Beyond Exploration: AI-facilitated Information Visualization Structure for Complex Context Exploration and Decision-Making in Environmental Sustainability

Abstract

Institutional sustainability campaigns often rely on static “playbooks” to disseminate pro-environmental advice. These linear, text-heavy resources are difficult to navigate when users must compare actions across effort, impact, and personal relevance. We investigate how different information visualization structures shape sustainability planning by comparing three interfaces: an AI-enhanced List, Quadrants, and an Interactive Map. The interfaces share a hybrid content pipeline in which sustainability practices are curated by human experts and enriched with LLM-generated metadata for visualization. In a within-subjects study with 115 university-affiliated participants, we found no significant differences in adoption volume or perceived discovery across interfaces. However, interface structure substantially affected workload and decision quality. The Map imposed significantly higher mental demand, frustration, and effort than both structured alternatives, lowered decision confidence, and was rated as less helpful than the institution’s original playbook. Quadrants emerged as the most preferred interface in post-study rankings, suggesting that lightweight semantic grouping can support exploration without the cognitive cost of free-form spatial navigation. These findings refine the design of AI-enhanced decision support tools for personal sustainability and other multi-criteria personal informatics domains.