AGI: Artificial General Intelligence for Education
Ehsan Latif, Gengchen Mai, Matthew Nyaaba, Xuansheng Wu, Ninghao Liu,, Guoyu Lu, Sheng Li, Tianming Liu, and Xiaoming Zhai

TL;DR
This paper discusses the potential of AGI, driven by large language models, to revolutionize education through personalized learning, improved assessments, and understanding social dynamics, while addressing ethical considerations.
Contribution
It provides a comprehensive review of AGI's capabilities, scope, and potential applications in education, highlighting new opportunities and challenges for future educational practices.
Findings
AGI can personalize learning experiences for students.
AGI enhances educational assessment and feedback.
Ethical issues like bias and privacy are critical in AGI deployment.
Abstract
Artificial general intelligence (AGI) has gained global recognition as a future technology due to the emergence of breakthrough large language models and chatbots such as GPT-4 and ChatGPT, respectively. Compared to conventional AI models, typically designed for a limited range of tasks, demand significant amounts of domain-specific data for training and may not always consider intricate interpersonal dynamics in education. AGI, driven by the recent large pre-trained models, represents a significant leap in the capability of machines to perform tasks that require human-level intelligence, such as reasoning, problem-solving, decision-making, and even understanding human emotions and social interactions. This position paper reviews AGI's key concepts, capabilities, scope, and potential within future education, including achieving future educational goals, designing pedagogy and…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Adam · Dense Connections · Label Smoothing · Dropout · Absolute Position Encodings · Position-Wise Feed-Forward Layer · Residual Connection
