Towards social generative AI for education: theory, practices and ethics

TL;DR

This paper discusses the development of social generative AI systems for education that engage in meaningful, goal-oriented conversations with humans and other AIs, emphasizing ethical considerations and practical design challenges.

Contribution

It introduces a conceptual framework for social AI in education, highlighting the need for systems capable of complex interactions, knowledge construction, and ethical responsibility.

Findings

Social AI can facilitate dynamic, goal-driven educational interactions.

Designing ethical and responsible social AI systems is crucial.

Future systems should integrate internet resources and external representations.

Abstract

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.