This paper presents a comprehensive examination of how multimodal artificial
intelligence (AI) approaches are paving the way towards the realization of
Artificial General Intelligence (AGI) in educational contexts. It scrutinizes
the evolution and integration of AI in educational systems, emphasizing the
crucial role of multimodality, which encompasses auditory, visual, kinesthetic,
and linguistic modes of learning. This research delves deeply into the key
facets of AGI, including cognitive frameworks, advanced knowledge
representation, adaptive learning mechanisms, strategic planning, sophisticated
language processing, and the integration of diverse multimodal data sources. It
critically assesses AGI's transformative potential in reshaping educational
paradigms, focusing on enhancing teaching and learning effectiveness, filling
gaps in existing methodologies, and addressing ethical considerations and
responsible usage of AGI in educational settings. The paper also discusses the
implications of multimodal AI's role in education, offering insights into
future directions and challenges in AGI development. This exploration aims to
provide a nuanced understanding of the intersection between AI, multimodality,
and education, setting a foundation for future research and development in AGI.

该研究全面探讨了多模态人工智能方法在教育环境中实现通用人工智能的途径，着重分析了人工智能在教育系统中的演进和整合，强调多模态学习（包括听觉、视觉、动觉和语言学习）的重要作用，研究深入探讨了通用人工智能的关键方面，包括认知框架、高级知识表示、自适应学习机制、战略规划、复杂语言处理和多样的多模态数据源整合，并批判性评估了通用人工智能在重塑教育范式、提高教学和学习效果、填补现有方法论空白以及在教育环境中解决伦理考量和负责任使用方面的潜力。该论文还讨论了多模态人工智能在教育中的意义，并提供对通用人工智能发展中未来方向和挑战的洞察。这项研究旨在为人工智能、多模态和教育的交叉领域提供细致的理解，为通用人工智能的未来研究和发展奠定基础。

人工智能教育的多模态性：迈向人工通用智能

Multimodality of AI for Education: Towards Artificial General  Intelligence

We present a categorical formulation of the cognitive frameworks of
Predictive Processing and Active Inference, expressed in terms of string
diagrams interpreted in a monoidal category with copying and discarding. This
includes diagrammatic accounts of generative models, Bayesian updating,
perception, planning, active inference, and free energy. In particular we
present a diagrammatic derivation of the formula for active inference via free
energy minimisation, and establish a compositionality property for free energy,
allowing free energy to be applied at all levels of an agent's generative
model. Aside from aiming to provide a helpful graphical language for those
familiar with active inference, we conversely hope that this article may
provide a concise formulation and introduction to the framework.

我们提出了一种基于范畴的认知框架，使用具有复制和丢弃功能的范畴内解释的字符串图表达方式，其中包括生成模型、贝叶斯更新、感知、规划、主动推理和自由能量的图表表示。我们通过自由能量最小化的图表导出了主动推理的公式，并建立了自由能量的组合性属性，使得自由能量可以应用于代理模型的所有层次。除了为熟悉主动推理的人提供一个有用的图形语言外，我们也希望本文能够提供一个简洁的框架和介绍。