How this framework applies to your specific context
The SFR framework affects different people in different ways. This page identifies eight specific contexts and explains what the framework changes in each one. Find the section that describes your role or organization.
New to SFR? This page assumes basic familiarity with the framework. For a plain-language 4-minute introduction — including what the classifications mean — read Start Here first.
Start Here →When an organization purchases a simulator, it is not only buying hardware. It is committing its drivers or participants to a specific training environment for a defined period. Whether that training environment is structurally valid determines whether the time invested produces correct outcomes.
The SFR framework changes procurement by providing a structural vocabulary that does not depend on manufacturer descriptions. Before this framework, a buyer had no formal basis for asking whether a system's motion was derived from vehicle physics or applied as an approximation. The framework provides that basis.
You can now ask structural questions during procurement that have defined, verifiable answers rather than marketing descriptions.
Does this system's motion come directly from the vehicle physics calculation — or is it added afterward as an approximation?
Systems that appear similar can produce structurally different training outcomes. Classification tells you which category a system falls into before you commit to it.
Drivers trained on surface-level systems can develop timing patterns specific to that system that do not transfer to the real vehicle.
University simulation facilities are typically used for two purposes: teaching students who will work in motorsport, automotive engineering, or related fields, and conducting research that produces publishable findings. The SFR framework affects both.
For teaching, classification determines what the facility is training students to understand. A surface-level system teaches the experience of surface-level simulation. That is a valid learning objective only if students know what they are learning about.
For research, classification determines what variables are actually being controlled. A study that compares "simulator training" to "no training" without specifying the structural category of the simulator cannot draw conclusions about simulation in general. It can only draw conclusions about that structural category.
Students should understand what type of simulation they are using and how that type relates to the real vehicle. Classification is the tool that provides that understanding.
All published simulation research should specify the structural category of the systems used. Without this, findings cannot be correctly interpreted or compared.
Research on simulation-based training has historically suffered from a confound: studies describe their simulation conditions in terms that do not map to structural properties. "High-fidelity simulation" in one study may refer to something structurally different from "high-fidelity simulation" in another. Cross-study comparison is therefore unreliable.
The SFR framework provides a controlled vocabulary for study design. If every study specifies the structural classification of its simulation conditions using SFR terminology, future meta-analyses and replication attempts will be able to compare like with like.
This is not a minor methodological detail. The question of whether simulation-based training transfers to real-world performance is one of the central questions in applied training research. That question cannot be answered without knowing what type of simulation was studied.
Use SFR classification to specify your simulation conditions as a controlled variable. In-the-Loop and Surface-Level are not equivalent conditions and should not be pooled.
When reviewing prior work, identify whether the structural category of simulation is specified. Treat unspecified category as a limitation in the evidence.
The framework predicts specific differences in training outcomes between structural categories. These predictions can be tested and falsified through empirical study.
Specify the SFR classification of your simulation conditions in the methods section. This makes your findings interpretable in the context of the growing body of SFR-classified literature.
In competitive motorsport, the margin between correct and incorrect timing is measured in milliseconds. The choice of simulation architecture is not a preference. It is a decision about what timing patterns a driver will practice and which patterns will be reinforced through repetition.
A driver who trains extensively on a surface-level system can develop a response to the perceived onset of oversteer that is structurally different from the correct response to real oversteer. The incorrect response is not just unhelpful. It can be actively counterproductive on track because it is automated to the wrong trigger.
The SFR framework gives teams a basis for evaluating their simulation assets against a structural standard rather than a marketing description. It also provides a formal vocabulary for communicating simulator limitations to drivers so they can adjust their use of simulation accordingly.
Use classification to determine which simulation assets are suitable for timing-critical training and which should be reserved for non-timing tasks (track learning, strategy).
Young drivers trained primarily on surface-level systems before real-vehicle exposure may need recalibration periods. The framework identifies this risk.
Simulation-based rehabilitation uses controlled sensory environments to promote neurological adaptation. The SFR framework is directly relevant to this context because the neurological validity of the sensory environment determines whether the adaptation produced is beneficial or counterproductive.
In rehabilitation contexts, the participant population may include individuals with vestibular disorders, post-concussion syndrome, Parkinson's disease, multiple sclerosis, or other conditions that affect sensory integration. For these populations, a surface-level simulation environment — one that provides visual motion cues without matching vestibular cues — can exacerbate sensory conflict symptoms rather than support recovery.
The framework identifies this risk formally and provides the structural vocabulary that rehabilitation practitioners need to evaluate simulation environments for clinical use.
Classify any simulation system used in rehabilitation before deployment. Surface-level systems carry specific risks for vestibular and neurologically compromised participants.
The framework includes a screening checklist for identifying participants who may be at elevated risk in surface-level simulation environments.
The SFR framework is not a certification body and does not prohibit any system architecture. It provides definitions that allow any system, at any price point, to be accurately described according to what it actually does structurally.
A manufacturer whose product is a well-engineered surface-level system can describe it accurately as such, specify what it is suitable for, and compete on the merits of its design within that category. This is more defensible than claiming properties that cannot be verified.
As the framework gains adoption in research, procurement, and training standards, systems that have been evaluated and classified will be distinguishable from those that have not. Participation in formal evaluation is a means of demonstrating structural transparency.
Use SFR classification terminology to describe your products accurately. Structural honesty builds long-term credibility in a market that is gaining classification awareness.
The In-the-Loop structural requirements define what properties are needed to produce valid training outcomes. These are engineering targets, not arbitrary preferences.
The SFR framework is a proposed standard, currently at Stage 0 (Proposed Standard) in its adoption roadmap. It is designed to be compatible with adoption by formal standards bodies without requiring modification to its core definitions or classification criteria.
The framework includes governance documentation — document classification system, versioning policy, revision criteria, and custodianship structure — that was designed with formal standards adoption in mind. The canonical definitions are structured to integrate with existing standards vocabulary in aviation, automotive, and medical simulation.
The framework invites formal review, technical challenge, and structured feedback through its community review process. Standards organizations that wish to engage with the framework prior to formal adoption can do so through the documented feedback and review pathways.
The simulation industry does not routinely disclose the structural properties of its systems to medical professionals or to the patients who use them. A neurologist, physiatrist, or vestibular therapist who refers a patient to a simulation-based program may have no way of knowing whether that program uses an In-the-Loop or Surface-Level system, and may not know that the distinction has clinical relevance.
The SFR framework documents the specific neurological risks associated with surface-level simulation for populations with compromised sensory integration. This includes post-concussion syndrome, vestibular disorders, Parkinson's disease, multiple sclerosis, and age-related vestibular decline. For these populations, visual-vestibular conflict — which surface-level simulation produces by design — can exacerbate symptoms.
This framework is not a clinical guideline and makes no specific treatment recommendations. It is a structural analysis that identifies risks that should be part of informed consent and referral decisions.
Before referring a neurologically compromised patient to a simulation program, ask whether the system is classified as In-the-Loop or Surface-Level. Surface-Level systems carry specific sensory conflict risks for vulnerable populations.
Patients using any simulation system should understand what type of system they are using. The framework provides the vocabulary to communicate this accurately.