Prompting Destiny: Negotiating Socialization and Growth in an LLM-Mediated Speculative Gameworld

2026-02-05

AI Summary

This study investigates how to support players’ reflection on socialization processes, moral responsibility, and educational roles within AI-mediated environments. To this end, we designed a large language model–based role-playing game that guides players through a “four seasons” narrative structure, accompanying a virtual prince across staged moral dilemmas. The system innovatively incorporates delayed feedback and phased growth prompts to balance open-ended expression with sustained engagement. By deliberately omitting immediate scoring and emphasizing reflective scaffolding, this work presents the first implementation of socialization theory into an interactive AI game system. A user study (N=12) demonstrates that players effectively negotiate their sense of responsibility and role identity, while also revealing a tension between openness in expression and the entry load required for continued participation.

Technology Category

• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Multiagent Systems: Mechanism Design

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

We present an LLM-mediated role-playing game that supports reflection on socialization, moral responsibility, and educational role positioning. Grounded in socialization theory, the game follows a four-season structure in which players guide a child prince through morally charged situations and compare the LLM-mediated NPC's differentiated responses across stages, helping them reason about how educational guidance shifts with socialization. To approximate real educational contexts and reduce score-chasing, the system hides real-time evaluative scores and provides delayed, end-of-stage growth feedback as reflective prompts. We conducted a user study (N=12) with gameplay logs and post-game interviews, analyzed via reflexive thematic analysis. Findings show how players negotiated responsibility and role positioning, and reveal an entry-load tension between open-ended expression and sustained engagement. We contribute design knowledge on translating sociological models of socialization into reflective AI-mediated game systems.

Problem

• socialization
• moral responsibility
• educational role positioning
• LLM-mediated gameworld
• reflective AI

Innovation

• LLM-mediated role-playing
• socialization theory
• reflective AI game design
• delayed feedback
• moral education simulation