•  
  •  
 

Author ORCID Identifier

Peter Anti Partey: https://orcid.org/0009-0008-8967-1690
Emmanuel Quayson: https://orcid.org/0000-0003-0400-401X

Abstract

Abstract

The integration of generative AI in education necessitates a clear understanding of the factors influencing its adoption by preservice teachers. This study examined the predictors of generative AI use and its impact on the academic performance of preservice teachers in Ghana, employing a quantitative cross-sectional survey design. Data were collected from 783 preservice teachers using structured questionnaires measuring task characteristics (TskC), technology characteristics (TechC), task–technology fit (TTF), attitude (ATT), subjective norms (SJN), perceived behavioural control (PBC), use of generative AI (USE), and academic performance (AP). The partial least squares structural equation modelling (PLS-SEM) analysis revealed that TskC and TechC significantly influenced TTF, which in turn influenced USE. SJN and PBC were significant predictors of USE, whereas ATT had no effect. Additionally, USE positively influenced the AP. The model explained 74.8%, 42.7%, and 14.9% of the TTF, USE, and AP variance, respectively, with strong predictive validity (Q² > 0). The findings highlight the contextual importance of task and technology alignment, social influence, and self-efficacy in shaping AI adoption, suggesting that teacher education should pay particular attention to these factors to enhance academic performance.

First Page

86

Last Page

123

Ethics Approval

Not Applicable

Declaration Statement

Data Availability Statement

The data on which the findings and conclusions of the study are derived will be available upon request from the corresponding author

Funding Statement

The authors did not receive any financial support. Thus, the work was not supported by grants from both internal and external sources.

Conflict of Interest Disclosure

No potential conflict of interest was reported by the authors.

Participation Consent Statement

Title of Study: Determinants of Generative AI Adoption in Teacher Education: An Integrated TTF–TPB Model

Principal Investigator: Emmanuel Quayson, Department of Business and Social Sciences Education, University of Cape Coast, Ghana

Purpose of the Study: This study seeks to examine the factors influencing the adoption and use of generative artificial intelligence (AI) among preservice teachers in Ghana and how such use impacts academic performance.

Participation Information: Participation in this research is entirely voluntary. The purpose, procedures, potential benefits, and any possible risks associated with the study have been clearly explained to me. I understand that my participation involves completing a questionnaire that will contribute to understanding how preservice teachers adopt and use generative AI tools.

I have been informed that all information I provide will remain strictly confidential and will be used solely for academic and research purposes. No identifying information will be published or disclosed. I am aware that I may withdraw from the study at any time without penalty or loss of benefit.

By signing below, I indicate that I have read (or have had read to me) the details of the research, that my questions have been answered to my satisfaction, and that I voluntarily agree to participate in this study.

Participant’s Name: ___________________________________________

Signature/Thumbprint: ________________________________________

Date: ___________________________

Researcher’s Signature: ________________________________________

Date: ___________________________

Permission to Reproduce materials from other Sources

N/A

Clinical Trial Registration

N/A

Download

Share

COinS