Exploring the determinants of mathematics teachers’ willingness to implement STEAM education using structural equation modeling DOI Creative Commons
Ming-sum Tang, Tommy Tanu Wijaya, Xinxin Li

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 21, 2025

Language: Английский

Unveiling learners’ intentions toward influencer-led education: an integration of qualitative and quantitative analysis DOI
Xiaojiao Chen, Teng Yu, Jian Dai

et al.

Interactive Learning Environments, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: Jan. 31, 2025

Language: Английский

Citations

3

Factors Influencing University Students' Behavioural Intention to Use Generative Artificial Intelligence for Educational Purposes Based on a Revised UTAUT2 Model DOI Open Access
Xin Tang, Zhiqiang Yuan, Shaojun Qu

et al.

Journal of Computer Assisted Learning, Journal Year: 2024, Volume and Issue: 41(1)

Published: Dec. 19, 2024

ABSTRACT Background Generative artificial intelligence (AI) represents a significant technological leap, with platforms like OpenAI's ChatGPT and Baidu's Ernie Bot at the forefront of innovation. This technology has seen widespread adoption across various sectors society is anticipated to revolutionise educational landscape, especially in domain tertiary education. However, there gap understanding factors influencing university students' behavioural intention use generative AI, leading hesitation its adoption. Objectives The primary objective this study was investigate that influence engage utilise AI. sought delve into fundamental reasons obstacles students encounter when contemplating for their academic endeavours. Methods used quantitative research design, utilising revised version Unified Theory Acceptance Use Technology 2 (UTAUT2) model. Data were collected from sample 380 Changsha, capital city Hunan China. Partial least squares structural equation modelling (PLS‐SEM) analyse relationships between variables model, which included performance expectancy (PE), effort (EE), social (SI), facilitating conditions (FC), learning value, habit intention. Results analysis revealed PE EE have direct impact on value. Additionally, SI FC found directly affect formation habit. Among these factors, value emerged as most potent predictor Habit also demonstrated significant, albeit smaller, effect Conclusions study's findings underscore importance driving AI among students. Efforts enhance could significantly increase uptake higher Furthermore, role habit, while less pronounced, suggests consistent exposure can foster greater inclination towards These insights provide foundation targeted interventions aimed improving integration application within settings. Stakeholders, including educators, policymakers designers leverage create an environment conducive effective

Language: Английский

Citations

7

Exploring the determinants of mathematics teachers’ willingness to implement STEAM education using structural equation modeling DOI Creative Commons
Ming-sum Tang, Tommy Tanu Wijaya, Xinxin Li

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 21, 2025

Language: Английский

Citations

1