Design objects, facilities, and environments to optimize human well-being and overall system performance, applying theory, principles, and data regarding the relationship between humans and respective technology. Investigate and analyze characteristics of human behavior and performance as it relates to the use of technology.
U.S. Workers
350,230
Median Salary
$101,140
10-Year Growth
+11.0%
Annual Openings
25,200
Typical entry: Bachelor's degree
25 of 26 tasks have some AI capability
Exposure Trend
This score reflects estimated AI technical capability for tasks in this occupation. It does not predict employment changes, and it does not account for company-specific constraints, regulation, or adoption barriers.
Prepare reports or presentations summarizing results or conclusions of human factors engineering or ergonomics activities, such as testing, investigation, or validation.
AI: Fully automatable - AI can synthesize test data, draft reports and presentations, and tailor outputs to audiences rapidly and accurately, enabling fully automated report generation in many cases by 2025.
Review health, safety, accident, or worker compensation records to evaluate safety program effectiveness or to identify jobs with high incidence of injury.
AI: Fully automatable - By 2025 AI systems can reliably ingest and analyze structured and unstructured safety/compensation records to identify patterns and high‑incidence jobs and assess program effectiveness.
Train users in task techniques or ergonomic principles.
AI: Fully automatable - AI by 2025 can deliver personalized training, virtual coaching, and interactive ergonomic instruction at scale, effectively training many users in techniques and principles.
Develop or implement research methodologies or statistical analysis plans to test and evaluate developmental prototypes used in new products or processes, such as cockpit designs, user workstations, or computerized human models.
AI: Fully automatable - AI tools can develop and implement research methodologies and statistical analysis plans, run simulations and analyses for prototypes, and automate much of the experimental design and evaluation workflow.
Write, review, or comment on documents, such as proposals, test plans, or procedures.
AI: Fully automatable - By 2025 AI systems can reliably generate, edit, and critique proposals, test plans, and procedures for clarity, consistency, and standards compliance, making them able to fully perform writing and review tasks with human validation as needed.
Perform statistical analyses, such as social network pattern analysis, network modeling, discrete event simulation, agent-based modeling, statistical natural language processing, computational sociology, mathematical optimization, or systems dynamics.
AI: Fully automatable - AI tools in 2025 are capable of performing a wide range of statistical and computational analyses (SNA, ABM, DES, NLP, optimization, systems dynamics) end-to-end given appropriate data and parameters.
Investigate theoretical or conceptual issues, such as the human design considerations of lunar landers or habitats.
AI: Fully automatable - AI can conduct literature reviews, generate conceptual analyses, propose human-centered design considerations, and run relevant simulations, enabling full support for theoretical and conceptual investigations.
Design cognitive aids, such as procedural storyboards or decision support systems.
AI: Fully automatable - AI can design cognitive aids such as procedural storyboards and prototype decision-support systems, producing usable artifacts and interaction designs though final validation and deployment require human testing.
Design or evaluate human work systems, using human factors engineering and ergonomic principles to optimize usability, cost, quality, safety, or performance.
AI: Partial - AI can generate and evaluate ergonomic designs and simulate human interactions, but fully reliable human-factors design still requires human validation, stakeholder engagement, and contextual nuance.
Collect data through direct observation of work activities or witnessing the conduct of tests.
AI: Partial - AI and computer-vision/sensor systems can collect observational data in many settings, yet direct, context-rich observation and some testing witnessing remain partially dependent on humans and instrumentation coverage.
Conduct interviews or surveys of users or customers to collect information on topics such as requirements, needs, fatigue, ergonomics, or interfaces.
AI: Partial - Surveys and scripted interviews can be automated and chatbots can conduct interviews, but eliciting deep qualitative insights about fatigue, needs, and ergonomics typically still requires human facilitation and interpretation.
Recommend workplace changes to improve health and safety, using knowledge of potentially harmful factors, such as heavy loads or repetitive motions.
AI: Partial - AI can analyze hazards and recommend workplace changes, but final recommendations and implementation require human expertise, legal/safety accountability, and on-site validation.
Assess the user-interface or usability characteristics of products.
AI: Partial - AI can perform automated usability heuristics, simulate user interactions, and analyze metrics, yet comprehensive usability assessment still benefits from human testing and contextual interpretation.
Perform functional, task, or anthropometric analysis, using tools such as checklists, surveys, videotaping or force measurement.
AI: Partial - AI can automate many elements of task and anthropometric analysis from surveys and video (e.g., pose estimation), but still cannot fully replace in‑person force measurements and some hands‑on validation.
Advocate for end users in collaboration with other professionals, including engineers, designers, managers, or customers.
AI: Partial - AI can generate user advocacy materials, synthesize user needs, and support collaboration, but it cannot fully substitute for human judgment, negotiation, and interpersonal advocacy in complex stakeholder contexts.
Conduct research to evaluate potential solutions related to changes in equipment design, procedures, manpower, personnel, or training.
AI: Partial - AI can design experiments, run simulations and analyze data to evaluate solutions, but real‑world trials, ethical oversight, and iterative human-driven selection still limit full automation.
Integrate human factors requirements into operational hardware.
AI: Partial - AI can produce human‑factors requirements, simulations, and integration plans, yet cannot physically implement or certify requirements into operational hardware without human engineers and onsite work.
Provide technical support to clients through activities such as rearranging workplace fixtures to reduce physical hazards or discomfort or modifying task sequences to reduce cycle time.
AI: Partial - AI can provide actionable technical recommendations (layout changes, sequence optimizations) and optimization plans, but cannot carry out physical rearrangements or on‑site adjustments autonomously.
Inspect work sites to identify physical hazards.
AI: Partial - AI-enabled image/video analysis and drones can detect many physical hazards remotely, but inspections still need human oversight for nuanced context, judgement, and access to constrained spaces.
Analyze complex systems to determine potential for further development, production, interoperability, compatibility, or usefulness in a particular area, such as aviation.
AI: Partial - AI can perform large-scale systems analysis, simulation, and compatibility checks, yet final determinations about development, production, and operational suitability require multidisciplinary human decision‑making and regulatory consideration.
Develop or implement human performance research, investigation, or analysis protocols.
AI: Partial - AI can draft and optimize research and analysis protocols and suggest experimental designs, but cannot fully implement or manage in-person human-subject procedures, ethical approvals, and hands-on adaptations without human oversight.
Apply modeling or quantitative analysis to forecast events, such as human decisions or behaviors, the structure or processes of organizations, or the attitudes or actions of human groups.
AI: Partial - AI can build and run many predictive and quantitative models (including agent-based and behavioral models) but forecasting complex human decisions and organizational behaviors still requires substantial domain expertise and human interpretation.
Establish system operating or training requirements to ensure optimized human-machine interfaces.
AI: Partial - AI can propose operating and training requirements for human–machine interfaces based on standards, simulations, and data, but final requirement-setting requires human judgement, stakeholder negotiation, and contextual validation.
Provide human factors technical expertise on topics such as advanced user-interface technology development or the role of human users in automated or autonomous sub-systems in advanced vehicle systems.
AI: Partial - AI can provide technical analyses and literature-informed recommendations about advanced UI and human roles in automated systems, but high-stakes synthesis and authoritative expert judgement remain human responsibilities.
Estimate time or resource requirements for ergonomic or human factors research or development projects.
AI: Partial - AI can generate time and resource estimates using historical data and models, but uncertainty, contextual factors, and programmatic constraints mean human oversight is usually needed for final budgets and schedules.
Operate testing equipment, such as heat stress meters, octave band analyzers, motion analysis equipment, inclinometers, light meters, thermoanemometers, sling psychrometers, or colorimetric detection tubes.
AI: Not automatable - Operating physical testing equipment requires hands-on manipulation, calibration, and on-site decision-making that AI alone cannot perform in typical settings as of 2025.