OceanChat: The Effect of Virtual Conversational AI Agents on Sustainable Attitude and Behavior Change
Pat Pataranutaporn, Alexander A. Doudkin, Pattie Maes
Abstract
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 * Both authors contributed equally to this research.
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 29d60d4c-e5a1-4a21-b4d3-d6fa1204cf1dBuilds on1
Related papers
- Speaking Through Chatbots or Text: How Format Shapes Information Agreement, Reactance, Environmental Awareness, and TrustYuri Hwang, Vera Maria Fahrner, Kai Tobias Horstmann, Marc HassenzahlCHI 2026 · 1 citation
- Exploring the Impact of Avatar Representations in AI Chatbot Tutors on Learning ExperiencesChek Tien Tan, Indriyati Atmosukarto, Budianto Tandianus, Songjia Shen et al.CHI 2025 · 16 citations
- Can LLM Agents Maintain a Persona in Discourse?Pranav Bhandari, Nicolas Fay, Michael J. Wise, Amitava Datta et al.EMNLP 2025
- EyeSee: Enhancing Art Appreciation through Anthropomorphic Interpretations from Multiple PerspectivesYongming Li, Hangyue Zhang, Andrea Yaoyun Cui, Zisong Ma et al.CHI 2025 · 7 citations
- Good for the Planet, Bad for Me? Intended and Unintended Consequences of AI Energy Consumption DisclosureMichael Klesel, Uwe MesserCHI 2026 · 2 citations
