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Ted M. Clark – Journal of Chemical Education, 2023
The artificial intelligence chatbot ChatGPT was used to answer questions from final exams administered in two general chemistry courses, including questions with closed-response format and with open-response format. For closed-response questions, ChatGPT was very capable at identifying the concept even when the question included a great deal of…
Descriptors: Artificial Intelligence, Science Tests, Chemistry, Science Instruction
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)
Zhao Wanli; Tang Youjun; Ma Xiaomei – SAGE Open, 2025
Deeper learning (DL) is firmly rooted in learning science and computer science. However, a dearth of review studies has probed its trajectory in DL in foreign languages (DLFL). Utilizing SSCI from the Web of Science Core Collection, we employ Citespace and Vosviewer to analyze the scientific knowledge graph of DLFL literature. Our analysis…
Descriptors: Bibliometrics, Second Language Learning, Computer Science, Educational Research
Sarah K. Cox; Elizabeth Hughes – School Science and Mathematics, 2025
Students with autism spectrum disorder (ASD) are included in the general education classroom more often than ever before. Despite mathematical strengths and early success, these students experience poor outcomes (academic and employment) compared to their typically developing peers. The language of mathematics increases in complexity, use, and…
Descriptors: Students with Disabilities, Autism Spectrum Disorders, Inclusion, Mathematics Instruction
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
Jose Berengueres – Discover Education, 2025
GPT-based models have enabled the creation of natural language chatbots that support both Inquiry-Based and Structured Learning approaches. This study offers a direct comparison of these two paradigms within a UNIX Shell scripting course by means of two chatbots: a Lesson Plan-Driven chatbot that ensures all students cover the same topics…
Descriptors: Lesson Plans, Artificial Intelligence, Technology Uses in Education, Natural Language Processing
Michael A. Smith – Information Systems Education Journal, 2025
Maloof & Associates (M&A), a well-regarded small auditing and accounting firm in Atlanta, kept a close eye on the media buzz surrounding ChatGPT. The partners knew that they must make decisions soon regarding the new technology, but they did not realize how soon until they lost a long-standing and substantial client to a rival that had…
Descriptors: Accounting, Barriers, Artificial Intelligence, Natural Language Processing
Yu Chen; Ting Wang; Enze Tang; Hongwei Ding – Journal of Speech, Language, and Hearing Research, 2025
Purpose: Neurotypical individuals show a robust "global precedence effect (GPE)" when processing hierarchically structured visual information. However, the auditory domain remains understudied. The current research serves to fill the knowledge gap on auditory global-local processing across the broader autism phenotype under the tonal…
Descriptors: Tone Languages, Attention, Autism Spectrum Disorders, Mandarin Chinese
Analí Rosa Taboh; Diego Edgar Shalom; Belén Alvares; Carolina Andrea Gattei – Journal of Speech, Language, and Hearing Research, 2025
Purpose: Children with hearing loss (CHL) who use hearing devices (cochlear implants or hearing aids) and communicate orally have trouble comprehending sentences with noncanonical order. This study explores sentence comprehension strategies in Spanish-speaking CHL, focusing on their ability to integrate morphosyntactic cues (word order,…
Descriptors: Sentences, Language Processing, Spanish Speaking, Hard of Hearing
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
Maria Korochkina; Kathleen Rastle – npj Science of Learning, 2025
Breaking down complex words into smaller meaningful units (e.g., "unhappy = un- + happy"), known as morphemes, is vital for skilled reading as it allows readers to rapidly compute word meanings. There is agreement that children rely on reading experience to acquire morphological knowledge in English; however, the nature of this…
Descriptors: Childrens Literature, Morphemes, Morphology (Languages), Reading Skills
Habeeb Yusuf; Arthur Money; Damon Daylamani-Zad – Educational Technology Research and Development, 2025
The ever-changing global educational landscape, coupled with the advancement of Web3, is seeing rapid changes in the ways pedagogical artificially intelligent conversational agents are being developed and used to advance teaching and learning in higher education. Given the rapidly evolving research landscape, there is a need to establish what the…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Higher Education
Amir Abdul Reda; Semuhi Sinanoglu; Mohamed Abdalla – Sociological Methods & Research, 2024
How can we measure the resource mobilization (RM) efforts of social movements on Twitter? In this article, we create the first ever measure of social movements' RM efforts on a social media platform. To this aim, we create a four-conditional lexicon that can parse through tweets and identify those concerned with RM. We also create a simple RM…
Descriptors: Social Media, Social Action, Natural Language Processing, Politics
Mike Perkins; Jasper Roe; Darius Postma; James McGaughran; Don Hickerson – Journal of Academic Ethics, 2024
This study explores the capability of academic staff assisted by the Turnitin Artificial Intelligence (AI) detection tool to identify the use of AI-generated content in university assessments. 22 different experimental submissions were produced using Open AI's ChatGPT tool, with prompting techniques used to reduce the likelihood of AI detectors…
Descriptors: Artificial Intelligence, Student Evaluation, Identification, Natural Language Processing
James A. Michaelov – ProQuest LLC, 2024
In recent years, converging evidence has suggested that prediction plays a role in language comprehension, as it appears to do in information processing in a range of cognitive domains. Much of the evidence for this comes from the N400, a neural index of the processing of meaningful stimuli which has been argued to index the extent to which a word…
Descriptors: Prediction, Language Processing, Brain Hemisphere Functions, Linguistic Input