🤖 AI Summary
This work addresses the paradox that, despite the growing prevalence of graphical user interfaces, terminal-based interactions remain dominant in AI collaboration. Drawing on human-computer interaction (HCI) theory, the paper proposes that effective human–AI–interface collaboration hinges on three core attributes: representational compatibility between the agent and the interface, interactional transparency of agent behavior, and low barriers to human participation. Through theoretical analysis, design principle derivation, and cross-modal comparison, the study systematically articulates and formalizes the terminal’s inherent advantages as an interaction paradigm for AI agents, demonstrating how it naturally fulfills these attributes. The research not only clarifies the irreplaceable role of terminals in AI collaboration but also distills generalizable design principles applicable to graphical and spatial interfaces, offering a theoretical foundation and practical framework for future multimodal human–AI collaborative systems.
📝 Abstract
While research on AI agents focuses on enabling them to operate graphical user interfaces, the most effective and widely adopted agent tools in practice are terminal-based. We argue that this convergence is not coincidental. It reflects three design properties central to effective human-AI-UI collaboration: representational compatibility between agent and interface, transparency of agent actions within the interaction medium, and low barriers to entry for human participants. We ground each property in established HCI theory, show how terminal-based tools satisfy them by default, and argue that any modality, including graphical and spatial interfaces, must be deliberately engineered to achieve them. Rather than a legacy artifact, the terminal serves as a design exemplar whose properties any agent-facing modality must replicate.