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Systematic Review

Systematic Literature Review of Simulation-Based Learning for Developing Teacher SEL

by
Ronen Kasperski
1,2,*,
Orna Levin
3 and
Merav Esther Hemi
1,2
1
Shaanan Academic Religious College, Special Education Department and SEL-C Simulation Centre, 7 HaYam HaTichon Street, Haifa 26109, Israel
2
Inclusive Education Department, Gordon College for Education, Tchernikhovski St 73, Haifa 3570503, Israel
3
School of Education and Simulation Research Centre, Achva Academic College, Shikmim 79804, Israel
*
Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(2), 129; https://doi.org/10.3390/educsci15020129
Submission received: 14 September 2024 / Revised: 22 December 2024 / Accepted: 9 January 2025 / Published: 23 January 2025
(This article belongs to the Special Issue The Multifaced Opportunities for Utilization of Simulation-Based Learning Models in K-12 and Professional Development in Education)
Browse Figures
Review Reports Versions Notes

Abstract

:
This systematic literature review provides a comprehensive analysis of simulation-based learning methods aimed at enhancing teachers’ social–emotional learning (SEL). The study follows PRISMA guidelines, focusing on peer-reviewed journal articles published in English between 2010 and 2023. Four academic databases—APA Psych, ERIC, Scopus, and Web of Science—were searched, resulting in 68 articles that met stringent inclusion criteria after thorough screening and validation. Findings indicate inconsistencies in terminology and the categorisation of simulation types across studies, highlighting the need for standardised definitions. Findings identify specific simulation types that prioritise SEL development, offering valuable insights into their effective implementation. The theoretical contribution of this systematic literature review lies in proposing a clear typology of simulations, distinguishing between various simulation types and their roles in advancing SEL in teacher training and professional development. In light of the findings, we suggest that, to optimise the use of simulations and enhance their effectiveness in developing SEL competencies within teacher education programs, educators and researchers should adopt a more comprehensive approach to SEL-targeted simulations.

1. Introduction

Social–emotional learning (SEL) is increasingly recognised as essential in teacher education, enhancing both student and teacher outcomes. Teacher SEL competencies improve classroom management, reduce stress, and support professional growth, making educators more effective and resilient (Jennings et al., 2020; Mattern & Bauer, 2014). Incorporating SEL into teacher education programs (encompassing both preservice teacher preparation and in-service professional development) enables educators to develop these competencies (Kasperski & Hemi, 2024), leading to positive impacts on student behaviour, academic achievement, and emotional well-being (Gimbert et al., 2023). Simulation-based learning (SBL) has emerged as a powerful tool for developing SEL competencies among teachers. By offering a controlled environment for practice and reflection, simulations allow educators to experiment with different strategies, receive feedback, and improve their skills without the immediate consequences of real-life situations (Chernikova et al., 2020; Theelen et al., 2019a). Despite the increased focus on SEL and the expansion of simulation-based learning, there remains a gap in understanding how simulation-based learning is utilised for SEL processes. This systematic literature review aims to explore the application of simulations for promoting SEL competencies by examining the types of simulations used and the specific SEL competencies targeted in these applications.

1.1. Theoretical Background

Social–emotional learning in teacher education.
Social–emotional learning (SEL) has gained recognition as an essential component of teacher education (Schonert-Reichl, 2017). Defined by the Collaborative for Academic, Social, and Emotional Learning (CASEL), SEL involves the processes through which individuals acquire and apply the knowledge, attitudes, and skills necessary to understand and manage emotions, set and achieve positive goals, feel and show empathy for others, establish and maintain positive relationships, and make responsible decisions (CASEL, 2023). The established CASEL model emphasises five core competencies: self-awareness, self-management, social awareness, relationship skills, and responsible decision-making. These competencies are foundational for fostering emotionally supportive and productive learning environments, for both students and educators (Kasperski & Hemi, 2024; Lozano-Pena et al., 2021).
In teacher education, SEL is increasingly valued for its positive impact on both students and teachers. For students, SEL fosters social and emotional development, leading to better academic performance and improved interpersonal relationships (Roffey, 2012). For teachers, strong SEL competencies enhance their ability to manage classroom stress, build positive relationships with students, and implement effective classroom management strategies (Cipriano et al., 2020). Additionally, SEL plays a vital role in supporting teachers’ well-being and professional growth (Mattern & Bauer, 2014). Finally, by promoting SEL among teachers, it is possible to reduce burnout and demoralisation, which are common in the teaching profession due to the high demands and emotional labour involved (Gimbert et al., 2023).
One of the reasons that the integration of SEL into teacher preparation programs and professional development is crucial is that teachers act as role models for their students (Jennings et al., 2020; Jennings et al., 2017). Research shows that when teachers receive SEL training, they are more likely to implement SEL practices effectively, resulting in improved student outcomes in areas such as academic performance, behaviour, and emotional well-being (Gimbert et al., 2023; Lozano-Pena et al., 2021). Scholarly literature identifies three primary types of SEL interventions for educators (Schonert-Reichl et al., 2017): designated interventions, mindfulness-based approaches, and in-class programs. This study focuses on designated interventions, and specifically on the simulation-based learning approach.

1.2. Simulations in Teacher Training Through the Lens of SEL

Simulations are widely recognised as powerful pedagogical tools for bridging the gap between theoretical knowledge and practical application in teacher education. By incorporating elements of real-world complexity into controlled environments, simulations enable preservice and in-service teachers to engage in experiential learning, practice decision-making, and refine their skills in a risk-free setting. The pedagogical foundation of simulations aligns closely with experiential learning models, such as Kolb’s (Kolb, 1984) cycle of experiential learning, which integrates four key elements: concrete experience, reflective observation, abstract conceptualisation, and active experimentation. Simulations provide concrete experiences through realistic, hands-on scenarios, such as managing a disruptive student or conducting a parent–teacher meeting. These scenarios are followed by opportunities for reflective observation and feedback, allowing participants to conceptualise their learning and refine their skills through iterative practice. Additionally, simulations draw from Vygotsky’s (Vygotsky, 1978) sociocultural theory, particularly the concept of the Zone of Proximal Development (ZPD). By offering structured yet challenging tasks within a supportive environment, simulations scaffold learners’ development of social–emotional learning (SEL) competencies. They provide guidance, feedback, and opportunities to practice skills just beyond the learners’ current capabilities. These tools are particularly effective for developing both technical and interpersonal competencies, such as SEL skills, which are essential for fostering supportive classroom environments (Chernikova et al., 2020; Dieker et al., 2023; Theelen et al., 2019a).
Simulations offer educators opportunities to experiment with different strategies, receive feedback, and reflect on their actions without the immediate consequences of real-life interactions. They vary in modality, ranging from human-based simulations to computer-based immersive or virtual reality environments (Lindberg & Jönsson, 2023; Spencer et al., 2019). Depending on the goals and contexts, simulations can range from single-event activities integrated into broader courses to standalone interventions focusing on specific competencies. For example, physical and mixed-reality simulations have been used to enhance classroom management and decision-making skills. Typically, simulations consist of an experiential phase lasting 10–15 min, where participants engage with realistic scenarios, followed by a debriefing phase of 45–60 min for reflective discussions and feedback (Ade-Ojo et al., 2022). During debriefing, participants receive targeted feedback to identify and expand their SEL skills. This structured approach enhances teachers’ preparedness and supports the development of reflective practitioners who are better equipped to address the multifaceted challenges of real-world classrooms. While most simulation research focuses on isolated, single-event experiences, some studies explore more extensive implementations. For instance, Levin and Flavian (2022) describe a semester-long course where preservice teachers participated in weekly simulation workshops over 26 weeks. Each simulation reflected various educational scenarios designed to challenge participants’ decision-making and interpersonal skills. Through written reflections and peer feedback, participants identified critical aspects of their professional identity and adaptability as educators, highlighting the role of continuous, iterative practice in fostering their SEL and teaching expertise.
Simulations in teacher education commonly aim to develop classroom management skills, instructional techniques, and interpersonal competence (e.g., see Angelini, 2021; Berg et al., 2023). These simulations recreate realistic classroom scenarios, allowing teachers to engage with students, manage disruptions, and cultivate a positive classroom environment, as well as interact with other stakeholders such as parents and teaching assistants (Chandler-Olcott & Dotger, 2023).
Research indicates that simulations effectively enhance teachers’ self-efficacy, resilience, professional identity and overall teaching competence. While these concepts are related to SEL, there is still a lack of emphasis on developing specific SEL competencies through simulations in teacher education. Improving the integration of SEL into simulation-based learning offers a valuable opportunity to enhance teacher preparation and professional development. By embedding SEL into simulation-based learning, teachers can gain the core SEL competencies that enable managing emotionally charged situations, building positive relationships, and making responsible decisions that consider students’ social and emotional needs (Walters et al., 2021). This intentional focus not only prepares teachers to navigate real-life classroom challenges more effectively but also addresses broader concerns, such as stress and reduced job satisfaction, which are often experienced by educators due to the profession’s demanding nature and emotional challenges.
In recent years, several systematic literature reviews have examined the role of simulations in teacher education, focusing on the development of key teaching competencies such as teaching strategies and classroom management skills. Chernikova et al.’s (2020) meta-analysis of simulation-based learning across higher education, including teacher education, serves as a comprehensive overview, demonstrating that simulations have a large positive effect on the development of complex skills, benefitting both preservice and in-service professionals. This broad analysis sets the foundation for exploring more specific types of simulations within teacher education.
Ade-Ojo et al. (2022) focused specifically on preservice teachers, reviewing both physical (e.g., role-play) and mixed-reality simulations. Their findings emphasised significant improvements in confidence, self-efficacy, classroom management, and communication skills, although the use of these simulations is still in its early stages compared to other fields, calling for more large-scale research.
Building on the potential of immersive technologies, Billingsley et al. (Billingsley et al., 2019) explored VR simulations in both preservice and in-service teachers, finding that VR offered opportunities to enrich learning experiences, particularly in classroom management and teaching strategies. However, they stressed that further research is needed to fully understand its long-term benefits. Similarly, Lindberg and Jönsson (Lindberg & Jönsson, 2023) focused on avatar-based simulations in preservice teacher education. These simulations helped preservice teachers hone classroom management skills and teaching strategies. Hillyar et al. (2024) expanded this by reviewing various types of simulations, including VR and other computer-based technologies, emphasising that, while these simulations were effective in improving teaching skills, the degree of success varied by simulation type. This highlights the importance of exploring which specific simulation technologies are most impactful for different aspects of teaching.
Theelen et al. (2019a), in their review of computer-based simulations, also confirmed the value of these simulations in enhancing teaching skills and classroom management. Unlike the other reviews, however, they specifically pointed out the limited evidence on the development of interpersonal competences, which is closely tied to building positive teacher–student relationships—an essential part of teaching that simulations could help address more fully.
Therefore, despite the promising findings across these reviews, there is still limited data regarding the role of simulation-based learning in the development of SEL competencies. Given the significance of SEL for teachers’ professional growth and the potential of simulations to address this area of competence, we believe this is a critical gap that should be addressed. This literature review aims to address this gap by exploring the following research questions:
What types of simulation are identified as promoting SEL in the simulations in the teacher education literature?
What specific SEL competencies are targeted by simulations in the teacher education literature?
What are the variations in SEL competencies developed across different types of simulations in the teacher education literature?

2. Methodology

2.1. Search Strategy and Inclusion and Exclusion Criteria

The systematic literature review was conducted following the PRISMA guidelines (Page et al., 2021). Studies were collected during February 2024 from the following academic databases: APA Psych (including PsyArticle and PsyInfo), ERIC, Scopus, and Web of Science. The search included titles, abstracts, and keywords utilising specific terms related to simulation in teacher education and SEL, as detailed in Table 1. The following limitations were defined: peer-reviewed journal articles, open access and/or university access, in English, published between the years 2010 and 2023 (an exception was made for the ERIC database, where the parameters necessitated a search covering the last 20 years. This search resulted in 614 papers (2004–2024), of which 111 papers were excluded: 105 from 2004 to 2009 and 6 from 2024).

2.2. Selection Process

The initial search yielded a total of 3143 records from the databases. By snowballing through the most frequently cited papers, we identified 16 more relevant studies, which were subsequently incorporated into the review. After combining the records and removing duplicates (n = 518), 2632 studies remained for screening. The meta-data of these articles were exported to Excel for initial screening. Titles and abstracts were read to determine their relevance. To ensure eligibility, each article was screened by one author against the inclusion criteria, and then each decision was reviewed by another researcher to validate the inclusion/exclusion decision. Discrepancies were resolved through discussion among the research team. After the initial screening, 2389 articles were excluded, leaving 243 articles for further review.
A deep reading of these 243 articles led to the exclusion of articles, and in cases of uncertainty, the research team held discussions until a consensus was reached. Articles were excluded for the following reasons: (1) review articles (e.g., see Theelen et al., 2019a); (2) articles on simulations as a means for evaluating teaching efficacy (e.g., see Klopfer et al., 2019); (3) articles on SEL assessment in simulations (e.g., see Hemi & Kasperski, 2023); (4) articles that did not fit the definition of simulation-based learning (e.g., cultural simulation, like Padua & Smith, 2020); (5) articles on simulations for enhancing teaching skills, self-efficacy, and confidence (e.g., see Ledger & Fischetti, 2020); (6) articles on R&D of simulation environments (e.g., see Dieker et al., 2023). This rigorous process resulted in a final selection of 68 articles. The process follows PRISMA guidelines and is described in Figure 1.

2.3. Analysis and Synthesis

The included studies were analysed based on predefined criteria related to the research questions, i.e., the type of simulation used and the type of SEL competencies. By following this rigorous methodology, the systematic literature review aimed to provide a comprehensive understanding of the current state of research on simulations and their effects on preservice and in-service teachers’ SEL competencies.

3. Findings

3.1. General Overview of the Corpus Data

In this section, we describe the general characteristics of the data corpus. Figure 2 illustrates the geographic distribution of studies focusing on simulations in teacher education as a method for promoting SEL across various countries. Interest in using simulations to enhance SEL initially emerged in the US, which has been a pioneer in the development of SEL theory (CASEL, 2023; Jones & Bouffard, 2012) and in exploring practical approaches for enhancing SEL, such as simulations. These publications subsequently influenced the adoption of simulations in teacher education as a means of promoting SEL in other countries. In recent years, there has been a notable increase in publications from other countries, including Israel, Australia, Spain, and Germany.
The following graph (Figure 3) illustrates the growing trend in the number of studies on simulations aimed at developing SEL among teachers from 2010 to 2023. Notably, there has been a significant increase in research interest, with a peak of 12 studies in both 2022 and 2023.
Table 2 provides a comprehensive overview of the distribution of data types across various study methodologies, including qualitative (n = 32), quantitative (n = 17), and mixed methods (n = 19). Qualitative studies rely heavily on reflections (n = 12), transcripts (n = 12), and interviews (n = 10), indicating a focus on capturing in-depth personal insights, experiences, and detailed verbal data. Reflections are narrative entries or written responses in which participants describe their thoughts, experiences, and learning processes. Transcripts often include verbatim records of simulated scenarios, group discussions, and debriefings, offering insights into participants’ interactions and reflections. Interviews typically involve direct, often face-to-face interactions, where interviewers engage with participants through a series of questions designed to elicit detailed qualitative data.
Observations (n = 6) are also utilised in qualitative studies, whether directly or through video recording, highlighting the emphasis on understanding behaviours and contexts in their natural settings. Less frequently (1 to 3 studies), qualitative studies employ focus groups to explore collective views, logs from online activities to track digital interactions, field notes to capture observational details, portfolios to document participants’ progress, and simulation documents such as written scenarios to provide context for the research.
Quantitative research primarily utilises questionnaires and surveys (n = 14), structured tools used to collect data from respondents through a series of standardised questions, often yielding quantifiable information. The use of observations, while present, is less frequent (n = 3). Additionally, two studies utilised performance outcomes, and one study relied on neurological measurements, such as EEG, to gather physiological data.
Mixed methods research, which combines both qualitative and quantitative techniques, is predominantly associated with the use of questionnaires (n = 18) and reflections (n = 11). Interviews (n = 5) and observations (n = 6) are also utilised in mixed methods research, albeit to a lesser extent.
Table 3 summarises the population types involved in studies on simulations in teacher education for promoting SEL. The majority, 46 studies (68%), focus on preservice teachers (students), examining how simulations help develop SEL competencies in future educators. In-service teachers are the subject of 13 studies (19%), highlighting the role of simulations in ongoing professional development. Multiple groups, combining preservice and in-service teachers, account for five studies (7%), exploring the broader impact of simulations across different educator levels. These studies are significant because they examine how simulations impact a diverse set of participants, offering insights into the cross-sectional applicability of SEL simulations across various levels of experience and roles within the educational ecosystem. Educational administrators are featured in only three studies (4%). This distribution emphasises the strong focus on preparing preservice teachers. A total of 7072 educators participated across the 68 studies reviewed (Table 3).

3.2. Simulation Types for Promoting SEL in Teacher Education

In addressing the first research question, it became clear that the term “simulation” is used inconsistently across studies. For instance, descriptions of immersive simulations may not align with their actual procedural details. Thus, it is imperative to critically evaluate the use of the term “simulation” to ensure that researchers establish a consistent and shared terminology across studies. Nevertheless, as seen in Table 4, five distinct types of simulations in teacher education were identified in the scientific literature.
The first type of simulation involves a case study analysis, typically utilising video clips that feature professional actors portraying complex educational scenarios. In these videos, one actor assumes the role of an educator, while others play pupils, parents, colleagues, etc. After viewing the case study, an analysis session is held in which SEL concepts are identified and discussed. For example, in Walker and Dotger (2012), 141 teacher candidates evaluated two videos showing different teacher approaches during a parent–teacher conference, focusing on communication skills.
The second type of simulative learning that arose from the data synthesis was virtual simulation (such as SimInClass and SimSchool), which is a computer-based program that depicts educational environments. In these simulations, participants interact with virtual students, select teaching responses and tasks, receive immediate feedback, and refine their teaching methods based on detailed reports. For example, in Rayner and Fluck (2014), 30 preservice teachers trained in SimSchool for two-hour sessions. They interacted with virtual students and then received feedback on student progress.
The third type of simulation encompasses immersive simulation, which refers to a mixed (blended) reality environment where virtual avatars are controlled either by live actors (e.g., Mursion) (Dalinger et al., 2020) or by BOTs (computer-controlled counterparts) (Dell’Aquila et al., 2022). The avatars can see, hear, and respond to the participants, mimicking real classroom dynamics. For example, in Dell’Aquila et al. (2022), 35 participants engaged in single-player role-play, interacting with a computer-controlled virtual agent (BOT) in scenarios simulating dialogue and conflict situations. Each interaction consisted of five to seven exchanges, where players selected responses from predefined options representing different conflict management styles. After each session, players received feedback on their performance and preferred conflict management style, along with suggestions for improvement.
The fourth type of simulation includes role-play, where participants embody roles and construct a narrative within the constraints of their character profiles. While role-play shares the intent of replicating real-world scenarios, it fundamentally differs from more immersive simulation modalities due to its lack of structured control, realism, and technological integration. Studies on simulations often included role-play, which is why it was retrieved in our search algorithm focused on simulations, as seen in other literature reviews on the topic (Ade-Ojo et al., 2022). Role-play relies on participant improvisation and subjective interpretation, offering the potential to foster creativity and perspective-taking, albeit with less control and realism compared to other simulation modalities. For example, in Angelini (Angelini, 2016), students collaborated in teams to design role-play scenarios based on human rights themes from selected literary texts. They created detailed scenarios with specific settings, situations, participant profiles, and norms, implementing the role-plays in three phases: briefing, action, and debriefing.
The fifth type of simulation was clinical simulation, as coined by Dotger (Dotger, 2010). In this method, participants engage with standardised participants—often professional actors but sometimes specialists (Mueller et al., 2019)—trained to act as parents, pupils, and other roles. The process begins with participants receiving background information about the scenario. During the interaction, they navigate the conversation, responding to the standardised participants’ scripted verbal and non-verbal cues. After the simulation, candidates receive immediate feedback from the standardised participants and facilitators, review video recordings for self-evaluation, and participate in debriefing sessions to reflect on their performance and learn from others’ experiences (Chandler-Olcott & Dotger, 2023).
The five types of simulative learning processes identified in the research can be arranged on a scale by immersion (see Figure 4). This scale reflects the depth of engagement and the realism of the learning environment. The lowest level of immersion is associated with case studies, which involve observation and analysis with no active participation in the simulation. Virtual simulations offer a higher degree of immersion by facilitating interactive engagement, although this interaction is still mediated by the digital interface and constrained to a predefined set of responses. Similarly, immersive simulations further increase the level of immersion by incorporating real-time interactions with virtual avatars. Nevertheless, the avatars do not fully replicate the authentic experience of real-life interactions, and the responses may remain limited.
Role-play significantly enhances immersion by requiring participants to actively embody roles and construct narratives, resulting in deeper emotional and cognitive engagement. However, in these simulations, participants may assume roles that are not directly relevant to their professional development, such as acting as “disturbed” pupils while training to be teachers. Finally, clinical simulations represent the highest level of immersion, involving unscripted interactions with standardised participants that closely replicate real professional encounters, wherein the active participant authentically assumes their professional role.
In summary, as the level of immersion increases in these simulation-based learning methods, the potential for realistic engagement and skill development also grows, better equipping participants to handle real-world challenges.

3.3. Targeted SEL Competencies in Simulations in Teacher Education

In addressing the second research question, we categorised the relevant SEL concepts identified in these studies according to the CASEL model. This categorisation process allowed us to systematically align the diverse SEL competencies observed in the research with the five core clusters established by CASEL, facilitating more robust comparisons and insights across the different studies. We included only those competencies reported to be enhanced or developed following the simulative experience, ensuring that our analysis accurately reflected the impact (or perceived experience) of the simulations. In some studies, determining the specific competency being measured was difficult due to vague descriptions. In these cases, the broader classification was maintained to accurately reflect the general nature of the findings.
The analysis revealed that all of the CASEL’s five SEL clusters were evident in the data (see Section S2 for more details on the SEL clusters and the wide array of SEL competencies in each study). However, significant differences were observed between the clusters as well as in the scope of each paper. Nine of 68 studies (13%) incorporated simulations that addressed three distinct SEL clusters. On average, these studies covered 4.56 specific SEL competencies (SD = 1.88). Twenty-six studies (38%) focused on two distinct SEL clusters, with a mean of 3.15 specific competencies per study (SD = 1.12). The remaining 33 studies (49%) addressed only one SEL cluster, covering a mean of 1.48 specific competencies (SD = 0.71). Overall, across all studies, the mean number of SEL competencies addressed was 2.53 (SD = 1.54).
Regarding the clusters’ variability, the social awareness cluster was the most frequently mentioned, appearing in 45 studies (40%), followed by the relationship skills cluster in 35 studies (31%). This underscores the primary objective of simulations in teacher education, which is to enhance interpersonal competencies. Figure 5 details the various SEL competencies identified in the review in these two clusters.
Within the social awareness cluster, specific competencies were addressed as follows: social understanding was highlighted in 21 of 45 studies (46%), empathy appeared in 19 studies (41%), inclusion was addressed in 9 studies (20%), social perspective was mentioned in 8 studies (17%), and emotional intelligence was highlighted in 4 studies (9%). Although these competencies share similarities, they remain distinct. Significant effort was made to precisely identify and delineate each specific competency. Social understanding refers to the ability to comprehend and interpret social cues, including recognising the emotions, intentions, and behaviours of others within social interactions (CASEL, 2023). Social perspective-taking builds on this by enabling individuals to consider and understand situations from others’ viewpoints (CASEL, 2023).
These foundational competencies directly contribute to empathy, which involves both cognitive understanding of another’s perspective and affective emotional resonance. Empathy is closely tied to emotional intelligence, which enhances the ability to identify and respond to the emotional states of others. The integration of these competencies creates a robust framework for fostering inclusive environments. Inclusion, the active practice of integrating diverse individuals into social settings (CASEL, 2023), is enabled by understanding others, resonating with them and valuing diverse perspectives.
The distribution of competencies in the relationship skills cluster highlights the importance of clear, constructive interactions in the classroom, as well as the necessity of managing classroom dynamics and fostering teamwork. Communication skills (13 studies, 37%) involve the ability to convey information clearly and effectively through both verbal and non-verbal means, including tone, body language, and facial expressions (CASEL, 2023). It is important to note that distinguishing between communication skills and conversation techniques was sometimes challenging, and in such cases, the broader concept of “communication skills” was retained from the original study to reflect the general nature of the findings.
In contrast, conversation techniques (20 studies, 57%) specifically focus on the verbal aspects of communication, enhancing interactions by refining how messages are exchanged during dialogue (van Leusen et al., 2016). For instance, active listening ensures that all participants feel heard, while clarity in verbal communication minimises misunderstandings. Additionally, probing questions encourage deeper exploration of topics, and techniques such as paraphrasing and summarisation help confirm that the intended message is accurately received. Structuring maintains the flow of discussions, ensuring that conversations are coherent and purposeful. The approach to the interaction (7 studies, 20%) adds depth to this cluster by encompassing non-judgmental attitudes, flexibility and empowerment. Together, these competencies provide a strong foundation for building and maintaining effective relationships.
Building on communication skills and conversation techniques, collaboration (14 studies, 40%) is a key competency in relationship skills. Effective collaboration requires working together toward shared goals, using clear communication to navigate group dynamics. Conflict resolution (12 studies, 34%) and assertiveness (5 studies, 14%) are crucial in addressing disagreements, relying on structured dialogue to find mutually acceptable solutions. Integrating these competencies—communication, conversation techniques, approach, collaboration, conflict resolution, and assertiveness—enables the creation of productive, inclusive environments that support teamwork and effective group dynamics (Behfar et al., 2008).
Ethical decision-making was addressed in 13 studies (12%). These studies concentrated on competencies such as equity, caring, human rights, and critical thinking, highlighting the importance of ethical considerations in educational practice. The self-awareness cluster was explored in 12 studies (11%), primarily focusing on reflection, indicating its role in enhancing professional growth and effectiveness in teaching. The self-management cluster, although less frequently addressed, was mentioned in seven studies (6%), with emotional regulation being the focus in six of these seven studies.
Overall, the distribution of competencies across these clusters illustrates how simulations in teacher education are designed to enhance a broad range of interpersonal and intrapersonal skills, preparing educators to create effective, nurturing and dynamic learning environments. This comprehensive approach aims to ensure that teachers are well-equipped to address the diverse needs of their students, while also maintaining ethical standards.

3.4. Variations in SEL Competencies Developed Across Different Simulation Types

When examining the number of SEL competencies addressed across different types of simulation studies (third research question), the variation in comprehensiveness becomes apparent, with each type offering distinct strengths (see Table 5). Clinical simulation studies emerge as the most comprehensive, covering a mean of 3.28 SEL competencies per study (SD = 1.90). Virtual simulations, while slightly less extensive, also cover multiple SEL competencies (M = 2.75, SD = 1.67). In contrast, the other three types of simulations tend to focus on a narrower range of competencies: immersive simulations (M = 1.93, SD = 0.88), role-play simulations (M = 2.00, SD = 0.92), and case studies (M = 1.80, SD = 0.84).
We can observe a spectrum of simulations, ranging from those that address a broader range of SEL competencies to those that focus on a more limited set. This variation likely stems from the origins of each simulation type, the expertise of the lead developers, and the specific goals that shaped their design. For example, clinical simulations were adopted from medicine (Dotger, 2011), where they were originally devised to improve physicians’ interpersonal communication with patients, thus naturally covering a wider range of SEL competencies. In contrast, other types such as immersive simulations were developed with more focused instructional objectives (Chernikova et al., 2020), resulting in a narrower emphasis on SEL competencies.
Figure 6 details the different SEL clusters across the various simulation types, to enhance our perception of these variations (Section S2 contains additional details about study characteristics, including simulation characteristics). Social awareness consistently emerges as the most addressed competency, indicating its central role in all types of simulations in teacher education. However, the emphasis on other competencies varies widely. Relationship skills are prominently featured in clinical and immersive simulations but receive less attention in role-play and virtual simulations, reflecting different priorities in how these simulations handle interpersonal interactions. Other competencies such as ethics and self-management are notably underrepresented. Ethics is completely absent in immersive and case study simulations. Similarly, self-management is minimally addressed, particularly in role-play and immersive simulations.

4. Discussion

The purpose of the current review was to explore the use of simulation-based learning in developing teachers’ social–emotional learning (SEL) competencies. Our review identified the use of five distinct types of simulations in teacher education: case studies, virtual simulations, immersive simulations, role-play, and clinical simulations. Using the CASEL framework as a basis for SEL competencies, we found that social awareness and relationship skills were the most commonly targeted SEL competencies across these studies. However, there were notable differences in the focus and depth of SEL coverage, which varied significantly depending on the type of simulation employed.
A key finding of this review is the inconsistency in the terminology and categorisation of simulation types across studies, an insight raised in previous research (Badiee & Kaufman, 2015). The current literature review shows that many studies do not clearly define the specific type of simulation employed, often lacking a systematic approach to categorisation. This ambiguity poses challenges for comparing and synthesising findings across different studies. For example, while some researchers classify case studies as a form of simulation (Walker & Dotger, 2012), others may not (Kasperski & Yariv, 2022). Therefore, this review makes a significant theoretical contribution by introducing a clearer and more structured categorisation of simulation types, which helps to establish a foundation for future research and promotes a more consistent understanding and application of simulation-based learning in developing teachers’ SEL competencies. By integrating these various simulation types, we propose the following definition of SEL-targeted simulations in the educational context:
Simulation is a means for replicating real-world scenarios and is utilised to enhance the development of specific skills, behaviours, and competencies through various modalities (Chernikova et al., 2020; Lindberg & Jönsson, 2023; Theelen et al., 2019a). These simulations encompass a spectrum of interaction levels, ranging from observing video clips and analysing case studies to actively engaging with virtual agents or standardised participants in immersive, real-time settings. The primary aim of SEL-targeted simulations is to offer a safe, structured environment where learners can explore diverse perspectives and enhance their social–emotional skills through active engagement, constructive feedback, and reflective debriefing.
The current findings also indicate that clinical simulations tend to place a stronger emphasis on developing SEL competencies compared to other types of simulations, which may account for their higher representation in this review. This focus on SEL might also explain the lower presence of leading researchers in simulations in teacher education (e.g., see Dieker et al., 2023; Ledger & Fischetti, 2020), as their work centres primarily on simulation protocols or the didactic aspects of teaching rather than SEL. Based on the proportional distribution of SEL competencies across different simulation types, we suggest that diversifying the use of simulations—rather than relying on a single type—could potentially broaden the range of SEL competencies addressed. However, this approach requires careful planning to avoid unintended negative effects, such as decreased motivation or fatigue after repeated simulations. Additionally, while certain simulations may complement one another, others could be mutually exclusive or even counterproductive.
The current review does not provide sufficient evidence to determine how and which specific simulation types should be adapted, combined, or sequenced to optimise outcomes. Therefore, we recommend that future studies investigate the interactions between various simulation types, their potential synergies, and any associated risks. This recommendation aligns with the study conducted by Chernikova et al. (2020), who concluded that combining multiple types of simulations within teacher education may yield greater learning outcomes. Furthermore, while Chernikova et al. (2020) emphasise the importance of designing simulations within the specific learning context, we suggest that incorporating SEL competencies into the design process is equally critical. This approach would not only benefit teacher education programs but also provide a broader educational system and researchers with more versatile and impactful tools for fostering supportive learning environments.
In this vein, it is worth acknowledging that only three studies have directly compared different types of simulations, such as role-play versus virtual simulations, for developing teachers’ SEL competencies (Krämer & Zimmermann, 2022; Lorenzo, 2014; Spencer et al., 2019). More comparative research is needed to identify the unique strengths and limitations of each simulation type as well as the benefits of combining simulation types. Such studies could help determine which types are most effective for specific SEL competencies, guiding educators and researchers in choosing the most appropriate simulation techniques for teacher professional development programs.
In addition to traditional simulation methods, the integration of artificial intelligence (AI) in educational simulations is emerging as a powerful tool for developing SEL competencies. AI-driven platforms enable teacher candidates to practice interactions with virtual students or other counterparts, providing realistic and adaptive learning experiences (Government Technology, 2024). Although these AI-based simulations are currently available in preliminary versions and their full potential has yet to be realised (Dieker et al., 2023), we expect their use to become increasingly prevalent. These innovations underscore the potential for AI to complement traditional simulations, offering more dynamic and personalised approaches to SEL development.

4.1. Limitations

This study has several limitations that should be acknowledged. First, it does not include a cost-effectiveness measurement, as the absence of comparative studies in the literature makes it difficult to evaluate the economic feasibility of different simulation-based SEL interventions. Second, this review does not distinguish between preservice and in-service teachers as separate audiences. This lack of distinction limits the study’s ability to assess potential differences in the application and effectiveness of simulation-based learning for these two groups. Third, the study does not provide sufficient evidence to determine how and which specific types of simulations should be adapted, combined, or sequenced to optimise SEL outcomes. Certain simulations may complement each other, while others could be mutually exclusive or even counterproductive. Moreover, the review did not explore the potential risks of simulation overuse, such as reduced motivation or negative effects on participants. Finally, there was no detailed analysis of the specific components within the simulations and how each of these elements directly promotes SEL competencies. Without this granular level of analysis, it is challenging to identify which aspects of simulation-based learning are most effective in enhancing particular SEL competencies.

4.2. Future Research Directions

Given that simulation-based learning has gained significant momentum in recent years, with a marked increase in the number of studies published (as demonstrated in Figure 3), it is advisable to continue conducting literature reviews that incorporate the most recent research, including publications from 2024 onwards. Moreover, future studies could benefit from a comparative analysis of the different simulation types and their SEL components, to gain a deeper understanding of the underlying learning mechanisms and outcomes in each learning environment. Future research should investigate potential differences in the application and effectiveness of simulations for preservice versus in-service teachers, as these groups may have distinct needs and learning outcomes (Kasperski, 2023). For instance, research has demonstrated the differential effect of simulations on various SEL subscales across professional phases, highlighting how preservice educators may benefit more from certain types of simulations compared to their in-service counterparts (Hemi, in press). Exploring these differences could provide valuable insights into tailoring simulation-based interventions for specific audiences. Quantitative studies assessing the overall effect of simulations on teachers’ SEL competencies would also provide valuable insights into the effectiveness of these interventions. Furthermore, future research could consider demographic factors alongside cultural contexts, which were not addressed in this study, to better understand the influence of such variables on SEL outcomes. Additionally, research could focus on specific design elements—such as feedback mechanisms, levels of immersion, and technological support (Chernikova et al., 2024)—and their direct impact on SEL development. Understanding these elements in greater detail would help optimise simulation design and improve learning outcomes. Finally, future studies could explore the risks and benefits of combining different simulation types and investigate the conditions under which they might complement or conflict with each other. Meta-analytic techniques could also be employed to quantify the relative effectiveness of various simulation types, providing a deeper empirical understanding of their impact on SEL development.

4.3. Conclusions

The current systematic literature review reveals both the potential and variability in addressing SEL skills in teachers’ professional development. The need for consistent terminology and categorisation to better compare and synthesise findings across studies is evident. By integrating comprehensive SEL competencies into a wider range of simulations and exploring innovative approaches such as AI-driven platforms, teacher education can be significantly enhanced. Broadening practices to include more SEL aspects, even when the primary focus is on teaching methods, will benefit educators, researchers, and the broader educational community by fostering more emotionally responsive classroom environments.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/educsci15020129/s1, Section S1: The PRISMA 2020 statement checklist. Section S2: Distribution of SEL competencies across simulation types.

Author Contributions

Conceptualization, R.K., M.E.H. and O.L.; methodology, R.K., M.E.H. and O.L.; formal analysis, R.K., M.E.H. and O.L.; investigation, R.K., M.E.H. and O.L.; data curation, R.K., M.E.H. and O.L.; writing—original draft preparation, R.K. and M.E.H.; writing—review and editing, O.L.; visualization, M.E.H.; supervision, R.K., M.E.H. and O.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author(s).

Conflicts of Interest

The authors declare no conflict of interest.

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Education 15 00129 g001
Figure 1. Stages of the study selection process, following PRISMA (Page et al., 2021).
Figure 1. Stages of the study selection process, following PRISMA (Page et al., 2021).
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Education 15 00129 g002
Figure 2. Geographic distribution of studies by country.
Figure 2. Geographic distribution of studies by country.
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Education 15 00129 g003
Figure 3. The number of studies on teachers’ SEL simulations.
Figure 3. The number of studies on teachers’ SEL simulations.
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Figure 4. Types of simulation arranged by level of immersion, from low (left side) to high (right side).
Figure 4. Types of simulation arranged by level of immersion, from low (left side) to high (right side).
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Education 15 00129 g005
Figure 5. SEL competencies in the social awareness and relationship clusters.
Figure 5. SEL competencies in the social awareness and relationship clusters.
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Figure 6. Proportional distribution of SEL competencies across different simulation types. Note: S-Awa—self-awareness; S-Man—self-management; Social A—social awareness; Relations—relationship skills; Ethics—Ethical Decision Making.
Figure 6. Proportional distribution of SEL competencies across different simulation types. Note: S-Awa—self-awareness; S-Man—self-management; Social A—social awareness; Relations—relationship skills; Ethics—Ethical Decision Making.
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Table 1. Search terms and exclusion criteria across databases.
Table 1. Search terms and exclusion criteria across databases.
APA PsychERICScopusWeb of Science
Search terms Title or Abstract or Keywords
simulat *
AND teach *
AND soci * OR emotion * OR interpersonal OR intrapersonal OR communicat * OR relation * 		TI OR AB = simulation OR simulate OR simulated OR simulative) AND (TI OR AB = teacher OR teachers OR teachers’ OR teacher’s OR teaching) AND (TI OR AB = social OR socio OR emotion OR emotional OR interpersonal OR intrapersonal OR communication OR communicate OR relation OR relationship OR relations OR relationshipsSearch parameters: title, abstract, keywords
Simulation OR simulate OR simulated OR simulative) AND (teacher or teachers OR teachers’ OR teacher’s OR teaching) AND (social OR socio OR emotion OR emotional OR interpersonal OR intrapersonal OR communication OR communicate OR relation OR relationship OR relations OR relationships		TS topic = title or abstract or keywords
simulat *
AND teach *
AND soci * OR emotion * OR interpersonal OR intrapersonal OR communicat * OR relation *
ExcludeNOT health OR medic * OR nurs *NOT TI OR AB = health OR medical OR medicine OR nurse OR nursingAND NOT health OR medical OR medicine OR nurse OR nursingNOT health OR medic * OR nurs *
Records4805031297863
Note. The symbol * at the end of a keyword indicates that variations of the keyword, including different word forms or plurals, were included as part of the search terms.
Table 2. Types of studies and data sources.
Table 2. Types of studies and data sources.
Data TypeQualitative
(n = 32)		Quantitative (n = 17)Mixed Methods (n = 19)Total
(n = 68)
Questionnaires--151732
Reflections12--1123
Interviews12--517
Observations63615
Transcripts11--213
Focus groups3--14
Logs2--13
Field notes2----2
Portfolios2----2
Performance--2--2
Documents1----1
Neurological
Measurements		--1--1
Table 3. Descriptive statistics for participant numbers by study design and population type.
Table 3. Descriptive statistics for participant numbers by study design and population type.
Qualitative
(32 Studies)		Quantitative
(17 Studies)		Mixed
(19 Studies)		Total
(68 Studies)
Preservice
(46 studies)		24 studies *
M = 35.37
(SD = 32.90)		10 studies
M = 84.56
(SD = 77.14)		12 studies
M = 99.30
(SD = 129.02)		46 studies *
M = 63.84
(SD = 81.99)
In-service
(13 studies)		5 studies
M = 38.00
(SD = 54.67)		4 studies
M = 223.00
(SD = 250.03)		4 studies
M = 117.25
(SD = 153.90)		13 studies
M = 134.09
(SD = 180.28)
Preservice and
in-service
(5 studies)		--2 studies
M = 361.00
(SD = 360.62)		3 studies
M = 350.33
(SD = 320.80)		5 studies
M = 354.60
(SD = 204.76)
Administrators
(3 studies)		2 studies
M = 9.50
(SD = 3.56)		1 study
M = 68.00
(SD = --)		--3 studies
M = 29.00
(SD = 33.87)
Assistants
(1 study)		1 study
M = 17.00
(SD = --)		----1 study
M = 17.00
(SD = --)
Total
(68 studies)		32 studies *
M = 32.88
(SD = 33.55)		17 studies
M = 152.69
(SD = 186.32)		19 studies
M = 147.82
(SD = 189.90)		68 studies *
M = 99.62
(SD = 152.21)
Note: M = mean number of participants; SD = standard deviation of the number of participants; five qualitative studies (one involving in-service teachers and four involving preservice teachers) did not specify the number of participants; * one qualitative study, which included over 1200 preservice participants, was determined as an outlier and thus excluded from the calculations.
Table 4. Types of simulations in teacher education.
Table 4. Types of simulations in teacher education.
Type of SimulationnStudies Included in the Systematic Review
Case Studies: Providing participants with a scenario through a video clip (usually), followed by a reflective discussion/analysis (e.g., Teacher Moments).5(Burden et al., 2010; De Jong et al., 2012; Landler-Pardo et al., 2022; Walker & Dotger, 2012; Theelen et al., 2019b)
Virtual Simulations: A virtual environment, where the participant selects their response from a predefined set (e.g., SimInClass and SimSchool).8(Arvola et al., 2018; Caglar-Ozhan et al., 2022; Rayner & Fluck, 2014; Krämer & Zimmermann, 2022; Lorenzo, 2014; Paz-Albo et al., 2023; Prieto, 2018; Yan & Ottenbreit-Leftwich, 2023)
Immersive (mixed-reality) Simulations: Mixed-reality environment, where the participant engages with avatars, controlled by real live actors or BOTs. The participant can be embodied by an avatar within the scene (e.g., TeachME) or not (e.g., Mursion, SimTEACHER).15(Accardo & Xin, 2017; Cohen et al., 2020; Dalinger et al., 2020; Dell’Aquila et al., 2022; Finn et al., 2020; Hirsch et al., 2023; Overland, 2017; Park et al., 2019; Rappa & Ledger, 2023; Robbins et al., 2019; Rosati-Peterson et al., 2021; Scarparolo & Mayne, 2022; Sebastian et al., 2023; Seufert et al., 2022; Wernick et al., 2021)
Role-Play: Two (or more) participants assume different roles and perform accordingly within a predefined situation.15(Angelini, 2016; Angelini et al., 2023; Arnett-Hartwick & Harpel, 2020; de Beer & Henning, 2011; Franck et al., 2016; Frederick et al., 2010; Goelman Rice et al., 2017; Goldin et al., 2021; Keeney et al., 2019; Lazar & Sharma, 2016; Scorgie, 2010; Shapira-Lishchinsky, 2013; Shapira-Lishchinsky, 2016; Tanghe, 2016; Wright-Maley, 2015)
Clinical Simulation: Hands-on, experiential learning method in a controlled yet realistic environment. Participants interact in an unscripted manner with standardised others enacted by actors.25(Chen, 2020; Cil & Dotger, 2015; Coughlin & Dotger, 2016; De Coninck et al., 2020; Dotger, 2010; Dotger & Alger, 2012; Dotger & Ashby, 2010; Dotger & Coughlin, 2018; Dotger et al., 2015; Freedman et al., 2021; Frei-Landau et al., 2022; Gerich & Schmitz, 2016; Goldin et al., 2018; Kasperski, 2023; Kasperski & Crispel, 2022; Kasperski & Hemi, 2022; Kozak et al., 2023; Levin, 2023; Levin & Flavian, 2022; Levin & Muchnik-Rozanov, 2022; Levin et al., 2023; Linder & Weissblueth, 2023; Magen-Nagar & Steinberger, 2022; Mueller et al., 2019; Yablon et al., 2022)
Table 5. Distribution of SEL clusters and competencies across different types of simulations.
Table 5. Distribution of SEL clusters and competencies across different types of simulations.
Type of SimulationNumber of SEL ClustersNumber of SEL Comp.S-AwaS-ManSocial ARelationsEthics
Clinical
(n = 25)		1.96 (0.79)3.28 (1.90)4416196
Virtual
(n = 8)		1.63 (0.74)2.75 (1.67)31432
Immersive
(n = 15)		1.47 (0.52)1.93 (0.88)3199--
Role-play
(n = 15)		1.40 (0.63)2.00 (0.92)1--1325
Case Studies
(n = 5)		1.40 (0.55)1.80 (0.84)1132--
ALL (n = 68)1.65 (0.71)2.53 (1.54)127453513
Note: Number of SEL Comp.—number of SEL competencies reported in the study; S-Awa—self-awareness; S-Man—self-management; Social A—social awareness; Relations—relationship skills; Ethics—Ethical Decision Making.
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Kasperski, R.; Levin, O.; Hemi, M.E. Systematic Literature Review of Simulation-Based Learning for Developing Teacher SEL. Educ. Sci. 2025, 15, 129. https://doi.org/10.3390/educsci15020129

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Kasperski R, Levin O, Hemi ME. Systematic Literature Review of Simulation-Based Learning for Developing Teacher SEL. Education Sciences. 2025; 15(2):129. https://doi.org/10.3390/educsci15020129

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Kasperski, Ronen, Orna Levin, and Merav Esther Hemi. 2025. "Systematic Literature Review of Simulation-Based Learning for Developing Teacher SEL" Education Sciences 15, no. 2: 129. https://doi.org/10.3390/educsci15020129

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Kasperski, R., Levin, O., & Hemi, M. E. (2025). Systematic Literature Review of Simulation-Based Learning for Developing Teacher SEL. Education Sciences, 15(2), 129. https://doi.org/10.3390/educsci15020129

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