes of independent research and 33,833 sample data. The results revealed that the main variables are strongly correlated with students' behavioural intention to use GenAI. Among them, performance expectancy (r = 0.389) and attitudes (r = 0.576) play particularly critical roles, and effort expectancy and habit are moderated by locational factors. Gender, notably, only moderated attitudes on students' behavioural intention to use GenAI. This study provides valuable insights for addressing the debate regarding students' intention to use GenAI in existed research, improving educational technology, as well as offering support for school decision-makers and educators to apply GenAI in school settings.

A Meta-analysis of College Students' Intention to Use Generative Artificial Intelligence

TL;DR

This study addresses inconsistent findings on college students' intention to use GenAI by conducting a meta-analysis of 27 empirical studies (87 effect sizes, n=33,833) within an integrated theoretical framework combining TPB, TAM/UTAUT, and EVT. It finds attitude (r=0.576) and performance expectancy (r=0.389) as the strongest predictors of behavioural intention, with other factors like habit, social influence, facilitating conditions, effort expectancy, and hedonic motivation showing significant, yet smaller effects, while perceived cost is negatively related. The analysis also reveals regional moderation—effort expectancy and habit vary between developed and developing regions—and gender moderation limited to the attitude–intention link. The findings offer evidence-based guidance for educators, policymakers, and GenAI developers to foster responsible adoption in higher education, highlighting the importance of shaping positive attitudes and realistic performance expectations.

Abstract

It is of critical importance to analyse the factors influencing college students' intention to use generative artificial intelligence (GenAI) to understand and predict learners' learning behaviours and academic outcomes. Nevertheless, a lack of congruity has been shown in extant research results. This study, therefore, conducted a meta-analysis of 27 empirical studies under an integrated theoretical framework, including 87 effect sizes of independent research and 33,833 sample data. The results revealed that the main variables are strongly correlated with students' behavioural intention to use GenAI. Among them, performance expectancy (r = 0.389) and attitudes (r = 0.576) play particularly critical roles, and effort expectancy and habit are moderated by locational factors. Gender, notably, only moderated attitudes on students' behavioural intention to use GenAI. This study provides valuable insights for addressing the debate regarding students' intention to use GenAI in existed research, improving educational technology, as well as offering support for school decision-makers and educators to apply GenAI in school settings.
Paper Structure (26 sections, 1 equation, 2 figures, 6 tables)

This paper contains 26 sections, 1 equation, 2 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Literature Review and Theoretical Framework
  3. Literature Review
  4. Theoretical Framework
  5. Theory of Planned Behaviour (TPB)
  6. Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT)
  7. Expectancy Value Theory (EVT)
  8. Research Design
  9. Literature Search
  10. Literature Screening
  11. Data Coding
  12. Results
  13. Publication Bias Analysis
  14. Heterogeneity Test
  15. Effect Size Calculation
  16. ...and 11 more sections

Figures (2)

  • Figure 1: PRISMA Flow Diagram
  • Figure 2: The Funnel Plots of Results of Publication Bias