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Yuan Chih Fu; Jin Hua Chen; Kai Chieh Cheng; Xuan Fen Yuan – Higher Education: The International Journal of Higher Education Research, 2024
Using data from approximately 342,000 course-taking records collected from 4406 college students enrolled at Taipei Tech during the 2009-2012 academic years, we examine the impact of multidisciplinarity on students' academic performance. Our study contributes to the literature in three ways. First, by applying natural language processing (NLP), we…
Descriptors: College Students, Interdisciplinary Approach, Academic Achievement, Natural Language Processing
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Nisar Ahmed Dahri; Noraffandy Yahaya; Waleed Mugahed Al-Rahmi – Education and Information Technologies, 2025
Enhancing student academic success and career readiness is important in the rapidly evolving educational field. This study investigates the influence of ChatGPT, an AI tool, on these outcomes using the Stimulus-Organism-Response (SOR) theory and constructs from the Technology Acceptance Model (TAM). The aim is to explore how ChatGPT impacts…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Career Readiness
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Muhammad Farrukh Shahzad; Shuo Xu; Hira Zahid – Education and Information Technologies, 2025
Artificial Intelligence (AI) technologies have rapidly transformed the education sector and affect student learning performance, particularly in China, a burgeoning educational landscape. The development of generative artificial intelligence (AI) based technologies, such as chatbots and large language models (LLMs) like ChatGPT, has completely…
Descriptors: Artificial Intelligence, Technology Uses in Education, Academic Achievement, Self Efficacy
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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
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Promethi Das Deep; Yixin Chen – Higher Education Studies, 2025
The COVID-19 pandemic significantly disrupted higher education. The sudden and profound transformations it necessitated had a direct and negative impact on higher education students, as evidenced by the widely reported instances of academic disengagement, decreased motivation, and lower performance. This was often due to student burnout caused by…
Descriptors: COVID-19, Pandemics, Electronic Learning, Fatigue (Biology)
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Louie Giray; Jonard Nemeño; Jelomil Edem – International Journal of Technology in Education, 2025
As artificial intelligence transforms education, ChatGPT has emerged as a tool for learning, reshaping how students engage with academic material. This study investigates ChatGPT's influence on student engagement among Filipino college students, focusing on self-management and intentional learning--two critical dimensions of self-directed…
Descriptors: Independent Study, Artificial Intelligence, Man Machine Systems, Natural Language Processing
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David Baidoo-Anu; Daniel Asamoah; Isaac Amoako; Inuusah Mahama – Discover Education, 2024
This study examined the perspectives of Ghanaian higher education students on the use of ChatGPT. The Students' ChatGPT Experiences Scale (SCES) was developed and validated to evaluate students' perspectives of ChatGPT as a learning tool. A total of 277 students from universities and colleges participated in the study. Through exploratory factor…
Descriptors: Student Attitudes, Artificial Intelligence, Higher Education, Foreign Countries
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Ibnatul Jalilah Yusof – Journal of Information Technology Education: Research, 2025
Aim/Purpose: This paper examines the potential of ChatGPT-assisted retrieval practice to enhance students' final exam performance. ChatGPT was utilized to generate questions and deliver timely feedback during retrieval practice, supporting learning in large class settings where providing personalized feedback is often challenging. Background:…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Scores
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Ravi Sankar Pasupuleti; Deepthi Thiyyagura – Education and Information Technologies, 2024
The aim of this research is to discover the continuance and recommendation intention of higher education students who are using ChatGPT. Specifically, we proposed an extend technology continuance theory (TCT) by integrating the recommendation intention. A structured Google form is used to collect the data from the higher education students…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Intention
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Sultan Hammad Alshammari; Mohammed Habib Alshammari – International Journal of Information and Communication Technology Education, 2024
The current study aims at assessing the factors which could affect students' use of ChatGPT. The study proposed a theoretical model that included five factors. Data were collected from 136 students using a questionnaire. The data were analyzed using two steps: CFA for measuring the model and SEM for analyzing the relationships and testing…
Descriptors: Influences, Technology Uses in Education, Artificial Intelligence, Natural Language Processing
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Mehedi Hasan Anik; Shahriar Nafees Chowdhury Raaz; Nushat Khan – International Journal of Artificial Intelligence in Education, 2025
Artificial intelligence (AI) technologies, especially language models like ChatGPT, are revolutionizing academic writing by generating human-like text and supporting thesis development. While some research explores ChatGPT in academic writing, none compares the experiences of young researchers using AI for the first time versus non-users in their…
Descriptors: Artificial Intelligence, Theses, Research, Science Education
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Yuxia Ma – European Journal of Education, 2025
The benefits of Generative Artificial Intelligence (GenAI) in enhancing second language (L2) learning are well established. However, these advantages can only be realised if learners are willing to adopt the technology. This study, grounded in the Theory of Planned Behaviour (TPB), investigated the factors influencing the behavioural intention to…
Descriptors: College Students, Student Attitudes, Artificial Intelligence, Technology Uses in Education
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Ananta Ardyansyah; Agung Budhi Yuwono; Sri Rahayu; Naif Mastoor Alsulami; Oktavia Sulistina – Journal of Chemical Education, 2024
The rapid development of artificial intelligence (AI) has transformed chatbots into generative pre-trained transformers (GPTs) capable of performing various tasks. The use of GPTs is expanding to learning, including natural sciences like chemistry. GPTs can assist students in understanding and solving chemistry problems. However, there are…
Descriptors: Foreign Countries, Artificial Intelligence, Man Machine Systems, Natural Language Processing
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Thi Thuy An Ngo; Gia Khuong An; Phuong Thy Nguyen; Thanh Tu Tran – Journal of Information Technology Education: Research, 2024
Aim/Purpose: The main goal of this study is to investigate the factors affecting students' satisfaction and continuous usage of ChatGPT in an educational context, using the Expectation-Confirmation Model (ECM) as the theoretical framework. Specifically, this investigation focuses on identifying how user expectations, perceived usefulness, and…
Descriptors: Student Satisfaction, Learner Engagement, Sustainability, Artificial Intelligence
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Jiahui Luo – Assessment & Evaluation in Higher Education, 2024
This study offers a critical examination of university policies developed to address recent challenges presented by generative AI (GenAI) to higher education assessment. Drawing on Bacchi's 'What's the problem represented to be' (WPR) framework, we analysed the GenAI policies of 20 world-leading universities to explore what are considered problems…
Descriptors: Artificial Intelligence, Educational Policy, College Students, Student Evaluation
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