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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
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)
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
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
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
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
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
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
Yucheng Chu; Peng He; Hang Li; Haoyu Han; Kaiqi Yang; Yu Xue; Tingting Li; Yasemin Copur-Gencturk; Joseph Krajcik; Jiliang Tang – International Educational Data Mining Society, 2025
Short answer assessment is a vital component of science education, allowing evaluation of students' complex three-dimensional understanding. Large language models (LLMs) that possess human-like ability in linguistic tasks are increasingly popular in assisting human graders to reduce their workload. However, LLMs' limitations in domain knowledge…
Descriptors: Artificial Intelligence, Science Education, Technology Uses in Education, Natural Language Processing
Stephanie Fuchs; Alexandra Werth; Cristóbal Méndez; Jonathan Butcher – Journal of Engineering Education, 2025
Background: High-quality feedback is crucial for academic success, driving student motivation and engagement while research explores effective delivery and student interactions. Advances in artificial intelligence (AI), particularly natural language processing (NLP), offer innovative methods for analyzing complex qualitative data such as feedback…
Descriptors: Artificial Intelligence, Training, Data Analysis, Natural Language Processing
Natalie V. Covington; Olivia Vruwink – International Journal of Artificial Intelligence in Education, 2025
ChatGPT and other large language models (LLMs) have the potential to significantly disrupt common educational practices and assessments, given their capability to quickly generate human-like text in response to user prompts. LLMs GPT-3.5 and GPT-4 have been tested against many standardized and high-stakes assessment materials (e.g. SAT, Uniform…
Descriptors: Artificial Intelligence, Technology Uses in Education, Undergraduate Study, Introductory Courses
Adam B. Lockwood; Joshua Castleberry – Contemporary School Psychology, 2025
Technological Advances in Artificial Intelligence (AI) have Brought forth the Potential for Models to Assist in Academic Writing. However, Concerns Regarding the Accuracy, Reliability, and Impact of AI in Academic Writing have been Raised. This Study Examined the Capabilities of GPT-4, a state-of-the-art AI Language Model, in Writing an American…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Writing (Composition)
Jie Yang; Ehsan Latif; Yuze He; Xiaoming Zhai – Journal of Science Education and Technology, 2025
The development of explanations for scientific phenomena is crucial in science assessment. However, the scoring of students' written explanations is a challenging and resource-intensive process. Large language models (LLMs) have demonstrated the potential to address these challenges, particularly when the explanations are written in English, an…
Descriptors: Artificial Intelligence, Technology Uses in Education, Automation, Scoring
Lawrence Ibeh; Noah Cheruiyot Mutai; Olufunke Mercy Popoola; Nguyen Manh Cuong; Sandra Ejiofor – Research in Learning Technology, 2025
For this study, 350 university students in Germany were surveyed to understand how they perceive ChatGPT's educational advantages and challenges. Using a combination of quantitative and qualitative methods, it found out that students tend to see ChatGPT as helpful for academic performance (53.14%), writing (47.14%), and exam preparation (50.00%).…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Technology Uses in Education
Jussi S. Jauhiainen; Agustín Garagorry Guerra – Innovations in Education and Teaching International, 2025
The study highlights ChatGPT-4's potential in educational settings for the evaluation of university students' open-ended written examination responses. ChatGPT-4 evaluated 54 written responses, ranging from 24 to 256 words in English. It assessed each response using five criteria and assigned a grade on a six-point scale from fail to excellent,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Student Evaluation, Writing Evaluation

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