Situated, Dynamic, and Subjective: Envisioning the Design of Theory-of-Mind-Enabled Everyday AI with Industry Practitioners

TL;DR

This paper explores how industry practitioners envision integrating Theory of Mind into everyday AI, emphasizing situated, dynamic, and subjective aspects to improve human-AI interactions.

Contribution

It provides novel design recommendations for ToM-enabled AI, grounded in practitioner insights, highlighting the importance of context, dynamism, and individual differences.

Findings

Three key design recommendations for ToM-enabled AI

Identification of tensions between envisioned futures and current practices

Advocacy for continuous, interaction-based ToM capabilities

Abstract

Theory of Mind (ToM) -- the ability to infer what others are thinking (e.g., intentions) from observable cues -- is traditionally considered fundamental to human social interactions. This has sparked growing efforts in building and benchmarking AI's ToM capability, yet little is known about how such capability could translate into the design and experience of everyday user-facing AI products and services. We conducted 13 co-design sessions with 26 U.S.-based AI practitioners to envision, reflect, and distill design recommendations for ToM-enabled everyday AI products and services that are both future-looking and grounded in the realities of AI design and development practices. Analysis revealed three interrelated design recommendations: ToM-enabled AI should 1) be situated in the social context that shape users' mental states, 2) be responsive to the dynamic nature of mental states, and 3) be attuned to subjective individual differences. We surface design tensions within each recommendation that reveal a broader gap between practitioners' envisioned futures of ToM-enabled AI and the realities of current AI design and development practices. These findings point toward the need to move beyond static, inference-driven approach to ToM and toward designing ToM as a pervasive capability that supports continuous human-AI interaction loops.