AGI: Artificial General Intelligence for Education
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 22 of its 130 references that are in the map. First posted on arXiv 24 Apr 2023.
Builds on
22 of 130 references.
References · 22
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10 more
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Built on by
Citing papers per year: 2023: 3; 2024: 2; 2025: 2.
7 papers.
- 2023AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiologycited by 34
- 2023Towards Automatic Boundary Detection for Human-AI Collaborative Hybrid Essay in Educationcited by 17
- 2024A Systematic Assessment of OpenAI o1-Preview for Higher Order Thinking in Educationcited by 16
- 2024Can generative AI and ChatGPT outperform humans on cognitive-demanding problem-solving tasks in science?cited by 6
- 2025The Jumping Reasoning Curve? Tracking the Evolution of Reasoning Performance in GPT-[n] and o-[n] Models on Multimodal Puzzlescited by 4
- 2023Understanding Place Identity with Generative AIcited by 1
- 2025AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Spacecited by 0
- 2025AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space24 Jun 2025
- 2025The Jumping Reasoning Curve? Tracking the Evolution of Reasoning Performance in GPT-[n] and o-[n] Models on Multimodal Puzzles3 Feb 2025
- 2024A Systematic Assessment of OpenAI o1-Preview for Higher Order Thinking in Education11 Oct 2024
- 2024Can generative AI and ChatGPT outperform humans on cognitive-demanding problem-solving tasks in science?7 Jan 2024
- 2023Towards Automatic Boundary Detection for Human-AI Collaborative Hybrid Essay in Education23 Jul 2023
- 2023AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology16 Jun 2023
- 2023Understanding Place Identity with Generative AI7 Jun 2023