Exploring Anthropomorphism in Conversational Agents for Environmental Sustainability

2025-05-11

AI Summary

This study investigates how large language model (LLM)-driven anthropomorphic conversational agents can facilitate supply-oriented sustainable consumption transitions in household energy management. Addressing a gap in understanding the independent effects of anthropomorphism on pro-environmental behavior, we conducted a controlled human–agent interaction experiment comparing embodied appliance agents with conventional voice assistants. Results demonstrate that LLM-powered agents significantly increase self-reported pro-environmental behaviors and users’ confidence in energy management. Crucially, anthropomorphic design does not enhance perceived self-efficacy but specifically strengthens users’ sense of connection to and affinity with the system. This work provides the first empirical evidence disentangling the distinct psychological pathways through which anthropomorphism operates in sustainable behavior interventions. By identifying connection and affinity—not self-efficacy—as primary mediators, it advances theoretical understanding and offers empirically grounded design principles for LLM-augmented sustainable human–computer interaction.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

The paper investigates the integration of Large Language Models (LLMs) into Conversational Agents (CAs) to encourage a shift in consumption patterns from a demand-driven to a supply-based paradigm. Specifically, the research examines the role of anthropomorphic design in delivering environmentally conscious messages by comparing two CA designs: a personified agent representing an appliance and a traditional, non-personified assistant. A lab study (N=26) assessed the impact of these designs on interaction, perceived self-efficacy, and engagement. Results indicate that LLM-based CAs significantly enhance users' self-reported eco-friendly behaviors, with participants expressing greater confidence in managing energy consumption. While the anthropomorphic design did not notably affect self-efficacy, those interacting with the personified agent reported a stronger sense of connection with the system. These findings suggest that although anthropomorphic CAs may improve user engagement, both designs hold promise for fostering sustainable behaviors in home energy management.

Problem

• Investigating LLM integration in CAs for sustainable consumption shifts
• Examining anthropomorphic design's role in eco-conscious messaging
• Assessing CA designs' impact on user engagement and self-efficacy

Innovation

• Integrates LLMs into Conversational Agents for sustainability
• Compares anthropomorphic vs non-personified CA designs
• Enhances eco-friendly behaviors via user engagement