Factors Influencing University Students' Behavioural Intention to Use Generative Artificial Intelligence for Educational Purposes Based on a Revised UTAUT2 Model

IF 5.1 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Journal of Computer Assisted Learning Pub Date : 2024-12-19 DOI:10.1111/jcal.13105

Xin Tang, Zhiqiang Yuan, Shaojun Qu

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引用次数: 0

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 of society and is anticipated to revolutionise the educational landscape, especially in the domain of tertiary education. However, there is a gap in understanding factors influencing university students' behavioural intention to use generative AI, leading to hesitation in its adoption.

Objectives

The primary objective of this study was to investigate the factors that influence university students' behavioural intention to engage with and utilise generative AI. The study sought to delve into the fundamental reasons and obstacles that university students encounter when contemplating the adoption of this technology for their academic endeavours.

Methods

The study used a quantitative research design, utilising a revised version of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. Data were collected from a sample of 380 university students in Changsha, the capital city of Hunan in China. Partial least squares structural equation modelling (PLS-SEM) was used to analyse the relationships between the variables of the model, which included performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), learning value, habit and behavioural intention.

Results

The analysis revealed that PE and EE have a direct impact on learning value. Additionally, SI and FC were found to directly affect the formation of habit. Among these factors, learning value emerged as the most potent predictor of university students' behavioural intention to use generative AI. Habit also demonstrated a significant, albeit smaller, effect on behavioural intention.

Conclusions

The study's findings underscore the importance of learning value in driving the adoption of generative AI among university students. Efforts to enhance the learning value of generative AI could significantly increase its uptake in higher education. Furthermore, the role of habit, while less pronounced, suggests that consistent exposure and use can foster a greater inclination towards generative AI. These insights provide a foundation for targeted interventions aimed at improving the integration and application of generative AI within educational settings. Stakeholders, including educators, policymakers and designers of generative AI, can leverage these findings to create an environment conducive to the adoption and effective use of generative AI in higher education.

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背景生成式人工智能（AI）是一项重大的技术飞跃，OpenAI 的 ChatGPT 和百度的 Ernie Bot 等平台处于创新的前沿。这项技术已在社会各领域得到广泛应用，预计将彻底改变教育领域，尤其是高等教育领域。然而，在了解影响大学生使用生成式人工智能的行为意向的因素方面还存在差距，导致他们在采用该技术时犹豫不决。研究目的本研究的主要目的是调查影响大学生参与和使用生成式人工智能的行为意向的因素。研究试图深入探讨大学生在考虑采用该技术进行学术研究时遇到的根本原因和障碍。研究方法本研究采用定量研究设计，使用了修订版的 "技术接受和使用统一理论 2"（UTAUT2）模型。数据收集自中国湖南省会城市长沙的 380 名大学生样本。采用偏最小二乘结构方程模型（PLS-SEM）分析模型中各变量之间的关系，这些变量包括绩效期望（PE）、努力期望（EE）、社会影响（SI）、便利条件（FC）、学习价值、习惯和行为意向。结果分析表明，PE 和 EE 对学习价值有直接影响。此外，SI 和 FC 也直接影响习惯的形成。在这些因素中，学习价值是预测大学生使用生成式人工智能行为意向的最有力因素。习惯对行为意向也有显著影响，尽管影响较小。结论本研究的结论强调了学习价值在推动大学生采用生成式人工智能方面的重要性。努力提高生成式人工智能的学习价值可以大大提高其在高等教育中的普及率。此外，习惯的作用虽然不那么明显，但表明持续的接触和使用可以促进对生成式人工智能的更大倾向。这些见解为采取有针对性的干预措施，改善生成式人工智能在教育环境中的整合与应用奠定了基础。包括教育工作者、政策制定者和创生式人工智能设计者在内的利益相关者可以利用这些发现来创造一个有利于在高等教育中采用和有效使用创生式人工智能的环境。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computer Assisted Learning EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

9.70

自引率

6.00%

发文量

116

期刊介绍： The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope