LFTutor: Teaching People about Logical Fallacies through Intent-based Socratic Questioning and Critical Argumentation

ACL ARR 2026 January Submission2742 Authors

Keywords: LLM for Education, Dialogue System, Agentic System

Abstract: Identifying logical fallacies (LFs) in everyday discourse is challenging for many people. This challenge is amplified in the era of Large Language Models (LLMs), where malicious agents can deploy fallacious arguments to disseminate misinformation at scale. In this work, we explore the potential of LLMs as part of the solution. We introduce LFTutor, an intelligent tutoring system which uses LLMs to tutor humans and help them learn about logical fallacies. LFTutor integrates intent-driven Socratic questioning and critical argumentation principles to actively engage learners to reflect on their reasoning. Through both automatic and human evaluations, we demonstrate that LFTutor significantly outperforms baseline LLMs lacking such pedagogical strategies. This work highlights the promise of combining LLMs with pedagogical scaffolding to foster critical thinking and argument literacy in the age of AI.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: NLP educational applications

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 2742