Experimenting With Soft Robotics in Education: A Systematic Literature Review From 2006 to 2022

Educational robotics (ER) is a discipline of applied robotics focused on teaching robot design, analysis, application, and operation. Traditionally, ER has favored rigid robots, overlooking the potential of soft robots (SRs). While rigid robots offer insights into dynamics, kinematics, and control, they have limitations in exploring the depths of mechanical design and material properties. In this regard, SRs present an opportunity to expand educational topics and activities in robotics through their unique bioinspired properties and accessibility. Despite their promise, there is a notable lack of research on SRs as educational tools, limiting the identification of research avenues that could promote their adoption in educational settings. This study conducts a systematic literature review to elucidate the impact of SRs across academic levels, pedagogical strategies, prevalent artificial muscles, educational activities, and assessment methods. The findings indicate a significant focus on K-12 workshops utilizing soft pneumatic actuators. Furthermore, SRs have fostered the development of fabrication and mechanical design skills beyond mere programming tasks. However, there is a shortage of studies analyzing their use in higher education or their impact on learning outcomes, suggesting a critical need for comprehensive evaluations to determine their effectiveness, rather than solely relying on surveys for student feedback. Thus, there is an opportunity to explore and evaluate the use of SRs in more advanced settings and multidisciplinary activities, urging for rigorous assessments of their influence on learning outcomes. By undertaking this, we aim to provide a foundation for integrating SRs into the ER curriculum, potentially transforming teaching methodologies and enriching students' learning experiences.

electric motors or pressurized fluid cylinders [2].The bioinspired philosophy in soft robotics can be exemplified by soft artificial muscles (SAMs), which are built from flexible materials, replicating the compliance and forms of their biological counterparts, and can produce a wide range of movements [3], [4].
Soft robotics employ diverse actuation strategies, including tendon-driven, fluidic actuation, and stimuli-responsive smart materials [5].Pneumatic artificial muscles (PAMs), such as McKibben muscles or PneuNets, are the most commonly used due to their low cost, ready availability of components, and simple manufacturing processes [6].Other technologies, such as electroactive polymers (EAPs), dielectric elastomers actuators (DEAs), ionic polymer-metal composites (IPMCs), and shape memory alloys (SMAs), although cost-effective, might require specific operating conditions, including high voltages, aqueous environments, or elevated temperatures, restricting their usage in some fields [7].
Innovations in this field are expanding into areas traditionally dominated by conventional robotics, such as the creation of grippers inspired by biology that provide a gentle yet firm grasp on delicate objects [6].In addition, soft robotics has facilitated navigation through obstacle-ridden, unstructured terrains [8].Moreover, the potential for human-robot collaboration is increased by the inherent safety of soft robots, creating service and assistive robotics opportunities.This emerging field has the potential to revolutionize various industries, notably in Science, Technology, Engineering, and Mathematics (STEM) education [9].
Educational robotics aims to improve learning outcomes through the integration of robot-centered activities, technologies, and artifacts, using a student-centered constructivist pedagogical approach [10], [11].This experiential approach aligns with the nature of robotics and helps learners construct knowledge.Students are prompted to conceptualize, design, build, program, and test robots to execute specific tasks, thereby honing digital skills [12].This process not only equips them with technical abilities, such as mechanical design and computer programming, but also promotes critical thinking and problem-solving skills [13], [14].Students gain a profound understanding of robotics principles by observing the direct effects of their actions and revising their strategies based on outcomes, fostering a sense of ownership and independence in their learning journey [15], [16].
Platforms such as LEGO Mindstorms serve this purpose well [17], yet they primarily belong to conventional rigid robotics and focus on predefined activities.In this regard, several studies have presented the development, implementation, and assessment of robotic platforms based on rigid robots for educational purposes [18], [19], [20], [21], [22], [23].While these systems allow in-depth exploration of rigid body kinematics, dynamics, and implementation aspects, they fail to showcase the advantages of compliant structures and novel actuation mechanisms.According to Yu et al. [24], when working with robot kits, the students spend most of their time programming rather than designing robot bodies.Thus, the students mainly focus on solving control tasks without considering the robot's mechanical design and material properties.Moreover, rigid robots have certain disadvantages that mitigate their use, such as the lack of safety in close contact with humans, their higher cost, and their more significant weight than soft robots [25].Hence, soft robotics offers a unique chance to stimulate scientific and engineering thinking, fostering understanding of related disciplines, such as materials science and biology [26], [27].Furthermore, soft robotics can provide an interdisciplinary learning experience that rigid robotics cannot replicate, especially in terms of biological mimicry and interaction safety.By incorporating soft robotics into education, it could be possible to spark a lifelong interest in STEM fields and robotics.An overview and comparison of rigid and soft robotics within educational robotics are presented in Fig. 1.
Although comprehensive reviews have been conducted in educational robotics, their focus does not encompass the implementation of soft robotics.They mainly concentrate on the application of existing robotic kits or the development of robotic platforms for potential use in education [28], [29], [30], the use of social robots to assess human-robot interaction (HRI) [31], and the potential and challenges of educational robotics in engaging students and teachers [32], [33], [34], [35], with some even emphasizing mathematics [36].However, a literature review on soft robotics use in educational robotics has yet to be conducted.Thus, there is an opportunity to examine the uses of soft robots in education by reviewing the recent literature.
The potential of soft robotics in education highlights the importance of assessing its impact across all levels of education.
As educational robotics has already demonstrated its benefits, it would be worthwhile to investigate the platforms and methods that introduce students to the foundational scientific and technological concepts that underlie soft robotics.To achieve this, a thorough and systematic literature review (SLR) could be conducted to comprehensively evaluate the existing research, allowing for a methodical examination of its current state.
This article presents an SLR that aims to explore the field of bioinspiration in educational robotics.The review focuses on the countries and years in which soft robotics has been utilized for education, the types of bioinspired muscles employed, the educational activities to which soft robotics has been applied, and how these activities have been evaluated.The review addresses the need for more research on using soft robots in education and emphasizes the significance of research in this area.The findings of this review are expected to serve as a roadmap for future research, highlighting the primary research themes, methodologies, and platforms currently used.
The rest of this article is organized as follows.The review begins with Section II that focuses on the two main topics discussed in this study: educational robotics and soft robotics.Consequently, Section III describes the methodology used to perform the SLR, highlighting the critical aspects addressed.In Section IV, we present our results, outlining key findings enriched with illustrative graphics for improved comprehension.In Section V, we analyze these crucial insights, and we also provide recommendations for researchers, educators, and policymakers eager to advance this field further.In Section VI, we suggest successful steps and materials for implementation.Finally, Section VII concludes this article.

II. BACKGROUND
This section presents an overview of the main topics discussed in this article: educational robotics and soft robotics.It is important to highlight that the term soft robotic education has not been defined in the current state of the art.Nevertheless, since this study is focused on the areas of educational robotics and soft robotics, a general background on these two concepts is presented as follows.

A. Educational Robotics
Educational robotics aims to utilize robots as a teaching tool that helps students tackle problems in areas unrelated to robotics.The aforementioned seeks to let the students construct their understanding of concepts and representations in both science and technology [11].Hence, robotics in education follows the constructivism theory of learning.According to constructivism's underlying principles, knowledge is viewed as an active experience created through interactions with the environment [37].In constructivism, students' prior knowledge and experience are the foundation for generating additional knowledge.
A second theory related to educational robotics is constructionism.The theory of constructionism shares ideas with the view of constructivism but expands it by not only supporting student-centered learning but also giving importance to discovering learning through tangible objects to connect prior knowledge and new information [38], [39].The key difference between constructionism and constructivism is that while constructivism mainly focuses on the mental process of learners or students, constructionism is interested in a physical process, such as creating a physical model or generating a mathematical equation [40].The aforementioned makes the thinking and learning process visible [41].
Robotics kits, such as those of LEGO Mindstorms, provide a modular approach to programming and construction, frequently utilized as creativity-enhancing tools in the classroom.By working with these robotic kits, the student can have the opportunity to apply engineering knowledge and clever solutions to a wide range of problems that can involve making a robot move from one position to another.Moreover, educational robotics has been directed by principles, such as problem-based learning and gamification [42].Furthermore, robotics's playful and physical embodiment of learning has impacted students' engagement, as presented by Apiola et al. [43] and Nemiro et al. [44].

B. Soft Robotics
Soft robots represent a blend of biologically inspired actuation, sensory, and cognitive functions, combined with a design that promotes safe, intuitive, and sensitive interactions [45].While conventional robots are frequently constructed using high-stiffness materials, such as steel, aluminum, titanium, or stainless steel, soft robots are primarily made of hyperelastic materials, such as polymers, rubber, or silicone.These flexible materials mirror the elasticity and malleability of biological systems, providing soft robots with an increased degree of freedom in their movement compared to their rigid counterparts.This flexibility also allows soft robots to interact safely with their environment and quickly adapt to unpredictable circumstances [46].The unique characteristics of soft robots afford them several advantages over more conventional, rigid robots.These include safer human-machine interaction, the capacity to create wearable devices, and the ability to develop simple yet effective gripping systems.These unique capabilities have enabled the application of soft robotics across a diverse range of fields [47].Examples of successful applications include the exploration of delicate deep-sea fauna, performing minimally invasive procedures such as endoscopies and surgeries, and the delivery of targeted drug therapies.Furthermore, combining soft and rigid robots provides advanced object manipulation skills that can benefit various industrial environments [46].Soft robots leverage several different actuation mechanisms [46], from which the most common ones are the following.
1) Fluidic: These robots use pressurized fluid in an inflatable compartment designed to deform into a specific shape when inflated.This category includes devices, such as PAMs, Pneu-nets, or McKibben Muscles.2) Thermal: In these robots, the use of SMAs or shape memory polymers allows the robot to revert to its original shape when the temperature exceeds a specific threshold [48].3) Electrostatic and electrochemical: Electrostatic soft robots use a high-voltage current applied to elastic electrodes (e.g., conductive ink) surrounding a stretchable dielectric membrane.The electric attraction deforms the dielectric material, creating a DEA [49].If the dielectric membrane is replaced with a shell containing a dielectric fluid, such as oil, this creates a hydraulically amplified self-healing elastomer [50].On the other hand, electrochemical actuation, as found in IPMC actuators, replaces the dielectric membrane with an ion-conductive polyelectrolyte, which deforms and creates motion when subjected to voltages of around 5 V in an aqueous medium [46].In light of this versatility and adaptability, this review aims to explore academic literature that has employed soft robots in educational settings.These soft robots are viewed as potential replacements or alternatives to the traditional rigid robots commonly used in educational robotics.

III. METHODOLOGY
This SLR follows the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method [51].The procedure is divided into four stages: formulating research questions (RQs); devising a search strategy; performing screening,; and data synthesis.Fig. 2 depicts the flowchart of the search and screening process.

A. Research Questions (RQs)
The RQs aim to guide our study toward understanding the role and impact of soft robotics in education, specifically within bioinspiration.These RQs allow us to extract relevant data from the existing literature and create a structured dataset for thorough Authorized licensed use limited to the terms of the applicable license agreement with IEEE.Restrictions apply.

TABLE I RESEARCH QUESTIONS
analysis.We have formulated these questions based on the need to explore the position, impact, incorporation, and technical choices of soft robotics in education.The resulting RQs and their potential answers are detailed in Table I.
In our review, it is essential to determine the position of soft robotics in education within the broader domain of bioinspiration.This understanding is formulated with the first RQ, which allows for the classification and quantification of the relevance of soft robotics in education and related bioinspired fields.If a single record covers multiple areas, including soft robotics, it will be categorized under soft robotics.If different areas are addressed, the dominant one will be considered.
Following this, we examine the impact and evolution of soft robotics in education as represented in scientific literature.For this purpose, we frame two RQs encompassing the keywords and temporal and geographical contexts.RQ2 aims to provide an overview of the related concepts associated with using soft robotics in education.To achieve this, the keywords of each of the review studies were surveyed.RQ3 explores where and when soft robotics has been incorporated into education.The aforementioned is done to show the trend in applying soft robotics in education and in which countries its use has been tested.
To understand the educational levels targeted to employ soft robots, RQ4 was formulated.We anticipate explicit distinctions such as K-12 or higher education.However, the category general public (i.e., K-12, higher education, and research students) is included to capture instances where the technology targets a broader audience rather than a specific educational level.On the other hand, RQ5 is established to understand the main approaches of the studies that have been published, determining whether they were focused on testing the technology or developing soft robots to be used for educational purposes.RQ6 examines the types of SAMs commonly used and identified in the literature.The answers to these questions help us to understand the underlying choices and limitations considered across a range of SAMs.
RQ7 aims to elucidate the activities utilized in implementing soft robotics in education.Examples of these activities include programming the robots or undertaking mechanical design to construct the robot.Another facet that has been scrutinized is the method of validating the usage of soft robotics in an educational setting, that is, how the impact and perception of this technology have been confirmed.To address this, RQ8 was formulated.Finally, RQ9 strives to highlight the existing gaps in using soft robotics in education and identify potential future research directions.These directions could be proposed based on the advantages and disadvantages observed or could emerge from existing research.This final question is crucial for outlining a roadmap for the development and application of soft robotics in education and determining the best ways to leverage this technology to enrich learning experiences.

B. Search Strategy
A comprehensive search strategy was designed and executed to identify pertinent records that could provide answers to the RQs.This strategy was applied to two major databases: SCOPUS and Web of Science (WoS).These databases were preferred due to their broad coverage of peer-reviewed publications across various academic fields, including engineering and education, and our accessibility to them.Moreover, SCOPUS and WoS were selected to reduce the bias related to the quality and reporting mechanism of the reviewed studies by collecting data from reputed databases similar to the study of Anwar et al. [41].
To gain a holistic view of the research conducted in our area of interest, the search included diverse types of documents: articles, conference papers, and reviews.The aforementioned is done to uncover any existing literature review or pioneering work.
The keywords for the search were carefully chosen to capture a wide range of topics under the umbrella of education and robotics, specifically within the bioinspired domain.These keywords were applied to the titles, abstracts, and keywords sections Authorized licensed use limited to the terms of the applicable license agreement with IEEE.Restrictions apply.

TABLE II SEARCH QUERIES
of potential documents.The first group of terms sought out articles with a clear educational aspect, covering any education, teaching, or learning.The term "robot*" ensured that the articles pertained to robotics.The third group of keywords was aimed at identifying articles specifically within the bioinspired or soft robotics domain.Finally, the search criteria ensured that the articles covered educational practices at various levels, from K-12 schools to university studies.We specifically targeted articles that touched upon broader fields of science, technology, or engineering and required the appearance of the terms "course" or "student" to narrow down the results and avoid irrelevant entries.
The final search queries applied to SCOPUS and WoS databases, with their corresponding equivalent query syntax, are detailed in Table II.In the search query for the SCOPUS database, "TITLE-ABS-KEY" performs a search where the terms appear in the title, keywords, or abstract, "LIMIT-TO" limits the search to the given input, "DOCTYPE" stands for document type, "cp" stands for Conference Paper, "ar" stands for Article, and "re" stands for Review.The Boolean operator AND encounters just those documents that contain all the terms, while the Boolean operator OR encounters documents with any of the terms.For the search query of the WoS database, "TS" performs a search where the terms appear in the title, keywords, or abstract, and "DT" stands for the document type.On the other hand, the Boolean operator's function is similar to that of the SCOPUS database.The asterisk is used on characteristic word stems for recovering term variations.The search was carried out in November 2022 and covered all records up to that date.

C. Screening
We identified 149 records from the databases, of which 54 were excluded due to duplication.In particular, 53 records from WoS were already present in SCOPUS, and one record appeared twice in SCOPUS.After this, a further screening was conducted to exclude irrelevant publications or those not meeting the analysis criteria.This process comprised two steps: initial screening based on titles, abstracts, and keywords, followed by a thorough full-text examination.During the first screening phase, 26 documents were excluded.Although the search terms were designed to yield only relevant results, these publications did not clearly or explicitly focus on education, even though they all related to bioinspiration or soft robotics.After full-text analysis, no articles were excluded based on scope, but seven were inaccessible.However, two non-English articles were identified, one in Chinese and the other in Japanese.Since language was a predefined exclusion criterion, 60 articles are included in this study, with 24 focusing on soft robotics.The inclusion and exclusion criteria during both steps are detailed in Table III.Records focusing on bioinspiration were utilized to answer RQ1, whereas those focusing on soft robotics were used to address RQ2-RQ9.

D. Synthesis
After the data screening, we compiled and interpreted the results descriptively and graphically.The use of VOS Viewer, MATLAB, and Tableau facilitated the creation of intuitive graphical representations to help understand the data.Our analysis process with these tools involved examining trends, correlations, and patterns in the data following our RQs.This process aids in formulating comprehensive insights, which are then detailed in the subsequent sections.

IV. RESULTS
This section provides insights from examining the 60 selected candidate studies focused on bioinspiration and 24 candidate articles focused on using soft robotics in education.
The second most prevalent category is soft robotics, whose unique attributes are elaborated upon in the fifth and sixth RQs (RQ5 and RQ6).Bioinspired control forms the third domain, being closely associated with neuroscience, and encompasses eight  papers whose primary focus is the formulation of neuromechanical control strategies.Examples include the application of swarm optimization [89], [90], [91], neural networks [92], [93], [94], Braitenberg vehicle behavior [95], and genetic algorithms [96].
A single paper about the domain of HRI in education, exploring biomimetics through the actions of an android that displays human-like behaviors [97].

B. Main Keywords
To answer RQ2 ("What are the main keywords?"),we analyzed the screened 24 records presented in Fig. 2 focused exclusively on soft robotics in education.This section and those that follow focus on soft robotics, constituting 24 of the 60 records surveyed in this study.The word cloud in Fig. 4 collates the index keywords to address RQ2.Notably, the keywords demonstrate soft robotics in education's interdisciplinary essence, encapsulating materials science, electronics, robotics, engineering design, computing, and education.Even though bioinspiration and educational robotics form the bedrock of this study, they do not emerge as the most frequently appearing terms.Nevertheless, related concepts, such as biomimetics and engineering education, surface consistently.Most keywords can be categorized into four sections: education and outreach; design; aterials and fabrication; and electronics, computing, and actuation.
The "education and outreach" category covers terms pertinent to pedagogical methods, stages of education, and learning subjects.This includes keywords such as educational robotics, engineering education, educational resources, K-12, students, high school teachers, curricula, STEM, teaching module, computational thinking, career interest, student motivation, gender disparities, and women's role in engineering.
The "design" category encompasses both an engineering essence and the influence of biomimicry.It comprises keywords such as product design, machine design, anthropomorphic models, biomimetic robotic fish, design-based learning, design-based research, design competitions, design experience, and design variations.
The "materials and fabrication" category pictures the technologies employed, especially those emulating diverse biological systems.This leads to the creation of robots exhibiting more organic, softer, and life-like movements.Keywords in this category include 3-D Printers, biomaterials, biocompatibility, biomimetic materials, composite materials, polymers, silicone, elastic properties, biodegradation, foam, compliant systems, and smart textiles.
Finally, the 'electronics, computing, and actuation" category includes terms critical to the system's mobility, control, and functionality.They encompass Arduino, pneumatics, end effectors, EAPs, IPMCs, grippers, control systems, data collection, data technologies, embedded systems, high-level languages, open-source projects, open-source software, and software toolkits.
We utilized the VOS viewer software to elucidate the relationships among different keywords.This software facilitated the creation of a co-occurrence network of keywords, including those that appeared a minimum of seven times.This network is depicted in Fig. 5, highlighting the temporal emergence of each keyword.The co-occurrence network considered keywords that appeared at least seven times.The years displayed in Fig. 5 only covered from 2015 to 2019, since within those years, the displayed terms appeared a minimum of seven times.That is why all the years considered in this review (i.e., from 2006 to 2022) are not shown in Fig. 5.The significance of robotics, engineering education, and students is consistently underscored.A historical examination of keyword usage reveals that terms such as robots, biomimetics, bioinspired robots, and education were frequently employed in academic publications circa 2015.A key observation is that "robots" and "education" were initially used as separate keywords.However, the term "educational robotics" has gradually emerged as a pivotal concept in contemporary literature.
Authorized licensed use limited to the terms of the applicable license agreement with IEEE.Restrictions apply.Moreover, some keyword relationships appeared weaker than expected.For instance, despite "soft robotics" being linked with "engineering education," it does not exhibit a strong association with "biomimetics" or "bioinspired robots," concepts that are fundamentally integral to the field of soft robotics.Finally, "design," whether referred to directly or as "machine design," which may indicate a more specific evolution of the term, consistently exhibits strong connections with all other keywords, thereby confirming its central role within this domain.

C. Geographical and Temporal Distribution of Research in Soft Robotics Education
To address RQ3 ("When was the research published, and what is the geographical location of the authors ?"), an examination of the temporal and geographical distribution of the compiled records relevant to soft robotics in education was conducted.The origin of research in this field can be traced back to 2006, as evidenced by a singular article published by Tan et al. [98].This initial article succeeded in [54], as illustrated in Fig. 6.Following a hiatus in research activity, a revival was noticed in 2013, and since then, there has been a consistent annual publication of at least one article.Recent years have witnessed a constant and sometimes upward trend in research output, reaching its pinnacle in 2017.Notably, this apex corresponded with the publication of three articles from the same research group [61], [99], [100].
Turning to the geographical spread of the research, Fig. 6 highlights the conspicuous predominance of the United States, which is credited with 19 out of the total 24 records.This overwhelming presence overshadows other countries, even against the flurry of publications in 2017.The remaining articles originate from four separate nations: Denmark [63], Germany [57], Taiwan [65], and Switzerland [55], [56].These publications indicate a geographical concentration of research within Central Europe and the United States, thus identifying the principal activity centers in Soft Robotics Education.

E. Approach Type
Regarding the pedagogical approach that soft robotics has adopted in education, in response to RQ5 ("What is the approach the technology has towards education?"), the 24 screened articles were analyzed and grouped into R&D and hands-on approaches.Fig. 8 illustrates the distribution of both approaches according to the 24 sampled studies.First, hands-on learning, accounting for 50% of the 24 records [52], [53], [54], [55], [56], [59], [63], [64], [65], [100], [101], [103], primarily involves  situations where students or participants are introduced to the subject, and subsequently, asked to design and construct a robot using SAMs.The second category, R&D, comprises 37.5% [57], [58], [60], [98], [99], [102], [104], [105], [106], and pertains to the development of a fabrication process, material, or system that can be later applied to soft robotics in education.The category of Didactic tools encompasses two unique cases (8.3%).The study by [9] is centered on developing a platform, SeaPerch II, to introduce soft robotics to younger students, emphasizing marine applications.In the second case, SAMs are employed to create an instructional platform to teach a distinct subject, precisely respiratory physiology [107].Finally, the remaining category includes a single study in which the authors summarize their findings within soft robotics in education [61].

F. Soft Robotics Technology
Concerning RQ6 ("What type of soft artificial muscle is being employed?"),we collected the utilized SAM in each of the 24 selected studies.Fig. 9 illustrates that, to date, only five types have been utilized: PAMs, IPMCs, textiles, paper-based systems, and elastic support elements.Among these, PAMs dominate with 58.3% of the records [52], [55], [58], [59], [60], [61], [64], [99], [100], [101], [102], [103], [105], [107].Textiles [63], [65], IPMCs focused on robotic fish [54], [98], and elastic support elements [53], [56] each appear twice in the 24 records.In the latter case, it was observed that materials such as silicone rubber or even glue are utilized to create compliant structures without necessarily creating PAMs as traditionally applied.However, in this context, they do not strictly fall under SAMs compared to the rest.Paper actuation is explored in a single record where the actuation resulting from water application is used to drive a system [57].Three articles do not focus on a singular technology but explore multiple types from those already mentioned by working on new platforms or toolkits [9], [104], [106].

G. Soft Robotics Activities in Education
Regarding RQ7 ("What kind of educational activities have been carried out with soft robotics?"),we analyzed the activities the authors conducted to test the developed soft robots.For this RQ and RQ8, particular emphasis is placed on a hands-on approach covered by 50% of the selected articles.Of the 12 articles analyzed, ten required participants to design their own SAM or structure based on given specifications [53], [54], [55], [56], [59], [63], [65], [100], [101], [103].For instance, there is a case in which the locomotion of a robot must be improved by modifying the morphology, dynamics, and gait with the use of soft components [56].However, it is crucial to note that all the articles necessitate participant involvement in fabricating the actuator or structure.Consequently, two articles concentrate solely on fabricating a predefined system [52], [64].Particularly in the case of PAMs, the utilization of silicone rubber materials such as EcoFlex is widespread.In addition, two articles encompassing both design and fabrication extend the scope of the SAM development process by requiring students to implement the activation code for their SAM [63], [65].

H. Validation Methods for Soft Robotics in Education
Regarding RQ8 ("What has been the main methodology or validation procedure to evaluate the effect of soft robotics in education?").In this context, the hands-on approach necessitates continuous monitoring of participants as they engage in activities.This highlights the imperative to identify alternative methods to evaluate their impact.Various researchers have proposed assessing the influence of soft robotics on students by scrutinizing their perceptions.Typically, this assessment is conducted using instruments, such as surveys, questionnaires, or tests, which students complete postinteraction with the soft robots or soft robotic platforms [52], [53], [56], [59], [64], [65], [100], [101], [103].In line with this, many studies advocate using pre-and postactivity surveys to ascertain students' perceptions after interacting with soft robotics.One particularly innovative strategy that has been employed for this purpose is the adaptation of the draw-a-scientist test into two distinct formats: the draw-an-engineer test (DAET) and the draw a robot task (DART) survey [52], [53].These modifications allow for a more in-depth analysis of students' engagement and creativity, even incorporating considerations of gender perspectives [108].
In instances where surveys were not employed as the primary means of evaluation, an alternative method has been used to measure the influence of soft robotics on the development of engineering competencies.Instead of assessing participants' perceptions, this approach focuses on gauging the performance and effectiveness of the proposed soft robotics prototypes [54], [55].This assessment is achieved by conducting task-based evaluations where students utilize soft robots for problem solving, providing a direct measure of learning outcomes and skills development.These studies highlight the multifaceted nature of educational assessments and emphasize the potential of soft robotics as innovative educational tools in the engineering domain.
Notably, in one particular study [63], observational assessments were the sole method employed to validate the impact.Despite this, the study's key insights and findings were meticulously presented.Consequently, these multifaceted methods collectively contribute to a comprehensive understanding of the role and efficacy of soft robotics in education.

I. Open Questions and Future Work Considering the Identified Advantages and Disadvantages
To answer RQ9 ("Considering the identified advantages and disadvantages, what open questions and future areas of research exist related to the use of soft robotics in education?"),we examined the conclusions and future work proposed by the 24 articles under consideration.Our review revealed that recent studies have concentrated their future research on two primary aspects.The first aspect involves an ongoing effort to refine the proposed soft robotic platform designed for educational use [9], [54], [55], [56], [58], [60], [64], [98], [104], [107].This focus stems from the observation that the simplicity and accessibility of the current methodology or platforms invite further investigation into how soft robots can be enhanced or improved in their design and features to better serve as educational tools.
The second area of interest focuses on the ongoing validation of the proposed soft robotics platform or soft robot, in association with various student groups [52], [53], [56], [59], [61], [65], [99], [100], [101], [103].The main objective of these studies is to refine approaches to measure the efficacy of soft robots in pedagogical contexts, targeting a broad audience spectrum that includes K-12 students as well as undergraduate and postgraduate learners.These studies validate the real-world application of soft robotics in education, emphasizing these tools' hands-on, engaging, and flexible nature.However, it is essential to note that there have been instances where the authors of certain studies have not indicated any future directions for their research [57], [63], [102], [105].Furthermore, we found no explicit limitations or drawbacks associated with using soft robotics in education in most of the reviewed articles.Nevertheless, in [63], it is argued that rigid robotics could be seen as more reconfigurable and more straightforward to operate and debug than their soft counterparts.Despite this, soft robotics's unique advantages and critical benefits are underscored.

V. DISCUSSION
The RQs of the present SLR aim to provide an overview of fields of bioinspiration covered within education, the main concepts associated with soft robotics in education, when and where studies have been developed to study the use of soft robotics in education, the educational levels in which soft robots have been utilized, the approaches toward education, the SAMs employed, the educational activities design to use them, and their validation procedures.The review considered 24 peer-reviewed journal and conference articles published between the years 2006 to 2022 to provide evidence of the use of soft robotics in education.This literature differs from other reviews on educational robotics since they mainly focus on conventional rigid robots instead of discussing the use of soft robotics in education.The screened articles were grouped and analyzed to respond to each RQ presented in Table I.

A. Regarding the First RQ
Based on the results presented in Fig. 2, it is possible to observe that only 95 records passed to the first screening process, a low value if we consider that the search query included terms that sought to be broad while being selective at the same time (see Table II).When we narrow the search terms to only include education and only keep robot* AND (bio-insp* OR bioinsp* OR biomim* OR "artificial muscle" OR "soft robot*"), the number of records in SCOPUS surpasses 20 000.With this volume of results, bioinspired robotic systems in education account for less than 0.5% of the total research.It is true that the inclusion and exclusion criteria limit the results and that some modifications in the search query could show a larger proportion of results regarding education.However, the percentage could rarely increase to more than 2%.An increase in research on bioinspired robotics, perhaps not directly focused on education, but considering it, is required and can be highly beneficial to broaden the mindset of future engineers and scientists.
Locomotion, bioinspired control, and soft robotics represent major areas of bioinspired robotics in education.Fig. 3 shows that locomotion has the most significant percentage by a slight margin, although it mainly focuses on legged robots and their biomechanics.This result can be expected since locomotion has been one of the first and most fundamental areas addressed by research on robotics, along with the fact that it is even one of the unique bioinspired aspects covered by educational robots or platforms, from which LEGO Mindstorms stands out as an example [17].Incorporating more diverse forms of bioinspired mechanisms and areas, such as bioinspired sensing or actuation, can broaden students' interest in the field.
Although bioinspired control is closely related to locomotion (e.g., gait generation), it has not been extensively explored in bioinspired robotics in education.Research in this area is closely related to neuroscience, specifically in neuromechanics, which is less than half that of locomotion, as shown in Fig. 3.Moreover, it concentrates on using neural networks, without explicitly applying central pattern generators, a tool widely employed to perform locomotion [26].Developing dedicated tools and processes to teach bioinspired control can benefit higher education.
Despite being relatively recent in its conception, soft robotics has a vital role in bioinspired robotics in education.This can be noted in Fig. 3 by being in a closed second place with 40%, just 5% below locomotion, and three times the preponderance of bioinspired control.The fact that many soft robotics actuator paradigms are low cost and relatively simple to manufacture [3] favors their direct and natural implementation in education.A further impulse in soft robotics in education could make this field prominent and widely implemented when introducing robotics to science and engineering students.

B. Regarding the Second RQ
The keyword analysis reveals the remarkable transdisciplinary nature of soft robotics, spanning science, technology, education, and even social aspects.Regarding science and technology, Soft robotics in education covers various topics, such as materials, electronics, actuators, fabrication methods, and biomimetics.The data also emphasize design-related terms and "engineering education," which highlight both theoretical and practical skills crucial for comprehensive engineering training.Even though "biology" or "bioinspiration" are not explicitly listed as keywords, the inclusion of a variety of biology-related terms bridges this gap, suggesting areas that roboticists can explore to enhance and propose innovative designs and solutions.
Another notable aspect, discernible from the title, abstract, and frequent mention in keywords, is promoting STEM interest among young and female students.This observation underscores the fundamental role of soft robotics in education.If integrated at early stages of education, soft robotics in education could enhance scientific and engineering skills and address persisting gender disparities.This potential growth could raise the profile of soft robotics in education in future curricula, particularly in courses that emphasize hands-on workshops imparting practical skills.This development should be considered by educators and policymakers who are aiming to revolutionize the teaching of technology.Introducing and expanding such courses can stimulate students' curiosity and foster their interest in this dynamic field.
Moreover, the co-occurrence analysis presented in Fig. 5 suggests that soft robotics can provide a powerful impetus for a constructivist and constructionism approach to engineering education due to its link with the term educational robots since, as mentioned in Section II of this study, robotics in education follows the constructivism and constructionism theories of learning [11].While the co-occurrence network in Fig. 5 does not illustrate a direct link between soft robotics and teaching, it is intricately connected to students.Interestingly, these keywords have become increasingly prominent in recent years, contrasting with traditional terms, such as "robots" and "teaching."This student-centered approach (due to its foundations in constructivism and constructionism), paired with active, experiential learning, supports the escalating trend of "learning by doing," a concept vigorously promoted by educational robotics as discussed by Schina et al. [12].With this in mind, soft robotics in education could be vital in accelerating the shift from traditional pedagogical methods to more engaging, interactive, and student-centered educational approaches.Doing so could empower learners to develop essential skills, such as critical thinking, problem-solving, and creativity, which are invaluable in the ever-evolving engineering field [109].

C. Regarding the Third RQ
Soft robotics as a field of study in education is relatively new, and it has mainly been developed in the United States.The first article on soft robotics in education was published 15 years ago, as shown in Fig. 6.There was a lack of literature on this topic for five years until 2013, which marked the beginning of a new era, with an average of fewer than three results.The increase in publications in 2021 could indicate a growing trend in soft robotics in education.However, despite this increase in publications, only a few studies have explored the use of soft robotics for educational purposes.Moreover, the presence of the United States with constant research throughout all years where there have been publications can be attributed to two reasons: the first one is its prolific scientific production that certainly touches a vast number of fields, and the second is the existence of initiatives from universities, such as Purdue [59], [61], [99], [100], [101], or Harvard [104], which look to expand soft robotics in education.The most evident example is the Soft Robotics Toolkit initiative.This platform invites students and makers to explore, design, and create this robotic system in terms of actuation and sensing [110].It is important to note that all research covered comes from highly developed countries, but the experiences and know-how provided could set the stage for expansion to broader regions of the world.
On the other hand, it is worth noting that the amount of research in which soft robots have been used for educational purposes is low compared to that of rigid robots.The aforementioned can be observed in the number of studies and reviews that have focused on the use of rigid robotics as in [28], [29], and [30].Furthermore, recent studies have developed educational platforms composed primarily of rigid robots [20], [21], [23], [111], [112].The aforementioned suggests that while robotics has been an element of interest in recent years, soft robotics are underused as an educational platform.

D. Regarding the Fourth RQ
Based on the results presented in Fig. 7, soft robots have been primarily used in K-12 students; nevertheless, their use in higher educational levels has been underexplored.While it is beneficial to continue encouraging soft robotics in education implementation in K-12 and high schools to foster STEM interest [9], undergraduate education must also develop its unique methodologies and platforms to solidify further research in graduate degrees [54].The aforementioned highlights the need for developing activities in which undergraduate and graduate students could use soft robotics and, consequently, studies that verify its implementation and higher educational levels.Another aspect to point out is that since soft robotics is mainly used for the K-12 level, the activities that could be implemented could be limited due to the lack of theoretical background that K-12 students have related to mechanical design, material properties, embedded systems, and programming.Thus, studies can be developed to test soft robotics at undergraduate and graduate Authorized licensed use limited to the terms of the applicable license agreement with IEEE.Restrictions apply.

TABLE IV
RECORDS COVERING SOFT ROBOTICS levels to design activities for more complex topics such as control engineering, artificial intelligence, embedded systems design, and physical and mathematical modeling [113], [114], [115].In addition, this lack of implementation of soft robotics at higher educational levels is a gap also presented by rigid robots where most of their use is for children or K-12 students [32], in which few works have explored the use of robotics tools at higher educational levels [21], [23].Hence, the development of studies that employed educational robotics at undergraduate and graduate levels is an aspect that could contribute to the overall field of educational robotics.

E. Regarding the Fifth RQ
Fig. 8 indicates that only 37.5% of the records pertain to R&D, significantly lower than the 50% attributed to hands-on activities.Moreover, only a handful of articles focus on developing novel methods, materials, or systems that address the engineering aspects of soft robots use in education, which can also be observed in Table IV.Progress in this area could generate a platform, potentially transforming it into the LEGO equivalent in soft robotics.In this regard, an open-access platform for soft robotics similar to the Soft Robotics Toolkit presented by Holland et al. [104] is an area that could be tackled by generating R&D studies, which could increase the use of soft robotics in education by performing hands-on approaches.It is noteworthy that SeaPerch II, is already leveraging the advantages of soft robotics in education as a dedicated didactic tool [9].In this regard, it is crucial to continue developing studies that combine both R&D and hands-on approaches since, on the one hand, R&D serves to create a new soft robotics platform.In contrast, hands-on approaches allow testing the platform with students and validating its effectiveness [56], [65], [100], [101].Thus, both approaches cannot be treated exclusively.

F. Regarding the Sixth RQ
Regarding SAMs, PAMs are the most widely used in educational settings.According to Fig. 9, 58.3% of the total soft robotics in education-related records were based on PAMs.This prevalence is mainly due to their straightforward manufacturing and testing process, which does not necessitate specialized materials or tools [116].By using silicone rubber, molds, and a source of pressurized air, students can easily test the behavior of their muscles.Textiles also offer a user-friendly introduction to SAMs, although their application range is constrained due to their limited force and activation frequency.On the other hand, EAPs are primarily limited to IPMCs, which require activation voltages around 5 V, starkly contrasting the kilovolt range for DEAs.IPMCs' need to operate in aqueous environments explains the selection of bioinspired soft robotics based on fish morphology.It is important to note that safety and operating conditions are paramount considerations when designing soft robotics for education and may limit their applications, primarily when intended for close human interaction.

G. Regarding the Seventh RQ
As noted by Yu et al. [24], when traditional rigid robots are utilized for educational purposes, their primary role is in control tasks such as programming.However, rigid robot kits often overlook other vital aspects of robotic design, including mechanical and material properties considerations.
In contrast, as evident from the data presented in Fig. 10, soft robotics has fostered the expansion of educational activities beyond just programming.Fabrication and design of soft robots have become commonplace activities in this field.It is noteworthy, however, that programming activities seem to be less frequently undertaken in the context of soft robotics.This suggests a broader range of educational activities facilitated by soft robotics compared to traditional rigid robots, primarily used for programming.Furthermore, studies such as that of Yu et al. [56] have introduced activities where improvements in robot locomotion are achieved through proposed variations in mechanical designs and material properties, rather than solely through enhancements in programming.Similarly, Zhang et al. [103] proposed using soft robots to enable students to understand how design choices influence system performance.The studies aforementioned highlight the expansive range of activities that can be developed via soft robotics.
Despite this progress, it is crucial to underscore that the symbiotic relationship between mechanical design and programming has not been exhaustively explored as a cross-cutting process.This omission identifies a significant opportunity that would encapsulate the complete process of designing a robotic system, thereby broadening the scope of educational activities and enriching the learning experience.Furthermore, Chen et al. [65] noted that K-12 students find it more challenging to program soft robots than select their materials; this suggests that equal emphasis should be given to fabrication and programming tasks when designing activities via soft robots.

H. Regarding the Eighth RQ
According to the data presented in Fig. 11, surveys emerge as the most commonly employed tool for evaluating the influence of soft robotics and their potential to shift students' viewpoints on a particular subject matter.While surveys are valuable for understanding learners' perceptions and attitudes, they may not necessarily provide objective measures to determine the extent of new knowledge acquired [117], [118].Direct observation, another evaluation method identified in the review, could also suffer from a similar lack of objectivity unless guided by welldefined parameters [118].In addition, conducting observations without a structured evaluation rubric may unintentionally introduce personal bias or subjectivity.This potential bias might compromise the validity of the collected data, underscoring the necessity for structured and standardized observation practices to ensure reliable and accurate results.
However, at higher educational levels, assessing the performance of student-created prototypes provides a more tangible and objective measure of understanding and learning.This form of evaluation allows a more precise assessment of students' comprehension of the subject matter and enables the clear identification of acquired engineering competencies.Such an approach might include assessing the design process, the functionality of the completed prototype, and its resilience under various conditions.
It is essential to highlight that most of the research has been focused on evaluating the students' perceptions rather than directly measuring the learning outcomes that can be accomplished using the technology.For instance, studies that have focused on the use of rigid robots in education have mainly been concerned with evaluating the motivation and learning interest that robotics could have on the students [111].Nevertheless, contrasting the students' learning outcomes with the proposed soft robotics has been underperformed.Hence, future work should be focused on analyzing the potential learning outcomes that soft Authorized licensed use limited to the terms of the applicable license agreement with IEEE.Restrictions apply.robotics could enhance such as those proposed by Bloom's Taxonomy [119].
Given the potential limitations of surveys and observation, combining assessment methods might provide a more holistic understanding of students' learning outcomes.Such a multifaceted approach might involve pairing surveys or interviews with performance evaluations of prototypes and other objective measures.Furthermore, future research could explore the development of more refined, objective measures for assessing the efficacy of soft robotics in education, thereby contributing to a more nuanced understanding of learning in this field.For example, Orlando et al. [120] proposed using a conjunctive knowledge tracing model based on a hidden Markov model in a web-based learning environment to provide teachers with information about student learning progress during a physics class.Similar methodologies could be incorporated into soft robotics in education to evaluate its effectiveness.

I. Regarding the Ninth RQ
The continuing evolution of soft robotics in education necessitates a twofold approach: the consistent refinement of existing platforms and methodologies and a comprehensive evaluation of its educational impact as presented in Section IV-I.On one side of this dichotomy, the simplicity and accessibility of the methods and materials employed in soft robotics present considerable advantages.However, these same features simultaneously raise challenges compared to the reconfigurability and ease of use typically associated with rigid educational robots.This contrast highlights a crucial open question: how can we enhance the reconfigurability and debuggability of soft robotics to meet or even exceed that of their rigid counterparts?It is crucial to note that this added complexity, despite being a challenge, could offer an enriching opportunity for cultivating engineering and scientific skills.Moreover, it may also stimulate further innovation within the field.
On the other side, there is an urgent need to evaluate the effectiveness of these technologies on student outcomes.To this end, it would be advantageous to integrate soft robotics more extensively into actual robotics courses across a broad spectrum of educational levels.This integration should span K-12 to higher education, enabling the perception of and actual learning from these technologies to be rigorously evaluated and validated.Furthermore, the widespread diffusion of this topic could present an excellent opportunity to elevate interest in the area among society, research communities, and government bodies.
This review of prospective work and open questions underscores the dynamic and swiftly evolving nature of the field of soft robotics in education.As researchers continue refining these tools and validating their applications, it is paramount to maintain a balance between innovation and practical effectiveness.The research community must actively address the potential limitations of soft robotics, rather than avoid them.These critical evaluations will prove instrumental in guiding the field's trajectory and establishing its practical relevance in diverse educational contexts.

VI. SUGGESTED APPROACH FOR INTEGRATING SOFT ROBOTICS IN EDUCATION
Drawing upon the findings from our analysis and the lack of soft robots in education, we propose an initial framework for incorporating soft robotics into educational curricula, as illustrated in Fig. 12.The first step involves clearly defining the educational level, the field of study, and the specific learning objectives.It is important to recognize the scope for exploring various concepts associated with science and engineering.Science-related concepts might include biomechanical modeling and materials science, while engineering-focused areas could involve validating existing systems through experimental design or even proposing innovative actuators or robot forms.
Once these learning objectives are established, a specific SAM can be selected based on availability, safety, and cost.For instance, hosting a workshop where students design their systems may be an excellent introduction to the basics of soft robotics.This could potentially involve manufacturing PAMs using silicone rubbers, such as EcoFlex or DragonSkin [121].Advanced educational levels might delve deeper into materials science challenges by implementing IPMCs or SMAs.
The proposed activities should comprise a clear step-by-step tutorial, yet retain enough flexibility to encourage students to experiment with the technology.If the learning objective is design-focused, computer-assisted design and 3-D printing could be indispensable tools for creating molds.Engaging students meaningfully can bolster their confidence and foster innovative problem-solving perspectives.Throughout the activity, the role of the instructor is not merely to explain the new concepts, but also to highlight the challenges and potential areas of improvement that students may encounter firsthand.
Finally, it is crucial to document key experiences, challenges encountered, and potential advice, while also identifying innovative directions.This approach can assist future participants and researchers in pushing the boundaries of soft robotics in education through the creation of novel methodologies and learning technologies.

VII. CONCLUSION
This work presented an SLR focused on using soft robotics in education.Thanks to its inherently multidisciplinary character, bioinspired robotics holds significant potential as an influential educational tool.It can introduce a wide array of scientific and engineering concepts to future learners.Nevertheless, this SRL divulged insufficient research on incorporating these progressive technologies into education.The study delved into three core areas: locomotion, bioinspired control, and soft robotics.These subjects could benefit greatly from an expanded focus, embracing broader domains, such as bioinspired sensing and biomaterials.Developing a comprehensive educational platform that integrates these facets could signal a promising path forward for educational advancements in this field.The findings from this review offer a valuable point of departure for educational researchers and instructional designers.
Focusing explicitly on soft robotics in education, the review points to limited research regarding the volume of publications and geographic coverage.However, soft robotics in education is burgeoning with potential for development, marking it as an emerging discipline with untapped opportunities for educators and policymakers alike.The simplicity and cost-effectiveness of certain soft robotics in education technologies, such as PAMs, make the field ripe for global adoption.This inclusive research approach could help bridge gaps in regions traditionally sidelined in technological research and educational innovation due to financial constraints.
Every educational level can gain from soft robotics in education, but considerable room for expansion remains, particularly at higher education levels that directly shape future researchers.While various soft robotics in education approaches offer unique benefits, it is essential to emphasize soft robotics in education's role as a hands-on educational tool to inspire practical, experiential learning.Moreover, R&D of innovative methods or technologies to advance SAMs represents a tremendous opportunity.Such research should prioritize safety, a vital aspect of these technological advancements.Our review observed that while various SAMs exist, research primarily centers on those presenting minimal user risk.With adequate safety measures to mitigate inherent risks such as high voltages associated with DEAs, their application in education or other human-centric contexts will be unrestricted.
Refining existing methodologies and platforms in soft robotics education presents an opportunity to develop solutions that can compete with traditional rigid educational robots' ease of use and versatility.This process could amplify the skillset related to soft robotics activities, including design, fabrication, and programming tasks.Moreover, it is vital to verify soft robotics's effectiveness in education consistently.Combining subjective metrics like students' perceptions with objective evaluations of a prototype's performance using predefined criteria is crucial for comprehensively assessing the field's educational impact.
Exploring a unified educational approach that concurrently addresses mechanical design and programming in soft robotics should be a key focus of future research.Additional studies might also consider developing innovative, objective evaluation methods to determine the effectiveness of soft robotics in education, thus enriching our understanding of learning in this emerging field.As soft robotics evolves, an important research area will be enhancing these systems' adaptability and fault detection capabilities to match their rigid counterparts.Continuous assessment of these technologies' impact on student outcomes across different educational levels is also a crucial future direction.The research community needs to balance the drive for innovation with the need for practical effectiveness as the field continues to grow.Researchers should thoughtfully consider potential challenges while striving to harness soft robotics's vast educational potential fully.
In conclusion, this study lays a solid foundation for future SLRs in soft robotics in education and offers a fundamental blueprint for incorporating soft robotics into education.We underscore the importance of starting with hands-on activities geared toward both scientific and engineering exploration.Educators could use this framework to introduce this emerging technological trend to future scientists and engineers.Although step-by-step tutorials can ease the initial learning process, fostering an environment that encourages creative thinking is equally essential.This approach advocates for a student-centered learning environment and fuels the generation of innovative ideas and designs.Documenting and critically evaluating these learning experiences can help shape novel pedagogical methodologies and lay the groundwork for future soft robotics in education platforms.

Fig. 1 .
Fig. 1.General overview and comparison of rigid and soft robotics within educational robotics.

Fig. 6 .
Fig. 6.Number of publications by year and country.

Fig. 8 .
Fig. 8. Type of approach employed in Soft Robotics in Education.

Fig. 9 .
Fig. 9. Types of SAMs technology used in soft robotics in education.

Fig. 10 .
Fig. 10.Types of hands-on activities for soft robotics in education.

Fig. 11 .
Fig. 11.Validation methods for hands-on activities in Soft Robotics in Education.

Fig. 12 .
Fig. 12. Recommended steps to implement soft robotics in education.