Designing an Adaptive Storytelling Platform to Promote Civic Education in Politically Polarized Learning Environments

TL;DR

This paper presents an AI-driven storytelling platform designed to enhance civic education by reducing political polarization through emotionally adaptive narratives that promote perspective-taking.

Contribution

It introduces a novel AI-mediated civic storytelling system that personalizes narratives using affective computing and GPT-4 to foster emotional engagement and reduce polarization.

Findings

Real-time emotion and attention tracking improves story engagement.

Personalized narratives increase perspective-taking among politically diverse students.

The platform demonstrates potential for AI-supported civic education interventions.

Abstract

Political polarization undermines democratic civic education by exacerbating identity-based resistance to opposing viewpoints. Emerging AI technologies offer new opportunities to advance interventions that reduce polarization and promote political open-mindedness. We examined novel design strategies that leverage adaptive and emotionally-responsive civic narratives that may sustain students' emotional engagement in stories, and in turn, promote perspective-taking toward members of political out-groups. Drawing on theories from political psychology and narratology, we investigate how affective computing techniques can support three storytelling mechanisms: transportation into a story world, identification with characters, and interaction with the storyteller. Using a design-based research (DBR) approach, we iteratively developed and refined an AI-mediated Digital Civic Storytelling (AI-DCS) platform. Our prototype integrates facial emotion recognition and attention tracking to assess users' affective and attentional states in real time. Narrative content is organized around pre-structured story outlines, with beat-by-beat language adaptation implemented via GPT-4, personalizing linguistic tone to sustain students' emotional engagement in stories that center political perspectives different from their own. Our work offers a foundation for AI-supported, emotionally-sensitive strategies that address affective polarization while preserving learner autonomy. We conclude with implications for civic education interventions, algorithmic literacy, and HCI challenges associated with AI dialogue management and affect-adaptive learning environments.