• Joel Cooper, PhD, Red Scientific Inc.
• John Shutko, toXcel LLC

Description: Scientists using AI tools publish substantially more and receive nearly five times more citations than those who do not (Hao et al., 2026), with 36-60% increases in manuscript output across major preprint servers (Kusumegi et al., 2025). But AI adoption can also narrow research focus, reduce collaboration and produce work that is polished but substantively weak. These are human-AI interaction problems, and HF/E professionals have the frameworks to address them. This full-day workshop teaches human factors and ergonomics researchers and practitioners how to use current AI systems as collaborative tools in their research practice, moving beyond casual chatbot use to cover persistent memory, structured context, agent architectures and tool integration, connecting each capability to HF/E frameworks participants already know.

The workshop is structured across four layers: (1) how LLMs work, including token prediction, context windows as working memory constraints and hallucinations as architectural consequence; (2) persistent memory, structured context and agent architectures mapped onto episodic, semantic and procedural memory models, building toward a four-level autonomy spectrum aligned with levels-of-automation frameworks; (3) three hands-on research workflow applications including adversarial design review, participant simulation and structured data extraction; and (4) connecting hands-on experience to function allocation, trust calibration, and supervisory control frameworks, with a review of publication policies across major societies and documentation practices for reproducibility.

Participants will leave able to explain how LLMs work and why they hallucinate, build a structured project context for persistent AI collaboration, direct AI agents through multi-step research tasks, use AI for design review and data extraction, apply HF/E frameworks to evaluate AI-assisted workflows and document those workflows for reproducibility.

Prerequisites: No programming expertise is required.

Who Should Attend: All HF/E professionals and students

Required Materials: Participants should bring a laptop with internet access. All exercises use browser-based tools and participants' own research materials.

• Thomas Watkins, 3Leaf

Description: Artificial intelligence can now generate dashboards, charts and summaries in seconds. Yet the rapid adoption of generative tools has outpaced careful consideration of their cognitive consequences. Speed does not replace the psychological requirements of data sense-making. In performance-critical environments — health care, aviation, finance, public policy, enterprise analytics and operational decision-making — poor visualization design can amplify bias, overload working memory, distort risk perception and erode calibrated trust. As AI becomes embedded in analytic workflows, the responsibility of human factors professionals becomes more, not less, central. The defining challenge is not automation, but ensuring that visualizations — AI-assisted or otherwise — measurably support human judgment and performance.

This interactive workshop centers on the design psychology of data visualization, grounding visual decision-support in perceptual and cognitive science. Participants will examine how human factors principles govern perceptual clarity, attention allocation, cognitive workload management, uncertainty communication and bias mitigation. AI is positioned not as a replacement for design judgment, but as a tool that can assist in data cleaning, transformation, and exploratory structuring when integrated within a rigorous human-centered process.

Through live demonstrations and guided redesign exercises, attendees will systematically critique both AI-assisted and traditionally produced visualizations, diagnose misleading encodings and cognitive risks and iteratively improve charts and dashboards using established HF/E frameworks grounded in perception and cognition. Participants will also examine how organizational pressures and aesthetic preferences can distort functional clarity and how to advocate for evidence-based design decisions in AI-integrated environments.

Participants will leave with a practical, repeatable workflow for combining AI efficiency with cognitively rigorous design principles, along with evidence-informed heuristics they can immediately apply in research, enterprise and operational contexts. Prior experience with basic data visualization or human factors concepts is helpful but not required. The workshop is designed for HF/E practitioners, students, UX professionals, researchers, analysts and technical leaders developing decision-support or AI-enabled systems.

In the age of generative automation, the question is not whether AI can visualize data, but whether those visualizations enhance human judgment, resilience and performance. This workshop equips participants to lead that standard.

Prerequisites: None

Who Should Attend: HF/E students and professionals

Required Materials: None

• Dr. Judi See, Sandia National Laboratories
• Dr. Julie Gilpin-McMinn, Spirit AeroSystems
• Dr. Rachel Yang, Johns Hopkins University Applied Physics Laboratory

Description: The purpose of this three-hour workshop is to support knowledge and application of the newly revised HFES 400 standard for Human Readiness Levels (HRL). Application of the HRL scale ensures proper attention to human systems design throughout system development, which minimizes or prevents human error and enhances user experience. Participants will learn about the changes that have been made in HFES 400 and how the revisions impact evaluation, tracking and communication of a technology's human readiness.

Learning objectives for the workshop include:(1) Understand HFES 400 and the latest revisions – Instructors will describe the development of the original HFES 400 standard to provide a foundation for understanding the revisions that were incorporated in the current version. (2) Learn how the HRL scale is applied in current and historical acquisition programs – Instructors will provide examples of how the HRL scale has been applied to date. (3) Apply the HRL scale to practical real-world problems – Students will gain hands-on experience applying the revised standard during group exercises that simulate teamwork during the system development process. Group exercises incorporate three different scenarios representing both material and non-material solutions at various stages of technological development. The hands-on exercises will specifically address the practical application of the latest HFES 400 revisions.

Prerequisites: Workshop attendees do not need prior knowledge of or expertise in HF/E or the original HFES 400 standard. The workshop will include an introduction to the HRL scale and standard before describing specific revisions.

Who Should Attend: All HF/E professionals and students

Required Materials: Students should download a free copy of the ANSI/HFES technical standard and bring it to the workshop in electronic or hard copy format. Laptops are not necessary for the workshop but may facilitate note-taking and completion of group exercises.
 

• Professor John Lee, Industrial and Systems Engineering, University of Wisconsin - Madison

Description: Generative AI (GenAI) has become a design material that reshapes how people work, learn, and make decisions. Yet most HF/E practitioners lack a structured framework for designing human-AI systems that are effective, trustworthy and safe. This half-day workshop addresses that gap by identifying human-relevant characteristics of generative AI and the underlying features that designers can adjust.

AI systems are being deployed across safety-critical domains — health care, transportation, defense and manufacturing — often without adequate human factors consideration. HF/E professionals are uniquely positioned to bridge this gap but need updated frameworks that account for generative AI's probabilistic outputs, emergent behaviors, and capacity to shape trust.

The workshop comprises five modules: (1) the current AI landscape, distinguishing meaningful advances from hype; (2) AI as a design material, examining how large language models' stochastic nature, context-window constraints and multimodal capabilities define design possibilities; (3) human-AI interaction design principles synthesized from empirical research and human-automation guidelines, including trust calibration and error recovery; (4) evaluation methods covering trust measurement, explainability assessment and failure-mode identification; and (5) an extended hands-on exercise in which teams prototype an AI-assisted workflow for a realistic HF/E scenario.

Objectives: Participants will learn to characterize GenAI capabilities and failure modes, apply human-AI interaction design principles, conduct heuristic evaluations using a structured checklist, assess trust calibration and explainability,\ and identify ethical and organizational considerations for responsible AI deployment. Participants will leave with a practical design toolkit: design heuristics, an evaluation checklist, AI-enabled system development tools, and experience applying them to realistic problems.

Prerequisites: No prior AI or programming experience is required. Basic familiarity with human factors concepts is helpful but not necessary.

Who Should Attend: This workshop is designed for HF/E professionals, UX designers, systems engineers, product managers and students who work with AI-infused systems, both within and outside the HF/E field.

Required Materials: Participants should bring a laptop with internet access. Access to a GenAI tool (e.g., ChatGPT, Claude, Gemini, and Antigravity) is recommended.