By Division Chief (ret.) Jim Brown, Dr. Ryan Falkenstein-Smith, David Little and Battalion Chief Chad Crouse
Generative artificial intelligence (AI) is quickly becoming part of the fire service workplace. It is showing up in report drafting, document review, policy comparison, meeting summaries, training support, data analysis and public education content. For many people, however, the conversation still begins and ends with prompting, or engaging with a chatbot to try to get a useful answer. Although that may be a practical starting point, it doesn’t scratch the surface of the power this tool can provide.
To move beyond tool familiarity, fire service leaders should consider a broader approach to AI competence — an approach first achieved by defining what firefighters, officers, analysts and executives need to know about using AI.
A useful framework for this may be AI knowledge, skills and attitudes (KSAs), which identify increasing levels of competence and describe them in terms that trainers and agencies already understand. This framework provides a training pathway that moves beyond collection of isolated classes. (Note: In this framework, the “A” in KSA refers to attitudes — such as judgment, skepticism and accountability — rather than abilities, a usage that may be more familiar in some fire service job qualification and professional standards contexts.)
The right distinction: Human vs. AI KSAs
Humans and AI do not possess the same competencies.
A human brings understanding, lived experience, judgment, values and accountability. AI does not. AI brings access to data, knowledge, patterns, functional capabilities and predictable response tendencies. That distinction matters because many current AI risks stem from overestimating what the system “knows” and whether confident output reflects real understanding.
For a human, knowledge suggests familiarity with facts and skills that lead to understanding. For AI, “knowledge” is the patterns it was trained in, the information placed in context and the information it can retrieve through tools or connected systems. AI does not know things the way people know things. It matches patterns and generates responses.
For a human, skills are practiced competencies developed through repetition, feedback and real-world performance. For AI, “skills” are functional capabilities: summarizing, comparing, extracting, drafting, analyzing, searching and, in some cases, carrying out multi-step workflows across tools. AI can perform tasks, but it does not develop mastery through practice.
For a human, attitudes are mindset, values and disposition — the product of culture, training and professional identity. For AI, “attitudes” are not beliefs or ethics. They are behavioral tendencies — a tendency to be helpful, to answer even when uncertain, to mirror the user’s tone, to fill in gaps and to sound more confident than may be warranted. These tendencies can be useful, but they can also be consequential if they are not met with strong human counterweights.
| Dimension | Human KSAs | AI KSAs |
Knowledge | Understanding, experience, judgment, context | Learned patterns, provided context, retrieved information |
Skills | Practiced competence applied in real situations | Functional capabilities such as generation, analysis, tool use and multi-step task completion |
Attitudes | Values, ethics, mindset, motivation, accountability | Behavioral tendencies such as agreeability, helpfulness, confidence, compliance and gap-filling |
This distinction has direct operational importance. If a firefighter, company officer or chief officer treats current AI as though it has judgment, common sense or mission understanding, they are likely to be overly confident in its responses. Trust in people and trust in technology are not always equal. The safer approach is to say humans have KSAs in the full professional sense; AI has a parallel set of capabilities and tendencies that can be understood and managed.
Why KSAs matter for the fire service
The fire service already understands competency-based development. Through testing systems and training personnel, awareness and technical specialization are distinguished from operational capability and command-level decision-making. AI implementation may be no different: Its expectations are defined, performance standards for its use are established, qualifications are well documented, and outcomes are accurately measured.
Right now, AI adoption often falls into one of two paths. In one path, AI is treated as a novelty. Personnel are shown a chatbot, maybe given a short class on prompting, and then left to experiment on their own. In the other path, AI is treated as too complex or too risky to engage at all. In either case, there is no sustainable pathway to build trust in technology as a reliable and useful tool.
A KSA framework creates structure. It helps agencies answer practical questions that extend beyond the technology:
- What should every member of the organization know about AI?
- What should frequent users be able to do?
- What additional competencies are needed for technical implementers?
- What should strategic leaders understand before selecting tools, approving use cases or setting policy?
Questions like these provide deeper insight into AI use, but more importantly, they foster more effective adoption of the technology through a training and professional development approach.
Attitudes: The missing piece in AI training
While knowledge and skills enable AI usage, attitude may be the critical training component that determines the integrity and persistence of its adoption. Two people can have similar technical ability and use AI in completely different ways, depending on their attitude. The fire service may intentionally cultivate attitudes that support responsible AI use, such as curiosity, healthy skepticism, humility, adaptability, accountability and service orientation.
Curiosity keeps people open to new uses. Skepticism guards against polished but inaccurate output. Humility reminds us that both the tool and the user have limits. Adaptability is essential because the technology and the surrounding policies will keep changing. Accountability keeps responsibility where it belongs: with people. Service orientation ensures AI is used to strengthen the mission, not just produce more content faster.
This is also where the comparison with AI itself becomes useful. AI lacks ethics and professional commitment. Although AI may have tendencies that can be helpful in answering questions or filling in gaps, its usage is reliant on strong human attitudes that filter its outputs into something of value.
The road ahead: The AI champion
The next step for the fire service may not just be more AI tools. It could be AI training grounded in the knowledge, skills and attitudes required for responsible public service use. This framework may also require educated leaders who act as AI champions to encourage and guide AI integration. AI competency is not just about who can write the best prompt. The value of this technology is rooted in a user’s understanding of the system, ability to use it effectively, and judgment and mindset to apply it responsibly. That applies at every rank and in every functional area. As AI becomes more common in administration, training, planning, prevention, analysis and knowledge management, the real question is whether fire service personnel will be prepared to use it with the same discipline, accountability and professional competence as expected in every other area of the job.
ABOUT THE AUTHORS
Jim Brown is a retired division chief from Monterey, California, now living in Hawaii. He is the lead for the Fire Service Working Group of the GovAI Coalition and a member of the IAFC Technology Council’s Data and AI subcommittee. Brown is a California-certified chief officer and master instructor, holds a bachelor’s degree in fire science from Columbia Southern University, and is a contract instructor for Analytical Tools for Decision-Making at the National Fire Academy.
Ryan Falkenstein-Smith, PhD, is a mechanical engineer in the Firefighting Technology Group of the Fire Research Division (FRD) at the National Institute of Standards and Technology (NIST). As the leader of the Smart Fire Fighting Project, Dr. Falkenstein-Smith focuses his research on developing measurement science designed to enhance situational awareness, operational effectiveness and firefighter safety. Having extensive publications in sensor technology, Dr. Falkenstein-Smith has conducted research that enables the fusion of data from various sources with reliable predictive artificial intelligence (AI) models.
David Little is a retired CEO at ISG/Infrasys Thermal Imaging, one of the first companies globally to provide thermal imaging to the fire services, sold to Scott Safety in 2014. Little has 35 years of experience in the fire service, 30 focused on thermal imaging. He currently serves as chair of the AI Task Group, NFPA Electronic Safety Equipment Technical Committee.
Chad Crouse is an operational battalion chief with the St. Lucie County Fire District in Florida and chair of the IAFC Technology Council’s AI and Data Subcommittee. A former division chief who oversaw IT, communications, community risk reduction, and emergency management, Crouse pairs administrative experience with daily field command — a perspective he brings to his writing and national presentations on data and AI. He is also a contract instructor for the National Fire Academy’s Managing Officer and Executive Fire Officer programs.
