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Deconstructing University Learners' Adoption Intention towards AIGC Technology: A Mixed-Methods Study Using ChatGPT as an Example
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Chengliang 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
You're (Not) My Type -- Can LLMs Generate Feedback of Specific Types for Introductory Programming Tasks?
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Dominic Lohr; Hieke Keuning; Natalie Kiesler – Journal of Computer Assisted Learning, 2025
Background: Feedback as one of the most influential factors for learning has been subject to a great body of research. It plays a key role in the development of educational technology systems and is traditionally rooted in deterministic feedback defined by experts and their experience. However, with the rise of generative AI and especially large…
Descriptors: College Students, Programming, Artificial Intelligence, Feedback (Response)
College Students' Behavioural Intentions of AI-Assisted Language Learning: Based on the Technology Acceptance Model
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Wenji Wang; Wenjuan Wang – Journal of Computer Assisted Learning, 2025
Background Study: The combination of artificial intelligence (AI) and foreign language learning is emerging as a significant trend in language education. Objectives: This study aimed to investigate the impact of technology acceptance, attitude and motivation on behavioural intentions regarding the use of AI in language learning. Methods:…
Descriptors: College Students, Student Behavior, Intention, Educational Technology
AI-Based Adaptive Feedback in Simulations for Teacher Education: An Experimental Replication in the Field
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Elisabeth Bauer; Michael Sailer; Frank Niklas; Samuel Greiff; Sven Sarbu-Rothsching; Jan M. Zottmann; Jan Kiesewetter; Matthias Stadler; Martin R. Fischer; Tina Seidel; Detlef Urhahne; Maximilian Sailer; Frank Fischer – Journal of Computer Assisted Learning, 2025
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks…
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural Language Processing
The Physical, Social, and Mental Conditions of Machine Learning in Student Health Evaluation
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Gulnur Tyulepberdinova; Madina Mansurova; Talshyn Sarsembayeva; Sulu Issabayeva; Darazha Issabayeva – Journal of Computer Assisted Learning, 2024
Background: This study aims to assess how well several machine learning (ML) algorithms predict the physical, social, and mental health condition of university students. Objectives: The physical health measurements used in the study include BMI (Body Mass Index), %BF (percentage of Body Fat), BSC (Blood Serum Cholesterol), SBP (Systolic Blood…
Descriptors: Artificial Intelligence, Algorithms, Predictor Variables, Physical Health
Students' Reflections on Their Experience with ChatGPT
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Josef Šedlbauer; Jan Cincera; Martin Slavík; Adéla Hartlová – Journal of Computer Assisted Learning, 2024
Background: The emergence of Generative Artificial Intelligence has brought a number of ethical and practical issues to higher education. Solid experimental evidence is yet inadequate to set the functional rules for the new technology. Objectives: The objective of this study is to analyse the experience of undergraduate students' interaction with…
Descriptors: Artificial Intelligence, Higher Education, Undergraduate Students, Interaction
Factors Influencing University Students' Behavioural Intention to Use Generative Artificial Intelligence for Educational Purposes Based on a Revised UTAUT2 Model
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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
Is ChatGPT a Menace for Creative Writing Ability? An Experiment
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Ahnaf Chowdhury Niloy; Salma Akter; Nayeema Sultana; Jakia Sultana; Sayed Imran Ur Rahman – Journal of Computer Assisted Learning, 2024
Background: The increasing prevalence of Artificial Intelligence (AI) language models, exemplified by ChatGPT, has sparked inquiries into their influence on creative writing skills in educational contexts. This study aims to quantitatively investigate whether ChatGPT's use negatively affects university students' creative writing abilities,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Creative Writing, Writing Ability