Conceptual Proposal for a Computational Platform to Assist in the Learning and Cognitive Development Process of Children with Autism Spectrum Disorder: A Solution Based on a Multicriteria Structure

1. Introduction

The increasing global prevalence of Autism Spectrum Disorder (ASD) has become a pressing concern in both academic and clinical communities [1,2] . According to recent epidemiological studies, diagnostic rates have risen sharply over the last two decades, with current estimates indicating that approximately 1 in 36 children are affected worldwide [3]. This upward trend highlights an urgent need for scalable and personalized technological solutions capable of supporting the cognitive, emotional, and social development of individuals with ASD. The growing demand for these tools is also reflected in the educational and healthcare systems, which require adaptable platforms to meet diverse neurodevelopmental needs in real-time and across different contexts. The use of computing platforms plays a crucial role in supporting the cognitive development of children with Autism Spectrum Disorder (ASD), especially in social communication and visual interaction [4]. Integrating technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) enables innovative solutions, such as heart rate sensors to monitor emotional state and mobile applications to facilitate communication [5]. In addition, interactive virtual environments are used to overcome barriers to visual contact, contributing to the development of social skills and promoting greater participation of children in the community. Individuals with ASD face significant challenges in developing the socioemotional skills essential for social contact and emotional balance [6]. In this context, ICT (information and communication technologies) are presented as fundamental tools to promote inclusion and support the socio-emotional development of students with ASD [7]. These technologies provide personalised strategies that contribute to improving the ability of these individuals to interact and adapt to educational environments [8]. The fragility of the diagnostic process is an aspect to be considered, where behavioural deviations due to the absence and monitoring of parents can be confused with a disorder [9]. Therefore, validation by experienced professionals, together with the family, is essential in this approach [10]. The cognitive development of children with ASD can significantly benefit from therapeutic approaches based on structured and scientifically validated models [11], such as ABA, DIR/Floortime, JASPER, and SCERTS. The ABA model has been widely used due to its effectiveness in modifying behaviour and improving social and academic skills through positive reinforcement. However, the DIR/Floortime model emphasises relationship building and emotional regulation through playful interaction, promoting gains in spontaneous communication and social interaction. The JASPER model, which focuses on developing joint attention and symbolic play, has proven effective in expanding the social participation and emotional regulation of children [12]. The SCERTS model adopts a multidisciplinary approach, focusing on transactional support and improving communication and emotional regulation skills [13]. These approaches, when applied in an integrated manner and adapted to the individual needs of children, contribute to a more favourable estimate and higher quality of life for individuals. Multimodal therapeutic interventions, which include the ABA, DIR/Floortime, JASPER, and SCERTS models, show significance in individuals’ cognitive and social development. The ABA model is recognised for its approach based on positive reinforcement, standing out for its effectiveness in improving executive functions, such as attention, working memory, and problem-solving. The DIR/Floortime model, by prioritising the construction of affective bonds and playful interactions, contributes to functional communication and social reciprocity development. The JASPER model stimulates joint attention and symbolic play, promoting significant advances in social interaction and emotional regulation. Finally, the SCERTS model employs a multidisciplinary approach, integrating assistive technologies and social skills training to improve social communication and emotional management. Early initiation of interventions and active family involvement are essential factors to enhance results and favour the integral development of individuals [14]. The definition of a technological tool for learning for students with disabilities currently considers aspects such as accessibility, pedagogical effectiveness, and adaptation to the specific needs of students. The selection of these resources is aligned with the Universal Design for Learning (UDL) principles, which seek to create inclusive environments [15]. In the context of ASD, technological resources from robotics and programming are widely used to develop cognitive and social skills. Interaction with humanoid robots, for example, has proven effective in assisting educational therapies and providing stimuli that favour learning and socialisation [16]. AI has been increasingly used to monitor cognitive and emotional states, allowing for more effective personalisation of teaching. The use of convolutional neural networks for emotion recognition makes it possible to predict emotional states and adapt educational approaches for children with autism, contributing to improving learning and social interaction, in addition to reducing barriers in verbal communication [17]. Based on it, it was realised that there was a need to address the following research questions:

• How are these resources currently chosen in the learning process? AI-based assistive technologies support the adaptive functioning of individuals with neurodevelopmental conditions in everyday scenarios. When choosing technological resources for the learning process [18]. AI-based assistive devices are selected based on their effectiveness in personalising support to individual needs and are often applied to stimulate social skills, communication, and daily living capabilities [19]. Thus, criteria such as accessibility, applicability in educational and domestic environments, and the ability to adapt the device to the specific demands of the user. [20] this choice should be made using e-learning recommendation systems, which involve criteria such as individual interests, specific needs, and user preferences. These platforms use collaborative filters and personalisation techniques to adapt the content to the child’s characteristics, promoting more accessible and practical learning. The choice of technological resources in special education teaching should be based on the personalisation of learning, using assistive technologies and adaptive platforms to meet individual needs [21]. In this analytical composition, it is essential to consider accessibility criteria, support for differentiated learning, and teacher training.
• What elements should be considered in these protocols that contribute to the learning process of the respective audience? Protocols for the adoption of educational technologies must consider criteria such as accessibility, applicability in different contexts (school, home and social) and adaptation to the specific needs of their users. Furthermore, it is vital to evaluate the effectiveness and usefulness of these devices in everyday life, ensuring their consistent and beneficial use [18]. For an educational resource aimed at children with ASD to be effective, it is essential to take into account sensory engagement, personalised teaching and integration with pedagogical practices [22]. The inclusion of multimodal elements, such as visual and auditory stimuli, as well as adaptation of the learning pace, contributes to improving the educational experience of these students [23]. Considering this perspective, educational protocols aimed at people with ASD must encompass aspects such as accessibility, adaptation to the user profile, personalised teaching and use of assistive technologies. Furthermore, it is essential to assess students’ visual engagement to ensure that technological resources are used effectively, promoting improvements in interaction and cognitive development [20]. Teachers play a crucial role in building interactions. They can foster collaboration by facilitating turn-taking, allowing students to build a mutual relationship rather than just responding to the teacher [6]. Fostering these social interactions allows students to express collaboration. In this way, they develop social competence in navigating group dynamics and enhance their educational experience.
• What are the reasonable criteria on the available platforms that should be considered? Considering that the evaluation criteria for these educational platforms should include accessibility, ease of use, the ability to support social and communication development, and effective integration into the school curriculum [23];[7]. While the study emphasises the training’s importance and its focus on sensory processing needs for children with autism, it does not delineate elements for broader protocols applicable to all learning processes. It is necessary to include integration criteria with Universal Design for Learning (UDL) principles, ease of use by teachers and students, and the availability of technical and ongoing training for educators. Possible constraints should also be considered, such as lack of time to develop digital materials, limited access to technology by vulnerable students, and the absence of guidelines on integrating emerging technologies [21]. The present study aims to provide a methodological structure based on criteria extracted from the various protocols to support the decision-maker in indicating a technological resource. This paper is organised into four sections. In the first section, we try to understand the scenario of the problem to be addressed, subdivided into three research questions, and how the protocols are applied. In the second section, we focus on the understanding and applicability of the ABA, JASPER, SCERTS, and DIR protocols, which we apply to structure and understand the problem [24], understanding that it is a complex and ill-defined problem, aiming to identify standard criteria that serve as a standardised input for a multicriteria decision analysis approach. In the third section, we perform an systematic review of the literature (SLR) based on the six-stage process method [15]; we took a scientometrics approach, conducted to identify research opportunities, highlighted existing gaps, and reinforced the need for a structured methodology to evaluate computational platforms and technological resources to support their cognitive and social development. Finally, we present the results with the respective discussions.

2. Methodological Flow

This paper is divided into four blocks: stage 1 addressed the analysis of the problem, understanding the limitations that exist regarding the learning process of this population in question and theoretical background; stage 2 dealt with the literature review based on the cognitive and social development of children with autism and the technological elements that assist them in this process, using databases with academic relevance (Scopus, Wos, IEEE, Village, and Emerald), we apply the search structure: ("Autism" OR "Autism Spectrum Disorder" OR "ASD" OR "TEA") AND ("computer programs" OR "educational software" OR "learning platforms" OR "assistive technology" OR "digital tools") AND ("learning" OR "education" OR "cognitive development" OR "early intervention")); In stage 3, the methodology was structured considering recognised protocols (ABA, Jasper, SCERTS, and DIR), applied to children between 4 and 6 years old, with this classification. Based on these protocols, we sought attributes and criteria to understand, from the perspective of experts (emphasis on early childhood education), the relevance of each criterion, and thus use algorithms and multicriteria methods to generate a ranking of computational platforms aimed at the learning process of these children. Finally, in stage 4, we focus on discussing the results, based on the SLR, to validate our methodological proposal by generating knowledge to propose new platforms, considering these criteria and attributes in the context of the multi-criteria algorithm (Figure 1).

Based on protocols, which served as a starting point for the study on recommendations in the learning process of children aged 4 to 6 in elementary school. Within this context, computational platforms designed for teaching children with this disorder were examined to identify relevant requirements and qualifying criteria that contribute to the cognitive development of these students. Subsequently, the SAPEVO-M multicriteria method was applied to prioritise these criteria and rank the respective platforms, considering the perspective of specialists (early childhood education teachers, psychologists, and therapists in the field of study), particularly in their contribution to the learning process of these children. After defining established criteria obtained from the analyses of the respective protocols, these criteria were weighted by these experts. Each alternative was then analysed based on these criteria. This analysis was applied to the computational platform (https://www.sapevo m.com/home.php) [25]. This methodological structuring established a decision-making process for selecting computational platforms to support the learning process of children with ASD (Figure 2). We based our systematic review on the six-stage process proposed by [26,27], as listed below:

• Field mapping through a scoping review;
• Comprehensive search;
• Quality evaluation, which encompasses the reading and the selection of papers;
• Data extraction, which relates to the collection and capture of relevant data into a pre designed spreadsheet;
• Synthesis, which comprises the synthesis of extracted data to show the known and provide the basis for establishing the unknown; and
• Write.

3. Scientometric Aspects of Literature

The use of scientometrics as a methodological foundation in the field of inclusive education has proven essential for mapping, analyzing, and deepening scientific production in increasingly relevant areas, such as computational platforms for the development of children with Autism Spectrum Disorder (ASD) [28]. When consulting and analysing the databases, considering the available documents, 1,756 documents resulted (Figure 3).

In this sample, we considered only journal articles, which resulted in 1,049 documents. Considering this total, 329 documents are duplicates, representing 31.36% (Table 1).

When we analysed the papers, we found two significant (consolidated) clusters, the USA and China. Canada and Australia flanked these. However, after 2022, research emerged from emerging countries in this study area, such as Brazil, Israel, the Csech Republic, and Indonesia (Figure 4).

Based on methodological elements of a rapid review [46], considering the time frame from 2014 to 2025, we note that some researchers, or groups of researchers, are consolidated. However, a new generation is emerging in this study area (Figure 5).

Considering that this population requires differentiated, more specific, interactive monitoring that provides engagement without losing the humanised focus, we sought this basis with the validated protocols and the literature to propose a structured approach. Thus, we built a timeline, looking for reference authors to understand the approach of the family, professionals, and the insertion of technological resources, which criteria or requirements were considered to enhance cognitive and social development.

Therefore, when analysing this database, we identified consolidated authors and a new generation of researchers in the aforementioned area of study (Table 2).

It is noted that the keywords applied between ASD and technology already stand out through assistive technology. However, we propose to support the process of selecting or indicating these technologies by identifying criteria and weighting them from the perspective of professionals who directly work with these children. (Figure 6).

The latest research points to a more humanised approach, bringing human beings to the centre of the theme and understanding that the environment in which they are inserted will have impacts. [60] Therefore, the emergence of the aspect of social and cognitive development of this population analysed is understood (Figure 7).

When analysing the Journal focused on this area of study, based on the year of publication, it is possible to see the relevance of this topic. However, it is worth noting the relevance of the Journal of Autism and Development compared to other databases (Figure 8).

The scientometric stage plays a fundamental role in consolidating the theoretical foundation of this study, revealing not only the main thematic elements in the literature but also a clear gap in the development and application of computing platforms specifically designed for that audience. This analysis allowed us to identify a shortage of structured approaches that integrate educational methodologies, personalized technological resources, and evidence-based interventions. Given this scenario, it becomes evident that there is a need for a conceptual proposal that addresses this gap, justifying and informing the next stage of this work, which aims to outline an applied framework for learning and developing children with TEA through specialized digital technologies.

4. Structural Analysis of Protocols

To identify convergent aspects between the approaches to the two protocols used in this research, the points in the table were identified (Table 3). Considering the SCERTS perspective, this is a transactional approach centered on the family to enhance the communication and socio-emotional skills of children [29]. The DIR/Floortime Protocol promotes emotional and social development through playful interactions and meaningful relationships [30]. O ABA uses behavioral strategies to modify behaviors, promote social skills, and reduce inappropriate behaviors [7]. O JASPER promotes joint attention, symbolic play, engagement, and regulation in autistic children through playful interactions and development strategies [31]. The aim is to identify common aspects, which will be converted into analytical criteria.

The choice of approach for these protocols will depend on the individual needs of the child, the therapeutic objectives, and the family and educational context. Although each approach has unique methods and philosophies, they share some fundamental aspects. They all aim to promote social inclusion and improve the quality of life of these children, although each has its own particularities and specific emphases. Considering the perspective of the intersection point between the respective protocols, it was highlighted that these are therapeutic approaches aimed at the development of children with ASD [32]. Based on these protocols, it was possible to convert the aspects into attributes, which were the structure of the essential criteria for the insertion of the multicriteria analysis and which significantly impacted the quality of life of these children [33]. We segmented them into six classification blocks (Table 4). Thus, the first column of this table contains these attributes, and the following columns contain the aspects and characteristics of each protocol.

While protocols provide a solid foundation for behavioural improvements, technological advances can increase scalability and adaptability [47]. Integrating AI and data driven learning models can enable personalised interventions, optimise behavioural reinforcement techniques, and ensure more effective skill transfer beyond controlled environments. Motivational strategies that leverage children’s interests and choices resulted in increased engagement, thus accelerating the development of communication skills [59]. Personalised apps with AI capabilities that adapt stimuli based on children’s preferences can further increase motivation and engagement. At the same time, digital monitoring tools can track progress in real time, allowing immediate adjustments to intervention strategies. According to this segmentation of protocols, we identified potential criteria and elements that a computational platform, or a technological resource used in this child’s cognitive development, learning, or socialisation process, must contain (Table 5).

Considering the diversity of characteristics and behaviors of diagnosed children, we list six structural criteria that take into account the sensory aspects of children, such as the safety requirements of the system. Criteria aim at cognitive development but develop their characteristics of socialization and communication in a personalized way. Given this structure, we consider other platforms, typically used in the learning process of children, to analyze their conceptual structure through segmentation and attribution of these criteria (Table 6).

Based on this framework, we identified distinct approaches to these platforms: interactive and educational, whose proposal is to monitor the gradual progression of difficulty. We identified a gamified software application designed for children with average cognitive abilities, which allows for the personalization of teaching and the management of progress [34]. Finally, we identified approaches whose main characteristic is the continuous stimulation to provide communication and develop the child’s social aspects.

This approach optimizes investment in educational technologies and ensures they are used coherently to promote cognitive and social development, fostering inclusive and practical learning [63]. Technological interventions targeting the cognitive development of children with ASD must be carefully designed and implemented [53]. The rational use of technological resources should prioritize learning effectiveness and individuals’ emotional well-being. Thus, by integrating technologies in an ethical and informed way, we can potentially minimize trauma and maximize cognitive development, promoting a safer and more nurturing environment for these children.

5. Multicriteria Fundamentals

Decision-making scenarios are composed of subjective aspects [35], whose measurements are more complex because they are personal and challenging to externalise when choosing priorities between criteria; they are considered complex scenarios and require assertive measures [36]. We can define the foundations of MCDA as those that can address different problems, whether selection, classification, ordering, or description [37], among several possible alternatives, have multiple criteria, present conflicts between the criteria, and present different units of measurement for the criteria [38]. When assigning or applying a multicriteria method, it is an elementary requirement to understand the problem’s structure [39], considering whether it is a matter of choosing, generating a ranking, or order and the data available in this context [40]. Based on this, the literature has the most diverse methods that can be applied to the respective solution of the problem. SAPEVO-M method is an evolution of the SAPEVO method [41], which was intended only for a mono-decision analysis. In addition to the new algorithm providing a multi criteria analysis with multiple decision-makers, a process of standardisation of matrices was integrated by correcting negative and null criteria weights, thus increasing the model consistency [36]. The method consists of two processes: preliminary, the transformation of ordinal preference between criteria should be performed, expressed by a vector representing the criteria weights. Then, the ordinal transformation of the preference between alternatives is made within a given set of criteria, expressed by a matrix. A series of pairwise comparisons between variables, whether criteria or alternatives within a given criterion, denote the individual preference information of each decision-maker [41] (Figure 9).

The application of multicriteria methods in decision-making requires a fundamental preliminary phase [42]. The lack of adequate structuring can compromise the effectiveness of multicriteria analysis, resulting in inconsistent choice [43]. Structuring methods help build a more robust decision model by integrating conceptual modelling and knowledge representation techniques [44]. In this way, the initial structuring phase reduces subjectivity in defining criteria and ensures transparency and reliability in the results obtained for decision-making (Figure 10).

After structuring and understanding the expected objective, the decision-making process was summarized by identifying the weighting of the criteria in relation to the alternatives, adapted [45] (Figure 11). Therefore, each criterion will have a weight (w) depending on the respective alternative.

The SAPEVO-M method is classified as a $\gamma$-type problem (ranking) and follows the axiomatic structure below:

• Convert ordinal preferences among the criteria into a vector of criteria weights;
• Convert the ordinal preferences among the alternatives for each criterion into partial utilities of the alternatives;
• Determine the overall weight (global utility) of each alternative.

Step 1: The Criteria

$C_i$ and $C_j$ be criteria from a set or preference scale:

$$C=\{C_1,C_2,C_3,\dots ,C_i,\dots ,C_j\}$$

(1)

The pairwise comparison $\delta C_i{C}_j$ follows these rules:

$$\begin{array}{cc}\hfill \delta C_i{C}_j=1& \iff C_i\approx C_j\hfill \\ \hfill \delta C_i{C}_j>1& \iff C_i\succ C_j\hfill \\ \hfill \delta C_i{C}_j<1& \iff C_i\prec C_j\hfill \end{array}$$

• ≈ “as important as”
• > “more important than”
• < “less important than”

Step 2: 7-Point Preference Scale

The intensity of preference is expressed on a 7-point ordinal scale between two elements (criteria or alternatives).

• $\ll \ll \ll \leftrightarrow -3$ (much less important)
• $\ll \ll \leftrightarrow -2$ (moderately less important)
• $\ll \leftrightarrow -1$ (slightly less important)
• $\approx \leftrightarrow 0$ (equally important)
• $>\leftrightarrow 1$ (slightly more important)
• $\gg \leftrightarrow 2$ (moderately more important)
• $\gg \gg \gg \leftrightarrow 3$ (much more important)

Step 3: The Decision Maker

A comparison matrix is generated based on the preference order provided by each decision maker (DM), as follows:

$$D=\{D{M}_1,D{M}_2,D{M}_3,\dots ,D{M}_k\}$$

(2)

$$MD{M}_k=\left[\delta C_i{C}_j\right]$$

(3)

Where D is the set of all decision makers, and $MD{M}_k$ represents the preference matrix built from the evaluations provided by the k-th decision maker.

Normalization of the Decision Matrix

In the pairwise comparison analysis between the criteria listed in the decision matrix, each decision maker (DM) validates the degree of relevance between criteria. This process generates a column vector $\left[v_i\right]$, which must then be normalized to eliminate the influence of different units of measurement among criteria. The normalization is performed using the following equation:

$$v={\displaystyle \frac{a_{ij}-min\left(a_{ij}\right)}{max\left(a_{ij}\right)-min\left(a_{ij}\right)}}$$

(4)

Where $a_{ij}$ represents the value of alternative i under criterion j. This operation scales the data to a range between 0 and 1, enabling fair aggregation in subsequent multicriteria analysis steps.

6. Applied Case Study

The decision-making stage involved comparing alternatives based on paired judgments and assigning relative weights to established criteria. The objective was to identify, in a structured and conceptual way, which computing platforms best meet the educational needs of these children.

6.1. Analytical Report of Criteria Evaluation

The application of this method enabled the systematic evaluation of seven criteria considered essential for computer platforms designed to support the educational development of these children. Each criterion was comparatively analysed by paired decision makers, and only consistent evaluations were considered in the final score, reinforcing the robustness of the method. To facilitate this application, we used the platform https://www.sapevo-m.com/home.php [64].

The consistency of criteria evaluation was 23.81%, considered low. However, the method discards inconsistent pairwise comparisons, which ensures methodological integrity by filtering out incoherent responses. This distribution shows a clear preference for Alternative n, which received 42.01% of the overall utility, demonstrating superior alignment with the defined criteria. The second most preferred platform was Alternative 1 (20.37%).

6.2. Consistency and Methodological Robustness

Each criterion presented an associated inconsistency level in the judgment matrices, which is a safeguard of the SAPEVO-M method. For instance, the criterion “Communication and Language” showed a 50% inconsistency, considered very high. The SAPEVO-M method disregards such incoherent judgments, ensuring that only reliable assessments contribute to the final model.

Logically inconsistent judgments are automatically discarded during the aggregation process. Thus, the consistency percentage serves as a methodological safeguard, reinforcing the reliability of the model without compromising the results, which is a distinctive advantage of this approach.

The final ranking indicated that Alternative n (42.01% overall preference) was the most suitable platform according to the defined criteria, followed by Alternative 1 (20.37%) and Alternative 3 (17.3%).

This approach optimises investment in educational technologies and ensures they are used coherently to promote cognitive and social development, fostering inclusive and practical learning [63]. While this new approach applies to children, the same is not valid for adolescents with ASD, who, despite having average cognitive abilities, present significant deficits in adaptive skills, particularly in socialisation and activities of daily living [49]. Technology assisted interventions should not only focus on cognitive improvement but also prioritise the development of adaptive skills to facilitate independent living. Although e-learning recommender systems provide personalised learning opportunities tailored to the cognitive and social needs of individuals with ASD, significant challenges remain in designing effective and adaptable platforms [20]. The lack of well-defined design principles and technological constraints in the development of recommender systems are factors that impact this context.

We found that the insertion of a conceptual model to support the decision to choose a computational tool or a technology in the learning and development process of these children is valid. However, the family, the professionals, as well as the school environment are the pillars that will emanate the natural essence of real life, not just looking at structural criteria of the proposal of platforms or technological resources, considering that each part has defined roles and will directly impact the formation and development of the child (Figure 12).

7. Conclusion

This research aimed to, based on findings, we found that the approach these professionals take to diagnose children is influenced by personal beliefs, values , and experiences acquired throughout their work, so subjectivity impacts this monitoring. Thus, the insertion of the multicriteria structure in this methodological proposal aimed to minimize this burden of subjectivity and enhance the decision-making scenario (objective, structuring of the problem, definition of criteria, and respective weighting) in the decision-making process. This research identified standard requirements (criteria) in the aforementioned ASD protocols. Therefore, regardless of the protocol adopted in the monitoring, this methodological approach is applicable since the emphasis was on the criteria.

The level of training of professionals demonstrates greater awareness of sensory strategies. Therefore, the selection of technological resources may be influenced by these professionals’ knowledge and prior experience. We found that professionals follow the line of thought based on their qualifications in the respective protocols. Therefore, some attributes and characteristics may not be considered in this indication.

The integration of telehealth technology to provide training, supervision, and consulting for behavioural interventions for these professionals who deal directly with these diagnosed individuals. However, they emphasise that resources are typically selected based on their ability to facilitate communication between interventionists. The choice leans toward resources that allow for interactive learning experiences, such as videoconferencing, that allow for feedback.

This study applied criteria based on protocols validated by the professional classes that deal with these children and considered possible alternatives with platforms already known and validated by these professionals. However, the structure of this research is broad. It can be replicated with other technological or non-technological resources to support this public’s learning process and social and cognitive development.

When analysing the literature over a timeline, considering the last eleven years, regarding the learning issues of children with ASD, we identified that there is a technological dependency and that the professionals who accompany these children understand that this methodology presents favourable aspects to the process. We understand that technological resources are a support element in the learning process and should not be the main element in this journey. The intensified use of digital elements can trigger other disorders in the child. Therefore, the family component is fundamental in controlling and monitoring this resource. The contribution of this research was to propose a methodology based on the multicriteria algorithm in order to minimise the subjectivity of the decision-maker when indicating technological resources for the child’s development since this analysis incorporated criteria from the family and the professionals who accompany the child, which must be considered in this decision. Therefore, regardless of the protocol to be used, the decision-maker will analyse the technological resources to be employed based on the relevance of the criteria and adherence to the expected objective.

7.1. Theoretical Implications

Developing a clear framework for decision-making specifically tailored to technology selection for children with ASD contributes to the body of knowledge in educational technology. It lays out structured criteria that professionals can use to evaluate various resources, emphasizing the need for evidence-based practices in selecting technologies that enhance learning. This framework can also guide future research and practice, fostering a standardized approach in the field.

The study enriches existing MCDA methodologies by introducing a multi-criteria structure incorporating validated protocols relevant to ASD. By highlighting the importance of criteria such as personalization, interactivity, and engagement, this research adds to the theoretical development of MCDA frameworks, demonstrating their applicability in complex, multi faceted decision-making scenarios. This contribution encourages further exploration of how MCDA techniques can be adapted and refined for various contexts within education and therapy.

By grounding the decision-making framework in established autism intervention protocols, the research bridges the gap between theoretical models and practical applications. It highlights the importance of integrating evidence-based practices and intervention strategies into the technological selection process. This alignment not only strengthens the validity of the proposed criteria but also ensures that the selected technologies support the therapeutic goals outlined in these protocols.

7.2. Practical Implications

The lack of adequate structuring can compromise the effectiveness of the analysis, resulting in biased and inconsistent choices. By integrating conceptual modelling and knowledge representation techniques, structuring methods help build a more robust decision model, considering solid structural elements and requirements, ensuring that the professionals who deal directly with these children can have an axiomatic and mathematical model that helps them choose an appropriate computational resource, in the construction of the learning path.

The research can serve as a basis for further validating multi-criteria decision-making frameworks specifically tailored to selecting technology resources. Such validation can increase the credibility and applicability of these frameworks in real-world settings, leading to more effective tools that specifically serve these children. By providing a structured methodology, the study can influence how professionals choose the technology tools they use. A more objective approach can reduce bias and improve outcomes for children, as professionals will be better equipped to assess the suitability of platforms based on defined criteria and the specific needs of the children they work with.

The methodologies and criteria established in the research can be scaled and adapted for various populations beyond children with ASD, allowing for broader application within educational technology. This adaptability can extend to different age groups or special education needs, facilitating the development of inclusive technological solutions across diverse educational settings. The framework promotes greater accessibility to educational resources for children with ASD and their families. By prioritizing key criteria such as usability and adaptability, technology developers can create more inclusive platforms, ensuring that all children can engage in meaningful learning experiences.