Unveiling the Drivers of AI Integration Among Language Teachers: Integrating UTAUT and AI-TPACK DOI

Nguyen Hoang Mai Tram

Computers in the Schools, Год журнала: 2024, Номер unknown, С. 1 - 21

Опубликована: Дек. 14, 2024

Artificial intelligence (AI) offers numerous benefits to the field of language education, making it crucial understand factors influencing teachers' adoption these technologies. This study investigates determinants AI chatbots in educational settings. Drawing on Unified Theory Acceptance and Use Technology (UTAUT) Technological Pedagogical Content Knowledge (TPACK) framework, a comprehensive model among teachers is proposed tested. Data were collected from 276 Vietnam through an online survey. Partial Least Square-Structural Equation Modeling (PLS-SEM) was employed analyze data. Results indicate that intent significantly predicts integration, while performance expectancy, effort self-efficacy are key intent. AI-TPACK emerges as factor, strongly self-efficacy, expectancy. Facilitation found be significant predictor AI-TPACK. These findings enhance theoretical framework education provide valuable insights for fostering effective integration teachers.

Язык: Английский

From Technology‐Challenged Teachers to Empowered Digitalized Citizens: Exploring the Profiles and Antecedents of Teacher AI Literacy in the Chinese EFL Context DOI Open Access
Ziwen Pan, Yongliang Wang

European Journal of Education, Год журнала: 2025, Номер 60(1), С. 1 - 16

Опубликована: Янв. 27, 2025

ABSTRACT Artificial Intelligence (AI) literacy has come to the spotlight, empowering individuals adeptly navigate modern digitalised world. However, studies on teacher AI in English as a Foreign Language (EFL) context remain limited. This study aims identify intraindividual differences and examine its associations with age years of teaching experience among 782 teachers. Given absence reliable instrument measure literacy, we first constructed validated scale encompassing five sub‐scales: Knowledge , Use Assessment Design Ethics . Subsequently, latent profile analysis (LPA) was conducted using Mplus 7.4, results revealing four distinct profiles: Poor (C1: 12.1%), Moderate (C2: 45.5%), Good (C3: 28.4%), Excellent (C4: 14.1%). Multinomial logistic regression analyses indicated significant between both experience. Additionally, 32 respondents participated semi‐structured interviews. The qualitative data analysed MAXQDA 2022 triangulated quantitative offered deeper insights into teachers’ perceptions their literacy. provides theoretical practical implications for understanding Chinese EFL context.

Язык: Английский

Процитировано

3
Personalized stem education empowered by artificial intelligence: a comprehensive review and content analysis DOI
Daner Sun, Gary Cheng, Philip L. H. Yu

и другие.

Interactive Learning Environments, Год журнала: 2025, Номер unknown, С. 1 - 23

Опубликована: Фев. 11, 2025

Язык: Английский

Процитировано

0
Exploring human and AI collaboration in inclusive STEM teacher training: A synergistic approach based on self-determination theory DOI
Tingting Li, Zehui Zhan, Yu Ji

и другие.

The Internet and Higher Education, Год журнала: 2025, Номер unknown, С. 101003 - 101003

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Latent Profile Analysis of AI Literacy and Trust in Mathematics Teachers and Their Relations with AI Dependency and 21st-Century Skills DOI Creative Commons
Tommy Tanu Wijaya, Qingchun Yu, Yiming Cao

и другие.

Behavioral Sciences, Год журнала: 2024, Номер 14(11), С. 1008 - 1008

Опубликована: Окт. 30, 2024

Artificial Intelligence (AI) technology, particularly generative AI, has positively impacted education by enhancing mathematics instruction with personalized learning experiences and improved data analysis. Nonetheless, variations in AI literacy, trust dependency on these technologies among teachers can significantly influence their development of 21st-century skills such as self-confidence, problem-solving, critical thinking, creative collaboration. This study aims to identify distinct profiles trust, examines how correlate the aforementioned skills. Using a cross-sectional research design, collected from 489 China. A robust three-step latent profile analysis method was utilized analyze data. The revealed five literacy teachers: (1) Basic Engagement; (2) Developing Literacy, Skeptical AI; (3) Balanced Competence; (4) Advanced Integration; (5) Expertise Confidence. found that an increase directly correlates decrease findings underscore need for careful integration educational settings. Excessive reliance lead detrimental dependencies, which may hinder essential contributes existing literature providing empirical evidence impact professional teachers. It also offers practical implications policymakers institutions consider balanced approaches integration, ensuring enhances rather than replaces thinking problem-solving capacities educators.

Язык: Английский

Процитировано

2
The impact of TPACK on teachers’ willingness to integrate generative artificial intelligence (GenAI): The moderating role of negative emotions and the buffering effects of need satisfaction DOI

Yiming Yang,

Qi Xia,

C. C. Liu

и другие.

Teaching and Teacher Education, Год журнала: 2024, Номер 154, С. 104877 - 104877

Опубликована: Ноя. 26, 2024

Язык: Английский

Процитировано

2
STEM-TPAB Öz-Yeterlik Ölçeği: Türkçeye Uyarlama Çalışması DOI Creative Commons
İdris Aktaş, Haluk Özmen

Uludağ Üniversitesi Eğitim Fakültesi Dergisi, Год журнала: 2024, Номер 37(2), С. 798 - 829

Опубликована: Авг. 13, 2024

Bütünleştirilmiş Fen, Teknoloji, Mühendislik ve Matematik (b-STEM) eğitimi 21. yüzyılda ilerletmenin en iyi pedagojik yollarından birisi olarak görülmektedir. Ancak STEM eğitimini güçlü bir şekilde uygularken öğretmen adaylarının ihtiyaç duyduğu bilgi türleri üzerine geliştirilmiş geçerli güvenilir ölçekler oldukça sınırlıdır. Bu çalışmanın amacı Chai diğerleri (2019) tarafından geliştirilen öğretmenlerin/öğretmen Teknoloji Pedagoji Alan Bilgisi (TPAB) çerçevesinde öz-yeterliklerini ölçmeyi amaçlayan STEM-TPAB ölçeğinin Türkçeye uyarlamasını yapmaktır. Orijinali 17 maddeden oluşan ölçeğin C.S. sağlanan 24 maddelik ön madde havuzu üzerinden uyarlama çalışması gerçekleştirilmiştir. Uyarlama çalışmasına 14 akademisyen çeşitli aşamalar için fen bilgisi, matematik, sınıf, BÖTE İngilizce öğretmenliği bölümlerinden olmak üzere toplam 523 adayı katılmıştır. Madde-toplam korelasyonu, açımlayıcı doğrulayıcı faktör analizleri yeterli güvenirlik geçerlik değerlerine sahip olduğunu göstermiştir. Ölçek orijinal yapısına uygun uyarlanmıştır. Uyarlanan ölçek gelecek çalışmalarda TPAB belirlemek, derslerini yürütmek ihtiyaçlarını STEM’in çoklu bileşenlerini desteklemek mesleki gelişim kurslarının çıktılarını ölçmek karşılaştırmalar yapmak amacıyla kullanılabilir.

Процитировано

0
Unveiling the Drivers of AI Integration Among Language Teachers: Integrating UTAUT and AI-TPACK DOI

Nguyen Hoang Mai Tram

Computers in the Schools, Год журнала: 2024, Номер unknown, С. 1 - 21

Опубликована: Дек. 14, 2024

Artificial intelligence (AI) offers numerous benefits to the field of language education, making it crucial understand factors influencing teachers' adoption these technologies. This study investigates determinants AI chatbots in educational settings. Drawing on Unified Theory Acceptance and Use Technology (UTAUT) Technological Pedagogical Content Knowledge (TPACK) framework, a comprehensive model among teachers is proposed tested. Data were collected from 276 Vietnam through an online survey. Partial Least Square-Structural Equation Modeling (PLS-SEM) was employed analyze data. Results indicate that intent significantly predicts integration, while performance expectancy, effort self-efficacy are key intent. AI-TPACK emerges as factor, strongly self-efficacy, expectancy. Facilitation found be significant predictor AI-TPACK. These findings enhance theoretical framework education provide valuable insights for fostering effective integration teachers.

Язык: Английский

Процитировано

0