Education Domain
Research in the education domain is rapidly evolving, driven by the integration of artificial intelligence, particularly large language models (LLMs) and multimodal AI, to enhance teaching, learning, and assessment. Current efforts focus on leveraging these models for personalized learning, automated feedback, and analyzing student data (e.g., through time series analysis and emotion detection) to improve educational outcomes. This work highlights the need for responsible AI development in education, addressing ethical concerns and ensuring equitable access to these powerful technologies, while also exploring the use of other AI models such as those for visual question answering and nonverbal immediacy analysis.
Papers
Bringing Generative AI to Adaptive Learning in Education
Hang Li, Tianlong Xu, Chaoli Zhang, Eason Chen, Jing Liang, Xing Fan, Haoyang Li, Jiliang Tang, Qingsong Wen
Generative AI for Education (GAIED): Advances, Opportunities, and Challenges
Paul Denny, Sumit Gulwani, Neil T. Heffernan, Tanja Käser, Steven Moore, Anna N. Rafferty, Adish Singla
Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges
Qingyao Li, Lingyue Fu, Weiming Zhang, Xianyu Chen, Jingwei Yu, Wei Xia, Weinan Zhang, Ruiming Tang, Yong Yu
Gemini Pro Defeated by GPT-4V: Evidence from Education
Gyeong-Geon Lee, Ehsan Latif, Lehong Shi, Xiaoming Zhai