AI Pedagogy: Dialogic Social Learning for Artificial Agents

TL;DR

This paper introduces a socially mediated learning environment for AI, inspired by Vygotsky, where dialogic interactions significantly improve knowledge acquisition in language models compared to traditional training methods.

Contribution

It proposes the 'AI Social Gym', a novel environment for dyadic pedagogical dialogues, demonstrating how structured social interactions enhance AI learning beyond conventional datasets.

Findings

Dialogic pedagogical strategies improve knowledge acquisition.

Mixed-direction interactions outperform unidirectional methods.

Socially mediated learning enhances response quality.

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in processing extensive offline datasets. However, they often face challenges in acquiring and integrating complex, knowledge online. Traditional AI training paradigms, predominantly based on supervised learning or reinforcement learning, mirror a 'Piagetian' model of independent exploration. These approaches typically rely on large datasets and sparse feedback signals, limiting the models' ability to learn efficiently from interactions. Drawing inspiration from Vygotsky's sociocultural theory, this study explores the potential of socially mediated learning paradigms to address these limitations. We introduce a dynamic environment, termed the 'AI Social Gym', where an AI learner agent engages in dyadic pedagogical dialogues with knowledgeable AI teacher agents. These interactions emphasize external, structured dialogue as a core mechanism for knowledge acquisition, contrasting with methods that depend solely on internal inference or pattern recognition. Our investigation focuses on how different pedagogical strategies impact the AI learning process in the context of ontology acquisition. Empirical results indicate that such dialogic approaches-particularly those involving mixed-direction interactions combining top-down explanations with learner-initiated questioning-significantly enhance the LLM's ability to acquire and apply new knowledge, outperforming both unidirectional instructional methods and direct access to structured knowledge, formats typically present in training datasets. These findings suggest that integrating pedagogical and psychological insights into AI and robot training can substantially improve post-training knowledge acquisition and response quality. This approach offers a complementary pathway to existing strategies like prompt engineering