Recently, comedian Nathan Fielder, best known for his HBO show ‘The Rehearsal” made headlines during an appearance on ‘CNN’s The Situation Room” when he criticized the FAA’s communication training for pilots. His argument? That simply presenting pilots with a PowerPoint slide instructing them to “speak up” during flight emergencies was wildly insufficient preparation for real-world, high-stakes conversations in hierarchical environments like a cockpit.
Fielder’s blunt assessment, “Well, that’s dumb. They’re dumb.” wasn’t just headline bait. It sparked a broader reflection on what effective training really looks like, especially in fields that demand complex interpersonal skills under pressure.
As someone involved in the training of investigative interviewers, Fielder’s comments hit close to home.
Science-Based Interviewing Requires Science-Based Teaching
In the field of investigative interviewing, we rightly champion the move from confession-driven models to rapport-based, evidence-informed approaches grounded in psychological science. But shouldn’t we apply that same scientific rigor to how we ‘teach’ those methods?
In the introduction to our training, we often show a video that highlights an important truth: “knowledge does not equal understanding”. Knowing the structure of a good interview doesn’t instill curiosity, develop an investigative mindset, or build confidence to navigate emotionally and socially complex conversations.
In my opinion, too often, training becomes a "bums on seats" exercise, where delivering information in lecture form, checking boxes, hoping for internalization and presenting a certificate of attendance is the objective. But research from educational psychology gives us far better tools.
Why Scenario-Based Training Works
Effective teaching in domains like investigative interviewing should incorporate “scenario-based training (SBT)”. This is an approach that emphasizes experiential learning, reflection, and iterative practice. This is especially critical when the subject matter involves navigating discomfort, establishing rapport, or strategic use of evidence to confront wrongdoing in an ethical, non accusatorial way.
Key theories supporting scenario-based methods include:
- Cognitive Load Theory (Sweller, 1988): Learners have limited working memory. Scenario-based learning helps by embedding information within realistic, manageable contexts.
- Constructivism (Piaget, Vygotsky): People learn by “doing”. SBT allows learners to construct knowledge by experiencing and reflecting on realistic challenges.
- Experiential Learning Theory (Kolb, 1984): Learners cycle through experience, reflection, conceptualization, and experimentation. SBT fits this cycle perfectly.
- Andragogy (Knowles, 1980): Adults learn best when training is relevant and problem-focused. SBT offers authenticity and agency.
These models reinforce the idea that scenario-based practice is not just a “nice to have” it’s essential for skills that rely on emotional intelligence, ethics, and adaptive decision-making.
The After-Training Gap: A Persistent Challenge
The implementation of the PEACE interview model in the UK is instructive. While the model itself is evidence-based, post-training evaluations often revealed inconsistencies in performance. These gaps were linked to a lack of assessor expertise, insufficient follow-up support, and poorly designed evaluation tools.
Simply put: “effective training is not complete when the course ends”. If learners aren't given the chance to apply, reflect, and receive feedback on their skills in real-world contexts, the benefits of training quickly erode.
This is why any serious training provider should be able to demonstrate not just how their program is taught, but how learners are supported ‘afterwards’ through mentorship, reflective practice, or structured feedback.
Enter AI: Opportunity and Caution
New tools like “Echomind” and similar AI-based assessment systems offer promise. They provide realistic simulations, consistent feedback, and repeatable practice opportunities. These tools could bridge the gap between classroom learning and field application, if implemented thoughtfully. However, we must remain vigilant. AI tools are only as good as their design, oversight, and ethical governance. They must complement, not replace, human judgment and professional standards.
A Personal Note
For transparency: I currently train FAA employees in the PEACE model of investigative interviewing using a scenario-based, experiential approach. In this domain at least, the FAA has embraced robust, science-based training that moves far beyond PowerPoint slides. So, Nathan Fielder’s critique—while provocative—doesn’t hold across the board.
Paul Chambers
