C9159 - HUMAN FACTORS IN TECHNOLOGY, SAFETY AND RISK MANAGEMENT

Learning outcomes

By the end of the course, students will be able to identify key theories in applied human factors and to illustrate assessment methods and tools

Course contents

the course is organized into six thematic modules.

• foundations of human factors and ergonomics: definitions, history, and scope (physical, cognitive, and organizational ergonomics); the sociotechnical systems perspective; the SHELL and SEIPS models; human-centred design principles.
• human performance and human error: models of attention, workload, and situation awareness; taxonomies of human error (Reason's slips, lapses, mistakes, and violations); active vs latent failures and the Swiss cheese model; performance-shaping factors (fatigue, stress, time pressure); fundamentals of human reliability analysis.
• human–automation interaction in safety-critical systems: levels and stages of automation; automation bias, complacency, and the ironies of automation; trust, overtrust, and overreliance; design of alarms, warnings, and decision-support systems; collaborative robots, exoskeletons, and AI under human oversight (Industry 5.0 perspective).
• safety management and safety culture: safety culture and safety climate; just culture and accountability; Safety-I vs Safety-II and resilience engineering; safety management systems and ISO 45001; leading and lagging safety indicators; psychosocial risks at work.
• risk management and assessment: risk concepts and risk perception; hazard identification; qualitative and quantitative risk-assessment methods; bow-tie analysis; the management of risk in dynamic sociotechnical systems.
• accident analysis and human-factors methods: accident-causation models (sequential, epidemiological, systemic); HFACS, AcciMap, root-cause analysis and learning from incidents and near-misses; reporting systems; applied methods and tools (hierarchical and cognitive task analysis, observation, interviews and surveys, workload and situation-awareness measurement, field studies, and simulation).

Readings/Bibliography

This is an indicative list of references. The specific readings required to prepare for the written exam will be provided during the classes.

Dekker, S. (2016). Just culture: balancing safety and accountability (3rd ed.). Routledge.

Hollnagel, E. (2014). Safety-I and Safety-II: the past and future of safety management. Ashgate.

Hollnagel, E., Woods, D. D., & Leveson, N. (Eds.). (2006). Resilience engineering: concepts and precepts. Ashgate.

Wickens, C. D., Helton, W. S., Hollands, J. G., & Banbury, S. (2021). Engineering psychology and human performance. Routledge.

Salvendy, G., & Karwowski, W. (Eds.). (2021). Handbook of human factors and ergonomics (5th ed.). Wiley.

Wiegmann, D. A., & Shappell, S. A. (2003). A human error approach to aviation accident analysis: the human factors analysis and classification system (HFACS). Ashgate.

Leveson, N. G. (2011). Engineering a safer world: systems thinking applied to safety. MIT Press.

Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37(1), 32-64.

Parasuraman, R., & Riley, V. (1997). Humans and automation: use, misuse, disuse, abuse. Human Factors, 39(2), 230-253.

Rasmussen, J. (1997). Risk management in a dynamic society: a modelling problem. Safety Science, 27(2-3), 183-213.

Carayon, P., Hundt, A. S., Karsh, B.-T., Gurses, A. P., Alvarado, C. J., Smith, M., & Brennan, P. F. (2006). Work system design for patient safety: the SEIPS model. Quality & Safety in Health Care, 15(suppl. 1), i50-i58.

Panchetti, T., Pietrantoni, L., Puzzo, G., Gualtieri, L., & Fraboni, F. (2023). Assessing the relationship between cognitive workload, workstation design, user acceptance and trust in collaborative robots. Applied Sciences, 13(3), 1720.

Fraboni, F., Brendel, H., & Pietrantoni, L. (2023). Evaluating organizational guidelines for enhancing psychological well-being, safety, and performance in technology integration. Sustainability, 15(10), 8113.

Morandini, S., Fraboni, F., Hall, M., Quintana-Amate, S., & Pietrantoni, L. (2025). User perspectives on AI explainability in aerospace manufacturing: a card-sorting study. Frontiers in Organizational Psychology, 3, 1538438.

European resources and repositories: EU-OSHA, Eurofound.

Teaching methods

The course combines lectures with applied, case-based work.

Theoretical lectures introduce the core models and frameworks; these are paired with the guided analysis of real accident and near-miss cases, hands-on exercises with selected human-factors methods and tools (e.g., task analysis, workload and situation-awareness assessment, HFACS coding), and small-group project work in which students apply an assessment method to a chosen sociotechnical scenario.

Assessment methods

Learning is assessed through a final written examination covering the whole programme, designed to verify both knowledge of the key theories and frameworks and the ability to illustrate and apply human-factors assessment methods and tools.

The written exam consists of 40 multiple-choice questions and two open-answer questions. Each multiple-choice question offers four options, with one correct answer and three incorrect ones. The open-answer questions include the analysis of a brief applied scenario. All questions are based on the prescribed reading material and the lecture slides; students are responsible for sourcing the full text of the papers and chapters listed in the bibliography.

Active participation is assessed throughout the course and contributes additional bonus points to the final mark. It is evaluated through a set of behavioral indicators, including asking questions, taking notes, staying focused on course activities, drawing on prior knowledge and personal experience, and engaging in both in-class and flipped-classroom group work. Attendance also contributes to this component.

The final mark is expressed in thirtieths (/30); the exam is passed with a minimum of 18/30, and honours (lode) may be awarded for excellent performance. Evaluation criteria include the accuracy and completeness of content, the correct use of terminology, and the ability to connect theory to applied human-factors practice.

Teaching tools

Lecture slides, case materials, and supplementary readings are made available through the university's virtual learning environment (Virtuale). Teaching is supported by software and templates for human-factors assessment (task analysis, workload and situation-awareness measurement, accident-analysis coding), real and anonymized case datasets, video material, and guest seminars. Students are encouraged to bring laptops for the hands-on sessions.

Office hours

See the website of Luca Pietrantoni