AGI: Artificial General Intelligence for Education
- URL: http://arxiv.org/abs/2304.12479v5
- Date: Wed, 13 Mar 2024 16:47:04 GMT
- Title: AGI: Artificial General Intelligence for Education
- Authors: Ehsan Latif, Gengchen Mai, Matthew Nyaaba, Xuansheng Wu, Ninghao Liu,
Guoyu Lu, Sheng Li, Tianming Liu, and Xiaoming Zhai
- Abstract summary: This position paper reviews artificial general intelligence (AGI)'s key concepts, capabilities, scope, and potential within future education.
It highlights that AGI can significantly improve intelligent tutoring systems, educational assessment, and evaluation procedures.
The paper emphasizes that AGI's capabilities extend to understanding human emotions and social interactions.
- Score: 41.45039606933712
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 curriculum, and performing
assessments. It highlights that AGI can significantly improve intelligent
tutoring systems, educational assessment, and evaluation procedures. AGI
systems can adapt to individual student needs, offering tailored learning
experiences. They can also provide comprehensive feedback on student
performance and dynamically adjust teaching methods based on student progress.
The paper emphasizes that AGI's capabilities extend to understanding human
emotions and social interactions, which are critical in educational settings.
The paper discusses that ethical issues in education with AGI include data
bias, fairness, and privacy and emphasizes the need for codes of conduct to
ensure responsible AGI use in academic settings like homework, teaching, and
recruitment. We also conclude that the development of AGI necessitates
interdisciplinary collaborations between educators and AI engineers to advance
research and application efforts.
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