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Latif, E., Mai, G., Nyaaba, M., Wu, X., Liu, N., & Lu, G., et al. (2024). AGI: Artificial General Intelligence for Education. Added by: alexb44 (03/09/2026, 08:25) Last edited by: alexb44 (03/09/2026, 08:27) |
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| Resource type: Miscellaneous Published BibTeX citation key: Latif2024 Email resource to friend View all bibliographic details  |
Categories: AI/Machine Learning Keywords: Artificial General Intelligence, Artificial Intelligence Creators: Latif, Li, Liu, Liu, Lu, Mai, Nyaaba, Wu, Zhai |
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| Abstract |
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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. AGI aims to replicate human intelligence through computer systems, one of the critical technologies that has the potential to revolutionize education. 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 inter- actions. In this position paper, we have articulated the 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 concerns about 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.
Added by: alexb44 Last edited by: alexb44 |