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

arXiv: https://arxiv.org/abs/2507.00161 · HTML: https://mlsanity.com/paper/2507.00161/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 76 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances](https://mlsanity.com/paper/2203.06935/) (2022)
- [Colin: A Multimodal Human-AI Co-Creation Storytelling System To Support Children's Multi-Level Narrative Skills](https://mlsanity.com/paper/2405.06495/) (2024)
- [Physiologically Driven Storytelling: Concept and Software Tool](https://mlsanity.com/paper/2003.09333/) (2020)

## Abstract (arXiv)

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.