Social Life Simulation for Non-Cognitive Skills Learning
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Abstract (arXiv)
Non-cognitive skills are crucial for personal and social life well-being, and such skill development can be supported by narrative-based (e.g., storytelling) technologies. While generative AI enables interactive and role-playing storytelling, little is known about how users engage with and perceive the use of AI in social life simulation for non-cognitive skills learning. Additionally, the benefits of AI mentorship on self-reflection awareness and ability in this context remain largely underexplored. To this end, we introduced Simulife++, an interactive platform enabled by a large language model (LLM). The system allows users to act as protagonists, creating stories with one or multiple AI-based characters in diverse social scenarios. In particular, we expanded the Human-AI interaction to a Human-AI-AI collaboration by including a Sage Agent, who acts as a bystander, providing users with some perspectives and guidance on their choices and conversations in terms of non-cognitive skills to promote reflection. In a within-subject user study, our quantitative results reveal that, when accompanied by Sage Agent, users exhibit significantly higher levels of reflection on motivation, self-perceptions, and resilience & coping, along with an enhanced experience of narrative transportation. Additionally, our qualitative findings suggest that Sage Agent plays a crucial role in promoting reflection on non-cognitive skills, enhancing social communication and decision-making performance, and improving overall user experience within Simulife++. Multiple supportive relationships between Sage Agent and users were also reported. We offer design implications for the application of generative AI in narrative solutions and the future potential of Sage Agent for non-cognitive skill development in broader social contexts.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 13 of its 153 references that are in the map. First posted on arXiv 1 May 2024.
Builds on
13 of 153 references.
References · 13
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1 more
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Built on by
Citing papers per year: 2024: 1; 2025: 0; 2026: 2.
3 papers.
- 2024From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agentscited by 82
- 2026Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Makingcited by 0
- 2026LLMs struggle to simulate human belief updates in controlled environmentscited by 0
- 2026LLMs struggle to simulate human belief updates in controlled environments30 Jul 2026
- 2026Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making12 Feb 2026
- 2024From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents4 Dec 2024