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AGI: Artificial General Intelligence for Education

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arxiv 2304.12479 v5 pith:KMMRWJBI submitted 2023-04-24 cs.AI

classification cs.AI
keywords educationeducationalfutureintelligencemodelsstudentartificialcapabilities
verification ladder T0 review T1 audit T2 compute T3 formal
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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. 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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. UrbanMind: Towards Urban General Intelligence via Tool-Enhanced Retrieval-Augmented Generation and Multilevel Optimization

    cs.LG 2025-07 reject novelty 4.0 of 10

    The paper introduces UrbanMind, a tool-enhanced RAG framework with a multilevel optimization formulation for continual adaptation in urban AI, but offers only qualitative prototype results.

  2. The Revolution Has Arrived: What the Current State of Large Language Models in Education Implies for the Future

    cs.HC 2025-07 unverdicted novelty 2.0 of 10

    A narrative review of LLMs in education that speculates, without new evidence, that conversational interfaces will replace traditional WIMP-style interaction as the default.

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