Toward a Probability-Based Framework for Cognitive Ergonomics in Future AI User Interfaces

Abstract

Based on a year-long autoethnographic study of ChatGPT and DALL-E, this paper examines the intersection of cognitive ergonomics, focusing on mental processes in human-technology interactions, and the design of both prompt and graphical user interfaces (UI) for generative artificial intelligence (GenAI) models. It investigates how probabilistic reasoning frameworks can be optimized to enhance user interaction in design and graphical content production tasks. By exploring the cognitive and logical foundations of these interactions, the study confirms that the inherent probabilistic nature of GenAI demands a shift from rule-based interfaces to those that embrace the ubiquitous uncertainty present in both the system and the user. A probabilistic approach to UI design is proposed to improve clarity and effectiveness amid increasing complexity and unpredictability, addressing the limitations of current natural language-based prompts and feedback interfaces in professional workflows. Drawing on the newly developed Networked Two-Way Communication Channels (NTCC) theory and its entropy alignment techniques, the paper evaluates interdisciplinary comparisons across various design practices to determine the effectiveness and limitations of GenAI as a design aid, providing both qualitative and quantitative insights into tool performance. Additionally, the paper introduces a set of situation-specific graphical “Turing tests” as benchmark assessment procedures for evaluating AI models’ readiness in graphical production tasks. The findings underscore that randomness will persist in GenAI technology and that embracing probability-based mental modeling in complex UI design is likely essential for harnessing the transformative potential of AI models, thereby fully realizing their creative and productive capabilities.

Keywords: Large language models (LLM), ChatGPT, DALL-E, Information theory, Design practices, User interface design, Multimodal interaction

DOI: 10.54941/ahfe1006132