Simulation-Based Learning – Is It One Size Fits all? A Model for Designing in Diversity of Learners to Engender Learner Inclusivity

Abstract

The study presented here explores the nuanced design elements of the ADELIS© model to create inclusive educational simulation-based learning exercises and environments. The ADELIS© model offers a framework that guides the design of learning experiences to enable scalable adaptations across cognitive levels and abilities, premised on educational rigour and authentic, fit-for-purpose exercises and environments. Pivotal expectations for simulation-based learning are for learners to engage in experiential, risk-free practice that enhances critical thinking, decision-making, develop an understanding of how to transfer learning from the classroom to the field of operation and support learning efficacy.

Purpose

This paper proposes an expansion of the design process to enable accessibility principles to accommodate diverse learning needs, including individuals with physical, sensory, or cognitive disabilities be designed into the ADELIS© model. In the focus on creating authentic, replicated real-world experiences through simulation, the nuanced characteristics of the learners can be unintentionally overlooked when the design is built for a generic application and guided by false assumptions that one size fits all.

Method

This paper explores combining two fundamental design paradigms – educational fidelity and learner inclusivity and offers a guide for educators, instructional designers, and learning facilitators to achieve recognition of learner attribute diversity.

Findings and Conclusion

Whilst oriented in the simulation-based learning domain, the guide is equally applicable across the serious games field, and the range of formats utilised to deliver authentic learner-centric experiences and promotion of inclusive participation.

Keywords

evaluation experiential learning field learner diversity learning theory simulation design model simulation/gaming

Introduction

Traditional approaches to curriculum design tend to prioritise the acquisition of explicit knowledge, which can be clearly articulated, written down, and stored permanently, over tacit knowledge. This emphasis is informed and underpinned by the relative ease of assessing explicit knowledge through the use of standardised written and spoken tasks. However, this creates a significantly disproportionate emphasis on what a person knows (their knowledge) and potentially neglects the equally important tacit domain of demonstrated capacity, i.e the physical ability to perform. The potential for this imbalance in the education domain has a domino effect on the approach to learning, teaching and assessment, further complicated by the need to ensure such delivery is cognizant of learner diversity.

Unlike explicit knowledge, tacit knowledge is inherently less readily articulated because it is demonstrated through action and exhibition of integrated practical skills and abilities rather than via verbal or written expression. Although tacit knowledge is crucial for real-world problem-solving and effective performance in professional contexts, it continues to remain difficult to assess. Three key factors contribute to the complexity of assessing tacit knowledge. The first of these is the way in which tacit knowledge is context-dependent, involving the potential performance of multiple actions at the same time within specific settings. Secondly, this leads to the need to design assessment tasks as complex interactive events, since an effective demonstration of tacit knowledge must engage the whole body well beyond the limitations of speaking and writing. A third factor, contributing to the complexity of teaching for and assessing tacit knowledge, is designing opportunities for learners to demonstrate holistic understanding, i.e. tacit and explicit knowledge. Where assessment continues to rely on using tasks dependent on assessing skill and knowledge components in a disassociated format, the resulting gap between knowing and being able to apply into action will remain unresolved, and learners and future employers will continue to feel short-changed.

Addressing this challenge requires education policymakers and educators to rethink and redesign curricula to attain not so much a ‘balance’ between the apparently competing needs of explicit and tacit knowledge, rather to achieve their integration. The concept of an integrated curriculum is not new; however, it is only in more recent decades that an increased body of studies has emerged examining this form of curriculum for its veracity and efficacy (Brewer, 2002; Elendu et al., 2024; Hernandez-Acevedo, 2021; Mohamed & Sicklinger, 2022). A study by Elendu et al. (2024) signposted the interrelationship in a study of medical education:

The integration of simulation with other training modalities and its adoption in diverse global contexts highlight its potential to revolutionise medical education worldwide (p.1).

Simulation-based learning offers a means of such integration through the provision of controlled environments within which learners can develop - and then demonstrate - practical skills, the learners’ performance based on transferring explicit knowledge into a tacit demonstration of the associated skill. The key challenge here is to design and deliver simulation-based learning that has the capacity to accommodate learners with diverse learning characteristics. The ADELIS© model for curriculum development provides a structured framework for integrating simulation into standard curricula, thereby supporting the development of an integrated curriculum that supports balancing the demonstration of explicit and tacit knowledge. Building on previous work on this model, this article demonstrates (with case studies) how focusing on alignment of relevant elements (education theories, curriculum development process, and procedures for ensuring constructive alignment of learning objectives) throughout the curriculum design can present simulation-based learning events that accommodate learner diversity and allow for both tacit and explicit knowledge to be demonstrated and assessed.

Literature Review

The extant literature that explores the influence of simulation-based learning on the development of learners’ knowledge and skills advocate the benefits of such an approach for supporting (a) a student’s understanding of knowledge and skills application and (b) the transfer of the knowledge and skill to real world application (Chernikova et al., 2024; McAlpin et al., 2025; Yu et al., 2025). Fundamental to achieving (a) and (b) is the embodiment of the well-established and evaluated experiential cycle of learning. As advocated by Garner (2000) and Morris (2020), the experiential learning cycle/process as prescribed in the work of Kolb (1984) is grounded in providing opportunity for learners to experience, reflect on the experience and consider future performance options, and apply such reflection on further performance (Figure 1).

Figure 1.

Kolb’s (1984) Experiential learning cycle (Source: https://educationaltechnology.net/kolbs-experiential-learning-theory-learning-styles/)

The experiential approach resonates with the literature that suggests learners who are provided learning opportunities that encourage active engagement demonstrate efficacy of the learning transfer to real-world application (Alkhabra et al., 2023; Tutal, & Yazar, T. 2023; Maceiras et al., 2025). Participating in simulations offers the opportunity to have the initial experience (Phase 1), reflect on the performance (Phase 2), adjust approaches - consider adjustment based on the reflection (Phase 3) and apply the adjusted knowledge and skill (Phase 4).

In parallel, studies indicate the critical contribution of the level of realism, authenticity and engagement experienced by the participants within a simulation exercise and environment has a direct correlation with the learners’ level of transfer of theory to practice and retention of the lessons learnt from the experience (Alam, 2023; Davies, 2013; Davies & Krame, 2024; Davies & McCarthy, 2025; Huang et al., 2021). The work of Brown et al. (1989) engenders advocacy for educators to be cognizant of and action the important influence of the social and physical context of the learning environment for its influence on the transfer of learning. This theory contends that to maximise the transfer of theory to practice, learning should be contextually specific. This premise has been well supported by studies through the intervening years across multiple disciplines and professions. For example. and not limited to, Herrington et al. (2003) whose work has been front and centre in examining and advocating for the role simulation based learning has in providing situated learning contexts; Caroca et al. (2016) examined the application of a situated learning approach in a virtual environment for improving disaster risk reduction; Choi & Ahn, (2021)investigated the role of situated learning and simulation for enhancing nurse education; the work of Qorbani et al. (2021) explored the role of situated learning through simulation for STEM education; and Seufert et al. (2022) studied the application of simulation-based learning for its capacity to situate pre-service teachers in an authentic environment to enable transfer of learning to the simulated reality of a classroom. Such studies identify the positive influence of a simulation-based learning approach for encapsulating situated learning characteristics, which lead to the potential for enhancing student learning transfer from theory to practice and the efficacy of such learning experience/s.

The core attribute of simulation-based learning is the potential for authentic environments to be created enabling the learner to apply explicit knowledge to demonstrate tacit knowledge– the being able to do. A key challenge for educators is to ensure that the simulation-based learning design enables evidence-based integration of curriculum and the capacity to (a) situate the learner in an authentic environment relative to the topic of learning; (b) enable demonstration of transfer of knowledge to practice; (c) ensure the simulation-based learning approach is fit for purpose and (d) aligns to the educational standards of the program/course in which it is situated.

It is in the context of developing simulation-based learning exercises, environments and learning experiences for learners with a range and level of disability that there is an evident paucity of studies and published literature discussing this nuanced area of simulation-based learning design.

In the context of designing learning experiences for persons with learning disabilities, there is a substantial level of literature devoted to studies examining the application and outcome/s of implementing simulation-based learning designed to develop awareness of the challenges for learning experienced by persons with disabilities. Examples of this approach to training are evidenced in the work of Chowdhury et al. (2021), which investigates how experiencing Virtual Reality disability simulation influences implicit association with people with disabilities. Similarly, the work of Billon et al. (2016) and of Saunder and Berridge (2015) discusses using simulation training to support healthcare professionals to meet the health needs of people with disabilities. There is a further theme in the literature that discusses studies associated with identifying whether virtual reality and computer-generated (online) simulation-based learning engenders learning acquisition and efficacy for people with disabilities. It appears the gap in the literature is related to studies that include simulation-based learning (and associated elements) that address features that may impact on enabling people with disabilities to achieve learning acquisition, transfer to real-world applications and efficacy of such learning acquisition.

There is no suggestion that simulation-based learning that accommodates learners with disabilities does not exist (the work of Berlanga-Macias et al., 2023 discusses the range of learning designs, including simulation for those with disabilities). Rather, no readily available literature stands as a guide for designing in such inclusivity in the simulation-based learning design process.

To place the following discussion into context, it is appropriate here to distinguish the characteristics of human disabilities that should be considered in the context of designing simulation-based exercises. The Australian National Disability Services Federal Government Department https:/www.nds.org.au(www.nds.gov.au) identifies the following core categories of disorders. This is not a comprehensive list; rather, it is adapted from the Australian website and offers a guide to the attributes and characteristics to be considered in the context of learning and, in particular, simulation-based learning.

• Neurodevelopmental Disorders: this is a group of disorders that includes intellectual disability, autism spectrum disorder (ASD), Attention-deficit hyperactivity disorder (ADHD), Communication disorders, Specific learning disorder and Motor disorders (DSM-5 American Psychiatric Association 2013)

• Intellectual disability is a lifelong condition that affects a person’s intellectual skills and their behaviour in different situations. It can include difficulties with reasoning, problem solving, planning, abstract thinking, academic learning, judgment and learning from experience.

• Autism Spectrum Disorder: commonly known as ‘ASD’, impacts how people make sense of the world and interact with others;

• Attention deficit hyperactivity disorder: Symptoms include difficulties with attention and/or hyperactivity and impulsivity, which are inconsistent with a person’s age or developmental level and interfere with a person’s family life or participation in their community.

• Communication disorders: Symptoms include language disorder, speech sound disorder and social (pragmatic) communication disorders

• Specific learning disorder: as the name implies, is diagnosed when there are specific deficits in an individual’s ability to perceive or process information efficiently and accurately – for example, dyslexia (Dyslexia is a learning disorder that involves difficulty reading due to problems identifying speech sounds and learning how they relate to letters and words - decoding and encoding)

• Physical disability: is a condition that affects a person’s mobility, physical capacity, stamina, or dexterity

• Vision impairment: is defined as a limitation of one or more functions of the eye or visual system.

• Hearing impairment: refers to hearing loss, or deafness and refers to partial or total inability to hear speech and sounds and can range from mild to severe

• Speech language impairment: refers to impairment that manifests in a person having problems with their speech sounds or understanding and using language. People may be born with conditions or disabilities that impact their speech, language and communication skills, such as Autism Spectrum Disorder, Intellectual disability, Down syndrome, hearing Impairment and Acquired Brain Injury or Speech Language Disorder, and these may occur in combination or isolation www.nds.org.au (2025).

An additional key area of consideration, especially in the context of simulations where there is the potential for loud sounds, bright lights, and flashing of images, is the condition of Photosensitive Epilepsy, where for some people, these circumstances may precipitate seizures can be triggered by flashing or flickering lights, and/or by certain geometric shapes or patterns (www.epilepsy.org)

Importantly, it is not proposed that the ADELIS© model offers a design solution for each specific disorder; rather the extended model offers guidance as to the place within the design process where these human characteristics are considered, accommodated and engender inclusivity of learners.

The ADELIS© model (Davies et al., 2023; Leigh et al., 2023; Shepherd et al., 2019) simulation-based learning design incorporates the fundamental elements to be considered and incorporated to meet the challenges presented by (a), (b), (c) and (d). The following section presents the ADELIS© model, illustrating the nuanced details to enable inclusion of and accommodation of learners with learning disorders.

Methodology

This study is premised on a qualitative approach to research. As explained by Busetto et al. (2020), a common data collection method for qualitative research is the study of document/s. Similarly, the work of Hammarberg et al. (2016) advocates that qualitative research includes examinations of documents and text. At the centre of this study is the examination of the steps in the ADELIS© model and in what manner there is an opportunity to develop further/expand the model to include area/s that are critical to creating well-designed simulation-based learning exercises and environments. Specifically, an examination and presentation of the model is guided by extant literature to accommodate learner diversity and engender inclusivity in simulation-based learning design.

The ADELIS© Model and Inclusivity of Learner Diversity

This section presents the current design of the ADELIS© model and expands the model to present a guide for building simulation-based learning that seeks to accommodate simulation participants with learning disorders.

The ADELIS© Model consists of 4 primary phases as illustrated in Figure 2.

Figure 2.

ADELIS© model

The first step in the model has a holistic influence on the approach and design implications for all steps within the design process.

Step 1

At this stage consideration is given to whether simulation-based learning is an appropriate pedagogical approach to be incorporated within the course (e.g. a simulated crime scene investigation for police cadets), and alignment of the learning objectives – for example, what are the course objectives? For what level of certification is the course designed, who is the audience for the course (professional certification, school leavers, learners with prior degrees/certifications). These considerations are pivotal as they signpost the role that simulation-based learning will have in the overall context of the course delivery. At the next level it is the consideration of the specific unit of learning (e.g. Principles of crime scene preservation).

Finally, in Step I (Figure 3), a key consideration is whether simulation-based learning is contextualised to the student graduate attributes to be achieved (e.g. is this form of pedagogy recognised as relevant to the future workplace practice of the graduate?). Here, the example of crime scene investigation is globally considered a fundamental area of knowledge and skill for police officers. It therefore follows that there is a reasonable expectation that the simulation-based learning approach is appropriate because the graduate will be expected to be able to demonstrate understanding of the fundamental tasks in crime scene management.

Figure 3.

Step 1 ADELIS© model

Appreciatively, one of the critical considerations in terms of the suitability of simulation-based learning in a course is the consideration of any prior qualifying attributes of the learner to join the course. Inclusion of a strategy for identifying potential students with learning disorders, e.g. dyslexia, epilepsy, offers the opportunity to ensure the simulation exercise and environment are appropriate and the design/mechanics that may require adjustment to accommodate the characteristics of the participant cohort.

Step 2

At Step 2 (Figure 4), the relationship between simulation-based learning and learner disorders becomes more nuanced. It is at Step 2 that the learning outcomes for the simulation exercise are articulated and constructively aligned with the assessment format to be applied to the learner’s performance. The learning outcomes should reflect the level of educational taxonomy that aligns with the level of the course. As illustrated in Figure 5, the educational taxonomy will be dependent upon the knowledge and skills that are expected to be demonstrated by the learner.

Figure 4.

Step 2 ADELIS© model

Figure 5.

Bloom’s taxonomy (source: https://spark.scu.edu.au/kb/tl/design/bloom-s-taxonomy)

The drafting of learning outcomes is central to the subsequent development of assessment design and, importantly, the simulation exercise design and capacity to enable learners to demonstrate their knowledge acquisition and ability to transfer this to practice. An example of a learning outcome (LO) (utilising the crime scene investigation context) is exemplified as follows and is premised on a Level 7 course (bachelor’s degree): LO1 Demonstrate the chronological steps for crime scene preservation. Assessment of this learning outcome may be configured from a range of options. Importantly, the learning outcome clearly defines the acquisition of knowledge and the knowledge to be demonstrated, and this therefore guides the assessment format and tools. Assessment options may include verbal explanation (for those less comfortable with their written expression); diagrammatic flow of the chronological steps; written explanation; a quiz or test; and or a physical walk-through of the steps in chronological order. A further dimension for assessment may include a group/team presentation, utilising collaboration, such as an approach that engenders mindfulness of the potential learner diversity in a cohort. These are only limited examples of the range of assessment options; the assessment design will also be influenced by the nature of the topic of learning, and the core consideration is offering a range of options to accommodate the diversity of learners.

Step 3

As illustrated in Figure 6, Step 3 is focused on the design of the simulation event, which, irrespective of its format, is generally labelled as a scenario.

Figure 6.

Step 3 ADELIS© model

The first consideration here is the educational theory under which the scenario event will be designed. The common thread between the five educational theories of Behaviourism, Cognitivism, Constructivism, Humanism and Connectivism in the simulation environment is that each requires learner demonstration of knowledge and understanding. It is not the aim of this section to offer an extensive discussion of educational theoretical frameworks, rather to illustrate the relationship between educational theory and simulation design and the influence on inclusivity of learner diversity. The work of Shepherd (2017), Shepherd and Burton (2019) and Ross (2021) offers a valuable review of learning theories and their interconnection with simulation-based learning pedagogy. Table 1 offers a brief description of the key educational theories as adapted from the work of Zambrano & Campuzano (2020). As Marougkas et al. (2023) explain in their work reviewing learning theories and approaches in virtual reality education for the last decade:

Comprehending learning theories is crucial for educators since they offer a foundation for developing effective teaching approaches, learning materials and evaluation tools (p.2).

Table 1.

Educational Theories (Zambrano & Campuzano, 2020)

Behaviourism	Is a view in which behaviour can be explained by external factors and behavioural conditioning can be used as a universal learning process.
Cognitivism	Is a learning theory developed by Jean Piaget in which a child develops cognitive pathways in understanding and physical response to experiences.
Constructivism	Central to this theory is the concept that people use previous knowledge and experience and link this to new information and experiences to develop their understanding of the world
Humanism	Focuses on the individual as the subject and asserts that learning is a natural process that helps a person reach self-actualisation
Connectivism	This is a relatively new learning theory, developed and based upon the idea that people process information by forming connections. The emergence of increased digital connectivity has also progressed this theory more broadly

The relationship between the educational theoretical approach and the inclusivity of learners lies in the consideration of the social interaction that is desired for participation in the scenario. A central consideration for the scenario/activity development is the alignment with the course/unit learning outcomes and the educational theoretical approach that underpins the expected learning experience. For example, and returning to the crime scene investigation course example, the constructivist theory of learning is readily applicable. The constructivist theory proffers that learners will develop their understanding by using what they know based on previous experiences in the process of linking new information to these experiences. As Zajda (2021) explains, drawing on Jonassen's (1994) description of 8 pedagogical practices that define constructivist theory, the common thread is that ‘constructivist learning environments enable context – and content-dependent knowledge construction’ (p.42). Here, it is envisaged that a learner would apply what they have learnt in theory about attending crime scenes and apply this in practice, which enables them to experience the application of the knowledge and thereby make sense of the knowledge and its transfer into demonstration/performance.

Scenario Development

The scenario design is a key element of Step 3, which by its intrinsic nature is interdependent with the fundamental elements of educational design, including psychological, physical and technical fidelity to ensure optimising opportunity for learner engagement, immersion and presence in the simulated scenario. In parallel, the authenticity of the scenario is advocated as a critical element in enabling the learner to make sense of their knowledge through application in circumstances reflective of the real world of their future practice (Corves et al., 2024; Davies, 2015; Mikeska & Howell, 2021). Advances in technologies have enabled an ever-increasing array of opportunities to embed tactile elements within simulation-based learning environments. There is no definitive conclusion within the published literature that advocates more or less tactility is required to engage the learner, rather the important factor is ensuring the level of tactility enables learner engagement, immersion, sense of presence and authenticity (Davies & Heysmand, 2021; Huang et al., 2023; Novak & Schwan, 2021; Yang et al., 2024). This enables the participant to prepare according to their own personal circumstances to mitigate learning loss. The psychological fidelity (the extent to which the task to be performed replicates real-world circumstances) is referred to by Dieckmann et al. (2009) as the degree of perceived realism, including psychological factors such as emotions, beliefs, and self-awareness of participants in simulation scenarios and has been widely supported in the literature as an essential aspect of simulation scenario design to elicit strong levels of immersion, presence, and perception of realism and authentic learner experience.

A chief consideration in Step 3 is to identify that the scenario/s activity offers an opportunity for the learner to demonstrate the transfer of knowledge, and this is interdependent on the psychological and technical fidelity of the simulation exercise and environment. The focus on the levels of fidelity can be fraught with losing sight of the constructive alignment between learning outcomes and assessment (where the design with all its ‘bells and whistles’ becomes the driver) however, the constructive alignment is a criterion that should be ever present at front and centre of the simulation design and environment to remain on target to achieve educational fidelity.

The development of the scenario, irrespective of the format (role play, desktop, immersive real-time simulation) should consider the diversity in learner characteristics and potential learning disorders and design to accommodate such diversity. Drawing on the work of Harrison et al. (2008); Volpe (2024); Brown et al. (2016); Parsons & Mitchell, (2002) and Politis et al. (2017), Table 2 (and later Table 3) synthesises a range of learning disorders and offers a guide for designers and facilitators in developing and delivering simulation scenario-based learning that engender accommodating learner diversity. The information in Table 2 is not limited; not all of the nuanced details of learning disorders are included here. Rather, it is a broad guide, and the nuanced design features that underpin the key features identified for design/facilitator considerations will be driven by the individual customised intent of the simulation-based learning exercise.

Table 2.

Primary Learning Disorders & Simulation Design Considerations

A range of primary learning disorders	Design/facilitation considerations
Dyslexia (reading disorder) (challenges coding and decoding words, slower speech and comprehension)	• Minimise large blocks of text - • Include audio narration • Plain language (avoid acronyms and jargon or colloquial language • Colour-coded cues
Nonverbal learning disorder (NVLD) (difficulty understanding nonverbal cues, spatial awareness or abstract concepts)	• Clear short instructions of a social cue nature or use visual elements for explanation • Activities with structure that do not require abstract concepts such as assumption or inference • Offer an opportunity to practice the understanding of the information provided
Attention deficit hyperactivity disorder (ADHD) (difficulty remaining engaged, easily distracted)	• Short, modular scenarios • Step-by-step prompts to keep the learner engaged • Opportunity to practice/repeat steps or activities/tasks • Interactive elements to maintain engagement • Minimise external distractions
Autism (learners prefer routine; ambiguity can cause distress and anxiety)	• Step-by-step guide to the scenario timeline • Straightforward flow of the simulation scenario, avoiding unexpected injections of activity (sound, voice incident) • Avoid situations that may cause cognitive overload

Table 3.

Autism-Inclusive Simulation Design Guide

Scenario structure (avoidance of unexpected injections and consequences)

⁃ Clear objectives; clear time frames – manageable steps and tasks deconstructed into smaller assets;

Communication design

⁃ Clear, unambiguous or colloquial language and acronyms; use multiple formats where possible – text, audio, visual; take breaks in the communication inputs

Tactile/Sensory elements

⁃ Avoid distracting noise, e.g. music, sound effects that are not required to elicit engagement, realism and presence
⁃ Where sound effects are necessary, consider less confronting and unexpected noise elements
⁃ Building a calm environment is preferable

Social, emotional and Cognitive support

⁃ A critical element here is to provide a sense of safety warnings – e.g. signpost what may be coming next – noise, bright light - the key is to provide the warning whether the simulation is desktop, real-time, virtual, the considerations remain common to all simulation activity/ies design
⁃ prepare for those who are distressed – have in place coping mechanism strategies

Assessment strategies

⁃ Multiple options – written, verbal, visual, physical demonstration, illustrative/diagrammatic/artistic presentation, group or individual presentation

To illustrate the application of information in Table 2 is offered in the following example of an autism -inclusive simulation design areas for consideration checklist (Table 3).

To best optimise the achievement of an inclusive learning environment when designing simulation-based learning activities, consideration should be given to developing the knowledge and understanding of learning disorders and the core neurodiversity influences on education for those responsible for simulation activity development. Inviting the review of these activities by an expert in learning disorders is a potential avenue for design validation. Further, the range of potential learning disorders within the learning cohort for which the simulation scenario activity/ies are to be delivered has the potential to enhance inclusivity.

Step 4

The final step in the ADELIS© model is to examine whether the simulation-based learning exercise/s and environment/s are fit for purpose and whether they achieve the intended outcomes for learning. Whilst it is appreciated that the concentration of effort in developing simulation-based learning instruments is centred on Steps one, two and three of the ADELIS© model, understanding the resultant simulation-based learning approach on student learning has equal importance. This Step 4 should be considered at the planning stage – how is the appropriateness, influence and efficacy of the learning approach to be evaluated. Such an evaluation may reach beyond the learning design and impact to elements of sustainability of the specific simulation-based learning activity in terms of return on investment (e.g. time, cost, workforce workload) – what is examined is a decision that lies with the individual education/training entity. The purpose of Step 4 is to ensure such an examination is incorporated in the planning and completion for embedding simulation-based learning activities within curriculum and units of education and or training delivery. As with Kolb’s (1984) experiential learning cycle, so also is the development of simulation-based learning – the cycle is complete when Step 4 is completed, and the outcomes are embedded in the next iteration of the simulation exercise (Figure 7).

Figure 7.

Step 4 ADELIS© model

There are several areas of enquiry within the evaluation process that offer an opportunity to inform continuous improvement of the design and delivery of the simulation exercise and environment. Table 4 presents a high-level guide to evaluation tools.

Table 4.

Evaluation Tools

Evaluation contributor	Data collection tool	Areas for evaluation
Student Facilitator Curriculum lead Subject matter expert Educational designer Technical staff	Surveys and or interviews Observation Scenario/activity experience Learner assessment outcomes	Adequacy of the simulation Did the activity and environment allow the student to demonstrate the transfer of knowledge from explicit to tacit? What factors inhibited What factors supported
		Activity & environment Levels of realism Authenticity Engagement Presence
		Course content alignment Does the simulation-based learning activity experience, task criterion and learner assessment align with the course learning outcomes?

Table 4 is a broad guide, and the application will be circumstance-specific. For example, a learner who has not attended a crime scene in the real world would have limited ability to identify the level of realism of a simulation-based learning environment dedicated to replicating a crime scene. There is, however, an important and often overlooked opportunity to gather more detailed, specific and informed responses by including in the evaluation process, inviting feedback from the learners once they are in their respective field of operation and or place of work (if the simulation-based learning is intended to develop practitioners). At this point, questions related to reflecting on the simulation-based learning experience and its influence on the learner’s experience in the real world of operation cannot be overestimated for the value it offers on the continuous improvement of specific learning activities and their influence on real-world operational practice.

For example, receiving evaluation of the crime scene investigation simulation scenario from learners who reflect on their learning experience from the perspective of attending crime scenes in the course of their subsequent policing duties is an invaluable source for all parties involved in the design, development, application and facilitation of simulation-based learning activities and environment. Of equal importance is to understand from the learners and facilitators to what extent the simulation scenario design and its environment were accommodating of learner diversity and engendering inclusivity. The data collection tools will vary depending on who or where the data is to be collected. Here also a range of strategies will be required to be employed to accommodate the diversity in learning disorders. For example, not all circumstances will be suitable for text based survey and response, rather it may require verbal discussion, and or a replay of the simulation scenario with weigh points at which stage the learner is asked questions about ease of understanding, comprehension, sense of confidence and comfort – these are only examples of areas of questions that could be utilised in these circumstances.

The Kirkpatrick model (Kirkpatrick, 1996; Paull et al., 2016; Smidt et al., 2009) for evaluation of learning design is a valuable and consistently supported framework for evaluating the effectiveness of learning and training programs. There are four levels of evaluation in the Kirkpatrick model:

• Reaction – which can be applied to measure engagement and the overall experience

• Learning – what knowledge and skills were learnt, what elements promoted learning, what elements were barriers and what suggestions for continuous improvement of the simulation-based learning experience

• Behaviour – what has changed as a result of the learning (this would be specific to the field-based post simulation data collection). This is an area that has the potential for substantial impact on the continuous improvement of the simulation-based approach to learning, as it focuses on the influence of the experience on the real world for which it is intended to replicate

• Results – Where simulation-based learning is utilised to develop practitioners, there is an opportunity to measure empirically the impact of the training approach through examining the impact/influence on the organisation (this would be training/education context specific). An example (referring to the earlier crime scene unit of learning) may be the change in timeframes for first officers at a crime scene to triage and log the incident as a consequence of their simulation experience. The flow-on effect is the potential number of crime scenes that can be processed in a given timeframe, impacting overall productivity.

Discussion

To contextualise identifying whether simulation-based learning is fit for purpose for a specific learning curriculum/unit/topic, it is valuable to reflect on the widely discussed (Almeqdad et al., 2023; Rose, 2000; Roski et al., 2021) Universal Design for Learning (UDL). UDL is a pedagogical approach designed by Moore et al. (2007) that offers a pragmatic approach to the pedagogical design of learning environments that are inclusive. Three key design considerations are proffered in the UDL approach, including:

- Multiple means of representation: Presenting content in various forms (texts, videos, images, concept maps) to support understanding, even for students with learning difficulties.

- Multiple means of action and expression: Allowing students to demonstrate what they have learned through differentiated activities, such as presentations, creative writing, or multimedia production.

- Multiple means of engagement: Designing activities that stimulate students’ motivation and interest, considering their preferences and passions (Volpe, 2024, p. 4).

In reflecting on the study offered in this paper, the proposed expansion of the ADELIS© model offers an opportunity to meet the UDL expectations for diverse learning by attending to nuanced design criteria that enable inclusiveness of learners. Further, the model provides guidance to accommodate those with learning disorders, as described by the National Disability Services (https://www.nds.org.au), to gain access to learning experiences that are increasingly adopted in modern teaching and learning practice across the globe. The limited literature that discusses developing simulations for learners with learning disabilities makes the expanded ADELIS© model an imperative.

In a further indication that the expanded ADELIS© model design process is reflective of good practice in respect of designing-in inclusivity of diverse learner traits is to consider the work of Tsikinas and Xinoglas (2018), who suggest in the design of Serious Games for learners with intellectual disabilities the following key guidelines:

• Actively include learners or educators in the process (ADELIS© model Step 4)

• Design a simple and clear graphical interface, with minimum input, to achieve an enjoyable experience for the players (Table 2 & Table 3)

• Provide sufficient feedback mechanisms, since they have proven to be an important feature in the design of serious games for people with intellectual disabilities (Table 3 and Assessment in ADELIS© model Step 2)

• Continuous challenge, by gradually increasing the game difficulty (Table 2, Table 3)

• Applying techniques to monitor players’ progress is considered an important asset (Table 2, Table 3)

• Customise in-game elements to provide uniqueness and further engage people with intellectual disabilities (ADELIS© model Steps 1, 2, 3, & Table 2).

As presented in this paper, developing simulation-based learning experiences and environments to accommodate those with learning disorders will vary; however, the steps to create engaging, immersive and authentic learning experiences remain constant as illustrated in the ADELIS© model. Attention to Step 4 in the model, evaluation, will offer validation of the level to which accommodating diverse learning characteristics has been achieved.

The limited extant literature that offers insight into the development of simulation-based learning exercises and environments that accommodate diverse learning traits and disorders has led to the development of a model for inclusivity based on the foundations of the ADELIS© model.

Limitations

The chief limitation of this study lies in the not-yet-applied expanded ADELIS© model. However, based on the literature associated with learning disorders and the ADELIS© model for designing educationally valid simulation-based learning exercises, the study offers a substantial baseline from which to develop and apply simulation-based learning that engenders inclusivity of those with learning disorders.

Future research endeavours will look to develop and trial simulation-based learning exercises and environments based on this guide offered in this study.

Conclusions

This study has been developed to support the work of educators, simulation design technicians, learning facilitators and those responsible for institutional education policy and processes. There is no suggestion that the work presented in this paper is not being undertaken, rather that there is a paucity in the published literature, this being a catalyst for the current study.

The study intends to provide a guide in an area that brings with it an array of challenges, chief among which is ensuring a safe and rewarding learning environment and experience. As the world continues to forge avenues for offering learning experiences for those with learning disorders, opportunities to engage in activities that enable growth, the simulation-based learning domain will continue to be in demand. This demand is driven in part by the advances in technologies to bring realistic, authentic, immersive and engaging environments into the learning domain and in part as the health sector continues to increase knowledge of learning disorders. Collaboration between learning disorder specialists (as referred to in this study) and simulation-based learning designers, educators, facilitators and technicians, with guiding models such as those offered in this paper, will enable pushing the boundaries of what is possible to sustain the momentum to create inclusive learning experiences and environments.

Footnotes

ORCID iD

Amanda Davies

Ethical Considerations

Ethics approval does not apply to this paper, there are no participants in the work reported in this article.

Informed Consent

No ethics approval or informed consent is required for this article, there are no participants or participant data associated with this article.

Funding

The author received no financial support for the research and authorship of this article. Publication was supported by Charles Sturt University.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Author Biography

Dr Amanda Davies, is a Professor in Policing and has been engaged in leading, developing and delivering police education and research for more than 20 years in Australia and the United Arab Emirates. Dr Davies research encompasses a wide range of policing, security and Emergency Management domains with a key focus on the design of education and training for officers on the frontline and leadership positions; developing models for building resilience in policing, security and emergency management, with a key focus on inclusivity of a simulation based learning approach. Dr Davies has researched and published widely on the development and evaluation of simulation-based learning initiatives for policing and the wider education community and is the recipient of a range of awards including: 2022 Distinguished Visiting Fellow, Home Team Academy, Singapore; 2021 International Teaching Award (Abu Dhabi Police); 2015 Australian Learning and Teaching Council ( Prime Minister’s Award) Citation, 2015 Vice Chancellors Award for Teaching Excellence, 2015 Visiting Fellow, China National Police, Shenyang, China, 2015 USA Best from around the Globe.

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