[Paper Review] AGI: Artificial General Intelligence for Education

Ehsan Latif, Gengchen Mai+7 more|arXiv (Cornell University)|Apr 24, 2023

Artificial Intelligence in Healthcare and Education36 citations

TL;DR

A position paper reviewing how AGI, enabled by large pre-trained models like GPT-4 and ChatGPT, could transform education through personalized tutoring, adaptive assessment, curriculum design, and ethical considerations, while emphasizing collaboration between educators and AI engineers.

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.

Motivation & Objective

• Define AGI and its core capabilities in relation to education.
• Survey how AGI can support pedagogy, curriculum design, and assessment.
• Discuss potential impacts on intelligent tutoring systems and educational outcomes.
• Highlight ethical considerations, data privacy, and the need for human-AI collaboration in education.

Proposed method

• Synthesize concepts from existing literature on AGI, LLMs, and education.
• Outline core AGI principles (cognition, knowledge representation, learning, planning, language, multimodal processing).
• Differentiate ANI vs AGI in educational contexts and analyze implications.
• Discuss practical applications in ITS, assessment design, and curriculum development with AGI.
• Examine ethical, equity, and governance considerations for responsible use in education.

Experimental results

Research questions

• RQ1 What are the defining characteristics and core capabilities of AGI relevant to education?
• RQ2 How can AGI enhance pedagogy, curriculum design, and assessment in educational settings?
• RQ3 What roles should human educators play alongside AGI to ensure effective and ethical use?
• RQ4 What are the key ethical, fairness, and privacy concerns when deploying AGI in education, and how can they be mitigated?

Key findings

• AGI could enable highly personalized, adaptive learning experiences and comprehensive, human-like feedback.
• LLMs (e.g., GPT-4, ChatGPT) are presented as stepping stones toward AGI with broad educational applications.
• AGI can support intelligent tutoring systems, pedagogical knowledge enhancement, and dynamic assessment approaches.
• The paper emphasizes the necessity of interdisciplinary collaboration between educators and AI engineers.
• Ethical considerations include data bias, fairness, privacy, and the need for codes of conduct for responsible AGI use in education.
• The role of human educators is framed as complementary to AGI, focusing on guidance, emotional support, and developmental appropriateness of content.