用AI动态调整政治叙事，帮学生接纳对立观点。
Designing an Adaptive Storytelling Platform to Promote Civic Education in Politically Polarized Learning Environments
- 基于情绪识别与注意力追踪，实时调节故事语言风格。
- 通过情感代入和角色认同，提升对异见群体的理解。
- 适合教育科技、人机交互与公民教育研究者参考。
政治极化加剧了身份认同对抗，阻碍民主公民教育。新兴AI技术为缓解极化、促进政治开放心态提供了新可能。本文基于政治心理学与叙事学理论，探索利用自适应且具情感响应能力的公民叙事策略，以维持学生在故事中的情感投入，进而促进其对政治外群体的共情。采用设计型研究（DBR）方法，迭代开发了人工智能媒介的数字公民叙事（AI-DCS）平台。原型整合面部情绪识别与注意力追踪技术，实时评估用户情感与专注状态；叙事内容基于预设故事框架，通过GPT-4实现逐节语言适配，个性化调整语调，以维持学生对自身立场相异的政治叙事的情感参与。本研究为支持情感极化的AI干预策略提供了基础，同时兼顾学习者自主性。最后讨论了公民教育干预、算法素养及AI对话管理与情绪自适应学习环境的人机交互挑战。
原文摘要 · Abstract (English)
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.
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