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Toni Taipalus; Hilkka Grahn; Saima Ritonummi; Valtteri Siitonen; Tero Vartiainen; Denis Zhidkikh – ACM Transactions on Computing Education, 2025
SQL compiler error messages are the primary way users receive feedback when they encounter syntax errors or other issues in their SQL queries. Effective error messages can enhance the user experience by providing clear, informative, and actionable feedback. Despite the age of SQL compilers, it still remains largely unclear what contributes to an…
Descriptors: Computer Science Education, Novices, Information Systems, Programming Languages
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Anastasia Sorokina – Multilingua: Journal of Cross-Cultural and Interlanguage Communication, 2025
Research has shown that bilingual individuals might encode autobiographical memories in either their first language (L1) or their second language (L2), depending on the language spoken at the time of the event. Although language mixing is a common occurrence among multilingual speakers, previous studies have largely overlooked mixed…
Descriptors: Psycholinguistics, Memory, Language Processing, Native Language
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Rob Hirschel; Kayoko Horai – Technology in Language Teaching & Learning, 2025
With the advent of generative AI that uses large language models such as ChatGPT, it is now relatively easy to provide automated written corrective feedback in a student's native language. This paper reports on an exploratory study using a ChatGPT-powered plugin recently developed for the Moodle learning management system. The classroom…
Descriptors: Artificial Intelligence, English (Second Language), Error Correction, Writing (Composition)
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Jiawei Li; Qianru Lyu; Wei Qiu; Andy W. H. Khong – International Educational Data Mining Society, 2025
Deep learning-based course recommendation systems often suffer from a lack of interpretability, limiting their practical utility for students and academic advisors. To address this challenge, we propose a modular, post-hoc explanation framework leveraging Large Language Models (LLMs) to enhance the transparency of deep learning-driven…
Descriptors: Artificial Intelligence, Information Systems, Technology Uses in Education, Course Selection (Students)
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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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Junbin Wang; Chuanbo Zhang – SAGE Open, 2025
This study aims to explore the criteria and success factors for the application of Artificial Intelligence Generated Content (AIGC) in higher education, and guide its practice through the construction of a comprehensive system and framework. This study first identifies seven primary criteria, encompassing technical robustness, integration with…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Higher Education
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Andy Nguyen; Faaiz Gul; Belle Dang; Luna Huynh; Tuure Tuunanen – Innovations in Education and Teaching International, 2025
Generative Artificial Intelligence (GenAI) technologies have introduced significant changes to higher education, but the role of Embodied GenAI Agents in Mixed Reality (MR) environments is still relatively unexplored. This study was carried out to develop an embodied GenAI system designed to facilitate active learning, self-regulated learning and…
Descriptors: Active Learning, Higher Education, Self Management, Learning Strategies
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Huanhuan Zhang; Yujie Su; Xiaosu Xu; Vivian Ngan-Lin Lei; Shanshan Hao – International Journal of Technology in Education, 2025
This study investigates the growing interest in ChatGPT's role in education, with a particular focus on its effects on postgraduate students' English-speaking proficiency--an area predominantly explored through theoretical perspectives with limited empirical evidence. The research aims to address this gap by examining the experiences and…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Graduate Students
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Anila Virani; Ahsan Mollani; Piper Jackson – Knowledge Management & E-Learning, 2025
Social media data has the potential to enable the exploration of public perspectives on health conditions, interventions and policies. However, the resource-intensive nature of qualitative analysis creates a barrier to the timely utilization of social media data. Artificial intelligence can provide innovative ways to reduce the burden by…
Descriptors: Artificial Intelligence, Natural Language Processing, Social Media, Public Health
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Michelle Ehrenpreis; John DeLooper – portal: Libraries and the Academy, 2025
In November 2019, the Leonard Lief Library implemented Ivy.ai, a proprietary chatbot on its website. This implementation was the first academic library installation of a vendor-supplied chatbot to be discussed in the professional literature. This chatbot functioned as a new tool that assisted users seeking information from the library website.…
Descriptors: Academic Libraries, Artificial Intelligence, Natural Language Processing, Intelligent Tutoring Systems
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Shella Gherina Saptiany; Rudi Hartono; Januarius Mujiyanto; Katharina Rustipa – Educational Process: International Journal, 2025
Background/purpose: Foreign language speaking anxiety poses a persistent challenge for Indonesian ESP students in hospitality programs. Traditional classroom settings often exacerbate this anxiety, limiting students' confidence and speaking fluency. This study investigates how ChatGPT's Voice Conversation Mode can create a low-pressure environment…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Audio Equipment
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Linh Huynh; Danielle S. McNamara – Grantee Submission, 2025
Four versions of science and history texts were tailored to diverse hypothetical reader profiles (high and low reading skills and domain knowledge), generated by four Large Language Models (i.e., Claude, Llama, ChatGPT, and Gemini). The Natural Language Processing (NLP) technique was applied to examine variations in Large Language Model (LLM) text…
Descriptors: Artificial Intelligence, Natural Language Processing, Textbook Evaluation, Individualized Instruction
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Lancaster, Thomas – Journal of Academic Ethics, 2021
Is academic integrity research presented from a positive integrity standpoint? This paper uses Natural Language Processing (NLP) techniques to explore a data set of 8,507 academic integrity papers published between 1904 and 2019.Two main techniques are used to linguistically examine paper titles: (1) bigram (word pair) analysis and (2) sentiment…
Descriptors: Cheating, Integrity, Natural Language Processing, Educational Research
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Gambi, Chiara; Jindal, Priya; Sharpe, Sophie; Pickering, Martin J.; Rabagliati, Hugh – Child Development, 2021
By age 2, children are developing foundational language processing skills, such as quickly recognizing words and predicting words before they occur. How do these skills relate to children's structural knowledge of vocabulary? Multiple aspects of language processing were simultaneously measured in a sample of 2-to-5-year-olds (N = 215): While older…
Descriptors: Preschool Children, Vocabulary Development, Ability, Prediction
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Thornton, Chris – Cognitive Science, 2021
Semantic composition in language must be closely related to semantic composition in thought. But the way the two processes are explained differs considerably. Focusing primarily on propositional content, language theorists generally take semantic composition to be a truth-conditional process. Focusing more on extensional content, cognitive…
Descriptors: Semantics, Cognitive Processes, Linguistic Theory, Language Usage
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