The Rise of AI Writing Tools and Their Impact on Academic Integrity
AI-powered writing tools like Grammarly are raising concerns among educators about their potential to enable student plagiarism and undermine the development of authentic student voices.
University Teachers' Perceptions and Adoption of Generative AI in Teaching: A Case Study
Over 50% of university teachers in this case study are open to adopting generative AI, such as ChatGPT, in their teaching activities, with a strong positive correlation between the positive impact of these tools on their teaching and their willingness to encourage students to use them ethically.
Examining the Relationship Between Gaze Synchrony and Self-Reported Attention During Video Lecture Learning
Attentive learners exhibit higher gaze synchronization during video lecture viewing, but gaze synchrony measures do not directly predict learning outcomes.
Leveraging Large Language Models as Interactive Academic Reading Companions: Exploring the Potential and Pitfalls
Large language models (LLMs) can serve as promising academic reading companions that enhance student learning when thoughtfully integrated with appropriate safeguards.
Large Language Models in Education: Survey and Outlook
Large language models (LLMs) are transforming education by providing personalized learning experiences and innovative tools for students and teachers.
Evaluating Authoring Tools for Explorable Education Content
Authoring tools for explorables in education need to meet specific requirements for functionality and usability to empower educators and students in the content creation process.
Enhancing Programming Education with ChatGPT: A Case Study on Student Perceptions and Interactions in a Python Course
ChatGPT enhances programming education by providing assistance with debugging, code generation, and explanations.
教育におけるAI デジタルアシスタントに対する遠隔学習学生の期待と懸念
遠隔学習学生は、AIデジタルアシスタントに対して、学習支援、個別化、アクセシビリティ、そして感情的・社会的サポートといった機能を期待している一方で、倫理的・社会的影響、データプライバシー、学習の完全性、運用上の課題などの懸念も示している。
KTbench: A Novel Data Leakage-Free Framework for Knowledge Tracing
Proposing a framework to prevent data leakage in knowledge tracing models and introducing model variations to enhance performance.
KTbench: A Novel Data Leakage-Free Framework for Knowledge Tracing
Preventing data leakage in knowledge tracing models is crucial for maintaining performance and accuracy.
© 2024 by Linnk AI