Main Article Content

Abstract

Early childhood is a critical golden period for development; however, children experiencing neurodevelopmental delays often face late diagnosis and limited access to traditional face-to-face rehabilitation, particularly in low-resource settings like Nepal. This qualitative study employs a narrative interpretative approach to explore the potential of Generative Artificial Intelligence (AI) in supporting early childhood development and rehabilitation. Data were gathered through semi-structured interviews, classroom observations, and interactive workshops with a purposively sampled group of teachers, parents, and therapists. The thematic analysis reveals that Generative AI offers transformative opportunities across five core domains: acting as a patient digital conversational partner to boost speech and language skills ; transforming physical exercises into playful experiences using motion-based games and augmented reality (AR) ; facilitating adaptive cognitive growth through real-time adjusted problem-solving challenges ; coaching socio-emotional learning via safe virtual role-playing scenarios ; and breaking distance barriers through home-based tele-rehabilitation platforms. Nevertheless, stakeholders express vital ethical and practical concerns, including child data privacy risks, the dangerous impact of systemic inaccuracies, digital divides driven by limited hardware access, and the risk of reducing essential human touch. This study concludes that technology must never substitute human warmth, empathy, or love; instead, AI should complement human efforts under a "shared caregiving" model controlled by adults. The implications highlight the urgent need to equip educators with leadership skills, empower parents as co-creators of learning environments, and establish rigorous ethical and policy frameworks to ensure AI deployment remains safe, equitable, and child-centered.


[Masa anak usia dini merupakan periode emas yang krusial bagi perkembangan anak , namun anak-anak dengan gangguan neuroperkembangan sering kali menghadapi keterlambatan diagnosis dan keterbatasan akses terhadap rehabilitasi konvensional berbasis tatap muka, khususnya di wilayah dengan sumber daya terbatas seperti Nepal. Penelitian kualitatif dengan pendekatan naratif interpretatif ini bertujuan untuk mengeksplorasi potensi pemanfaatan Artificial Intelligence (AI) Generatif dalam mendukung perkembangan dan rehabilitasi anak usia dini. Data dikumpulkan melalui wawancara semistruktur, observasi kelas, dan lokakarya interaktif yang melibatkan guru, orang tua, dan terapis melalui teknik purposive sampling. Hasil analisis tematik menunjukkan bahwa AI Generatif menawarkan peluang besar pada lima domain utama: bertindak sebagai mitra bicara digital yang sabar untuk mendukung kemampuan bahasa ; mentransformasi terapi fisik menjadi permainan interaktif berbasis sensor gerak dan augmented reality (AR) ; memfasilitasi stimulasi kognitif adaptif melalui teka-teki yang menyesuaikan kemampuan anak secara real-time ; melatih aspek sosio-emosional lewat skenario bermain peran virtual yang aman ; serta memperluas jangkauan tele-rehabilitasi bagi keluarga di area terpencil. Kendati demikian, antusiasme ini dibayangi oleh tantangan etis dan praktis yang signifikan, termasuk masalah kerahasiaan data anak , risiko ketidakakuratan output sistem , kesenjangan digital akibat keterbatasan perangkat , serta kekhawatiran berkurangnya interaksi manusia. Penelitian ini menyimpulkan bahwa AI Generatif tidak boleh menggantikan hubungan emosional, melainkan harus diposisikan sebagai komplemen (shared caregiving) di bawah pengawasan orang dewasa. Implikasi penelitian menekankan urgensi pelatihan kepemimpinan bagi pendidik , pelibatan aktif orang tua sebagai mitra ko-kreator , dan perumusan kebijakan yang mendukung pemanfaatan teknologi secara inklusif, aman, dan tepat sasaran.]

Keywords

generative AI rehabilitation children tele-rehabilitation development

Article Details

How to Cite
Generative AI for Young Children: New Pathways in Rehabilitation and Development: Kecerdasan Buatan Generatif untuk Anak Usia Dini: Jalur Baru dalam Rehabilitasi dan Perkembangan. (2026). SiRad: Pelita Wawasan, 2(2), 223-246. https://doi.org/10.64728/sirad.v2i2.art5

How to Cite

Generative AI for Young Children: New Pathways in Rehabilitation and Development: Kecerdasan Buatan Generatif untuk Anak Usia Dini: Jalur Baru dalam Rehabilitasi dan Perkembangan. (2026). SiRad: Pelita Wawasan, 2(2), 223-246. https://doi.org/10.64728/sirad.v2i2.art5

References

  1. Alawneh, Y. J. J., Radwan, E. N. Z., Salman, F. N., Makhlouf, S. I., Makhamreh, K., & Alawneh, M. S. (2024, April). Ethical considerations in the use of AI in primary education: Privacy, bias, and inclusivity. In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS) (Vol. 1, pp. 1-6). IEEE.
  2. Arcega, J., Autman, I., De Guzman, B., Isidienu, L., Olivar, J., O'Neal, M., & Surdilla, B. (2020). The Human Touch: Is Modern Technology Decreasing the Value of Humanity in Patient Care?. Critical Care Nursing Quarterly, 43(3), 294-302.
  3. Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in healthcare (pp. 25-60). Academic Press.
  4. Brehon, K., Carriere, J., Churchill, K., Loyola-Sanchez, A., O’Connell, P., Papathanasoglou, E., ... & Manhas, K. P. (2022). Evaluating the impact of a novel telerehabilitation service to address neurological, musculoskeletal, or coronavirus disease 2019 rehabilitation concerns during the coronavirus disease 2019 pandemic. Digital Health, 8, 20552076221101684.
  5. Britto, P. R. (2017). Early Moments Matter for Every Child. UNICEF. 3 United Nations Plaza, New York, NY 10017.
  6. Browning, R. M. (2017). Behavior modification in child treatment: An experimental and clinical approach. Routledge.
  7. Buijsman, S. (2024). Over what range should reliabilists measure reliability? Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7, 61-73.
  8. Caro, K., Tentori, M., Martinez-Garcia, A. I., & Zavala-Ibarra, I. (2017). FroggyBobby: An exergame to support children with motor problems practicing motor coordination exercises during therapeutic interventions. Computers in Human Behavior, 71, 479-498.
  9. Cary Jr, M. P., Bessias, S., McCall, J., Pencina, M. J., Grady, S. D., Lytle, K., & Economou‐Zavlanos, N. J. (2025). Empowering nurses to champion Health equity & BE FAIR: Bias elimination for fair and responsible AI in healthcare. Journal of Nursing Scholarship, 57(1), 130-139.
  10. Castner, D. J. (2020). Translating the implementation gap: Three versions of early childhood curriculum leadership. Early Childhood Education Journal, 48(4), 429-440.
  11. Ching, C. C., Basham, J. D., & Jang, E. (2005). The legacy of the digital divide: Gender, socioeconomic status, and early exposure as predictors of full-spectrum technology use among young adults. Urban Education, 40(4), 394-411.
  12. Classen, A. I., & Westbrook, A. (2022). Professional credential program: impacting early childhood inclusive learning environments. International Journal of Inclusive Education, 26(7), 719-736.
  13. Clin, E., & Kissine, M. (2023). Listener-versus speaker-oriented disfluencies in autistic adults: Insights from wearable eye-tracking and skin conductance within a live face-to-face paradigm. Journal of Speech, Language, and Hearing Research, 66(8), 2562-2580.
  14. Clini, E., Roversi, P., & Crisafulli, E. (2010). Early rehabilitation: much better than nothing. American Journal of Respiratory and Critical Care Medicine, 181(10), 1016-1017.
  15. Conein, S. (2018). New Skills For Working In Digitized Surroundings-In Depth Studies Of Workplaces In 12 Different Occupations. In INTED2018 Proceedings (pp. 5247-5250). IATED.
  16. Correa, T., & Pavez, I. (2016). Digital inclusion in rural areas: A qualitative exploration of challenges faced by people from isolated communities. Journal of Computer-Mediated Communication, 21(3), 247-263.
  17. Crompton, H., Jones, M. V., & Burke, D. (2024). Affordances and challenges of artificial intelligence in K-12 education: A systematic review. Journal of research on technology in education, 56(3), 248-268.
  18. De Raeve, L.; Cump˘at, M.-C.; van Loo, A.; Costa, I.M.;Matos, M.A.; Dias, J.C.; Mârt, u, C.;Cavaleriu, B.; Ghergut, , A.; Maftei, A.;et al. Quality Standard for Rehabilitation of Young Deaf Children Receiving Cochlear Implants. Medicina 2023, 59, 1354.https://doi.org/10.3390/ medicina59071354
  19. Delello, J., Sung, W., Mokhtari, K., & De Giuseppe, T. (2024, March). Are K-16 educators prepared to address the educational and ethical ramifications of artificial intelligence software?. In Future of Information and Communication Conference (pp. 406-432). Cham: Springer Nature Switzerland.
  20. Denee, R. (2017). Distributed leadership for professional learning: Effective leadership practices in early childhood education (Doctoral dissertation, Open Access Te Herenga Waka-Victoria University of Wellington).
  21. Ding, A. C. E., Shi, L., Yang, H., & Choi, I. (2024). Enhancing teacher AI literacy and integration through different types of cases in teacher professional development. Computers and Education Open, 6, 100178.
  22. Dornelas, L. F., Duarte, N. M., Morales, N. M., Pinto, R. M., Araújo, R. R., Pereira, S. A., & Magalhães, L. C. (2016). Functional outcome of school children with history of global developmental delay. Journal of Child Neurology, 31(8), 1041-1051.
  23. Douglass, A. (2018). Redefining leadership: Lessons from an early education leadership development initiative. Early Childhood Education Journal, 46(4), 387-396.
  24. Du, Y., & Juefei-Xu, F. (2023). Generative AI for therapy? Opportunities and barriers for ChatGPT in speech-language therapy.
  25. Dyson, B., Baek, S., Lee, Y., Stuttle, K., & Fowler, J. (2025). Teachers’ Perspectives of Social and Emotional Learning in Elementary Physical Education. Journal of Teaching in Physical Education, 1(aop), 1-8.
  26. Eder, R. A. (1990). Uncovering young children's psychological selves: Individual and developmental differences. Child Development, 61(3), 849-863.
  27. Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative ai. Business & Information Systems Engineering, 66(1), 111-126.
  28. First, L. R., & Palfrey, J. S. (1994). The infant or young child with developmental delay. New England journal of medicine, 330(7), 478-483.
  29. Gabriel, S. (2024). Generative AI and educational (in) equity. In International Conference on AI Research.
  30. García-Vergara, S., Brown, L., Park, H. W., & Howard, A. M. (2014). Engaging children in play therapy: The coupling of virtual reality games with social robotics. In Technologies of inclusive well-being: Serious games, alternative realities, and play therapy (pp. 139-163). Berlin, Heidelberg: Springer Berlin Heidelberg.
  31. Gibbs, L. (2022). Leadership emergence and development: Organizations shaping leading in early childhood education. Educational management administration & leadership, 50(4), 672-693.
  32. Girouard-Hallam, L. N., & Danovitch, J. H. (2022). Children’s trust in and learning from voice assistants. Developmental Psychology, 58(4), 646.
  33. Guo, J. X., Zhang, G. H., & Zhang, Y. M. (2025). AI-Powered Assessment of Motor Development: Using Platforms Like KineticAI to Analyze Fundamental Movement Skills in Children. Perceptual and Motor Skills, 00315125251357047.
  34. Haartsen, R., Jones, E. J., & Johnson, M. H. (2016). Human brain development over the early years. Current Opinion in Behavioral Sciences, 10, 149-154.
  35. Han, A., & Cai, Z. (2023, June). Design implications of generative AI systems for visual storytelling for young learners. In Proceedings of the 22nd annual ACM interaction design and children conference (pp. 470-474).
  36. Henriksen, D., Creely, E., Gruber, N., & Leahy, S. (2025). Social-emotional learning and generative AI: A critical literature review and framework for teacher education. Journal of Teacher Education, 76(3), 312-328.
  37. Holyfield, C., MacNeil, S., Caldwell, N., O'Neill Zimmerman, T., Lorah, E., Dragut, E., & Vucetic, S. (2024). Leveraging communication partner speech to automate augmented input for children on the autism spectrum who are minimally verbal: Prototype development and preliminary efficacy investigation (Vol. 33, No. 3, pp. 1174-1192). American Speech-Language-Hearing Association.
  38. Honey, A., Almomani, F., Chen, Y. W. R., Codd, Y., Kim, J. A., Kunishige, M., ... & McGrath, M. (2025). Supporting parents with disability and other challenges through occupational therapy: What is needed?. Australian Occupational Therapy Journal, 72(3), e70026.
  39. Jacobson, S., & Notman, R. (2018). Leadership in Early Childhood Education: Implications for Parental Involvement and Student Success from New Zealand. International Studies in Educational Administration (Commonwealth Council for Educational Administration & Management (CCEAM)), 46(1).
  40. Jamiat, N., & Othman, N. F. N. (2019, October). Effects of augmented reality mobile apps on early childhood education students' achievement. In Proceedings of the 3rd International Conference on Digital Technology in Education (pp. 30-33).
  41. Jauhiainen, J. S., & Guerra, A. G. (2023). Generative AI and ChatGPT in school children’s education: Evidence from a school lesson. Sustainability, 15(18), 14025.
  42. Javed, H., Jeon, M., Howard, A., & Park, C. H. (2018, March). Robot-assisted socio-emotional intervention framework for children with autism spectrum disorder. In Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (pp. 131-132).
  43. Kaswan, K. S., Dhatterwal, J. S., & Ojha, R. P. (2024). AI in personalized learning. In Advances in technological innovations in higher education (pp. 103-117). CRC Press.
  44. Kawakami, A., Sivaraman, V., Cheng, H. F., Stapleton, L., Cheng, Y., Qing, D., ... & Holstein, K. (2022, April). Improving human-AI partnerships in child welfare: understanding worker practices, challenges, and desires for algorithmic decision support. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-18).
  45. Kazdin, A. E. (1993). Psychotherapy for children and adolescents: Current progress and future research directions. American Psychologist, 48(6), 644.
  46. Kewalramani, S., Kidman, G., & Palaiologou, I. (2021). Using Artificial Intelligence (AI)-interfaced robotic toys in early childhood settings: a case for children’s inquiry literacy. European Early Childhood Education Research Journal, 29(5), 652-668.
  47. Khalid, U. B., Naeem, M., Stasolla, F., Syed, M. H., Abbas, M., & Coronato, A. (2024). Impact of AI-powered solutions in rehabilitation process: Recent improvements and future trends. International Journal of General Medicine, 943-969.
  48. Klatte, I. S., Bloemen, M., de Groot, A., Mantel, T. C., Ketelaar, M., & Gerrits, E. (2024). Collaborative working in speech and language therapy for children with DLD—What are parents’ needs?. International journal of language & communication disorders, 59(1), 340-353.
  49. Kolednjak, D. (2024). A comparison of human-authored and AI-generated picturebooks in readalongs with Young Learners (Doctoral dissertation, University of Zagreb. Faculty of Teacher Education).
  50. Kosoy, E. (2025). Youth in the Loop: Harnessing Children's Exploration, Causal Reasoning, and Data to Shape the Development and Evaluation of Artificial Intelligence (Doctoral dissertation, University of California, Berkeley
  51. Laxton, D., & Horn, A. (2023). An early years model of transformative leadership: Becoming a change agent. Early Years Educator, 23(21), 20-21.
  52. Laxton, D., Cooper, L., & Younie, S. (2021). Translational research in action: The use of technology to disseminate information to parents during the COVID‐19 pandemic. British Journal of Educational Technology, 52(4), 1538-1553
  53. Le, H. H., Loomes, M. J., & Loureiro, R. C. (2023). AI enhanced collaborative human-machine interactions for home-based telerehabilitation. Journal of Rehabilitation and Assistive Technologies Engineering, 10, 20556683231156788.
  54. Lewis, A., Dangol, A., Suh, H., Olszewski, A., Fogarty, J., & Kientz, J. A. (2025, April). Exploring AI-Based Support in Speech-Language Pathology for Culturally and Linguistically Diverse Children. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1-19).
  55. Li, J., Bhat, A., & Barmaki, R. (2021, October). Improving the movement synchrony estimation with action quality assessment in children play therapy. In Proceedings of the 2021 International Conference on Multimodal Interaction (pp. 397-406).
  56. Likhar, A., Baghel, P., Patil, M., & Patil, M. S. (2022). Early childhood development and social determinants. Cureus, 14(9).
  57. Lin, Y., Lemos, M., & Neuschaefer-Rube, C. (2021). Digital health and digital learning experiences across speech-language pathology, phoniatrics, and otolaryngology: interdisciplinary survey study. JMIR Medical Education, 7(4), e30873.
  58. Liu, B., & Lin, Y. C. (2024, June). Integrating Generative AI into Visual Perception Therapy for Children with Developmental Delays: An Empirical Eye-Tracking Study. In International Conference on Human-Computer Interaction (pp. 221-232). Cham: Springer Nature Switzerland.
  59. Louie, R., Gergle, D., & Zhang, H. (2022, April). Affinder: Expressing concepts of situations that afford activities using context-detectors. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-18).
  60. Lwin, M. O., Stanaland, A. J., & Miyazaki, A. D. (2008). Protecting children's privacy online: How parental mediation strategies affect website safeguard effectiveness. Journal of retailing, 84(2), 205-217.
  61. Lyu, Y., An, P., Zhang, H., Katsuragawa, K., & Zhao, J. (2024). Designing AI-enabled games to support social-emotional learning for children with autism spectrum disorders. arXiv preprint arXiv:2404.15576.
  62. Martin, F., Gezer, T., Anderson, J., Polly, D., & Wang, W. (2021). Examining parents perception on elementary school children digital safety. Educational Media International, 58(1), 60-77.
  63. Mayo, N. E. (1991). The effect of physical therapy for children with motor delay and cerebral palsy: a randomized clinical trial. American journal of physical medicine & rehabilitation, 70(5), 258-267.
  64. Mazon, C., Clément, B., Roy, D., Oudeyer, P. Y., & Sauzéon, H. (2023). Pilot study of an intervention based on an intelligent tutoring system (ITS) for instructing mathematical skills of students with ASD and/or ID. Education and Information Technologies, 28(8), 9325-9354.
  65. McStay, A., & Rosner, G. (2021). Emotional artificial intelligence in children’s toys and devices: Ethics, governance and practical remedies. Big Data & Society, 8(1), 2053951721994877.
  66. Mercer, N., & Howe, C. (2012). Explaining the dialogic processes of teaching and learning: The value and potential of sociocultural theory. Learning, Culture and Social Interaction, 1(1), 12-21. https://doi.org/10.1016/j.lcsi.2012.03.001
  67. Michaud, F., Salter, T., Duquette, A., & Laplante, J. F. (2007). Perspectives on mobile robots as tools for child development and pediatric rehabilitation. Assistive Technology, 19(1), 21-36.
  68. Muscott, H. S., & Gifford, T. (1994). Virtual reality and social skills training for students with behavioral disorders: Applications, challenges and promising practices. Education and Treatment of Children, 417-434Myers, M. D. (2019). Qualitative research in business and management.Nagy, M., Sisk, B., Lai, A., & Kodish, E. (2024). Will artificial intelligence widen the therapeutic gap between children and adults?. Pediatric Investigation, 8(01), 1-6.
  69. Na-songkhla, J., Mahakaew, V., & Peytcheva-Forsyth, R. (2024). The Emergence of Generative Artificial Intelligence: Enhancing Critical Thinking Skills in ChatGPTIntegrated Cognitive Flexibility Approach. Generative Artificial Intelligence in Higher Education: A Handbook for Educational Leaders, 52.
  70. NICHD Early Child Care Research Network. (2004). Type of child care and children’s development at 54 months. Early Childhood Research Quarterly, 19(2), 203-230.
  71. Nicholson, J., Capitelli, S., Richert, A. E., Bauer, A., & Bonetti, S. (2016). The Affordances of Using a Teacher Leadership Network to Support Leadership Development: Creating Collaborative Thinking Spaces to Strengthen Teachers' Skills in Facilitating Productive Evidence-Informed Conversations. Teacher Education Quarterly, 43(1), 29-50.
  72. Nikolajeva, M., & Scott, C. (2013). How picturebooks work. Routledge.
  73. Nikolic, S., Wentworth, I., Sheridan, L., Moss, S., Duursma, E., Jones, R. A., ... & Middleton, R. (2024). A systematic literature review of attitudes, intentions and behaviours of teaching academics pertaining to AI and generative AI (GenAI) in higher education: An analysis of GenAI adoption using the UTAUT framework. Australasian Journal of Educational Technology.
  74. Ning, Y., Teixayavong, S., Shang, Y., Savulescu, J., Nagaraj, V., Miao, D., ... & Liu, N. (2024). Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist. The Lancet Digital Health, 6(11), e848-e856.
  75. Novita, D., Kurnia, F. D., & Mustofa, A. (2020). Collaborative learning as the manifestation of sociocultural theory: Teachers’ perspectives. Jurnal Pendidikan Bahasa Inggris (Exposure Journal 13), 9(1), 13, 25
  76. Oberg, G. (2025). Moral injury in teaching: the systemic roots of ethical conflict and emotional burnout in education. Educational Review, 1-24.
  77. Oh-Young, C., & Karlin, M. (2025). Artificial Intelligence... In the Early Childhood Special Education Classroom!!?. TEACHING Exceptional Children, 57(5), 348-356.
  78. Oluwagbenro, M. B. (2024). Generative AI: Definition, concepts, applications, and future prospects. Authorea Prepr.
  79. Palethorpe, L. J. (2019). A change agent for improving quality: The educational leader in Australian early childhood education and care.
  80. PATEL, S. V. (2023). Experiences and expectations: Narratives of immigrant early childhood educators and influences on curriculum and pedagogical decision-making (Doctoral dissertation, Monash University).
  81. Ragone, G., Good, J., & Howland, K. (2021). How technology applied to music-therapy and sound-based activities addresses motor and social skills in autistic children. Multimodal Technologies and Interaction, 5(3), 11.
  82. Ravichandran, K. (2024). Identifying learning difficulties at an early stage in education with the help of artificial intelligence models and predictive analytics. International Research Journal of Multidisciplinary Scope (IRJMS), 5(4), 1455-1461.
  83. Reddy, K. J. (2025). Traditional Approaches and Limitations. In Innovations in Neurocognitive Rehabilitation: Harnessing Technology for Effective Therapy (pp. 39-51). Cham: Springer Nature Switzerland.
  84. Rous, B. S., & Hallam, R. A. (2006). Tools for Transition in Early Childhood: A Step-by-Step Guide for Agencies, Teachers, and Families. Brookes Publishing Company. PO Box 10624, Baltimore, MD 21285.
  85. Sapiets, S. J., Hastings, R. P., & Totsika, V. (2024). Predictors of access to early support in families of children with suspected or diagnosed developmental disabilities in the United Kingdom. Journal of autism and developmental disorders, 54(4), 1628-1641.
  86. Saracho, O. N. (2017). Literacy and language: new developments in research, theory, and practice. Early Child Development and Care, 187(3-4), 299-304.
  87. Scherzer, A. L., Chhagan, M., Kauchali, S., & Susser, E. (2012). Global perspective on early diagnosis and intervention for children with developmental delays and disabilities. Developmental Medicine & Child Neurology, 54(12), 1079-1084.
  88. Schunk, D. H. (1987). Peer models and children’s behavioral change. Review of educational research, 57(2), 149-174.
  89. Senadheera, I., Hettiarachchi, P., Haslam, B., Nawaratne, R., Sheehan, J., Lockwood, K. J., ... & Carey, L. M. (2024). AI applications in adult stroke recovery and rehabilitation: a scoping review using AI. Sensors (Basel, Switzerland), 24(20), 6585.
  90. Sezgin, E., & McKay, I. (2024). Behavioral health and generative AI: a perspective on future of therapies and patient care. npj Mental Health Research, 3(1), 25.
  91. Sharma, R. (2024). Engaging ECD Educators in Leadership Development: Unfolding the Practice. Journal of Durgalaxmi, 3, 236-254.
  92. Shih, T. Y., Wang, T. N., Shieh, J. Y., Lin, S. Y., Ruan, S. J., Tang, H. H., & Chen, H. L. (2023). Comparative effects of kinect-based versus therapist-based constraint-induced movement therapy on motor control and daily motor function in children with unilateral cerebral palsy: a randomized control trial. Journal of neuroengineering and rehabilitation, 20(1), 13.
  93. Shrestha, R., Dissanayake, C., & Barbaro, J. (2019). Age of diagnosis of autism spectrum disorder in Nepal. Journal of autism and developmental disorders, 49(6), 2258-2267.
  94. Skinner, A. C., & Slifkin, R. T. (2007). Rural/urban differences in barriers to and burden of care for children with special health care needs. The Journal of Rural Health, 23(2), 150-157.
  95. Smit, N. A., Van der Linde, J., Eccles, R., Swanepoel, D. W., & Graham, M. A. (2021). Exploring the knowledge and needs of early childhood development practitioners from a low-resource community. Early Childhood Education Journal, 49(2), 197-208.Stolarz, M., Mitrevski, A., Wasil, M., & Plöger, P. G. (2024). Learning-based personalisation of robot behaviour for robot-assisted therapy. Frontiers in Robotics and AI, 11, 1352152.
  96. Su, J., & Yang, W. (2023). A systematic review of integrating computational thinking in early childhood education. Computers and Education Open, 4, 100122.
  97. Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education, 6(3), 355-366.
  98. Su, J., Ng, D. T. K., & Chu, S. K. W. (2023). Artificial intelligence (AI) literacy in early childhood education: The challenges and opportunities. Computers and Education: Artificial Intelligence, 4, 100124.
  99. Tang, Y., Chen, L., Chen, Z., Chen, W., Cai, Y., Du, Y., ... & Sun, L. (2024, May). Emoeden: Applying generative artificial intelligence to emotional learning for children with high-function autism. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1-20).
  100. Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., & Rintel, S. (2024, May). The metacognitive demands and opportunities of generative AI. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1-24).
  101. Tiwari, S. P., Kostenko, O., & Yekhanurov, Y. (2025). Understanding Technology in the Context of National Development: Critical Reflections. SciFormat Publishing Inc.
  102. Tomlinson, C. A. (2002). Different learners different lessons: squeezing students into a one-size-fits-all curriculum has left many behind. By differentiating your instruction, you can meet the needs of every child.(Pathways to Reach Every Learner). Instructor (1990), 112(2), 21-26.
  103. Wang, G., Zhao, J., & Shadbolt, N. (2019). Are children fully aware of online privacy risks and how can we improve their coping ability?. arXiv preprint arXiv:1902.02635.
  104. Whyte, M. (2016). Working parents' perspectives and involvement in their young child's learning using the Initiating Parent Voice (Doctoral dissertation, University of Auckland).
  105. Wood, A., Hill, A., Cottrell, N., & Copley, J. (2024). Clinician experience of being interprofessional: an interpretive phenomenological analysis. Journal of Interprofessional Care, 38(6), 1035-1049.
  106. Xiang, H., Zhou, J., & Xie, B. (2023). AI tools for debunking online spam reviews? Trust of younger and older adults in AI detection criteria. Behaviour & Information Technology, 42(5), 478-497.
  107. Xu, Q., & Li, P. (2023). Computational modeling of language learning in the era of generative artificial intelligence: A response to open peer commentaries. Language learning, 73, 83-94.
  108. Xu, Y., Thomas, T., Yu, C. L., & Pan, E. Z. (2025). What makes children perceive or not perceive minds in generative AI? Computers in Human Behavior: Artificial Humans, 4, 100135.
  109. Yousif, J. H. (2025). Artificial Intelligence Revolution for Enhancing Modern Education Using Zone of Proximal Development Approach. Applied Computing Journal, 386-398.
  110. Yulianingsih, W., Susilo, H., & Nugroho, R. (2020, February). Optimizing golden age through parenting in saqo kindegarten. In 1st International Conference on Lifelong Learning and Education for Sustainability (ICLLES 2019) (pp. 187-191). Atlantis Press.

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