Human Factors Challenges in Small Modular Reactor Control Rooms: A Systematic Literature Review
Bibliographic record
Abstract
Small Modular Reactors (SMRs) introduce novel operational paradigms that pose unique human factor challenges for control room operators. This systematic review investigates what human factor challenges are faced by SMR control room operators, and how these challenges impact operational safety and decision-making effectiveness, following a Population-Exposure-Outcome (PEO) framework (Population: SMR control room operators; Exposure: human factor challenges such as cognitive workload, interface design, ergonomics; Outcome: operational safety and decision-making effectiveness). A comprehensive literature search (2013-2025) was conducted in scholarly databases including Scopus, Web of Science, IEEE Xplore, and ScienceDirect, yielding peer-reviewed studies on SMR control room human factors. Following PRISMA guidelines, studies were screened and appraised (using CASP checklists) and synthesized according to the SALSA (Search, Appraisal, Synthesis, Analysis) framework. Results: key challenge themes identified include cognitive workload and multi-unit operations, automation and operator situational awareness, human-system interface (HSI) design and ergonomics, and staffing, training and team organization. Shift control rooms often envisage one team overseeing multiple reactor modules, leading to higher cognitive workload and multitasking demands that can degrade situational awareness and decision quality [1]-[3]. Advanced automation and digital control systems are intended to mitigate human error, but if not carefully implemented they may reduce operator engagement and awareness, risking delayed responses in critical situations [4], [5]. Inadequate interface design or ergonomics can further exacerbate information overload or contribute to operator error, directly impacting safety. Conversely, studies suggest that optimized staffing (e.g., maintaining at least one operator per reactor during emergencies) and improved training, interface design, and real-time operator support systems can enhance decision-making effectiveness and maintain safety margins [6], [7]. Conclusion: Human factor challenges in SMR control rooms-especially those related to cognitive workload, automation, and interface usability-are critical determinants of safe operations and effective operator decision-making. Proactive human factors engineering, rigorous training, and evidence-based design of control room systems are essential to address these challenges. This review provides a consolidated understanding of current research, highlights gaps (such as limited empirical data from actual SMR operations), and offers best-practice recommendations to ensure that next-generation SMR control rooms support their operators and uphold nuclear safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".