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West Virginia Department of Education, 2025
This guidance centers around the users of artificial intelligence (AI) in various roles throughout West Virginia PK-12 schools. It is designed to assist individuals such as superintendents, district staff, educators, and support staff in the appropriate and effective use of AI, particularly generative AI technologies, within West Virginia schools.…
Descriptors: Technology Uses in Education, Elementary Secondary Education, Artificial Intelligence, Man Machine Systems
Brian W. Stone – Teaching of Psychology, 2025
Background: Students in higher education are using generative artificial intelligence (AI) despite mixed messages and contradictory policies. Objective: This study helps answer outstanding questions about many aspects of AI in higher education: familiarity, usage, perceptions of peers, ethical/social views, and AI grading. Method: I surveyed 733…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Technology Uses in Education
Ayse Merzifonluoglu; Habibe Gunes – European Journal of Education, 2025
Artificial intelligence (AI) is significantly shaping education and currently influencing pre-service teachers' academic and professional journeys. To explore this influence, the present study examines 389 Generation Z pre-service teachers' attitudes towards AI and its impact on educational decision-making at two state universities, using an…
Descriptors: Decision Making, Artificial Intelligence, Teacher Attitudes, Age Groups
Siqi Yi; Soo Young Rieh – Information and Learning Sciences, 2025
Purpose: This paper aims to critically review the intersection of searching and learning among children in the context of voice-based conversational agents (VCAs). This study presents the opportunities and challenges around reconfiguring current VCAs for children to facilitate human learning, generate diverse data to empower VCAs, and assess…
Descriptors: Literature Reviews, Children, Childrens Attitudes, Artificial Intelligence
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

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