Acceptance of Artificial Intelligence as a Teaching Strategy Among University Professors: The Role of Habit, Hedonic Motivation, and Competence for Technology Integration

IF 3 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Benicio Gonzalo Acosta-Enriquez, Luigi Italo Villena Zapata, Olger Huamaní Jordan, Carlos López Roca, Betty Margarita Cabrera Cipirán, Willy Saavedra Villacrez, Carmen Graciela Arbulu Perez Vargas
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Abstract

The immersion of artificial intelligence (AI) in higher education presents significant challenges and opportunities. This study examines the acceptance of AI as a teaching strategy among university teachers, following the extended UTAUT2 model with the inclusion of the teacher skills and knowledge for technology integration (SKTI) construct. Employing a quantitative cross-sectional research design, data were collected from 318 university teachers with prior experience using AI as a learning strategy through nonprobabilistic convenience sampling across 10 universities in northern Peru. Participants completed an online survey, and data were analyzed using descriptive statistics, Kruskal–Wallis tests with Dunn’s post hoc comparisons, and partial least squares structural equation modeling (PLS-SEM). The results showed that performance expectancy (β = 0.129∗∗), hedonic motivation (β = 0.167∗∗), habit (β = 0.405∗∗∗), and SKTI (β = 0.263∗∗∗) had a positive influence on the behavioral intention to adopt AI as a teaching strategy. Additionally, behavioral intention (β = 0.303∗∗∗), facilitating conditions (β = 0.115), and habit (β = 0.464∗∗) determine the behavioral use of AI by teachers. The Kruskal–Wallis test revealed significant differences among age groups in the performance expectancy, social influence, habit, and behavioral intention constructs, with the 37- to 48-year-old age group showing higher average ranks. The discussion highlights that these findings suggest a positive adoption of AI among teachers, driven by individual and contextual factors, and challenges assumptions about the relevance of certain constructs in this specific context. In conclusion, this study represents a significant advancement in understanding the adoption of AI in university teaching and provides valuable guidance for practical implementation efforts.

Abstract Image

大学教授接受人工智能作为一种教学策略:习惯、享乐动机和技术整合能力的作用
人工智能(AI)在高等教育中的渗透带来了重大的挑战和机遇。本研究考察了大学教师接受人工智能作为一种教学策略,遵循扩展的UTAUT2模型,包括教师技能和知识的技术整合(SKTI)结构。采用定量横断面研究设计,通过非概率方便抽样,从秘鲁北部10所大学的318名大学教师中收集数据,这些教师之前曾使用人工智能作为学习策略。参与者完成了一项在线调查,并使用描述性统计、Kruskal-Wallis检验和Dunn事后比较以及偏最小二乘结构方程模型(PLS-SEM)对数据进行分析。结果表明,成绩期望(β = 0.129∗∗)、享乐动机(β = 0.167∗∗)、习惯(β = 0.405∗∗)和SKTI (β = 0.263∗∗)对采用人工智能作为教学策略的行为意向有正向影响。此外,行为意向(β = 0.303∗∗)、促进条件(β = 0.115∗)和习惯(β = 0.464∗)决定了教师对人工智能的行为使用。Kruskal-Wallis测试显示,不同年龄组在表现预期、社会影响、习惯和行为意图结构方面存在显著差异,其中37至48岁年龄组的平均排名更高。讨论强调,这些发现表明,在个人和环境因素的推动下,教师积极采用人工智能,并挑战了有关特定背景下某些结构相关性的假设。总之,这项研究代表了在理解人工智能在大学教学中的应用方面的重大进步,并为实际实施工作提供了有价值的指导。
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来源期刊
Human Behavior and Emerging Technologies
Human Behavior and Emerging Technologies Social Sciences-Social Sciences (all)
CiteScore
17.20
自引率
8.70%
发文量
73
期刊介绍: Human Behavior and Emerging Technologies is an interdisciplinary journal dedicated to publishing high-impact research that enhances understanding of the complex interactions between diverse human behavior and emerging digital technologies.
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