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Lord J. Hyeamang; Tejas C. Sekhar; Emily Rush; Amy C. Beresheim; Colleen M. Cheverko; William S. Brooks; Abbey C. M. Breckling; M. Nazmul Karim; Christopher Ferrigno; Adam B. Wilson – Anatomical Sciences Education, 2025
Evidence suggests custom chatbots are superior to commercial generative artificial intelligence (GenAI) systems for text-based anatomy content inquiries. This study evaluates ChatGPT-4o's and Claude 3.5 Sonnet's capabilities to interpret unlabeled anatomical images. Secondarily, ChatGPT o1-preview was evaluated as an AI rater to grade AI-generated…
Descriptors: Artificial Intelligence, Anatomy, Identification, Man Machine Systems
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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)
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Nga Than; Leanne Fan; Tina Law; Laura K. Nelson; Leslie McCall – Sociological Methods & Research, 2025
Over the past decade, social scientists have adapted computational methods for qualitative text analysis, with the hope that they can match the accuracy and reliability of hand coding. The emergence of GPT and open-source generative large language models (LLMs) has transformed this process by shifting from programming to engaging with models using…
Descriptors: Artificial Intelligence, Coding, Qualitative Research, Cues
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Harpreet Auby; Namrata Shivagunde; Vijeta Deshpande; Anna Rumshisky; Milo D. Koretsky – Journal of Engineering Education, 2025
Background: Analyzing student short-answer written justifications to conceptually challenging questions has proven helpful to understand student thinking and improve conceptual understanding. However, qualitative analyses are limited by the burden of analyzing large amounts of text. Purpose: We apply dense and sparse Large Language Models (LLMs)…
Descriptors: Student Evaluation, Thinking Skills, Test Format, Cognitive Processes
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Gani, Mohammed Osman; Ayyasamy, Ramesh Kumar; Sangodiah, Anbuselvan; Fui, Yong Tien – Education and Information Technologies, 2023
The automated classification of examination questions based on Bloom's Taxonomy (BT) aims to assist the question setters so that high-quality question papers are produced. Most studies to automate this process adopted the machine learning approach, and only a few utilised the deep learning approach. The pre-trained contextual and non-contextual…
Descriptors: Models, Artificial Intelligence, Natural Language Processing, Writing (Composition)
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Reese Butterfuss; Harold Doran – Educational Measurement: Issues and Practice, 2025
Large language models are increasingly used in educational and psychological measurement activities. Their rapidly evolving sophistication and ability to detect language semantics make them viable tools to supplement subject matter experts and their reviews of large amounts of text statements, such as educational content standards. This paper…
Descriptors: Alignment (Education), Academic Standards, Content Analysis, Concept Mapping
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Debora Weber-Wulff; Alla Anohina-Naumeca; Sonja Bjelobaba; Tomáš Foltýnek; Jean Guerrero-Dib; Olumide Popoola; Petr Šigut; Lorna Waddington – International Journal for Educational Integrity, 2023
Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artificial intelligence (AI) generated content in an academic environment and intensified efforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for…
Descriptors: Artificial Intelligence, Identification, Man Machine Systems, Accuracy
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Andreea Dutulescu; Stefan Ruseti; Denis Iorga; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2024
The process of generating challenging and appropriate distractors for multiple-choice questions is a complex and time-consuming task. Existing methods for an automated generation have limitations in proposing challenging distractors, or they fail to effectively filter out incorrect choices that closely resemble the correct answer, share synonymous…
Descriptors: Multiple Choice Tests, Artificial Intelligence, Attention, Natural Language Processing
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Amal Abdullah Alibrahim – South African Journal of Education, 2024
After ChatGPT was released late in 2022, many arguments about its accuracy and use in education arose. In this article, I seek to provide evidence of the accuracy and validity of ChatGPT's responses to users' queries in education by applying a systematic review methodology to analyse publications in specific databases following PRISMA guidelines…
Descriptors: Artificial Intelligence, Technology Uses in Education, Reliability, Natural Language Processing
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Kun Sun; Rong Wang – Cognitive Science, 2025
The majority of research in computational psycholinguistics on sentence processing has focused on word-by-word incremental processing within sentences, rather than holistic sentence-level representations. This study introduces two novel computational approaches for quantifying sentence-level processing: sentence surprisal and sentence relevance.…
Descriptors: Reading Rate, Reading Comprehension, Sentences, Computation
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Ibrahim Talaat Ibrahim; Najeh Rajeh Alsalhi; Atef F. I. Abdelkader; Nidal Alzboun; Abdellateef Alqawasmi – Eurasian Journal of Applied Linguistics, 2024
Artificial intelligence (AI) has become an integral component of human existence, with individuals employing AI tools in various facets of life. Among the most significant applications of AI is its role in facilitating communication among humans. The present study focuses on the use of AI in translating a crucial type of text that falls within the…
Descriptors: Artificial Intelligence, Translation, Geography, Politics
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Gary D. Fisk – Teaching of Psychology, 2025
Introduction: Recent innovations in generative artificial intelligence (AI) technologies have led to an educational environment in which human authorship cannot be assumed, thereby posing a significant challenge to upholding academic integrity. Statement of the problem: Both humans and AI detection technologies have difficulty distinguishing…
Descriptors: Technology Uses in Education, Writing (Composition), Plagiarism, Identification
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Mohsin Murtaza; Chi-Tsun Cheng; Mohammad Fard; John Zeleznikow – International Journal of Artificial Intelligence in Education, 2025
As modern vehicles continue to integrate increasingly sophisticated Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV) functions, conventional user manuals may no longer be the most effective medium for conveying knowledge to drivers. This research analysed conventional, paper and video-based instructional methods versus a…
Descriptors: Educational Change, Driver Education, Motor Vehicles, Natural Language Processing
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Jionghao Lin; Eason Chen; Zifei Han; Ashish Gurung; Danielle R. Thomas; Wei Tan; Ngoc Dang Nguyen; Kenneth R. Koedinger – International Educational Data Mining Society, 2024
Automated explanatory feedback systems play a crucial role in facilitating learning for a large cohort of learners by offering feedback that incorporates explanations, significantly enhancing the learning process. However, delivering such explanatory feedback in real-time poses challenges, particularly when high classification accuracy for…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Feedback (Response)
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Zaki, Nazar; Turaev, Sherzod; Shuaib, Khaled; Krishnan, Anusuya; Mohamed, Elfadil – Education and Information Technologies, 2023
Quality control and assurance plays a fundamental role within higher education contexts. One means by which quality control can be performed is by mapping the course learning outcomes (CLOs) to the program learning outcomes (PLO). This paper describes a system by which this mapping process can be automated and validated. The proposed AI-based…
Descriptors: Program Evaluation, Outcomes of Education, Natural Language Processing, Higher Education
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