Student Friendly Knowledge
Student-friendly knowledge focuses on optimizing knowledge transfer and learning experiences for students, leveraging advancements in AI and machine learning. Current research emphasizes developing AI-powered tools like teachable agents and virtual teaching assistants, often employing models such as BERT, LLMs (including ChatGPT and similar architectures), and various ensemble learning methods, to personalize learning and provide effective feedback. This research aims to improve learning outcomes, enhance assessment methods, and address challenges like mitigating bias in AI-generated feedback and promoting ethical AI usage in education, ultimately impacting both pedagogical practices and the development of more effective educational technologies.
Papers
Your Student is Better Than Expected: Adaptive Teacher-Student Collaboration for Text-Conditional Diffusion Models
Nikita Starodubcev, Artem Fedorov, Artem Babenko, Dmitry Baranchuk
Students' Perceptions and Preferences of Generative Artificial Intelligence Feedback for Programming
Zhengdong Zhang, Zihan Dong, Yang Shi, Noboru Matsuda, Thomas Price, Dongkuan Xu