mlsanityData to 7 Oct 2026

OceanChat: The Effect of Virtual Conversational AI Agents on Sustainable Attitude and Behavior Change

arXiv 2502.02863 ↗First posted 5 Feb 2025cs.HC cs.AIcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

Marine ecosystems face unprecedented threats from climate change and plastic pollution, yet traditional environmental education often struggles to translate awareness into sustained behavioral change. This paper presents OceanChat, an interactive system leveraging large language models to create conversational AI agents represented as animated marine creatures -- specifically a beluga whale, a jellyfish, and a seahorse -- designed to promote environmental behavior (PEB) and foster awareness through personalized dialogue. Through a between-subjects experiment (N=900), we compared three conditions: (1) Static Scientific Information, providing conventional environmental education through text and images; (2) Static Character Narrative, featuring first-person storytelling from 3D-rendered marine creatures; and (3) Conversational Character Narrative, enabling real-time dialogue with AI-powered marine characters. Our analysis revealed that the Conversational Character Narrative condition significantly increased behavioral intentions and sustainable choice preferences compared to static approaches. The beluga whale character demonstrated consistently stronger emotional engagement across multiple measures, including perceived anthropomorphism and empathy. However, impacts on deeper measures like climate policy support and psychological distance were limited, highlighting the complexity of shifting entrenched beliefs. Our work extends research on sustainability interfaces facilitating PEB and offers design principles for creating emotionally resonant, context-aware AI characters. By balancing anthropomorphism with species authenticity, OceanChat demonstrates how interactive narratives can bridge the gap between environmental knowledge and real-world behavior change.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 6 of its 79 references that are in the map. First posted on arXiv 5 Feb 2025.

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6 of 79 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr

Other references · 5

  1. 2023Towards Understanding Sycophancy in Language Modelscited by 988
  2. 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 805
  3. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Surveycited by 240
  4. 2019Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Goodcited by 207
  5. 2024AI as a Child of Mother Earth: Regrounding Human-AI Interaction in Ecological Thinkingcited by 2

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  2. 2025Exploring Anthropomorphism in Conversational Agents for Environmental Sustainability
  3. 2025Simulating Persuasive Dialogues on Meat Reduction with Generative Agents
  4. 2026MASCOT: Multi-Agent Socio-Collaborative Companion Systems
  5. 2026From Codebooks to VLMs: Evaluating Automated Visual Discourse Analysis for Climate Change on Social Media
  6. 2025Sensorium Arc: AI Agent System for Oceanic Data Exploration and Interactive Eco-Art
  7. 2026Fine-Tuned Multi-Agent Framework for Detecting OCEAN in Life Narratives
  8. 2024Artificial social influence via human-embodied AI agent interaction in immersive virtual reality (VR): Effects of similarity-matching during health conversations