Advancing Mathematical Learning in Children and Adolescents with Autism Spectrum Disorder through Serious Games and Educational Robotics: A Mini Review

Fabrizio Stasolla ^1,*, Enza Curcio ¹, Antonio Zullo ², Mariacarla Di Gioia ², Anna Passaro ¹

1. University Giustino Fortunato of Benevento, Benevento, Italy

2. Universitas Mercatorum of Rome, Rome, Italy

* Correspondence: Fabrizio Stasolla

Academic Editor: Wagner Ferreira dos Santos

Received: November 04, 2025 | Accepted: February 04, 2026 | Published: February 10, 2026

OBM Neurobiology 2026, Volume 10, Issue 1, doi:10.21926/obm.neurobiol.2601323

Recommended citation: Stasolla F, Curcio E, Zullo A, Di Gioia M, Passaro A. Advancing Mathematical Learning in Children and Adolescents with Autism Spectrum Disorder through Serious Games and Educational Robotics: A Mini Review. OBM Neurobiology 2026; 10(1): 323; doi:10.21926/obm.neurobiol.2601323.

© 2026 by the authors. This is an open access article distributed under the conditions of the Creative Commons by Attribution License, which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is correctly cited.

1. Introduction and Theoretical Background

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by differences in social communication, restricted interests, and patterns of repetitive behavior [1]. Beyond these core features, many children and adolescents with ASD display distinctive cognitive and sensory profiles that shape how they perceive, process, and respond to information. Difficulties in executive functioning—such as working memory, cognitive flexibility, and inhibitory control—often coexist with strengths in visual processing and systematic thinking [2]. These characteristics influence how learners with ASD approach academic domains, including mathematics, which frequently becomes a source of frustration and anxiety when instructional methods rely heavily on abstract or language-based explanations [3].

Mathematics represents a multifaceted challenge for many children and adolescents with ASD [4]. Empirical evidence shows that differences in Executive Functioning (EF)—including working memory, inhibition, and cognitive flexibility—can negatively affect the acquisition of numerical concepts, problem-solving strategies, and abstract reasoning. Moreover, difficulties with linguistic and symbolic processing may limit comprehension of traditional instruction, which often relies on verbal explanations and rote repetition. Consequently, mathematics is usually perceived by learners with ASD as anxiety-inducing, marked by low engagement and reduced self-efficacy [5].

Despite the central role of mathematical competence for academic achievement and everyday functioning, research on technology-enhanced mathematics education for learners with ASD remains fragmented. Existing reviews have primarily examined Serious Games, robotics, or digital technologies separately, often focusing on social communication, emotional regulation, or general STEM engagement rather than mathematics-specific learning outcomes. As a result, there is currently a lack of integrative syntheses that explicitly address how interactive digital and embodied technologies support mathematical learning processes in ASD, limiting the translation of research findings into coherent educational and therapeutic practices.

Digital and embodied technologies, such as Serious Games (SG) and Educational Robotics (ER), introduce alternative pedagogical pathways that emphasize interactivity, sensory engagement, and intrinsic motivation. Recent international reviews have highlighted the growing role of AI-enhanced ICT, educational robotics, and adaptive digital environments in supporting personalized learning across diverse educational contexts, including special needs education [6]. SG situates mathematical tasks within immersive digital environments where learners can manipulate quantities, visualize relations, and receive immediate, adaptive feedback. ER, through the tangible programming and manipulation of robotic devices, connects mathematical reasoning with embodied sensory experience. Programming a robot to move a specified distance, rotate by a specific angle, or execute sequential instructions allows learners to concretize abstract concepts through direct action and perception [7].

SG and ER are examined together in this Mini Review because they share complementary pedagogical mechanisms grounded in embodied interaction, adaptive feedback, and executive functioning support. Previous analyses have demonstrated that game-based digital environments can enhance motivation and engagement in children with ASD, while robotic platforms foster hands-on problem-solving, sequencing, and spatial reasoning. However, these technologies are rarely considered within a unified analytical framework, despite their shared potential to externalize abstract mathematical concepts and scaffold cognitive processes essential for mathematical learning. Integrating SG and ER within a single review, therefore, enables a more comprehensive understanding of how digital and physical interactivity jointly contribute to mathematical cognition in ASD.

These approaches align with the theory of embodied cognition, which posits that knowledge emerges from sensorimotor experience rather than detached symbolic representation. This perspective is particularly relevant to ASD, where sensory and motor channels often support learning more effectively than purely verbal approaches [8]. By externalizing reasoning into visible and tangible actions, SG and ER transform mathematical abstraction into accessible, multisensory processes that match the preferences of learners with ASD for visual, structured, and predictable contexts [9].

At the same time, SG and ER embody the principles of neurodiversity, valuing cognitive difference as a resource rather than a deficit. Their rule-based, consistent, and feedback-rich designs align with the strengths of many learners with ASD—such as attention to detail and systematic thinking—while adaptive features promote autonomy and reduce frustration. The motivational dimension is equally important: SG and ER provide clear goals, safe spaces for experimentation, and opportunities for incremental success, helping to mitigate math-related anxiety [10]. When used collaboratively, such as in cooperative robot programming or multiplayer math challenges, they also support communication, turn-taking, and shared problem-solving in structured, low-stress environments [11].

From a broader educational perspective, these technologies represent tools for inclusive and equitable learning, offering multiple, accessible pathways to mathematical understanding. They not only make abstract concepts comprehensible but also restore agency and curiosity in learners who might otherwise disengage [12].

Despite promising outcomes, research on SG and ER for mathematics in ASD remains limited, fragmented, and methodologically diverse. Many studies [13,14,15] focus on engagement or social skills rather than domain-specific learning, leaving the mechanisms of mathematical improvement insufficiently explored. Small samples, brief interventions, and the absence of longitudinal data also limit generalizability [10]. Ethical and practical issues—such as accessibility, teacher preparation, and technological sustainability—are often underexamined. Moreover, research in technology-enhanced learning frequently relies on self-reported measures, which may be influenced by contextual factors such as social desirability or response biases related to survey structure and participation [16,17].

Despite the growing body of research on SG and ER in ASD, empirical studies explicitly targeting mathematics- or numeracy-related outcomes remain limited, highlighting a relevant gap in the current literature. For these reasons, a focused synthesis of existing evidence is warranted. Summarizing and critically examining theoretical and empirical studies on SG and ER in relation to mathematical learning allows for the identification of shared mechanisms, research gaps, and future directions, providing a clearer foundation for inclusive and evidence-based educational practice.

Therefore, this Mini Review synthesizes theoretical and empirical studies exploring how SG and ER can support mathematical learning in ASD. By integrating cognitive, motivational, and ethical perspectives, this study aims to clarify how digital and embodied tools foster understanding, engagement, and empowerment in mathematics among learners with ASD.

2. Method and Scope

This Mini Review adopts a narrative and integrative approach to synthesize theoretical and empirical contributions on the use of SG and ER to support mathematical learning in children and adolescents with ASD. Rather than following a meta-analytic protocol, the Review emphasizes conceptual depth and interdisciplinary interpretation, integrating insights from cognitive psychology, educational technology, and inclusive pedagogy. SG has consistently shown promise in enhancing motivation, engagement, and personalized learning among children with ASD [8], while ER-supported platforms for mathematics and cognitive training provide embodied, data-driven environments that can track behavioral progress and guide individualized instruction [18]. These complementary technologies address both the cognitive and motivational dimensions of mathematical development, supporting their joint analytical consideration.

Dynamically adaptive SG, integrated with social robotics, has been shown to improve attention and engagement in Applied Behavior Analysis-based learning contexts [7]. At the same time, multi-minigame platforms have also facilitated social and academic integration [19]. A formal quality appraisal of the included studies was not conducted, as the primary objective was to interpret how SG and ER facilitate learning processes—such as motivation, engagement, and executive functioning—rather than to quantify the effects of specific interventions [20].

In addition to examining study design and targeted mathematical domains, the analytical framework explicitly incorporated educational and therapeutic implications as core interpretative dimensions. Studies were therefore analyzed with respect to their potential to support inclusive teaching practices, executive functioning, emotional regulation, engagement, and socio-cognitive development across educational and therapeutic contexts. This methodological perspective was adopted to ensure coherence between the review approach and the subsequent Educational and Therapeutic Implications section, which directly builds on these analytical criteria.

2.1 Search Strategy

The literature search was conducted between August and October 2025 across three major databases—Scopus, PubMed, and Web of Science (WOS)—selected for their comprehensive coverage of educational, clinical, and technological research.

The following Boolean search strings were used:

• Scopus: TITLE-ABS-KEY ("serious games") OR TITLE-ABS-KEY ("robotics") AND TITLE-ABS-KEY ("autism") AND TITLE-ABS-KEY ("mathematics").
• PubMed: (((“serious games”) OR (“robotics”)) AND (“autism”)) AND (“mathematics”).
• WOS: combination of the terms “serious games”, “robotics”, “autism”, and “mathematics”.

Searches were limited to peer-reviewed journal articles published in English between 2015 and 2025. Duplicates were removed manually before screening, and only full-text articles were retained for eligibility assessment.

The initial search yielded 447 records—432 from WOS, 10 from Scopus, and 5 from PubMed. Titles and abstracts were screened for relevance, and full-text articles were evaluated to verify inclusion criteria.

2.2 Eligibility Criteria

Studies were included if they met the following criteria:

1. Described or evaluated the use of SG or ER to foster mathematical or logical-reasoning skills in children or adolescents with ASD [18];
2. Reported educational or therapeutic outcomes related to numeracy, sequencing, spatial reasoning, or problem-solving [7];
3. Employed an experimental, quasi-experimental, or design-based research design [20];
4. Included participants aged 6-16 years, representing the developmental stages in which foundational mathematical and executive skills are actively consolidated [19].

Studies were excluded if they:

• Focused exclusively on communication, emotion regulation, or social interaction without mathematical content [21];
• Included participants outside the 6-16 years range;
• Were reviews, theoretical, or conceptual papers without empirical data;
• Targeted neurotypical participants or lacked a confirmed ASD diagnosis.

The 6-16 years age range was chosen to ensure developmental consistency, corresponding to the period of primary and lower secondary education when children begin formal mathematics instruction and when cognitive flexibility, working memory, and inhibitory control—the executive functions crucial for mathematical problem-solving—are still maturing [7].

2.3 Study Selection

After title and abstract screening and removal of duplicates, five empirical studies met all inclusion criteria—four retrieved from Scopus and one from WOS. No studies from PubMed were retained, as most records focused on clinical or neurofeedback interventions rather than on educational mathematics.

All included studies featured participants formally diagnosed with ASD, aged between 6 and 16 years, and targeted numeracy, sequencing, or spatial reasoning using SG or ER. Each study was selected for its clear educational focus, explicit assessment of mathematical outcomes, and methodological transparency.

The small number of eligible studies reflects the emerging and still underexplored nature of this research field.

2.4 Included Studies

The five studies meeting the inclusion criteria are summarized below.

Hutchison et al. [22] investigated an informal after-school ER and coding program designed to enhance STEM engagement in students with ASD aged 6-11 years. Over seven weeks, 12 participants engaged with a variety of physical computing tools (e.g., Sphero Bolt, Ozobot, LEGO Spike, Finch, Makey Makey) and programming platforms (Scratch, OctoStudio) in both unstructured and structured formats. Activities progressed from open exploration to sequenced programming tasks, allowing observation of engagement dynamics over time.

Multilevel analyses [23] revealed that as session frequency increased, the ASD diagnostic markers observed decreased, while engagement significantly improved. These changes were independent of background variables such as ADHD symptoms, challenging behaviors, or sensory sensitivities, suggesting that comorbid traits did not constrain engagement. An inverse correlation between ASD markers and engagement further emphasized the role of interest-driven ER activities in modulating in situ behaviors.

This study was included due to its explicit focus on logical reasoning and sequencing—core components of mathematical cognition—delivered via ER in a population of school-aged learners with ASD. The findings underscore the potential of informal, interest-based STEM interventions to promote cognitive and behavioral adaptability in learners with ASD.

Gkiolnta et al. [24] examined the use of ER to promote STEM learning and social interaction in primary school children with ASD and intellectual disability (ID). The intervention employed the Codey Rocky robot, a block-based programming tool designed for young learners, within an eight-session program conducted in a natural school environment. Activities were structured to enhance coding, problem-solving, and social communication through collaborative robot-programming tasks.

The participant was a minimally verbal girl with ASD and moderate ID, paired with a typically developing classmate to foster triadic interaction. Throughout the sessions, improvements were observed in joint attention, eye contact, turn-taking, and emotional expression, whereas challenging behaviors such as tantrums and stereotyped movements decreased.

This study met the inclusion criteria because it focuses on the use of ER to develop both academic (STEM and coding) and social and behavioral competencies in children with ASD. It highlights the potential of robot-mediated activities to enhance communication and reduce maladaptive behaviors in learners with ASD within inclusive school settings.

Hughes et al. [25] explored the integration of ER and AI to support STEM learning in elementary school students with ASD. The intervention, titled RAISE (Robotics & AI to Improve STEM and Social Skills for Elementary School Students), involved children aged 8-10 years and combined the Dash™ robot with an AI-powered virtual companion Zoobee™. This setup created an interactive learning environment that supported both individual and collaborative engagement. The primary goal was to enhance students’ coding and foundational STEM skills, including mathematical logic, algorithmic thinking, sequencing, and conditional reasoning—competencies directly linked to mathematical cognition and problem-solving. The learning environment incorporated Universal Design for Learning (UDL) principles to accommodate the cognitive and sensory needs of children with ASD, offering clear task structures, immediate feedback, and multimodal interaction. The study met the inclusion criteria as it explicitly focused on developing mathematical reasoning and problem-solving through an ER platform tailored for learners with ASD. It provided a structured, iterative framework that promotes academic, cognitive, and Social-Emotional Learning (SEL) in students with ASD.

Arshad et al. [26] explored the use of ER as an assistive technology (AT) to enhance cognitive abilities and promote meaningful learning experiences in children with ASD. Using the LEGO^® Mindstorms EV3 platform, the authors developed a robot (PvBOT) programmed to teach place value concepts in mathematics. The study adopted a single-case design involving eight children with ASD (aged 10-13 years; mild to moderate level) and six special education teachers. Participants engaged in both traditional and robotic-assisted lessons, followed by standardized assessments. Quantitative results showed significant improvements in cognitive performance after the ER intervention compared with traditional learning (M = 91.25 vs. 61.25; p < 0.05). Qualitative analyses of teacher interviews revealed that ER sessions:

• increased student interest and engagement,
• enhanced attention and focus,
• promoted interaction and communication, and
• created a joyful, motivating classroom environment.

The authors concluded that ER can serve as an effective AT tool to support cognitive learning and socio-communicative engagement among children with ASD. Robots should be seen as complementary aids rather than replacements for teachers, offering repetitive, engaging, and individualized learning experiences. The study acknowledged limitations, including a small sample size, a short intervention duration, and a focus on a single cognitive skill, and suggested that future research should explore longer-term ER interventions and include children with ID and other neurodevelopmental disorders (NDDs).

Ishaq et al. [27] investigated the effectiveness of the Literacy and Numeracy Drive (LND), a mobile-assisted learning application implemented in public primary schools in Punjab, Pakistan, to improve English language and mathematics skills in grade 3 students. The study employed a mixed-methods design, involving 57 teachers and 300 students, including a subgroup of five children with ASD aged 6-8 years, to evaluate the app’s usability, accessibility, content quality, and assessment features.

Findings showed that the LND app had limited educational impact due to poor usability, a non-interactive design, misalignment with the curriculum, and restricted accessibility (e.g., only one tablet per class). Both teachers and students reported low engagement and motivation, citing the lack of animations, phonics, and feedback mechanisms. Teachers recommended incorporating game-based elements, interactive interfaces, and voice-guided activities to enhance the app’s effectiveness and improve student learning outcomes.

This study was included in the Review because it highlights how technology-enhanced learning tools, when poorly designed, may fail to support language acquisition or engagement—while emphasizing that game-based and interactive features can improve cognitive and linguistic outcomes, particularly in inclusive or low-resource educational contexts.

2.5 Excluded Studies

Although the primary objective of this Mini Review was to identify empirical studies directly evaluating the impact of SG and ER on mathematical learning in children and adolescents with ASD, several relevant works identified during the search did not meet the inclusion criteria. These studies were excluded after full-text review because, despite involving SG, ER, or other interactive technologies, they primarily addressed social communication, emotional regulation, attention, or general STEM education rather than mathematics- or numeracy-specific outcomes. Three representative studies illustrating these broader applications are briefly acknowledged to clarify the conceptual boundaries of the present Review [28,29,30].

Álvarez Ariza et al. [28] reviewed STEM and STEAM interventions involving robotics and coding for students with ASD, emphasizing inclusion and engagement rather than mathematical reasoning or numeracy development; therefore, they were excluded from the present Review.

Wan et al. [29] developed FECTS (Facial Emotion Cognition and Training System), a framework for human-computer and ER interaction aimed at enhancing emotion recognition in Chinese children with ASD. Although the intervention improved engagement and socio-emotional skills, it was excluded from the present Review because its primary outcomes focused on affective learning rather than mathematical or cognitive skills.

Rudrauf et al. [30] proposed a computational and theoretical framework - the Projective Consciousness Model (PCM) - applied to robotic systems to simulate how subjective perspectives influence adaptive and maladaptive behaviors. Although the model offers valuable insights into cognitive and affective regulation in ASD, it was excluded from the present Review because it is theoretical in nature and does not address educational or mathematics-related outcomes.

Together, these studies contextualize the educational and cognitive dimensions of SG and ER applications, clarify their boundaries, and inform future directions for mathematics-specific interventions in ASD.

2.6 Analytical Focus

To enhance methodological transparency, a two-stage screening and analysis process was adopted. During the initial screening phase, studies were identified broadly based on their focus on SG or ER in populations with ASD, regardless of the specific learning domain addressed. This strategy was adopted to avoid prematurely excluding potentially relevant interventions. In the second screening phase, full-text articles were reviewed in detail, and only studies explicitly reporting mathematics- or numeracy-related outcomes were retained for final inclusion.

The limited number of eligible studies reflects the current scarcity of empirical research—particularly in Western contexts—explicitly examining mathematics-related outcomes through SG and ER in ASD, rather than limitations of the search strategy.

The five included studies were analyzed according to their research design, targeted mathematical domains, participant profiles, and educational outcomes. The analysis prioritized the mechanisms through which SG and ER enhance cognitive engagement, embodied understanding, and executive functioning [22,24,25,26,27].

Rather than quantifying effects, this Review interprets how and why these technologies promote mathematical learning. By focusing on embodied interaction, adaptive feedback, and motivational reinforcement, the analysis reveals the potential of SG and ER to transform abstract reasoning into accessible, multisensory learning experiences for children and adolescents with ASD [31].

This methodological framework ensures both rigor and inclusivity, presenting a focused yet comprehensive overview of how digital and embodied tools are reshaping mathematical education in ASD contexts. Table 1 provides a synthetic overview of the participants, methodologies, and main outcomes of the five studies included in this Mini Review.

Table 1 The synthetic overview of the participants, methodologies, and main outcomes of the five studies included in this Mini Review.

3. Educational and Therapeutic Implications for ASD

The five studies included in this Mini Review collectively demonstrate that SG and ER can serve as powerful mediators between abstract mathematical concepts and the concrete, multisensory experiences through which many learners with ASD construct understanding [22,24,25,26,27]. Beyond their immediate educational benefits, these technologies carry broader therapeutic and developmental implications, promoting cognitive flexibility, emotional regulation, and social engagement. Examining each study in detail highlights both their individual contributions and their shared pedagogical message: mathematics education for ASD can be inclusive, motivating, and embodied when digital and robotic tools are meaningfully integrated into the learning process.

3.1 Learning through Playful and Visual Engagement

In several of the reviewed studies, SG and ER interventions designed to strengthen arithmetic skills provided children with ASD (aged 6-12) with structured, visually supported, and engaging environments for practicing mathematical concepts. For example, learners showed improvements in accuracy and motivation when interacting with coding-based tasks or robot-mediated games [22,26]. In contrast, Ishaq et al. [27] highlighted how the absence of feedback and interactivity in existing educational apps can undermine engagement and limit cognitive benefit. These findings suggest that SG and ER can reduce the anxiety often associated with mathematics by transforming it into a multisensory, rewarding, and low-pressure experience [32].

The pedagogical implication is clear: when mathematics is presented through interactive, play-based formats that leverage visual and procedural learning strengths, students with ASD can develop deeper conceptual understanding and greater self-efficacy. This aligns with neurodiversity-affirming educational practices, which focus on building competence and confidence rather than remediating deficits [22,24,27].

3.2 Embodied Interaction and Executive Function Development

The study by Arshad et al. [26] illustrates how ER supports both mathematical reasoning and EF—using LEGO^® Mindstorms EV3, eight children with ASD (aged 7-9) engaged in problem-solving tasks related to place value and sequencing. The physical manipulation of robotic components requires planning, inhibition, and coordination of working memory—cognitive processes that are often challenging for learners with ASD.

The implication extends beyond mathematics: ER promotes the integration of sensory-motor experience with abstract reasoning, allowing learners to feel the logic of numbers through action. Such embodied learning may help bridge the gap between procedural and conceptual knowledge while fostering autonomy and sustained attention [33]. These effects suggest that robotics can serve not only as instructional tools but also as therapeutic instruments for strengthening EF and self-regulation [34].

3.3 Collaborative Learning and Cognitive Flexibility

In Hughes et al. [25], the RAISE project combined ER with virtual interaction to enhance STEM and basic mathematical reasoning among elementary students with ASD (aged 6-11). The integration of the Dash™ robot and a virtual agent Zoobee™ enabled participants to collaborate, communicate, and solve coding tasks involving sequencing, spatial reasoning, and logical prediction.

This hybrid model demonstrates the potential of SG and ER to cultivate cooperative learning—an area often difficult for children on the autism spectrum. By situating mathematics within interactive, social contexts, ER promotes both academic and socio-cognitive growth. For educational practice, this means that mathematics instruction can double as a platform for developing flexibility, turn-taking, and peer collaboration—skills transferable to broader life domains [35].

3.4 Attention Regulation and Early Mathematical Cognition

Among the included studies, Hughes et al. [25] and Hutchison et al. [22] indirectly addressed attention regulation and self-monitoring, showing that both SG and ER can improve sustained focus during mathematical and coding tasks. In Hughes et al. [25], students with ASD who interacted with the Dash™ robot and the AI-based agent Zoobee™ demonstrated increased persistence and reduced distractibility during sequencing and problem-solving activities. Similarly, Hutchison et al. [22] observed that engagement in ER-based sessions was inversely correlated with ASD-related behavioral markers, suggesting that interactive, structured environments can naturally support attentional control and cognitive self-regulation.

These findings indicate that technology-mediated learning environments, even when not explicitly designed for attention training, can enhance the underlying cognitive and emotional conditions that facilitate mathematical learning [36]. By embedding numerical reasoning within interactive, feedback-rich activities, SG and ER foster both attentional stability and cognitive flexibility—key foundations for academic and adaptive functioning in learners with ASD [7].

3.5 Broader Educational and Therapeutic Takeaways

Across these studies [22,24,25,26,27], several consistent implications emerge. SG and ER promote motivation and emotional regulation by offering structured, feedback-rich, and predictable learning contexts—conditions that align with the cognitive and sensory preferences of individuals with ASD. They also foster cognitive flexibility and problem-solving, encouraging learners to test hypotheses, adapt strategies, and persist in the face of challenges. From a therapeutic perspective, such activities support EF development, attention management, and self-confidence—skills that generalize beyond the mathematics classroom [37].

At the systemic level, the integration of SG and ER entails a shift toward inclusive, strengths-based pedagogy. Teachers and therapists are encouraged to use technology not merely as an assistive add-on but as a core component of instruction that values diverse cognitive profiles. The use of visual and tactile interfaces reduces the linguistic and symbolic barriers that often hinder mathematical learning, enabling students with ASD to access content through alternative yet equally valid modes of reasoning [38].

Furthermore, these studies [22,24,25,26,27] highlight the importance of collaboration among educators, psychologists, and engineers in designing interventions that are both pedagogically meaningful and developmentally sensitive. Co-design approaches—in which learners and teachers actively contribute to the creation of SG and ER activities—can enhance engagement and ensure ecological validity [39].

The educational and therapeutic implications of the reviewed studies suggest that SG and ER have the potential to transform mathematical learning for students with ASD. By combining structure with creativity, predictability with exploration, and cognition with emotion, these tools offer pathways toward more inclusive, empowering, and enduring forms of learning [40].

Nevertheless, while these educational and therapeutic outcomes highlight the transformative potential of SG and ER for learners with ASD, they must be interpreted within the methodological and ethical boundaries of current research [41].

4. Methodological, Practical, and Ethical Challenges

The first critical limitation concerns sample size and participant diversity. Most of the selected interventions involved very small groups—typically between eight and twelve learners—recruited from a single school or therapeutic center. For instance, Arshad et al. [26] implemented ER activities focused on place value and sequencing with eight children aged 7-9, demonstrating improvements in task engagement and accuracy but with limited external validity. Similarly, Hutchison et al. [22] examined an after-school ER and coding program with twelve students with ASD aged 6-11, observing increased engagement and reduced ASD diagnostic markers over seven weeks. Although both studies [22,26] yielded promising outcomes, their small, homogeneous samples and short intervention periods limit the generalizability of the findings.

These small, localized samples fail to represent the heterogeneity of the ASD population, which encompasses wide variability in cognitive, communicative, and sensory profiles [42]. As a result, the capacity to generalize findings across different levels of functioning, age groups, or educational contexts remains modest.

A second limitation involves the lack of standardized outcome measures. Each study adopted distinct evaluation tools—ranging from arithmetic accuracy scores to behavioral observations or teacher-rated engagement—making cross-study comparison difficult [43]. In many cases [25,26], results were inferred from qualitative observations or subjective ratings rather than standardized mathematical assessments. This inconsistency hampers meta-analytic synthesis and weakens the evidentiary basis for the effectiveness of SG and ER. Future studies should therefore employ validated, multidimensional instruments that capture both cognitive and behavioral outcomes [44].

Additionally, most interventions were short-term pilot projects, typically lasting four to eight weeks [24]. Although these studies [22,24,26,27] demonstrated feasibility, they do not clarify whether the observed gains persist beyond the intervention period or transfer to authentic educational contexts. Longitudinal designs are essential to determine the durability of effects and the development of autonomous mathematical reasoning. Another recurring limitation is the absence of control groups, which restricts causal interpretation. Only a few studies [25,26] included comparative designs; others, such as Ishaq et al. [27], lacked control groups, limiting causal interpretation.

Beyond the limitations of the included studies, the present Mini Review has its own constraints. First, the small number of eligible studies reflects both the novelty of mathematics-focused SG and ER interventions in ASD and the strict inclusion criteria adopted, which may have excluded relevant work addressing related cognitive domains. Second, as a narrative Mini Review, this study did not employ a formal quality appraisal or meta-analytic approach, limiting the ability to quantify effect sizes or risk of bias. Finally, publication bias cannot be excluded, as studies reporting null or negative findings may be underrepresented in the available literature. These limitations should be considered when interpreting the conclusions of the present synthesis.

4.1 Practical and Educational Barriers

From an implementation perspective, SG and ER present several logistical and pedagogical barriers that can limit their scalability [25,26]. Cost is among the most frequently reported issues: hardware platforms such as LEGO^® Mindstorms or Dash™ robots require financial investment, maintenance, and teacher training [26]. Schools with limited technological infrastructure or funding may therefore struggle to adopt these tools, reinforcing existing educational inequities. Even SG, which often runs on more accessible devices, demands stable internet connections and a level of digital literacy that is not universal [45].

Teacher preparation represents another significant challenge [46]. The success of SG and ER depends not only on the technology itself but also on how educators integrate it within the curriculum. Many teachers still feel unprepared to align technological tasks with learning objectives, especially when working with students with ASD, who require individualized instruction. In Hughes et al. [25], educators collaborated with engineers to operate the ER platform, highlighting the importance of interdisciplinary cooperation. Establishing professional training in digital pedagogy and inclusive design would help transform SG and ER from experimental tools into sustainable educational practices [28].

Ecological validity is another key issue. Many interventions [24,26,27] were conducted in controlled laboratory or therapy settings, where distractions, social interaction, and time constraints were minimized. Such conditions, while useful for measurement, do not mirror the complexity of real classrooms. Even Hughes et al. [25], in their own early-stage pilot work, acknowledged that their interventions were not fully integrated into standard teaching routines. The transition from controlled environments to authentic educational contexts often reveals unanticipated challenges related to attention, peer collaboration, or sensory overload. For instance, students may struggle to maintain focus in noisier, more stimulating classrooms; collaborative tasks with peers may lead to anxiety or conflict; and unpredictable classroom dynamics can interfere with the structured routines many ER and SG systems rely on [24,25]. To enhance generalizability, future research should implement SG and ER directly within schools, integrating them into standard teaching routines and evaluating their real-world feasibility.

4.2 Ethical and Inclusive Considerations

The growing integration of SG and ER into educational contexts for ASD also demands careful attention to ethical and inclusivity concerns. Many of these technologies record sensitive behavioral or cognitive data, raising questions about privacy, data protection, and informed consent [25,26]. Compliance with international regulations such as the General Data Protection Regulation (GDPR) is essential, particularly when working with minors or individuals with cognitive vulnerabilities. Transparency in data collection and the use of anonymized systems are fundamental ethical requirements [47].

Accessibility and fairness are equally important. Technological inequality risks reinforcing pre-existing disparities between schools or families with differing resources. Ensuring equitable access to SG and ER is therefore not only a pedagogical issue but also a matter of social justice [22,24]. The adoption of universal design principles—including low-cost materials, open-source software, and customizable sensory features—can mitigate these disparities and ensure that inclusive education is recognized as a right rather than a privilege [25].

Another ethical dimension concerns the purpose and emotional impact of technology. SG and ER should promote autonomy, exploration, and self-efficacy—not passive dependence or standardized behavioral compliance [24,25]. Interventions that overemphasize performance feedback or competition may risk reinforcing anxiety or perfectionism, which are already common among learners with ASD [22]. Conversely, environments that encourage experimentation and error correction within safe, predictable frameworks can enhance emotional regulation and intrinsic motivation [25,26].

Environmental sustainability deserves consideration. Designing durable, repairable devices and reducing electronic waste aligns with broader ethical commitments to sustainable development. Incorporating these principles strengthens the moral legitimacy of educational innovation [24,25].

All included studies involved human participants and reported adherence to ethical standards, including institutional approval and informed consent procedures, as described in the original publications. However, the level of detail in ethical reporting varied across studies, highlighting the need for more standardized and transparent documentation in future research involving children with ASD.

4.3 Integrative Reflection and Future Directions

Overall, the evidence synthesized in this Review indicates that SG and ER can transform mathematical learning for students with ASD by providing multisensory, engaging, and conceptually grounded experiences [22,24,26]. Yet this potential will remain underexploited unless methodological rigor and contextual integration improve [25]. Future research should adopt mixed-method and longitudinal designs, integrate neurocognitive and behavioral measures, and explore differential effects across developmental profiles and educational settings [24,25].

Collaborative work among educators, psychologists, engineers, and ethicists is crucial to ensure that technological interventions are not only effective but also equitable and ethically responsible [24,25]. Moreover, future programs should prioritize co-design with teachers, students, and families, thereby promoting user-centered innovation that respects the diversity of learning preferences within the autism spectrum [22,25].

By addressing these methodological, practical, and ethical challenges, SG and ER can evolve from isolated pilot projects into sustainable instruments of inclusive education. Their true contribution lies not merely in improving arithmetic or problem-solving accuracy but in cultivating curiosity, confidence, and cognitive flexibility—transforming mathematics into an accessible and rewarding domain for all learners with ASD [24,26].

5. Future Directions and Conclusion

The integration of SG and ER into mathematics education for learners with ASD represents an emerging yet promising field that bridges cognitive science, pedagogy, and AT [34]. While current evidence highlights their potential to enhance numeracy, problem-solving, and engagement, the field still faces methodological, contextual, and ethical limitations that must be addressed to achieve broader educational impact [26].

Future research should prioritize methodological rigor and ecological validity. Most existing studies are small-scale or exploratory, limiting generalizability. Larger, longitudinal investigations are needed to evaluate not only immediate learning outcomes but also the sustainability and transfer of skills to everyday academic contexts [7]. A key direction involves examining how gains in attention, motivation, and EF achieved through SG and ER interventions influence long-term mathematical reasoning and school achievement [48].

Another essential avenue is to include more heterogeneous and representative samples. Learners with ASD exhibit a wide range of cognitive, communicative, and sensory profiles, and interventions must reflect this diversity. Future work should include participants across the full autism spectrum—including those with co-occurring intellectual disabilities or minimal verbal skills—to ensure that SG and ER interventions are accessible and effective for all learners [49]. Similarly, cross-cultural studies could determine how contextual factors such as classroom resources, teacher training, and educational systems affect the implementation and efficacy of these tools.

From a pedagogical perspective, future initiatives should explore curricular integration and teacher empowerment [50]. SG and ER will achieve lasting impact only if incorporated into everyday instructional practice rather than confined to short-term research projects. This requires sustained professional development programs that equip teachers to align technological activities with specific learning goals and adapt them to students’ sensory and emotional needs [51,52]. Co-design approaches—in which educators, learners, and developers collaborate in creating educational tools—can enhance engagement and ensure that technology reflects both pedagogical intentions and learner preferences [53].

In addition, inclusive and ethical design principles must guide innovation in SG and ER [54]. Future development should prioritize accessibility, affordability, and sustainability to prevent the widening of educational disparities [55]. Designing open-source, low-cost, and energy-efficient platforms can democratize access while aligning technological innovation with environmental responsibility [56]. Ethical reflection must also extend to data management and privacy, particularly as technologies become more personalized and data-driven [57,58]. Transparent communication with families and educators about how data are used will be crucial to maintaining trust and safeguarding learners’ rights [59].

Beyond the classroom, SG and ER hold therapeutic promise for supporting emotional regulation, attention control, and social participation—key developmental goals in ASD. Numerous studies [60,61,62] reviewed illustrate that mathematical learning can simultaneously nurture self-confidence, autonomy, and interpersonal skills when delivered through interactive and multisensory experiences [63,64]. Future research should therefore adopt a holistic framework that views cognitive and emotional development as intertwined outcomes of technology-enhanced learning.

SG and ER offer powerful avenues for reimagining mathematics education as an inclusive, adaptive, and engaging process. By transforming abstract concepts into embodied, interactive experiences, they enable learners with ASD to access mathematical reasoning through their own cognitive strengths [65,66]. These tools support not only academic progress but also personal growth, fostering a sense of agency and curiosity that extends beyond the mathematics classroom [67,68].

Moving forward, the challenge for researchers and educators alike is to translate the promise of SG and ER into sustainable educational practice—one that values neurodiversity, promotes ethical innovation, and builds bridges between cognitive science, pedagogy, and human development [69]. When designed and implemented with care, these technologies can transform mathematics from a domain of exclusion into one of empowerment, creativity, and shared discovery for all learners with ASD [70].

Author Contributions

F.S. Review of the entire process. E.C. Drafting and methodology. A.Z; M.D.G. Editing and revision. A.P. Review and validation. All authors reviewed and approved the final manuscript.

Competing Interests

The authors have declared that no competing interests exist.

AI-Assisted Technologies Statement

During the preparation of this manuscript, AI-assisted tools were used exclusively for language editing purposes, including improvements in clarity, grammar, and readability. All scientific content—including study selection, data extraction, critical analysis, and interpretation—was developed by the author. No AI tools were used for research design, data handling, or conceptual writing.