This paper explores educational interactions involving humans and artificial intelligences not as sequences of prompts and responses, but as a social process of conversation and exploration. In this conception, learners continually converse with AI language models within a dynamic computational medium of internet tools and resources. Learning happens when this distributed system sets goals, builds meaning from data, consolidates understanding, reconciles differences, and transfers knowledge to new domains. Building social generative AI for education will require development of powerful AI systems that can converse with each other as well as humans, construct external representations such as knowledge maps, access and contribute to internet resources, and act as teachers, learners, guides and mentors. This raises fundamental problems of ethics. Such systems should be aware of their limitations, their responsibility to learners and the integrity of the internet, and their respect for human teachers and experts. We need to consider how to design and constrain social generative AI for education.

翻译：本文探讨了人类与人工智能之间的教育交互，将其视为一种对话与探索的社会过程，而非简单的提示与响应序列。在这一理念中，学习者在由互联网工具和资源构成的动态计算媒介中，与AI语言模型持续对话。当这一分布式系统设定目标、从数据中构建意义、巩固理解、调和差异，并将知识迁移至新领域时，学习便得以发生。构建面向教育的社会生成式人工智能，需要开发强大的AI系统：它们既能与人类对话，也能彼此交流；能够构建外部表征（如知识图谱）；能够访问并贡献互联网资源；并能扮演教师、学习者、引导者和导师的角色。这引发了根本性的伦理问题：此类系统应认识到自身的局限性，理解其对学生及互联网完整性的责任，并尊重人类教师与专家。我们必须思考如何设计和约束面向教育的社会生成式人工智能。