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Direct linkChengliang Wang; Xiaojiao Chen; Zhebing Hu; Sheng Jin; Xiaoqing Gu – Journal of Computer Assisted Learning, 2025
Background: ChatGPT, as a cutting-edge technology in education, is set to significantly transform the educational landscape, raising concerns about technological ethics and educational equity. Existing studies have not fully explored learners' intentions to adopt artificial intelligence generated content (AIGC) technology, highlighting the need…
Descriptors: College Students, Student Attitudes, Computer Attitudes, Computer Uses in Education
Xin Tang; Zhiqiang Yuan; Shaojun Qu – Journal of Computer Assisted Learning, 2025
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…
Descriptors: Influences, College Students, Student Behavior, Intention
Irum Alvi; Hager Khechine – Journal of Computer Assisted Learning, 2025
Background: Collaborative learning, which emphasises cooperative group techniques, intersects with the evolving role of social media as a tool. Understanding how cultural values influence these dynamics is crucial for effectively integrating and utilising social media into collaborative learning environments. Objective: This research aims to…
Descriptors: Foreign Countries, College Students, Student Attitudes, Engineering Education
Hui-Tzu Hsu; Chih-Cheng Lin – Journal of Computer Assisted Learning, 2024
Background: Behavioural intention (BI) has been predicted using other variables by adopting the technology acceptance model (TAM). However, few studies have examined whether BI can predict learning performance. Objectives: The present study used an extended TAM to investigate whether students' BI is a predictor of their listening learning…
Descriptors: Intention, Vocabulary Development, Handheld Devices, College Students
Marco Rüth; Maria Jansen; Kai Kaspar – Journal of Computer Assisted Learning, 2024
Background: Online exams have become a more common form of assessment at universities due to the COVID-19 pandemic. However, cheating behaviour in online exams is widespread and threatens exam validity as well as student learning and well-being. Objective: To better understand the role of university students' needs, conceptions and reasons…
Descriptors: Foreign Countries, College Students, Computer Assisted Testing, Cheating
Ding, Lu; Er, Erkan – Journal of Computer Assisted Learning, 2018
Research has noted the effectiveness of online tools (e.g., discussion boards) for supporting help seeking among class members. However, help seeking is not necessarily warranted via online learning tools because some factors (e.g., low Internet self-efficacy) may influence students' intention to use them. This study aims to identify the…
Descriptors: College Students, Help Seeking, Computer Uses in Education, Influences
