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Kevin Peyton; Saritha Unnikrishnan; Brian Mulligan – Discover Education, 2025
Within the university sector, student recruitment and enrolment are key strategies as institutions strive to attract, retain and engage students. This strategy is underpinned by the provision of services, applications and technologies that facilitate lecturing and support staff. Universities that offer online learning have a particular incentive…
Descriptors: Universities, Artificial Intelligence, Computer Mediated Communication, College Students
Leveraging Large Language Models to Generate Course-Specific Semantically Annotated Learning Objects
Dominic Lohr; Marc Berges; Abhishek Chugh; Michael Kohlhase; Dennis Müller – Journal of Computer Assisted Learning, 2025
Background: Over the past few decades, the process and methodology of automatic question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational content. Objectives: This paper explores the potential of large language models…
Descriptors: Resource Units, Semantics, Automation, Questioning Techniques
Hui Wen Chua; Nagaletchimee Annamalai – International Journal of Technology in Education, 2025
The role of AI chatbots is undergoing a transformation, where it was firstly used for English native language learning; later, it shifted to the use for learning English as a second language (ESL) and English as a foreign language learning. Lastly, it is used to learn foreign languages. Hence, due to the changes in AI chatbots' role, there is a…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, English (Second Language)
Olena Bolgova; Paul Ganguly; Volodymyr Mavrych – Anatomical Sciences Education, 2025
Integrating artificial intelligence, particularly large language models (LLMs), into medical education represents a significant new step in how medical knowledge is accessed, processed, and evaluated. The objective of this study was to conduct a comprehensive analysis comparing the performance of advanced LLM chatbots in different topics of…
Descriptors: Comparative Analysis, Artificial Intelligence, Technology Uses in Education, Natural Language Processing
Brady D. Lund; Tae Hee Lee; Nishith Reddy Mannuru; Nikhila Arutla – Journal of Academic Ethics, 2025
The emergence of generative artificial intelligence tools, such as ChatGPT, presents new challenges impacting student perceptions of academic integrity. While extensive research exists on academic misconduct and student perceptions of various infractions, there is limited understanding of how AI tools impact these views and whether their use…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Student Attitudes
Nathan Lindberg – Writing Center Journal, 2025
In this essay, I suggest that we should embrace generative artificial intelligence (GenAI) writing tools, particularly chatbots (e.g., ChatGPT, Copilot, Claude), because they can enable linguistic equity by leveling the academic playing field for English as an additional language students. As writing experts, we can find ways to use this…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Technology Uses in Education
Hyeongdon Moon; Richard Lee Davis; Seyed Parsa Neshaei; Pierre Dillenbourg – International Educational Data Mining Society, 2025
Knowledge tracing models have enabled a range of intelligent tutoring systems to provide feedback to students. However, existing methods for knowledge tracing in learning sciences are predominantly reliant on statistical data and instructor-defined knowledge components, making it challenging to integrate AI-generated educational content with…
Descriptors: Artificial Intelligence, Natural Language Processing, Automation, Information Management
Victor-Alexandru Padurean; Tung Phung; Nachiket Kotalwar; Michael Liut; Juho Leinonen; Paul Denny; Adish Singla – International Educational Data Mining Society, 2025
The growing need for automated and personalized feedback in programming education has led to recent interest in leveraging generative AI for feedback generation. However, current approaches tend to rely on prompt engineering techniques in which predefined prompts guide the AI to generate feedback. This can result in rigid and constrained responses…
Descriptors: Automation, Student Writing Models, Feedback (Response), Programming
Saira Anwar; Ahmed Ashraf Butt; Muhsin Menekse – International Journal of STEM Education, 2025
Background: Technology-enhanced classrooms now integrate a range of educational apps designed to improve student outcomes. The effectiveness of these applications is influenced by multiple factors related to the courses and the applications themselves. A critical factor is student engagement, which involves interacting with the course content…
Descriptors: Natural Language Processing, Handheld Devices, Computer Oriented Programs, Learner Engagement
James G. Caling; Joanna Kyla T. Antonio; Ma. Fe. L. Dimatatac; Mitz D. Sabellano; Victoria Dhane R. Vicencio; Justin M. Prias; John Carlo M. Ramos – Journal of Interdisciplinary Studies in Education, 2025
This study examines how 10 pre-service teachers from a teacher education institution in Manila integrate ChatGPT into their academic tasks and navigate the resulting moral dissonance. Through semistructured interviews, the findings reveal that while ChatGPT is employed for paraphrasing, organizing ideas, information retrieval, and simplifying…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Preservice Teachers
Mickie De Wet; Margarita Oja Da Silva; René Bohnsack – Innovations in Education and Teaching International, 2025
This study explores the use of large language models (LLMs) to generate feedback on essay-type assignments in Higher Education. Drawing on a seminal feedback framework, it examines the pedagogical and psychological effectiveness of LLM-generated feedback across three cohorts of MBA, MSc, and undergraduate students. Methods included linguistic…
Descriptors: Higher Education, College Students, Artificial Intelligence, Writing Evaluation
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
Jaqueline Mora – Educational Linguistics, 2025
This study explores prototypical word associations in EFL learners' mental lexicon to determine how they categorize the words retrieved in response to prompts in a lexical availability task. We compare two groups of Spanish EFL learners: sixty children in the sixth grade of primary education and sixty adolescents in the ten grade of secondary…
Descriptors: English (Second Language), Associative Learning, Children, Adolescents
Shimmei, Machi; Matsuda, Noboru – International Educational Data Mining Society, 2023
We propose an innovative, effective, and data-agnostic method to train a deep-neural network model with an extremely small training dataset, called VELR (Voting-based Ensemble Learning with Rejection). In educational research and practice, providing valid labels for a sufficient amount of data to be used for supervised learning can be very costly…
Descriptors: Artificial Intelligence, Training, Natural Language Processing, Educational Research
Huawei, Shi; Aryadoust, Vahid – Education and Information Technologies, 2023
Automated writing evaluation (AWE) systems are developed based on interdisciplinary research and technological advances such as natural language processing, computer sciences, and latent semantic analysis. Despite a steady increase in research publications in this area, the results of AWE investigations are often mixed, and their validity may be…
Descriptors: Writing Evaluation, Writing Tests, Computer Assisted Testing, Automation

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