Exploring Anthropomorphism in Conversational Agents for Environmental Sustainability

TL;DR

This study explores how anthropomorphic design in conversational agents using large language models influences user engagement and eco-friendly behaviors in energy management, finding that personified agents increase user connection but both designs promote sustainability.

Contribution

It introduces a comparative analysis of anthropomorphic versus traditional conversational agents in promoting environmental sustainability through LLMs.

Findings

LLM-based CAs increase eco-friendly behaviors

Personified agents enhance user connection

Both designs support sustainable energy management

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.