Prospective Teachers’ Acceptance of virtual reality technology: a mixed study in Rural China

Li, Qian; Liu, Qian; Chen, Yi

doi:10.1007/s10639-022-11219-w

Cited by 15 publications

(2 citation statements)

References 76 publications

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“…The findings of this study validate that performance expectancy and social influence significantly impact pre-service teachers' behavioral intention to incorporate GenAI into teaching practices, which aligns with previous research [104][105][106]. Researchers have observed that, in a highly collectivist culture, Chinese teachers are particularly influenced by important social connections [107]. The recommendations from their peers and teachers significantly shape these pre-service teachers' behavioral intentions toward embracing GenAI.…”

Section: The Utaut Modelsupporting

confidence: 86%

“…Such challenges are likely to evoke anxiety among pre-service teachers, thereby influencing their perception of effort expectancy, performance expectancy, and behavioral intention toward the adoption of GenAI in teaching. In fact, anxieties towards technology, such as privacy and over-reliance on technology, are frequently cited as significant barriers to adopting new technological tools in previous studies [68,107,116].…”

Section: Genai Anxietymentioning

confidence: 99%

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Pre-Service Teachers’ GenAI Anxiety, Technology Self-Efficacy, and TPACK: Their Structural Relations with Behavioral Intention to Design GenAI-Assisted Teaching

Wang,

Ruan,

Zhang

et al. 2024

Behavioral Sciences

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Generative artificial intelligence (GenAI) has taken educational settings by storm in the past year due to its transformative ability to impact school education. It is crucial to investigate pre-service teachers’ viewpoints to effectively incorporate GenAI tools into their instructional practices. Data gathered from 606 pre-service teachers were analyzed to explore the predictors of behavioral intention to design Gen AI-assisted teaching. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this research integrates multiple variables such as Technological Pedagogical Content Knowledge (TPACK), GenAI anxiety, and technology self-efficacy. Our findings revealed that GenAI anxiety, social influence, and performance expectancy significantly predicted pre-service teachers’ behavioral intention to design GenAI-assisted teaching. However, effort expectancy and facilitating conditions were not statistically associated with pre-service teachers’ behavioral intentions. These findings offer significant insights into the intricate relationships between predictors that influence pre-service teachers’ perspectives and intentions regarding GenAI technology.

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