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Wu Xu; Zhang Wei; Peng Yan – European Journal of Education, 2025
This study investigates the use of Large Language Models (LLMs) by undergraduates majoring in Instrumentation and Control Engineering (ICE) at University of Shanghai for Science and Technology. We conducted a questionnaire survey to assess the awareness and usage habits of these LLMs among ICE undergraduates in ICE courses, focusing on the model…
Descriptors: Artificial Intelligence, Natural Language Processing, Engineering Education, Majors (Students)
Suping Yi; Wayan Sintawati; Yibing Zhang – Journal of Computer Assisted Learning, 2025
Background: Natural language processing (NLP) and machine learning technologies offer significant advantages, such as facilitating the delivery of reflective feedback in collaborative learning environments while minimising technical constraints for educators related to time and location. Recently, scholars' interest in reflective feedback has…
Descriptors: Reflection, Feedback (Response), Cooperative Learning, Natural Language Processing
Pierre-Alexandre Balland; Olesya Grabova; J. Scott Marcus; Robert Praas; Andrea Renda – European Union, 2025
This report examines the burgeoning generative artificial intelligence (GenAI) and foundation models landscape within the European Union, and analyses its impact, technological advancements, and regulatory implications. It details the GenAI value chain, identifying key players and investment trends, revealing a significant US dominance. The report…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Industry
Ingrid Del Valle García Carreño – European Educational Researcher, 2025
The main objective of this article is to search into the exploration of the ChatGPT trend in the field of Social Sciences, focusing on its trend and its widespread global application in the digital era. It is noted that ChatGPT is an artificial intelligence system that utilizes the GPT (Generative Pre-trained Transformer) language model developed…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Social Sciences
Rui Wang; Haili Ling; Jie Chen; Huijuan Fu – International Journal of Distance Education Technologies, 2025
This study adopted the Latent Dirichlet Allocation (LDA) to extract learners' needs based on 70,145 reviews from online course designed for software design and development in China and then applied Quality Function Deployment (QFD) to map learners' differentiated needs into quality attributes. Taking national first-class courses as the…
Descriptors: Educational Improvement, Student Needs, Computer Science Education, Foreign Countries
Navreet Kaur Rana – Higher Education for the Future, 2025
The article is an exploratory study assessing the stance selected higher education institutes (HEIs) have adopted regarding the usage of generative artificial intelligence (AI) applications in academic research. The HEIs are selected based on purposive sampling in order to showcase different stances they have adopted to curb plagiarism and uphold…
Descriptors: Artificial Intelligence, Technology Uses in Education, Higher Education, Plagiarism
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
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
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
Yi Lyu; Azhar Bin Md Adnan; Lijuan Zhang – Education and Information Technologies, 2025
This study presents a comprehensive examination of the applications, challenges, and strategies associated with the integration of natural language processing (NLP) technologies in university teaching. By employing qualitative analyses, including interviews, classroom observations, and document review, the study explores the diverse applications…
Descriptors: Foreign Countries, Natural Language Processing, Technology Integration, Teaching Methods
Muna Barakat; Nesreen A. Salim; Malik Sallam – Open Praxis, 2025
Integration of ChatGPT into higher education requires assessing university educators' perspectives regarding this novel technology. This study aimed to validate a survey instrument specifically tailored to assess ChatGPT usability and acceptability among university educators based on the Technology Acceptance Model (TAM). The survey instrument…
Descriptors: College Faculty, Teacher Attitudes, Artificial Intelligence, Man Machine Systems
Kristin Dutcher Mann – History Teacher, 2025
Historians sometimes view teaching and community engagement as peripheral to research. Self-reflection on the design of assignments, pedagogy techniques, and students' work aids teachers as they refine their teaching, and it can also inform research questions and methods. Teaching, research, and community engagement do not have to be separate…
Descriptors: Community Involvement, Authentic Learning, History Instruction, Teaching Methods
Steven Watson; Jonathan Romic – European Educational Research Journal, 2025
This paper presents a novel contribution to the discourse surrounding Large Language Models (LLMs) like ChatGPT in relation to education and society by using systems theory. We argue that ChatGPT can be understood not just as an 'artificial' intelligence but that it is entangled in the evolution of society and therefore education. ChatGPT is a…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Role
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)
Abdulla-All Mijan; Md Rabiul Hasan; Mehedi Hasan – International Journal of Technology in Education and Science, 2025
This study investigated the utilization of artificial intelligence (AI) platforms in Bangladeshi higher education institutions, with an emphasis on determining which AI platforms are most widely used and assessing the variables that affect AI adoption. The particular criteria influencing platform preferences and the comparative analysis of various…
Descriptors: Artificial Intelligence, Technology Integration, Technology Uses in Education, Foreign Countries