Modern tools for ADHD detection and diagnosis: Psychometric analysis and improvement perspectives

 Corresponding author: Zoe Karabatzaki Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Modern tools for ADHD detection and diagnosis: Psychometric analysis and improvement perspectives Christos Simos and Zoe Karabatzaki * Department of Greek Philology, Democritus University of Thrace, Greece. Global Journal of Engineering and Technology Advances, 2025, 24(02), 153-160 Publication history: Received on 06 July 2025; revised on 12 August 2025; accepted on 15 August 2025 Article DOI: https://doi.org/10.30574/gjeta.2025.24.2.0243 Abstract Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neuro developmental disorder characterized by symptoms of inattention, hyperactivity, and impulsivity. Accurate diagnosis is critical for effective intervention, yet current practices often rely on subjective measures. This article presents a systematic review of psychometric and neuropsychological tools commonly used in the diagnosis of ADHD, including SNAP-IV, Conners’ CRS-R, Vanderbilt, ADHD-RS-IV, the Continuous Performance Test (CPT), Stroop Test, and Wisconsin Card Sorting Test (WCST). Special attention is given to the integration of ICT (Information and Communication Technologies) in assessment procedures, enhancing diagnostic accuracy and accessibility. Furthermore, the article highlights the need for culturally adapted diagnostic tools in the Greek context and proposes directions for future research and interdisciplinary collaboration. Keywords: ADHD; Diagnosis; Psychometric Tools; Executive Functions; Neuropsychological Assessment; ICT. 1. Introduction Attention Deficit Hyperactivity Disorder (ADHD) is one of the most frequently diagnosed neuro developmental disorders in children, with a global prevalence of approximately 5% (Polanczyk et al., 2007). The disorder is defined by persistent symptoms of inattention and/or hyperactivity-impulsivity that interfere with functioning or development (APA, 2013). Beyond childhood, ADHD can persist into adolescence and adulthood, affecting educational attainment, occupational success, and interpersonal relationships (Faraone et al., 2015). Traditional diagnostic methods, primarily based on behavioral reports and questionnaires completed by parents and teachers, are often insufficiently objective. There is increasing emphasis on supplementing subjective assessments with standardized psychometric and neuropsychological tools (Barkley, 1997). Additionally, the advancement of digital technologies has enabled the digitization of several diagnostic tests, improving precision and offering new possibilities in remote assessment and monitoring (Brearly et al., 2017). Finally, we stress the importance of all digital technologies in the field of education and in ADHD training. These technologies are highly effective and productive and facilitate and improve assessment, intervention, and educational procedures through mobile devices that bring educational activities anywhere [21-23], various ICTs applications that are the main supporters of education [24-28], and AI, STEM, and ROBOTICS [29-32] that raise educational procedures to new performance levs. Furthermore, the development and integration of ICTs with theories and models of metacognition, mindfulness, meditation, and the development of emotional intelligence [33-46], accelerates and improves educational practices and results more than those, particularly in children with ADHD, treating domain. Global Journal of Engineering and Technology Advances, 2025, 24(02), 153-160 154 2. Theoretical Foundation: Attention Deficit Hyperactivity Disorder (ADHD) and the Need for Accurate Diagnosis Attention Deficit Hyperactivity Disorder (ADHD) is currently recognized as a neurobiological disorder arising from a combination of various biological, genetic, and environmental factors. Research has demonstrated that ADHD is associated with dysfunctions in brain regions responsible for executive functions and behavioral regulation, as well as with dysregulation of the neurotransmitters dopamine and no repinephrine, which affect executive functions and concentration capacity (Faraone et al., 2015). Moreover, heredity plays a significant role, with genetic factors estimated to account for 70–80% of ADHD cases (Barkley, 2006). The disorder manifests with characteristic inattention, hyperactivity, and impulsivity. Children with ADHD typically struggle to maintain focus on tasks, to be organized, to remain calm, and to control their impulses. Their symptoms are not only quantitatively but also qualitatively more severe compared to the behavior of other children (APA, 2013). The main diagnostic tools currently in use are the DSM-5 (American Psychiatric Association, 2013) and the ICD-11 (World Health Organization, 2022), which provide detailed diagnostic criteria based on a specific number of symptoms, duration, and severity. Although these definitions are valuable for conducting a clear diagnosis, their practical application is complex. The characteristics of ADHD, the frequent co-occurrence of other disorders (such as learning difficulties, anxiety, or behavioral disorders), and the differing expression of symptoms across age groups render diagnosis challenging and multifaceted (Du Paul et al., 2016). Furthermore, the diagnostic process is significantly influenced by the social and cultural context. Parental perceptions and attitudes, pedagogical beliefs, and school expectations all play a crucial role in how the child’s behaviors are assessed (Tannock, 2009). The educational environment itself can either actively contribute to recognizing ADHD or hinder it, depending on the accessibility of support services and the quality of teacher training. Recognizing the importance of these challenges, international research has highlighted the need for multicultural and multi methodological approaches to diagnosis. This entails activating and utilizing information from multiple sources (parents, educators, the child, clinical evaluators), employing multiple tools (psychometric, neuropsychological), and considering both symptoms and the child’s functioning across various settings (Du Paul et al., 2016). The use of highquality psychometric instruments, their cultural adaptation, and scientific validation are essential factors for the accuracy and reliability of diagnosis. Aim and Research Questions This review aims to comprehensively present and evaluate the core diagnostic tools used for ADHD assessment in children and adolescents, with a focus on their psychometric properties, cultural relevance to the Greek context, and compatibility with Information and Communication Technologies (ICT). The main research questions guiding this work are: • Which psychometric and neuropsychological instruments are most frequently used for ADHD diagnosis, and what are their strengths and limitations? • How culturally appropriate are these tools for use within the Greek educational and clinical context? • In what ways can ICT improve the efficiency, precision, and accessibility of ADHD diagnosis? 3. Method This study employs a literature review methodology aimed at a detailed and comprehensive evaluation of the primary tools used for the diagnosis and assessment of Attention Deficit Hyperactivity Disorder (ADHD) in the child and adolescent population. The selection of tools to be examined was based on specific, predefined criteria. In particular, an analysis was conducted considering their frequency of use in international clinical and research practice, their scientific foundation, their applicability in educational and psychiatric settings, as well as the availability of sufficient data supporting their psychometric validity and reliability. The literature analysis is organized around four main axes. First, the most widely used psychometric scales for diagnosing ADHD are presented, which are based on DSM-5 criteria and provide standardized methods of data collection, either through self-report or informant-report. Subsequently, the principal neuropsychological tests are Global Journal of Engineering and Technology Advances, 2025, 24(02), 153-160 155 discussed, offering objective measures of executive function difficulties related to the disorder. Thirdly, cultural issues affecting the validity and reliability of these tools are analyzed, especially in countries like Greece where comprehensive data for many instruments are lacking. Finally, the role of Information and Communication Technologies (ICT) in developing innovative forms of assessment and tele-diagnosis is examined, with the aim of improving accessibility and the accuracy of diagnostic procedures. The literature was gathered through databases including Psyc INFO, Pub Med, ERIC, and Google Scholar. The review covers the period from 2000 to 2024, emphasizing recent meta-analyses, primary research studies, and official guidelines from reputable organizations. Studies focusing on children and adolescents, involving comparative analyses or tool evaluations, and linked to DSM-5 and ICD-11 diagnostic criteria were prioritized. During the selection process, studies exclusively focusing on adults, employing non-standardized or insufficiently validated tools, or lacking a clear methodology for tool evaluation were excluded. Emphasis was placed on integrating both qualitative and quantitative data to present a complete and research-based overview of the available instruments. The overall approach of the study is analytical and interpretative; it does not merely provide a descriptive inventory of the tools but aims to highlight their strengths, limitations, and prospects for improvement. A key objective is to formulate recommendations that can be applied both in educational and clinical psychology practice, as well as in future research on the use of assessment tools for ADHD. 4. Results The findings of this literature review underscore the necessity of adopting a multidimensional diagnostic approach for Attention Deficit Hyperactivity Disorder (ADHD), which includes both psychometric rating scales and neuropsychological tests. These tools, structured upon the DSM-5 criteria, facilitate the systematic identification of ADHD symptoms but differ in scope, objectivity, and diagnostic accuracy. The analysis is organized into three major areas: psychometric tools, neuropsychological tools, and the synergistic role of digital technology in optimizing diagnostic practices. 4.1. Psychometric Tools Psychometric instruments are often the first-line tools in ADHD assessment. Their principal function is to collect standardized, quantifiable behavioral data from key informants, such as parents and teachers, based on observable symptomatology. Among the most widely used are the SNAP-IV, Conners’ Rating Scales – Revised (CRS-R), the Vanderbilt Assessment Scales, and the ADHD Rating Scale IV (ADHD-RS-IV). These tools vary in length, diagnostic focus, age applicability, and cross-cultural adaptation. 4.1.1. SNAP-IV The SNAP-IV Rating Scale is a 26-item questionnaire aligned with DSM-5 criteria for ADHD and Oppositional Defiant Disorder (ODD) (Swanson et al., 2001). It is designed for completion by both parents and teachers and has demonstrated excellent internal consistency (α> 0.90) across multiple studies (Bussing et al., 2008). The scale captures the three core dimensions of ADHD: inattention, hyperactivity, and impulsivity. Its brevity and ease of administration make it suitable for school-wide screenings. However, it is highly susceptible to contextual bias and subjectivity, as it relies exclusively on third-party observations. Teachers’ behavioral expectations or a parent’s tolerance level may skew responses. Moreover, its utility as a standalone tool has been questioned, as it can lead to over diagnosis if not combined with clinical interviews or other assessments (Gomez et al., 2013). 4.1.2. Conners’ Rating Scales – Revised (CRS-R) The Conners’ CRS-R includes multiple versions for parents, teachers, and self-reports by adolescents. The full version assesses a broader range of emotional and behavioral problems, including ADHD symptoms, executive functioning difficulties, conduct problems, anxiety, and academic issues (Conners, 2008). Its multidimensional structure, validated via factor analysis, makes it ideal for distinguishing between ADHD subtypes and comorbid disorders (Chang et al., 2009). The CRS-R demonstrates excellent psychometric properties (α> 0.95). Despite its clinical value, practical limitations exist: the full version can be time-consuming and requires specific training to interpret. In the Greek context, lack of localized normative data is a major limitation. The cultural irrelevance of reference norms may reduce the diagnostic specificity when applied to Greek school populations (Koutsoklenis & Honkasilta, 2017). Global Journal of Engineering and Technology Advances, 2025, 24(02), 153-160 156 4.1.3. Vanderbilt ADHD Diagnostic Rating Scales Developed for use by pediatricians and educators, the Vanderbilt Scales include symptom checklists and performance ratings in academic and social domains. These instruments are brief (approximately 35 items), freely available, and recommended by the American Academy of Pediatrics (Wolraich et al., 2003). They evaluate both ADHD symptom frequency and functional impairment, offering a holistic snapshot of the child’s daily behavior. A significant advantage is the capacity for repeated administration to monitor treatment outcomes. However, the absence of a self-report version from the child or adolescent limits the assessment’s multi-informant validity (Fabiano et al., 2006). 4.1.4. ADHD Rating Scale IV (ADHD-RS-IV) The ADHD-RS-IV is an 18-item scale designed for use by parents and teachers, with items directly reflecting DSM-5 criteria. It is widely used both in research and practice for diagnosis and intervention planning (DuPaul et al., 1998). Its strength lies in its ability to quantify symptom severity and track treatment response. When used in school settings, it demonstrates a high degree of sensitivity (84%) and specificity (76%) (Kahana et al., 2008). However, like other rating scales, its accuracy depends on the raters’ insight and observational skills. To optimize reliability, it should be integrated with structured interviews or observational data. 4.2. Neuropsychological Tools While psychometric tools provide essential symptom data, they are inherently subjective and context-dependent. Neuropsychological assessments, in contrast, offer objective performance-based measures of core cognitive functions affected in ADHD—particularly attention, inhibition, working memory, and cognitive flexibility. 4.2.1. Continuous Performance Test (CPT) The CPT is a computerized task designed to measure sustained attention and response inhibition. Participants must respond to target stimuli while inhibiting responses to non-targets, under time-pressured conditions. Key variables include omission errors (inattention), commission errors (impulsivity), mean reaction time, and variability (Rosvold et al., 1956). CPT performance patterns in children with ADHD typically involve more errors and greater reaction time inconsistency, reflecting self-regulatory deficits (Epstein et al., 2003). The digitization of the test allows for high scalability and automatic scoring. However, CPT does not capture the full complexity of executive dysfunction, necessitating its use alongside other measures. 4.2.2. Stroop Color-Word Test The Stroop Test assesses cognitive inhibition, a core component of executive function. In its interference condition, participants must name the ink color of incongruent color words, overriding the habitual reading response. This task places high demands on attentional control and inhibition. Children with ADHD show longer response times and more errors, particularly under interference conditions, which correlate with prefrontal cortex dysfunction (Lansbergen et al., 2007). The Stroop test is valuable for diagnosing impulsivity and attentional switching issues and is especially effective in identifying subtle executive deficits not easily observable in behavioral assessments. 4.2.3. Wisconsin Card Sorting Test (WCST) The WCST is a complex problem-solving task that evaluates cognitive flexibility, set-shifting, and error correction. Participants must sort cards based on changing and unspoken rules, adapting responses to examiner feedback (Heaton et al., 1993). Children with ADHD tend to display high preservative errors, suggesting rigidity in cognitive strategies and difficulties adapting to new contingencies. These impairments are linked to under activation in the dorsolateral prefrontal cortex and correlate with real-world challenges in behavior regulation and learning. Global Journal of Engineering and Technology Advances, 2025, 24(02), 153-160 157 4.3. Integration and Digital Advancements The integration of psychometric and neuropsychological methods enhances the validity, reliability, and depth of ADHD diagnosis. Psychometric tools provide an initial symptom profile, while neuropsychological tests verify and quantify cognitive impairments. This dual approach supports differential diagnosis, helps exclude mimicking conditions (e.g., anxiety, learning disabilities), and guides individualized interventions. In recent years, Information and Communication Technologies (ICT) have revolutionized assessment delivery. Tools like the Qb Test, which combines CPT tasks with movement tracking, offer real-time, multidimensional assessments of hyperactivity, attention, and impulsivity (Hult et al., 2018). Other innovations include the e-Stroop and computerized WCST versions, which provide consistent administration and advanced data analytics (Görzig et al., 2019). These digital tools are ideal for tele health settings, facilitate longitudinal monitoring, and support the standardization of complex cognitive data. However, they require clinical oversight, training, and contextual validation. Their utility is greatest when complementing—not replacing—professional clinical judgment. 5. Discussion The accurate diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) presents not only methodological challenges but also cultural and technological considerations that are frequently underestimated in both research and clinical practice. A growing body of literature emphasizes that many of the diagnostic tools currently in use were developed and standardized predominantly in English-speaking countries such as the United States, the United Kingdom, and Canada. As such, these tools are embedded with cultural assumptions and expectations that may not align with the norms and practices of other societies (van de Vijver & Tanzer, 2004). In the Greek context, this issue becomes particularly evident. Although several internationally recognized ADHD questionnaires have been translated into Greek and are commonly used in clinical and educational settings, they often lack updated national normative data. The absence of localized standardizations compromises the validity and diagnostic accuracy of these instruments (Papanastasiou & Chatzinikolaou, 2017). Furthermore, cultural elements such as social attitudes, gender stereotypes, and pedagogical expectations significantly influence the ways in which children’s behavior is interpreted. For instance, behaviors considered developmentally appropriate or even beneficial in one cultural setting may be viewed as problematic in another, leading to over-diagnosis or mis referrals (Koutsoklenis & Honkasilta, 2017). Given these cultural limitations, the adoption of a multimodal assessment framework is strongly recommended. As proposed by DuPaul et al. (2016), multimodal assessment involves the use of multiple diagnostic sources and tools, including psychometric questionnaires, direct behavioral observations across diverse environments, clinical interviews (structured or semi-structured), and neuropsychological testing. The triangulation of data from various informants (e.g., parents, teachers, and clinicians) and contexts (e.g., home, school, peer interactions) facilitates a more comprehensive, holistic, and accurate understanding of the child’s behavior and functional capacity. In addition to methodological pluralism, recent advances in Information and Communication Technologies (ICT) are reshaping the landscape of psychological and neuropsychological assessment. Digital tools, web-based platforms, and artificial intelligence (AI)-enhanced applications now allow for the remote administration of validated assessment instruments. These technologies have proven particularly valuable in expanding access to diagnostic services in geographically remote or underserved areas. Moreover, ICT solutions often enable automated scoring, real-time data analysis, and longitudinal tracking, which significantly improve diagnostic efficiency and reduce human error (Görzig et al., 2019). One notable example is the QbTest, which integrates the Continuous Performance Test (CPT) with motion tracking to generate a multidimensional cognitive and behavioral profile. This tool offers objective metrics of attention, impulsivity, and hyperactivity (Hult et al., 2018). Similarly, digital adaptations of traditional tests—such as the e-Stroop and computerized versions of the Wisconsin Card Sorting Test (WCST)—allow for precise measurement of executive functions and enhance the flexibility of assessment procedures. These tools are especially beneficial in complex or ambiguous clinical cases that demand detailed cognitive profiling. Nevertheless, the integration of digital technologies into diagnostic practice should be approached with caution. While ICT tools offer clear advantages, they cannot and should not replace the interpretive expertise of mental health Global Journal of Engineering and Technology Advances, 2025, 24(02), 153-160 158 professionals. Their application requires specialized training, ethical considerations regarding personal data protection, and continuous psychometric validation. Additionally, tools designed and validated for one cultural context may not function equivalently in others, especially when translated or adapted for use in new populations. Continuous crosscultural research and localized standardization are therefore essential. In conclusion, the diagnostic landscape of ADHD is evolving towards increased methodological diversity and technological sophistication. A balanced approach that combines traditional clinical methods with innovative ICT solutions can enhance the validity, reliability, and accessibility of ADHD assessment. However, such progress must be grounded in cultural sensitivity, scientific rigor, and ongoing professional reflection. 6. Conclusions and Research Prospects Accurate and scientifically grounded diagnosis of ADHD requires a multi factorial and interdisciplinary approach that combines the professional’s expertise with the use of cross-validated tools for measuring and analyzing behavior. The simple use of a single instrument is insufficient to capture the complexity of the disorder. The validity and reliability of diagnostic outcomes are significantly enhanced when psychometric tools (such as SNAP-IV, Conners’ CRS-R, Vanderbilt, and ADHD-RS-IV) are complemented by neuropsychological methods (such as CPT, Stroop, WCST), interviews, and observations of the child across various settings. The data from this review confirm that each tool possesses specific advantages and limitations. For this reason, their combined use is deemed essential, particularly in settings where resources and available personnel allow the implementation of multidimensional methodologies. The adaptation of tools to the Greek language and culture, the development of local norms, and the training of parents and educators regarding the nature and manifestations of ADHD are also critically important factors. The contribution of technology to assessment is particularly promising, through the creation of personalized digital tools, tele-diagnosis, and data analysis via algorithms. The development of tools that adapt to the child’s developmental profile and provide dynamic real-time assessment could transform diagnostic practice in the coming years. Emerging research directions include the creation of Greek standards for major diagnostic tools, empirical evaluation of digital tests for early detection, and the investigation of relationships between neuropsychological deficits, psycho educational characteristics, and academic performance. Through such research pathways, a deeper understanding of ADHD can be achieved, alongside a fairer, more accurate, and more effective intervention for every child experiencing it. Compliance with ethical standards Acknowledgments The Authors would like to thank the SPECIALIZATION IN ICTs AND SPECIAL EDUCATION: PSYCHOPEDAGOGY OF INCLUSION Postgraduate studies Team, for their support. Disclosure of conflict of interest The Authors proclaim no conflict of interest. References [1] American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). [2] Barkley, R. A. (2006). Attention-deficit hyperactivity disorder: A handbook for diagnosis and treatment (3rd ed.). Guilford Press. [3] Bussing, R., Fernandez, M., Harwood, M., Hou, W., Garvan, C. W., Eyberg, S. M., & Swanson, J. M. (2008). Parent and teacher SNAP-IV ratings of attention deficit hyperactivity disorder symptoms: Psychometric properties and normative ratings from a school district sample. Assessment, 15(3), 317–328. [4] Chang, H. L., Wang, C. Y., & Tsai, M. Y. (2009). Psychometric properties of the Chinese version of the Conners’ Parent Rating Scale–Revised. Journal of Pediatric and Neonatal Individualized Medicine, 2(3), 101–109. Global Journal of Engineering and Technology Advances, 2025, 24(02), 153-160 159 [5] Conners, C. K. (2008). Conners 3rd Edition Manual. Toronto, Canada: Multi-Health Systems. [6] DuPaul, G. J., Power, T. J., Anastopoulos, A. D., & Reid, R. (1998). ADHD Rating Scale–IV: Checklists, norms, and clinical interpretation. Guilford Press. [7] DuPaul, G. J., Stoner, G., & Weyandt, L. L. (2016). ADHD in the schools: Assessment and intervention strategies (3rd ed.). Guilford Press. [8] Epstein, J. N., et al. (2003). Evidence-based assessment of ADHD children using continuous performance tests. Journal of Clinical Child and Adolescent Psychology, 32(1), 82–93. [9] Fabiano, G. A., Pelham, W. E., et al. (2006). The utility of the Vanderbilt ADHD Diagnostic Parent Rating Scale in a clinical sample. Journal of Pediatric Psychology, 31(6), 660–669. [10] Faraone, S. V., Biederman, J., & Mick, E. (2006). The age-dependent decline of attention deficit hyperactivity disorder: A meta-analysis of follow-up studies. Psychological Medicine, 36(2), 159–165. [11] Gomez, R., et al. (2013). Parent ratings of ADHD symptoms: Associations with social and school functioning in a community sample. Journal of Attention Disorders, 17(7), 599–607. [12] Heaton, R. K., et al. (1993). Wisconsin Card Sorting Test Manual: Revised and Expanded. Psychological Assessment Resources. [13] Hult, N., Kadesjö, B., &Gillberg, C. (2018). Using QbTest to monitor medication in ADHD: A randomized controlled trial. European Child & Adolescent Psychiatry, 27(6), 733–742. [14] Kahana, S. Y., et al. (2008). Utility of the ADHD Rating Scale–IV for identifying inattentive-type ADHD in children. Journal of Attention Disorders, 11(5), 572–580. [15] Koutsoklenis, A., &Honkasilta, J. (2017). ADHD in Greek education: Discourses and practices. Discourse: Studies in the Cultural Politics of Education, 38(4), 546–558. [16] Lansbergen, M. M., et al. (2007). The Error-related negativity as a candidate endophenotype for ADHD. Biological Psychiatry, 62(10), 1198–1204. [17] Rosvold, H. E., et al. (1956). A continuous performance test of brain damage. Journal of Consulting Psychology, 20(5), 343–350. [18] Tannock, R. (2009). The educational outcomes of ADHD. In R. A. Barkley (Ed.), Attention-Deficit Hyperactivity Disorder: A Handbook for Diagnosis and Treatment (3rd ed., pp. 579–606). Guilford Press. [19] Van de Vijver, F. J. R., & Tanzer, N. K. (2004). Bias and equivalence in cross-cultural assessment: An overview. Revue Européenne de PsychologieAppliquée/European Review of Applied Psychology, 54(2), 119–135. [20] Wolraich, M. L., et al. (2003). Vanderbilt ADHD diagnostic teacher rating scale: Initial standardization of validity data. Pediatrics, 111(3), 1098–1104. [21] Stathopoulou, et all 2018, Mobile assessment procedures for mental health and literacy skills in education. International Journal of Interactive Mobile Technologies (iJIM), 12(3), 21-37, https://doi.org/10.3991/ijim.v12i3.8038 [22] Stathopoulou A, Karabatzaki Z, Tsiros D, Katsantoni S, Drigas A, 2019. Mobile apps the educational solution for autistic students in secondary education , Journal of Interactive Mobile Technologies (IJIM) 13 (2), 89-101, https://doi.org/10.3991/ijim.v13i02.9896 [23] Drigas A, DE Dede, S Dedes 2020 Mobile and other applications for mental imagery to improve learning disabilities and mental health International , Journal of Computer Science Issues (IJCSI) 17 (4), 18-23 DOI:10.5281/zenodo.3987533 [24] Drigas A, Petrova A 2014 ICTs in speech and language therapy , International Journal of Engineering Pedagogy (iJEP) 4 (1), 49-54 https://doi.org/10.3991/ijep.v4i1.3280 [25] Alexopoulou, A., Batsou, A., & Drigas, A. S. (2019). Effectiveness of Assessment, Diagnostic and Intervention ICT Tools for Children and Adolescents with ADHD. International Journal of Recent Contributions from Engineering, Science & IT (iJES), 7(3), pp. 51–63. https://doi.org/10.3991/ijes.v7i3.11178 [26] Bamicha V, Drigas A, 2022 The Evolutionary Course of Theory of Mind - Factors that facilitate or inhibit its operation & the role of ICTs , Technium Social Sciences Journal 30, 138-158, DOI:10.47577/tssj.v30i1.6220 Global Journal of Engineering and Technology Advances, 2025, 24(02), 153-160 160 [27] Galitskaya, V., & Drigas, A. (2020). Special Education: Teaching Geometry with ICTs. International Journal of Emerging Technologies in Learning (iJET), 15(06), pp. 173–182. https://doi.org/10.3991/ijet.v15i06.11242 [28] Chaidi I, Drigas A, 2022 "Parents' views Questionnaire for the education of emotions in Autism Spectrum Disorder" in a Greek context and the role of ICTs , Technium Social Sciences Journal 33, 73-9, DOI:10.47577/tssj.v33i1.6878 [29] Lytra N, Drigas A 2021 STEAM education-metacognition–Specific Learning Disabilities , Scientific Electronic Archives journal 14 (10) https://doi.org/10.36560/141020211442 [30] Demertzi E, Voukelatos N, Papagerasimou Y, Drigas A, 2018 Online learning facilities to support coding and robotics courses for youth , International Journal of Engineering Pedagogy (iJEP) 8 (3), 69-80, https://doi.org/10.3991/ijep.v8i3.8044 [31] Chaidi I, Drigas A 2022 Digital games & special education , Technium Social Sciences Journal 34, 214-236 https://doi.org/10.47577/tssj.v34i1.7054 [32] Doulou A, Drigas A 2022 Electronic, VR & Augmented Reality Games for Intervention in ADHD , Technium Social Sciences Journal, 28(1), 159-169. https://doi.org/10.47577/ tssj.v28i1.5728 [33] Drigas A, Mitsea E, Skianis C 2021 The Role of Clinical Hypnosis & VR in Special Education , International Journal of Recent Contributions from Engineering Science & IT (IJES) 9(4), 4-18. https://doi.org/10.3991/ijes.v9i4.26147 [34] V Galitskaya, A Drigas 2021 The importance of working memory in children with Dyscalculia and Ageometria , Scientific Electronic Archives journal 14 (10) https://doi. org/10.36560/141020211449 [35] Drigas A, Mitsea E, Skianis C. 2022, Virtual Reality and Metacognition Training Techniques for Learning Disabilities , SUSTAINABILITY 14(16), 10170, https://doi.org/10.3390/su141610170 [36] Drigas A,. Sideraki A. 2021 Emotional Intelligence in Autism , Technium Social Sciences Journal 26(1), 80-92, https://doi.org/10.47577/tssj.v26i1.5178 [37] Mitsea E, Drigas A,, Skianis C, 2022 Breathing, Attention & Consciousness in Sync: The role of Breathing Training, Metacognition & Virtual Reality , Technium Social Sciences Journal 29, 79-97 https://doi.org/10.47577/tssj.v29i1.6145 [38] Chaidi, I. ., & Drigas, A. (2022). Social and Emotional Skills of children with ASD: Assessment with Emotional Comprehension Test (TEC) in a Greek context and the role of ICTs. , Technium Social Sciences Journal, 33(1), 146–163. https://doi.org/10.47577/tssj.v33i1.6857 [39] Kontostavlou, E. Z., & Drigas, A. 2021. How Metacognition Supports Giftedness in Leadership: A Review of Contemporary Literature. , International Journal of Advanced Corporate Learning (iJAC), 14(2), pp. 4–16. https://doi.org/10.3991/ijac.v14i2.23237 [40] Drigas A, Mitsea E, Skianis C, 2022 Intermittent Oxygen Fasting and Digital Technologies: from Antistress and Hormones Regulation to Wellbeing, Bliss and Higher Mental States , Technium BioChemMed journal 3 (2), 55-73 [41] Drigas A, Mitsea E 2021 Neuro-Linguistic Programming & VR via the 8 Pillars of Metacognition X 8 Layers of Consciousness X 8 Intelligences. Technium Social Sciences Journal 26(1), 159–176. https://doi.org/10.47577/tssj.v26i1.5273 [42] Drigas A, Papoutsi C, Skianis C, Being an Emotionally Intelligent Leader through the Nine-Layer Model of Emotional Intelligence-The Supporting Role of New Technologies, Sustainability MDPI 15 (10), 1-18 [43] Drigas A, Mitsea E 2022 Breathing: a Powerful Tool for Physical & Neuropsychological Regulation. The role of Mobile Apps , Technium Social Sciences Journal 28, 135-158. https://doi.org/10.47577/tssj.v28i1.5922 [44] Drigas A, Karyotaki M, Skianis C, 2017 Success: A 9 layered-based model of giftedness , International Journal of Recent Contributions from Engineering, Science & IT 5(4) 4-18, https://doi.org/10.3991/ijes.v5i4.7725 [45] Drigas A, Mitsea E, Skianis C 2021. The Role of Clinical Hypnosis and VR in Special Education , International Journal of Recent Contributions from Engineering Science & IT (IJES) 9(4), 4-17. [46] Drigas A, Bakola L, 2021 The 8x8 Layer Model Consciousness-Intelligence-Knowledge Pyramid, and the Platonic Perspectives , International Journal of Recent Contributions from Engineering, Science & IT (iJES) 9(2) 57-72, https://doi.org/10.3991/ijes.v9i2.22497