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Rapid development of effective behaviorally aligned training simulations for human relations practitioners (Open Topic)


OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy, Human Machine Interfaces


OBJECTIVE: The goal is to use Artificial Intelligence (AI) and Modeling & Simulations (M&S) for rapid development of training scenarios with a scalable level of difficulty, sufficiently matched fidelity, and that elicits appropriate behavioral interactions and cues sufficient for effective learning and transfer of interpersonal skills. For use in sensitive topics of human relations and interaction.


DESCRIPTION: A true assessment still needs to be completed to test the effectiveness and adequacy of legacy and novel technological approaches to education and training (E&T) for Equal Opportunity (EO) practitioners. There is a need to develop and integrate adaptive learning approaches that tailor EO trainings that respond and adapt to the learner’s capabilities. Therefore, the purpose of this topic is to develop technological and methodological approaches that will utilize realistic synthetic representations of human relation training scenarios that are adaptive and scalable on the level of difficulty, sufficiently matched fidelity, and elicit the appropriate behavioral interactions and cues to sufficiently elicit learning and transferability of interpersonal skills for use in training EO practitioners such as interpersonal relationships, team development, and resiliency. The need for this approach will potentially overcome the limitations of current educational modalities (e.g., didactic lectures, case studies, observations), which do not provide the complexity to replicate the nuances of real-life situations accurately and may result in the underdevelopment of the necessary abilities to embrace the complex and varied roles an EO practitioner fully.


Modernization of E&T is necessary to ensure that future EO practitioners are fully equipped with the necessary tools and skills to address and prevent complex human relations issues. Simulation learning (SL) will fill the gap in current E&T approaches by providing a tailored, interactive, and scalable learning environment that ensure acquisition of the necessary knowledge, skills, and abilities (KSAs) with fidelity. SL is an appropriate approach to EO education and training because it has demonstrated evidence to increase knowledge structuring, which is particularly important skill set for EO practitioners. SL also provides a tailored and adaptive approach to problem-solving skill development. Wherein the learner can progress at a pace that encourages mastery over completion - this is accomplished using realistic synthetic representations of adverse human relations events, which promote creativity in the learner to develop new solutions, critical thinking (i.e., reflection), formulate one or more solutions, establish, or recall strategies to implement the solution(s), discover new possible solutions, and explain the problem (i.e., understanding) and provide a realistic and actionable solution (i.e., evaluation).


Previous research indicates an increased proficiency in tasks and skill development when simulation training is included, as opposed to traditional classroom training alone. Specifically, SL is more effective than traditional learning approaches at increasing knowledge structuring for human relation topics. SL also provides a tailored and adaptive approach to problem-solving skill development.


The advantage of SL over traditional educational modalities is that it provides an environment that encourages transformative learning for the EO practitioner. SL is an immersive experience to addressing complex human relation scenarios wherein the learner is allowed to remain fully emerged, creating a higher sense of presence over face-to-face learning modules, which increases human relation skillsets, such as establishing contact with the victim, sharing emotions or ideas, and developing cooperation skills, such as awareness of others' ideas, tolerance toward others, and empathy.


SL also provides instructors with real-time objective data to assess the learners' mastery of job tasks or acquisition of new skills. SL objective data reduces threats to internal validity typically associated with human relations assessments because it removes the instructors' subjective self-observations. SL gives the DoD the ability to train and educate a broader audience in a variety of locations, with potentially fewer human resources, leveraging already funded technologies in development by other research organizations, thus saving millions of dollars, advancing the methods currently utilized in training and education (E&T) in human relations domain, and provide readily accessible and realistic refresher training for EO practitioners.


The Defense Equal Opportunity Management Institute (DEOMI) seeks solutions leveraging AI and SL to support the training of equal opportunity, equal employment, and diversity and inclusion practitioners to respond to challenging social issues happening in our military and other government agencies. These technologies should aid with the rapid development of very immersive and realistic scenarios portraying today’s social challenges like racism, extremism, discrimination, harassment, etc. This training tool should be able to address the development of interpersonal skills, trigger a change in behavior, and deliver the knowledge-based training effectively.



  • Complexity – When multiple topics converge causing the lines of distinction to become unclear.
  • Fidelity – the emersion or level of simulation that re-creates (simulates) a complex phenomenon, environment, or experience.
  • Internal Validity – ability to accurately infer the causal relationship between two or more concepts.
  • Presence – the self-reported feeling of emersion experience of the user in the virtual or simulated environment.
  • Realistic Synthetic Representations - recreations of realistic equipment or people to represent real-world concepts or scenarios. 
  • Sensitivity - The underlying accepts of the human relation topic that is too difficult, complex, challenge to express without causing emotional distress to other members. 
  • Serious Game – highly interactive computer-based games or simulations that creates a sense of full emersion and engagement for the user.


PHASE I: Develop a concept for creating realistic synthetic representations of adverse human relations issues suitable for use in training and education for EO practitioners. Demonstrate the feasibility of the concept to meet all the requirements as stated in the Description. Establish feasibility through modeling and analysis of the design to include the initial design specifications and capabilities description to build a prototype solution in Phase II.


During Phase 1, the following questions will be answered:

1) How can we leverage simulation technology to enhance the learning experience?

2) What is the correct stimulation level for a given learning objective or outcome?

3) How does the fidelity of SL add or detract from the learning experience and E&T's effectiveness?

4) At what point does the sensitivity or complexity of the human relation topic(s) under review render them unfit for SL?

5) Will the learner perform accurate word tasks better after SL exposure?

6) When can SL become independent from the instructor, mediator, or facilitator?

7) What is the appropriate level of physical fidelity to accurately represent the necessary KSAs for EO practitioners?

8) Can SL in human relations present an opportunity to construct an adaptive learning environment that presents the learner with a personalized and adaptive curriculum and test based on ability?


PHASE II: Develop and demonstrate prototypes M&S tool in a realistic environment. Conduct testing to prove feasibility over extended operating conditions. Develop a rapid scenario development methodology and traditional training conversion to M&S at the applicable and appropriate level of fidelity and complexity, in addition to a methodology on how to properly evaluate the outcomes and performance of the students during training engagements.


PHASE III DUAL-USE APPLICATIONS: Supports the transition of EO modular simulation learning components to military training programs and the commercial market. The technology developed under this topic could create a dynamic training approach for industry and DoD programs. The innovation sought with this effort will reduce reliance on instructor capabilities, thus increasing T&E fidelity.

This simulation technology is applicable in a broad range of military and civilian E&T applications where the nature of topics is sensitive, and an expert instructor or SME is not easily accessible.




1. “National Defense Strategy”. 2022. Online at file:///C:/Users/1471179457A/Desktop/SBIR-%20Simulations/2022-NATIONAL-DEFENSE-STRATEGY-NPR-MDR.PDF


2. Defense Modeling and Simulation Coordination Office, “Defense Modeling and Simulation Reference Architecture”. 10 February 2020. Online at


3. Cook, D. A., Hamstra, S. J., Brydges, R., Zendejas, B., Szostek, J. H., Wang, A. T., Erwin, P. T. Hatala, R. (2013). Comparative effectiveness of instructional design features in simulation-based education: Systematic review and meta-analysis, Medical Teacher, 35:1, e867 e898, DOI: 10.3109/0142159X.2012.714886


4. Kara, N. A systematic review of the use of serious games in science education. (2021). Contemporary Education Technology, 13(2).


5. Sauve, L., Kaufman, D., & Renaud, L. (2007). A systematic review of the impact of games and simulations on learning. EdMedia, 4149-4157.


6. de Smale, S., Overmans, T., Jeuring, J., & van de Grint, L. (2016). The effects of simulations and games on learning objectives in tertiary education: A systematic review. A. De Gloria and R. Veltkamp (Eds.): GALA 2015, LNCS 9599, pp. 506–516, 2016. DOI: 10.1007/978-3-319-40216-1_55


7. Balint, B-N & Stevens, B. (2022). Transforming team training: The influence of virtual environment features. Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), Orlando, FL.


8. Garcia, A. & Winer, E. (2022). Blending AR and VR to increase situational awareness during training. Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), Orlando, FL.



KEYWORDS: Education and Training (E&T), Equal Opportunity (EO), Modeling and Simulations (M&S), Simulation Learning (SL), and Transformative Learning, Human machine interface, Trusted AI, Adaptive learning

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