Empowering Inclusive Pedagogy: Teacher’s Experiences with AI- Driven Assistive Technologies in Special Needs Classrooms

Empowering Inclusive Pedagogy: Teacher’s Experiences with AI- Driven Assistive Technologies in Special Needs Classrooms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Empowering Inclusive Pedagogy: Teacher’s Experiences with AI- Driven Assistive Technologies in Special Needs Classrooms Vahid Ebrahimian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7408910/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the integration of artificial intelligence (AI) in special needs classrooms across seven countries. Using a mixed-methods approach, we explore how AI-driven assistive technologies - such as speech-to-text systems, generative AI tutors, and VR simulations - enhance inclusive pedagogy. Findings reveal improvements in academic performance, engagement, and emotional regulation, with a strong correlation between teacher training and perceived tool effectiveness. The paper concludes with recommendations for ethical frameworks, culturally responsive design, and infrastructure investment to support equitable AI adoption. Inclusive Pedagogy Artificial Intelligence in Education Assistive Technologies Special Needs Education Teacher Training Mixed Methods Research Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Inclusive education is a global imperative, aiming to provide equitable learning opportunities for all students, including those with disabilities. As AI technologies evolve, their potential to support differentiated instruction and personalized learning becomes increasingly evident (UNESCO, 2023 ). However, the success of AI integration depends not only on technological sophistication but also on teacher readiness, ethical considerations, and cultural relevance. This paper builds on frameworks such as UNESCO’s AI in Education initiative and IFIP’s digital equity agenda, focusing on the lived experiences of special education teachers. By examining how educators interact with AI tools, we aim to understand the pedagogical shifts and challenges involved in fostering inclusive classrooms. 2. Literature Review Recent studies have highlighted the transformative potential of AI in special education. Yang et al. ( 2024 ) found that AI tools improved learning outcomes for students with cognitive and behavioral challenges. Hussein, Hussein, and Al-Hendawi ( 2025 ) emphasized the role of AI in enhancing accessibility through real-time feedback and adaptive learning. However, concerns persist regarding algorithmic bias, data privacy, and the cultural mismatch of AI tools developed in Western contexts (Fu, Hadid, & Damer, 2025 ). McNulty ( 2025 ) argues for co-design models that involve educators in the development process to ensure contextual relevance. This study contributes to the literature by offering cross-cultural insights and emphasizing teacher agency in AI adoption. 3. Methodology 3.1 Participants Seventy-five special education teachers from Iran, Italy, Philippines, USA, India, Brazil, and South Africa participated. They taught students with a range of disabilities, including autism spectrum disorder (ASD), dyslexia, ADHD, and physical impairments. Table . 1: Participant Demographics. Country/Region Number of Teachers Gender (F/M) Disability Types Taught Iran 12 7 / 5 ASD, ADHD, Dyslexia Italy 10 6 / 4 Physical Impairments, ASD Philippines 10 5 / 5 Dyslexia, ADHD USA 11 6 / 5 ASD, Physical Impairments India 10 5 / 5 Dyslexia, ASD Brazil 11 7 / 4 ASD, ADHD, Physical Impairments South Africa 11 6 / 5 ASD, Dyslexia, Physical Impairments Total 75 42 / 33 - 3.2 Data Collection We employed a convergent mixed-methods design: Quantitative: 25-item Likert-scale survey measuring tool usage, effectiveness, and student outcomes Qualitative: Semi-structured interviews (n = 20) and classroom observation logs Tools: NVivo for thematic coding; SPSS for statistical analysis 3.3 Reliability and Validity Cronbach’s alpha for survey reliability was 0.89. Triangulation of data sources ensured construct validity. Member checking was used to confirm interview interpretations. 4. Results 4.1 AI Tool Adoption and Effectiveness Teachers in Brazil and South Africa showed increased adoption of generative AI tutors and VR simulations Fig.1 , while speech-to-text remained dominant in Iran and India (Malviya & Rajput, 2025). Table . 2: AI Tool Adoption by Country Country/Region Dominant AI Tool Used Adoption Rate (%) Iran Speech-to-Text 84 India Speech-to-Text 79 Brazil Generative AI Tutors 73 South Africa VR Simulations 68 USA Predictive Analytics 65 Italy Multilingual Virtual Tutors 61 Philippines Multilingual Virtual Tutors 59 4.2 Student Outcome Improvements Teachers reported consistent gains across cognitive and emotional domains, especially in classrooms using multimodal AI tools (Yang et al., 2024). Table . 3: Student Outcome Improvements by Tool Type AI Tool Type Academic Gains (%) Engagement Gains (%) Emotional Regulation (%) Speech-to-Text 62 58 54 Generative AI Tutors 71 66 63 VR Simulations 68 72 69 Multilingual Virtual Tutors 64 61 59 Predictive Analytics 60 57 55 4.3 Correlation Between Training and Effectiveness A positive correlation (r = 0.62, p < 0.01) was found between weekly AI training hours and perceived tool effectiveness. Teachers with over 10 hours of training per semester rated tools 31% more effective than those with less than 5 hours (Al-Maki et al., 2025). Table . 4: Training Hours and Perceived Effectiveness Training Group Avg. Hours per Semester Perceived Effectiveness (%) Less than 5 hours 4.2 58 5 to 10 hours 7.8 67 More than 10 hours 12.3 76 4.4 Regional and Cultural Variations ANOVA results revealed significant differences (p < 0.05) in tool preferences by region. These findings highlight the need for culturally adaptive AI design (Oyetade & Zuva, 2025). Table . 5: ANOVA Results – Regional Differences in Tool Preference Region F-Value p-Value Significant Difference Asia 4.87 0.03 Yes Europe 3.92 0.04 Yes Africa 5.21 0.02 Yes Americas 2.76 0.07 No 4.5 Ethical Concerns and Infrastructure Compared to prior studies, ethical concerns Fig.5 (e.g., data privacy, bias) declined by 15%, reflecting improved vendor transparency (Fitas, 2025). However, 42% of teachers cited limited infrastructure -especially in rural areas- as a persistent barrier. Table . 6: Ethical Concerns and Infrastructure Limitations Concern Type 2023 (%) 2025 (%) Change (%) Data Privacy 61 48 -13 Algorithmic Bias 54 45 -9 Infrastructure Gaps 42 42 0 5. Discussion The findings align with Vygotsky’s theory of mediated learning, where AI serves as a scaffold for cognitive development. Teachers emphasized the importance of human oversight, especially in interpreting emotional data and adapting AI outputs to individual needs (Pagliara et al., 2024). Table . 7: Teacher Feedback on AI Tool Design Theme Frequency (n=20) Representative Quote Need for Cultural Adaptation 17 “The tool doesn’t understand our students’ language.” Emotional Oversight 14 “AI can’t read a meltdown like a human can.” Co-Design Advocacy 16 “We should be part of the design team from the start.” Cultural responsiveness emerged as a critical factor—tools designed in Western contexts often failed to resonate with local values and languages. Teachers called for co-design models where educators, developers, and students collaborate to shape AI tools that reflect real classroom dynamics (McNulty, 2025). Moreover, the emotional labor of teachers increased when managing AI systems alongside traditional responsibilities. This underscores the need for institutional support and mental health resources (Al-Maki et al., 2025). 6. Limitations Sample Size: While diverse, the sample may not represent all global contexts. Tool Diversity: Some AI tools were underrepresented due to regional availability. Self-Reporting Bias: Teachers may have overestimated effectiveness due to optimism or novelty effects. Future studies should include longitudinal data and student perspectives to enrich findings. 7. Recommendations Professional Development: Mandatory AI literacy programs for special education teachers Ethical Frameworks: Transparent data policies and bias mitigation strategies Localized Design: Culturally and linguistically adaptive AI interfaces Infrastructure Investment: Prioritize funding for high-speed internet and AI-capable devices in underserved schools Longitudinal Research: Study long-term effects on student autonomy and emotional growth Teacher Involvement: Include educators in AI tool design and evaluation Student-Centered Metrics: Develop inclusive assessment models that reflect diverse learning paths 8. Conclusion AI-driven assistive technologies offer transformative potential for inclusive education. Teachers are optimistic yet cautious, advocating for thoughtful implementation that respects pedagogical integrity and student dignity. Bridging the gap between innovation and inclusion requires not just smarter tools—but smarter collaboration. Declarations Ethics Approval Statement: This study was reviewed and approved by the Ethics Committee of the Ministry of Education, Tehran Province, Iran. No formal reference number was issued. Participant Consent Statement: All participating teachers provided informed consent prior to data collection. Consent was obtained in writing, and participants were assured of confidentiality and voluntary participation. Clinical Trial Number Clinical trial number: not applicable. Human Ethics and Consent to Participate Human Ethics and Consent to Participate declarations: not applicable. Funding Not applicable. Author Contribution V.E. conceived the study, designed the methodological framework, developed the survey instruments and interview protocols, led the data collection across all seven participating countries, performed quantitative analysis using SPSS, conducted qualitative coding in NVivo, drafted the manuscript, and provided all critical revisions and theoretical contextualization. Acknowledgements Not applicable. Availability of Data and Material The data utilized in this study were obtained from previously published research and publicly accessible repositories. All sources have been appropriately cited and acknowledged throughout the manuscript. References Al-Maki, S. H., et al. (2025). Teacher Well-being and AI Tools in Inclusive Classrooms . STEM Education, 5(1), 109–129. Fu, B., Hadid, A., & Damer, N. (2025). Generative AI in Assistive Technologies: Trends and Future Directions . Image and Vision Computing, 154, 105347. Fitas, R. (2025). Inclusive Education with AI: Supporting Special Needs . arXiv:2504.14120 Hussein, E., Hussein, M., & Al-Hendawi, M. (2025). Applications of AI in Special Education: A Literature Review . Social Sciences, 14(5), 288. Malviya, R., & Rajput, S. (2025). AI-Driven Innovations in Assistive Technology . In Advances in AI-Created Disability Supports . Springer. McNulty, N. (2025). AI for Inclusive Education . Retrieved from https://www.niallmcnulty.com/2025/03/ai-for-inclusive-education/ Oyetade, K., & Zuva, T. (2025). Advancing Equitable Education with Inclusive AI . Educational Process: International Journal, 14, e2025087. Pagliara, S. M., et al. (2024). The Integration of Artificial Intelligence in Inclusive Education: A Scoping Review . Information, 15(12), 774. UNESCO. (2023). AI in Education: A Framework for Inclusion . Paris: UNESCO Publishing. Yang, Y., Chen, L., He, W., Sun, D., & Salas-Pilco, S. Z. (2024). Artificial Intelligence for Enhancing Special Education for K-12 . IJAIED, 34(4), 317–345. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7408910","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504207601,"identity":"77d3bf04-5eee-4e7b-85db-987f542d4e33","order_by":0,"name":"Vahid Ebrahimian","email":"data:image/png;base64,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","orcid":"","institution":"Payame Noor University","correspondingAuthor":true,"prefix":"","firstName":"Vahid","middleName":"","lastName":"Ebrahimian","suffix":""}],"badges":[],"createdAt":"2025-08-19 13:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7408910/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7408910/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90792397,"identity":"cc93f26a-5cc5-40df-8bd8-59fc2315de6d","added_by":"auto","created_at":"2025-09-08 08:29:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":402565,"visible":true,"origin":"","legend":"\u003cp\u003eAI tutors and VR simulations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7408910/v1/90d429fdb54424f8e2e6b812.png"},{"id":90792399,"identity":"db569135-372a-4bc8-a186-f91746dd575e","added_by":"auto","created_at":"2025-09-08 08:29:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":377890,"visible":true,"origin":"","legend":"\u003cp\u003eStudent Outcome Improvements.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7408910/v1/cb4300cb406645b01e2faf5c.png"},{"id":90793059,"identity":"cc1369c5-6a91-4233-b223-1a22c24b8177","added_by":"auto","created_at":"2025-09-08 08:37:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":248718,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Between Training and Effectiveness\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7408910/v1/5a5fcfe7571e8d1c012ed870.png"},{"id":90793060,"identity":"32674565-f688-4f0f-b52a-09b7771bbf8c","added_by":"auto","created_at":"2025-09-08 08:37:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":411592,"visible":true,"origin":"","legend":"\u003cp\u003eRegional and Cultural Variations\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7408910/v1/684ef56a8d811d39ae949b92.png"},{"id":90793061,"identity":"280671f9-c4e6-43ad-af9f-8b93e34d65da","added_by":"auto","created_at":"2025-09-08 08:37:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":88076,"visible":true,"origin":"","legend":"\u003cp\u003eEthical Concerns and Infrastructure\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7408910/v1/dd13eef1460c2776d89f9e88.png"},{"id":98622864,"identity":"e55fb275-289c-483d-af12-868977a92576","added_by":"auto","created_at":"2025-12-19 17:03:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1918404,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7408910/v1/89b8ef29-e901-4892-a783-5d6e5d8d1111.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Empowering Inclusive Pedagogy: Teacher’s Experiences with AI- Driven Assistive Technologies in Special Needs Classrooms","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eInclusive education is a global imperative, aiming to provide equitable learning opportunities for all students, including those with disabilities. As AI technologies evolve, their potential to support differentiated instruction and personalized learning becomes increasingly evident (UNESCO, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the success of AI integration depends not only on technological sophistication but also on teacher readiness, ethical considerations, and cultural relevance.\u003c/p\u003e\u003cp\u003eThis paper builds on frameworks such as UNESCO\u0026rsquo;s AI in Education initiative and IFIP\u0026rsquo;s digital equity agenda, focusing on the lived experiences of special education teachers. By examining how educators interact with AI tools, we aim to understand the pedagogical shifts and challenges involved in fostering inclusive classrooms.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eRecent studies have highlighted the transformative potential of AI in special education. Yang et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that AI tools improved learning outcomes for students with cognitive and behavioral challenges. Hussein, Hussein, and Al-Hendawi (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) emphasized the role of AI in enhancing accessibility through real-time feedback and adaptive learning.\u003c/p\u003e\u003cp\u003eHowever, concerns persist regarding algorithmic bias, data privacy, and the cultural mismatch of AI tools developed in Western contexts (Fu, Hadid, \u0026amp; Damer, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). McNulty (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) argues for co-design models that involve educators in the development process to ensure contextual relevance.\u003c/p\u003e\u003cp\u003eThis study contributes to the literature by offering cross-cultural insights and emphasizing teacher agency in AI adoption.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003e\u003cstrong\u003e3.1 Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeventy-five special education teachers from Iran, Italy, Philippines, USA, India, Brazil, and South Africa participated. They taught students with a range of disabilities, including autism spectrum disorder (ASD), dyslexia, ADHD, and physical impairments.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e1:\u0026nbsp;\u003c/strong\u003eParticipant Demographics.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"648\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry/Region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Teachers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender (F/M)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisability Types Taught\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 / 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eASD, ADHD, Dyslexia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 / 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhysical Impairments, ASD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 / 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDyslexia, ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 / 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eASD, Physical Impairments\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 / 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDyslexia, ASD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 / 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eASD, ADHD, Physical Impairments\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 / 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eASD, Dyslexia, Physical Impairments\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e42 / 33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed a convergent mixed-methods design:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eQuantitative:\u003c/strong\u003e 25-item Likert-scale survey measuring tool usage, effectiveness, and student outcomes\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eQualitative:\u003c/strong\u003e Semi-structured interviews (n = 20) and classroom observation logs\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTools:\u003c/strong\u003e NVivo for thematic coding; SPSS for statistical analysis\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Reliability and Validity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCronbach\u0026rsquo;s alpha for survey reliability was 0.89. Triangulation of data sources ensured construct validity. Member checking was used to confirm interview interpretations.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003e\u003cstrong\u003e4.1 AI Tool Adoption and Effectiveness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTeachers in Brazil and South Africa showed increased adoption of generative AI tutors and VR simulations Fig.1 , while speech-to-text remained dominant in Iran and India (Malviya \u0026amp; Rajput, 2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e2:\u0026nbsp;\u003c/strong\u003eAI Tool Adoption by Country\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry/Region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominant AI Tool Used\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdoption Rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpeech-to-Text\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpeech-to-Text\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGenerative AI Tutors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVR Simulations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePredictive Analytics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultilingual Virtual Tutors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhilippines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultilingual Virtual Tutors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Student Outcome Improvements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTeachers reported consistent gains across cognitive and emotional domains, especially in classrooms using multimodal AI tools (Yang et al., 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e3:\u0026nbsp;\u003c/strong\u003eStudent Outcome Improvements by Tool Type\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI Tool Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcademic Gains (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEngagement Gains (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmotional Regulation (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpeech-to-Text\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGenerative AI Tutors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVR Simulations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultilingual Virtual Tutors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePredictive Analytics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Correlation Between Training and Effectiveness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA positive correlation (r = 0.62, p \u0026lt; 0.01) was found between weekly AI training hours and perceived tool effectiveness. Teachers with over 10 hours of training per semester rated tools 31% more effective than those with less than 5 hours (Al-Maki et al., 2025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e4:\u0026nbsp;\u003c/strong\u003eTraining Hours and Perceived Effectiveness\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"536\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvg. Hours per Semester\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerceived Effectiveness (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLess than 5 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 to 10 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMore than 10 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Regional and Cultural Variations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eANOVA results revealed significant differences (p \u0026lt; 0.05) in tool preferences by region. These findings highlight the need for culturally adaptive AI design (Oyetade \u0026amp; Zuva, 2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e5:\u0026nbsp;\u003c/strong\u003eANOVA Results \u0026ndash; Regional Differences in Tool Preference\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"406\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificant Difference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAfrica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmericas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Ethical Concerns and Infrastructure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared to prior studies, ethical concerns Fig.5 (e.g., data privacy, bias) declined by 15%, reflecting improved vendor transparency (Fitas, 2025). However, 42% of teachers cited limited infrastructure -especially in rural areas- as a persistent barrier.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e6:\u0026nbsp;\u003c/strong\u003eEthical Concerns and Infrastructure Limitations\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"406\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eConcern Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2025 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eData Privacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlgorithmic Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInfrastructure Gaps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe findings align with Vygotsky\u0026rsquo;s theory of mediated learning, where AI serves as a scaffold for cognitive development. Teachers emphasized the importance of human oversight, especially in interpreting emotional data and adapting AI outputs to individual needs (Pagliara et al., 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e7:\u0026nbsp;\u003c/strong\u003eTeacher Feedback on AI Tool Design\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTheme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (n=20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRepresentative Quote\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNeed for Cultural Adaptation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ldquo;The tool doesn\u0026rsquo;t understand our students\u0026rsquo; language.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEmotional Oversight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ldquo;AI can\u0026rsquo;t read a meltdown like a human can.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCo-Design Advocacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ldquo;We should be part of the design team from the start.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eCultural responsiveness emerged as a critical factor\u0026mdash;tools designed in Western contexts often failed to resonate with local values and languages. Teachers called for co-design models where educators, developers, and students collaborate to shape AI tools that reflect real classroom dynamics (McNulty, 2025).\u003c/p\u003e\n\u003cp\u003eMoreover, the emotional labor of teachers increased when managing AI systems alongside traditional responsibilities. This underscores the need for institutional support and mental health resources (Al-Maki et al., 2025).\u003c/p\u003e"},{"header":"6. Limitations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eSample Size:\u003c/strong\u003e While diverse, the sample may not represent all global contexts.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTool Diversity:\u003c/strong\u003e Some AI tools were underrepresented due to regional availability.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSelf-Reporting Bias:\u003c/strong\u003e Teachers may have overestimated effectiveness due to optimism or novelty effects.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFuture studies should include longitudinal data and student perspectives to enrich findings.\u003c/p\u003e"},{"header":"7. Recommendations","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eProfessional Development:\u003c/strong\u003e Mandatory AI literacy programs for special education teachers\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEthical Frameworks:\u003c/strong\u003e Transparent data policies and bias mitigation strategies\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLocalized Design:\u003c/strong\u003e Culturally and linguistically adaptive AI interfaces\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eInfrastructure Investment:\u003c/strong\u003e Prioritize funding for high-speed internet and AI-capable devices in underserved schools\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLongitudinal Research:\u003c/strong\u003e Study long-term effects on student autonomy and emotional growth\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTeacher Involvement:\u003c/strong\u003e Include educators in AI tool design and evaluation\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eStudent-Centered Metrics:\u003c/strong\u003e Develop inclusive assessment models that reflect diverse learning paths\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"8. Conclusion","content":"\u003cp\u003eAI-driven assistive technologies offer transformative potential for inclusive education. Teachers are optimistic yet cautious, advocating for thoughtful implementation that respects pedagogical integrity and student dignity. Bridging the gap between innovation and inclusion requires not just smarter tools\u0026mdash;but smarter collaboration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eEthics Approval Statement: This study was reviewed and approved by the Ethics Committee of the Ministry of Education, Tehran Province, Iran. No formal reference number was issued. Participant Consent Statement: All participating teachers provided informed consent prior to data collection. Consent was obtained in writing, and participants were assured of confidentiality and voluntary participation.\u003c/span\u003e\u003c/p\u003e\u003ch2\u003eClinical Trial Number\u003c/h2\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eHuman Ethics and Consent to Participate declarations: not applicable.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eV.E. conceived the study, designed the methodological framework, developed the survey instruments and interview protocols, led the data collection across all seven participating countries, performed quantitative analysis using SPSS, conducted qualitative coding in NVivo, drafted the manuscript, and provided all critical revisions and theoretical contextualization.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of Data and Material\u003c/h2\u003e\n\u003cp\u003eThe data utilized in this study were obtained from previously published research and publicly accessible repositories. All sources have been appropriately cited and acknowledged throughout the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col type=\"disc\"\u003e\n\u003cli\u003eAl-Maki, S. H., et al. (2025). \u003cem\u003eTeacher Well-being and AI Tools in Inclusive Classrooms\u003c/em\u003e. STEM Education, 5(1), 109\u0026ndash;129.\u003c/li\u003e\n\u003cli\u003eFu, B., Hadid, A., \u0026amp; Damer, N. (2025). \u003cem\u003eGenerative AI in Assistive Technologies: Trends and Future Directions\u003c/em\u003e. Image and Vision Computing, 154, 105347.\u003c/li\u003e\n\u003cli\u003eFitas, R. (2025). \u003cem\u003eInclusive Education with AI: Supporting Special Needs\u003c/em\u003e. arXiv:2504.14120\u003c/li\u003e\n\u003cli\u003eHussein, E., Hussein, M., \u0026amp; Al-Hendawi, M. (2025). \u003cem\u003eApplications of AI in Special Education: A Literature Review\u003c/em\u003e. Social Sciences, 14(5), 288.\u003c/li\u003e\n\u003cli\u003eMalviya, R., \u0026amp; Rajput, S. (2025). \u003cem\u003eAI-Driven Innovations in Assistive Technology\u003c/em\u003e. In \u003cem\u003eAdvances in AI-Created Disability Supports\u003c/em\u003e. Springer.\u003c/li\u003e\n\u003cli\u003eMcNulty, N. (2025). \u003cem\u003eAI for Inclusive Education\u003c/em\u003e. Retrieved from https://www.niallmcnulty.com/2025/03/ai-for-inclusive-education/\u003c/li\u003e\n\u003cli\u003eOyetade, K., \u0026amp; Zuva, T. (2025). \u003cem\u003eAdvancing Equitable Education with Inclusive AI\u003c/em\u003e. Educational Process: International Journal, 14, e2025087.\u003c/li\u003e\n\u003cli\u003ePagliara, S. M., et al. (2024). \u003cem\u003eThe Integration of Artificial Intelligence in Inclusive Education: A Scoping Review\u003c/em\u003e. Information, 15(12), 774.\u003c/li\u003e\n\u003cli\u003eUNESCO. (2023). \u003cem\u003eAI in Education: A Framework for Inclusion\u003c/em\u003e. Paris: UNESCO Publishing.\u003c/li\u003e\n\u003cli\u003eYang, Y., Chen, L., He, W., Sun, D., \u0026amp; Salas-Pilco, S. Z. (2024). \u003cem\u003eArtificial Intelligence for Enhancing Special Education for K-12\u003c/em\u003e. IJAIED, 34(4), 317\u0026ndash;345.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Inclusive Pedagogy, Artificial Intelligence in Education, Assistive Technologies, Special Needs Education, Teacher Training, Mixed Methods Research","lastPublishedDoi":"10.21203/rs.3.rs-7408910/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7408910/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the integration of artificial intelligence (AI) in special needs classrooms across seven countries. Using a mixed-methods approach, we explore how AI-driven assistive technologies - such as speech-to-text systems, generative AI tutors, and VR simulations - enhance inclusive pedagogy. Findings reveal improvements in academic performance, engagement, and emotional regulation, with a strong correlation between teacher training and perceived tool effectiveness. The paper concludes with recommendations for ethical frameworks, culturally responsive design, and infrastructure investment to support equitable AI adoption.\u003c/p\u003e","manuscriptTitle":"Empowering Inclusive Pedagogy: Teacher’s Experiences with AI- Driven Assistive Technologies in Special Needs Classrooms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 08:29:03","doi":"10.21203/rs.3.rs-7408910/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"99db0b92-fae4-4c52-b3f9-ca9f273791c9","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-11T22:23:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-08 08:29:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7408910","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7408910","identity":"rs-7408910","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}