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Zijing Hu – Educational Process: International Journal, 2025
Background/purpose: The need for effective, accessible, and culturally sensitive training models has become increasingly important, particularly in cross-cultural and resource-limited contexts, such as Africa. Traditional Chinese Medicine has gained global recognition for its holistic and preventive approach to health. This study explores the…
Descriptors: Asian Culture, Medicine, Health Education, Artificial Intelligence
Benjamin Brummernhenrich; Christian L. Paulus; Regina Jucks – British Journal of Educational Technology, 2025
Generative AI systems like chatbots are increasingly being introduced into learning, teaching and assessment scenarios at universities. While previous research suggests that users treat chatbots like humans, computer systems are still often perceived as less trustworthy, potentially impairing their usefulness in learning contexts. How are…
Descriptors: Higher Education, Artificial Intelligence, College Students, Feedback (Response)
Minkai Wang; Jingdong Zhu; Gwo-Jen Hwang; Shao-Chen Chang; Qi-Fan Yang; Di Zhang – Journal of Computer Assisted Learning, 2025
Background: STEM education aims to develop innovation and problem-solving skills through interdisciplinary learning, yet struggles to foster student engagement and interdisciplinary thinking. Whilst alternate reality games (ARGs) can boost motivation via game-based problem-solving, integrating large language models (LLMs) remains underexplored.…
Descriptors: Learner Engagement, STEM Education, Natural Language Processing, Artificial Intelligence
Lin, Jiayin; Sun, Geng; Beydoun, Ghassan; Li, Li – Educational Technology & Society, 2022
A newly emerged micro learning service offers a flexible formal, informal, or non-formal online learning opportunity to worldwide users with different backgrounds in real-time. With the assist of big data technology and cloud computing service, online learners can access tremendous fine-grained learning resources through micro learning service.…
Descriptors: Translation, Natural Language Processing, Informal Education, Online Courses
Hsu, Hao-Hsuan; Huang, Nen-Fu – IEEE Transactions on Learning Technologies, 2022
This article introduces Xiao-Shih, the first intelligent question answering bot on Chinese-based massive open online courses (MOOCs). Question answering is critical for solving individual problems. However, instructors on MOOCs must respond to many questions, and learners must wait a long time for answers. To address this issue, Xiao-Shih…
Descriptors: Foreign Countries, Artificial Intelligence, Online Courses, Natural Language Processing
Almotairi, Maram; Fkih, Fethi – Journal of Education and e-Learning Research, 2022
The Question answering (QA) system plays a basic role in the acquisition of information and the e-learning environment is considered to be the field that is most in need of the question-answering system to help learners ask questions in natural language and get answers in short periods of time. The main problem in this context is how to understand…
Descriptors: Semantics, Natural Language Processing, Intelligent Tutoring Systems, Ambiguity (Semantics)
Mead, Alan D.; Zhou, Chenxuan – Journal of Applied Testing Technology, 2022
This study fit a Naïve Bayesian classifier to the words of exam items to predict the Bloom's taxonomy level of the items. We addressed five research questions, showing that reasonably good prediction of Bloom's level was possible, but accuracy varies across levels. In our study, performance for Level 2 was poor (Level 2 items were misclassified…
Descriptors: Artificial Intelligence, Prediction, Taxonomy, Natural Language Processing
Larranaga, Mikel; Aldabe, Itziar; Arruarte, Ana; Elorriaga, Jon A.; Maritxalar, Montse – IEEE Transactions on Learning Technologies, 2022
In a concept learning scenario, any technology-supported learning system must provide students with mechanisms that help them with the acquisition of the concepts to be learned. For the technology-supported learning systems to be successful in this task, the development of didactic material is crucial--a hard task that could be alleviated by means…
Descriptors: Computer Assisted Testing, Science Tests, Multiple Choice Tests, Textbooks
Adam Keath; James Wyant; Brooke Towner – Journal of Physical Education, Recreation & Dance, 2024
AI tools can revolutionize physical education (PE) by assisting teachers in various ways, such as curriculum development, providing feedback, enhancing content knowledge and data analysis, and promoting student engagement. This article explores various ways in which PE teachers can utilize AI tools like ChatGPT to improve their instruction and…
Descriptors: Physical Education, Physical Education Teachers, Technology Uses in Education, Artificial Intelligence
William Cain – TechTrends: Linking Research and Practice to Improve Learning, 2024
This paper explores the transformative potential of Large Language Models Artificial Intelligence (LLM AI) in educational contexts, particularly focusing on the innovative practice of prompt engineering. Prompt engineering, characterized by three essential components of content knowledge, critical thinking, and iterative design, emerges as a key…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Prompting
Jacqueline Zammit – Technology in Language Teaching & Learning, 2024
The Chat Generative Pretrained Transformer (ChatGPT) is a state-of-the-art artificial intelligence (AI) language model developed by OpenAI. It employs advanced deep-learning algorithms to generate text that mimics human language. ChatGPT, launched on November 30, 2022, has rapidly gained widespread recognition. Its influence on the future of…
Descriptors: Artificial Intelligence, Computer Software, Synchronous Communication, Second Language Learning
Yu Bai; Jun Li; Jun Shen; Liang Zhao – IEEE Transactions on Learning Technologies, 2024
The potential of artificial intelligence (AI) in transforming education has received considerable attention. This study aims to explore the potential of large language models (LLMs) in assisting students with studying and passing standardized exams, while many people think it is a hype situation. Using primary education as an example, this…
Descriptors: Instructional Effectiveness, Artificial Intelligence, Technology Uses in Education, Natural Language Processing
Andrew Williams – International Journal of Educational Technology in Higher Education, 2024
The value of generative AI tools in higher education has received considerable attention. Although there are many proponents of its value as a learning tool, many are concerned with the issues regarding academic integrity and its use by students to compose written assessments. This study evaluates and compares the output of three commonly used…
Descriptors: Content Area Writing, Artificial Intelligence, Writing Assignments, Biomedicine
Giovanni Zimotti; Claire Frances; Luke Whitaker – Technology in Language Teaching & Learning, 2024
This study explores the perceptions of second language (L2) educators on the surge of Large Language Models (LLMs) like ChatGPT, and their potential impact on language education. We surveyed over 100 L2 instructors, asking questions about their ideas for AI-proofing assignments, their policies, and their perceptions of how this tool will impact…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Second Language Instruction
Bima Sapkota; Liza Bondurant – International Journal of Technology in Education, 2024
In November 2022, ChatGPT, an Artificial Intelligence (AI) large language model (LLM) capable of generating human-like responses, was launched. ChatGPT has a variety of promising applications in education, such as using it as thought-partner in generating curricular resources. However, scholars also recognize that the use of ChatGPT raises…
Descriptors: Cognitive Processes, Difficulty Level, Artificial Intelligence, Natural Language Processing

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