CORTEXA
← Browse
arxivcs.HCcs.AI2026-07-22

A Framework of User Experience Principles for Human-AI Agent Interaction in the Workplace

Kathrin Paimann, Elizangela Valarini, Sebastian Juhl

As AI agents become integral to business workflows, establishing guiding user experience (UX) principles is crucial for ensuring user trust and successful adoption. To address this, our study uses a multi-method approach - combining participatory design workshop, paper-and-pencil, expert review, meta-analysis, and in-depth interviews - to identify and validate a design framework of eight core UX principles for human-AI agent interaction in the workplace. Together with their underlying criteria, these principles provide actionable guardrails for designers and software engineers, creating a foundation for developing effective and human-centered AI agent interactions. This study contributes to a structured foundation for future empirical studies on agentic AI in enterprise settings.

View free PDFSource page

Related papers

arxivcs.AIcs.HC2026-07-08

Learning social norms enhances compatibility in dynamic human-AI coordination

Yi Yang, Siyuan Liu, Xin Gao, Huamu Sun, Chao Liu, Qing Zhou, et al.

Humans continuously coordinate with others in dynamic interactions, often through implicit, hard-to-quantify social norms that act as shared tacit expectations among interacting agents. As AI agents, including large language models (LLMs), become embedded in daily life, they incr…

View free PDFSource page
arxivcs.HCcs.AI2026-07-04

CoGen3D: An Agentic Human-AI Co-Design Pipeline for 3D Asset Generation for Virtual Reality

Weiwei Jiang, Wanyu He, Zheyu Tan, Zheyuan Kuang, Difeng Yu, Shinobu Hasegawa, et al.

Creating 3D assets for virtual reality requires modeling expertise, which restricts the authorship of immersive experiences. Existing generative AI tools rely on unconstrained, command-driven prompting, lacking the conversational scaffolding needed for users to articulate their i…

View free PDFSource page
arxivcs.AIcs.CEcs.HC2026-07-14

Networked Intelligence: Active Shared Context Graphs for Human-AI Team Science

Sutanay Choudhury, Jeffrey J. Czajka, Lummy M. O. Monteiro, Erin Bredeweg, Jason McDermott, Katherine Wolf, et al.

Most AI-for-science systems focus on scaling a single reasoning process by using better models, larger context windows, long-horizon agentic execution, or digital co-scientists working with one principal user. However, challenging scientific problems are rarely solved by one reas…

View free PDFSource page
arxivcs.HCcs.AI2026-07-08

Creativity from Friction: Human-AI Interaction for Exploratory Structural Design

Ricardo Maia Avelino, Rita Sevastjanova, Tom Van Mele, Philippe Block, Mennatallah El-Assady

AI agents that generate final answers based on user input often do not meet the needs of creative fields. Fields such as structural design and architecture need interactive systems that help users externalise and develop ideas, explore alternatives, and refine partial solutions.…

View free PDFSource page
arxivcs.AIcs.HC2026-07-13

Designing Agent-Ready Websites for AI Web Agents: A Framework for Machine Readability, Actionability, and Decision Reliability

Said Elnaffar, Farzad Rashidi

Online shopping is increasingly shifting toward a model in which AI agents independently search for products, compare options, evaluate constraints, and carry out parts of the purchasing process for users. Website design must now support both human and agent-mediated interaction.…

View free PDFSource page
arxivcs.HCcs.AI2026-07-02

What Types of Human-AI Teams Exist?

Nathan Hughes, Ibrahim Habli

Human-AI teaming has received increasing attention in the literature. However, the range of studies conducted in multiple domains make it difficult to understand what types of teams are being studied, and in what ways are they similar/different from one another. In this study, we…

View free PDFSource page