Talk to the machine: Unleashing the potential of AI to scale dialogic education and reduce polarization

Abstract Dialogic education is largely advocated as a means to promote dialogue and reduce polarization. Chatbots based on large language models (LLMs) carry the potential to scale dialogic education by serving as conversation partners and sustaining a dialogic space on various topics. They combine human-like conversational abilities with machine patience. To explore this potential, we fine-tuned an LLM-based chatbot, LlamaLo, using a corpus of productive discussions. We analyzed ten discussions with LlamaLo on contentious topics, such as liberalism and cultural appropriation. Our findings show that LlamaLo effectively opens dialogic spaces by questioning interlocutors’ assumptions, presenting alternative perspectives, and providing relevant knowledge. However, challenges, such as negative tone and bias, could undermine the dialogic space and should be addressed computationally and pedagogically. We conclude that dedicated LLM-based chatbots have the potential for enhancing dialogic education and enabling seamless scripting responsive to real-time needs.