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

• Published in: Behavioral sciences Vol. 14; no. 5; p. 373
• Main Authors: Wang, Kai, Ruan, Qianqian, Zhang, Xiaoxuan, Fu, Chunhua, Duan, Boyuan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.05.2024
• Subjects: Anxiety; Chatbots; Computational linguistics; Education; Generative artificial intelligence; Hypotheses; Influence; Information management; Knowledge; Language processing; Learning; Literacy; Methods; Natural language interfaces; Pedagogy; Perceptions; pre-service teachers; Preservice teachers; Self-efficacy; Social networks; Students; Teachers; Teaching; Technology adoption; the UTAUT model; TPACK; Tutoring; United Kingdom; China; TPACK; the UTAUT model; anxiety; pre-service teachers; generative artificial intelligence
• ISSN: 2076-328X; 2076-328X
• DOI: 10.3390/bs14050373

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.