Emotive Architectures: The Role of LLMs in Adjusting Work Environments

2026-04-28

AI Summary

This study addresses the challenges of diminished focus, well-being, and engagement in remote and hybrid work settings by proposing a co-adaptive environment framework that seamlessly integrates physical and virtual spaces. Leveraging large language models (LLMs), the framework dynamically interprets emotional and behavioral cues from natural language in real time to adjust environmental parameters such as lighting, acoustics, and interface elements. By positioning the LLM as an affective-aware intermediary, the approach transforms static workspaces into responsive, human-centered intelligent environments. Designed with strict adherence to user privacy and autonomy, this method significantly enhances user experience and establishes a novel paradigm for ethically grounded, inclusive design of smart office spaces.

Abstract

In remote and hybrid work contexts, the integration of physical and digital environments is revolutionizing spatial experiences, collaboration, and interpersonal interactions. This study examines three fundamental spatial conditions: the physical environment, characterized by material and sensory attributes; the virtual environment, influenced by immersive technologies; and their fusion into hybrid environments where digital and physical components interact dynamically. The increasing number of AI tools in contemporary society, extensively utilized in both professional and personal spheres, has led to a varied landscape of developing technologies. For instance, ChatGPT has emerged as one of the most downloaded applications, a statistically substantiated fact that demonstrates the swift incorporation of language-based AI into daily life. It also underscores the function of large language models (LLMs) as meaningful bridges between concepts at reading emotional and behavioral signals via natural language. These models provide real-time modifications such as altering illumination, acoustics, or interface configurations, converting static settings into dynamic, emotionally receptive environments. We investigate the integration of language models into professional settings and their potential to enhance user experience by promoting focus, well-being, and engagement. The study investigates ethical concerns, including privacy, emotional tracking, and user agency, emphasizing the importance of inclusive and transparent design. This research formulates a framework for creating co-adaptive environments that merge technological innovation with human-centered experiences, offering a fresh viewpoint on responsive and supportive hybrid workspaces.

Problem

• emotive environments
• large language models
• hybrid workspaces
• emotional tracking
• user agency

Innovation

• Large Language Models
• Emotive Environments
• Hybrid Workspaces
• Co-adaptive Systems
• Affective Computing