Journals / İstanbul Gelişim Üniversitesi Sağlık Bilimleri Dergisi / 2026 / Sayı: 29 Özel Sayı

Evaluation of Artificial Intelligence-Based Early Intervention and Education Applications

Yapay Zekâ Temelli Erken Müdahale ve Eğitim Uygulamalarının Değerlendirilmesi

Hatice Keser(SIVAS CUMHURIYET UNIVERSITY)0000-0002-8283-7394
Şenay Şirin(ISTANBUL SISLI VOCATIONAL SCHOOL, İSTANBUL ŞİŞLİ VOCATIONAL SCHOOL)0000-0001-9294-3855
Kübra Aldırmaz(ISTANBUL GELISIM UNIVERSITY)0000-0002-7403-5324
Pages
80–95
Publication Date
2026-08-02
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings indicated that Duolingo ABC, Khanmigo, and DreamBox primarily supported academic skills, individualized learning, and cognitive development through adaptive mechanisms, whereas TADİS and Suufle contributed to the language, communication, and social participation skills of children with autism spectrum disorder.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Aim: This study was conducted to evaluate artificial intelligence (AI)-based early intervention and educational applications developed for children from the perspective of Child Development experts, in line with the parameters of the target population, area of use, technological infrastructure, advantages, limitations, and developmental appropriateness.
📊
Key Findings & Data:A total of 17 early intervention and educational applications were identified through a web-based search; among these, five applications that met the predetermined criteria were included in the study.
💡
Conclusion & Impact:The findings indicated that Duolingo ABC, Khanmigo, and DreamBox primarily supported academic skills, individualized learning, and cognitive development through adaptive mechanisms, whereas TADİS and Suufle contributed to the language, communication, and social participation skills of children with autism spectrum disorder.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Yapay zeka · Gelişimsel uygunluk · Eğitim uygulamaları

Abstract

Aim: This study was conducted to evaluate artificial intelligence (AI)-based early intervention and educational applications developed for children from the perspective of Child Development experts, in line with the parameters of the target population, area of use, technological infrastructure, advantages, limitations, and developmental appropriateness. Method: The study was designed as an expert-based qualitative screening study. Data were collected by examining the official websites, product promotion pages, user information pages, and privacy policies of the applications. A total of 17 early intervention and educational applications were identified through a web-based search; among these, five applications that met the predetermined criteria were included in the study. The applications reviewed were Duolingo ABC, Khanmigo, DreamBox, Suufle, and TADİS. The evaluation process was carried out by four researchers who are experts in child development, and the consistency rate among expert responses was 86.11%. Results: The applications were evaluated across seven main parameters. The findings indicated that Duolingo ABC, Khanmigo, and DreamBox primarily supported academic skills, individualized learning, and cognitive development through adaptive mechanisms, whereas TADİS and Suufle contributed to the language, communication, and social participation skills of children with autism spectrum disorder. However, limitations were identified in certain applications regarding technological transparency, cultural adaptation, data security, and independent scientific evidence of effectiveness. Conclusion: The results showed that AI-based early intervention and educational applications for children offer significant opportunities for personalized learning, motivation, accessibility, language development, and support for social communication skills. However, these applications should be carefully evaluated with regard to ethics, data security, scientific evidence, and developmental appropriateness. In this respect, it is recommended that AI applications developed for children be addressed not only through technological innovation but also within a holistic framework grounded in pedagogical and developmental principles.

Özet

Amaç: Bu araştırma; çocuklara yönelik geliştirilen Yapay Zekâ (YZ) temelli erken müdahale ve eğitim uygulamalarını; hedef kitle, kullanım alanı, teknolojik altyapı, avantajlar, sınırlılıklar ve gelişimsel uygunluk parametreleri doğrultusunda Çocuk Gelişimi uzmanları perspektifiyle değerlendirmek amacıyla yapılmıştır. Yöntem: Araştırma, uzman görüşlerine dayalı nitel bir tarama araştırmasıdır. Veriler, uygulamaların resmî web sayfaları, ürün tanıtım sayfaları, kullanım açıklamaları ve gizlilik politikaları incelenerek toplanmıştır. Web tabanlı taramada toplam 17 adet erken müdahale ve eğitim uygulaması listelenmiş; bunlardan belirlenen ölçütleri karşılayan beş uygulama araştırmaya dahil edilmiştir. İncelemeye Duolingo ABC, Khanmigo, DreamBox, Suufle ve TADİS uygulamaları alınmıştır. Değerlendirme süreci çocuk gelişimi alanında uzman dört araştırmacı tarafından yürütülmüş; uzman yanıtlarının tutarlılığı %86,11 olarak hesaplanmıştır. Bulgular: Araştırma bulgularına göre uygulamalar yedi ana parametre altında değerlendirilmiştir. Bulgular; Duolingo ABC, Khanmigo ve DreamBox’ın daha çok akademik becerileri, bireyselleştirilmiş öğrenmeyi ve uyarlanabilir mekanizmalarıyla bilişsel gelişimi desteklediği; TADİS ve Suufle uygulamalarının ise otizm spektrum bozukluğu olan çocukların dil, iletişim ve sosyal katılım becerilerine katkı sağladığı belirlenmiştir. Bununla birlikte bazı uygulamalarda teknolojik şeffaflık, kültürel uyarlama, veri güvenliği ve bağımsız bilimsel etkililik kanıtları açısından sınırlılıklar saptanmıştır. Sonuç: Araştırma sonucunda, çocuklara yönelik AI temelli ve erken müdahale ve eğitim uygulamalarının kişiselleştirilmiş öğrenme, motivasyon, erişilebilirlik, dil gelişimi ve sosyal iletişim becerilerinin desteklenmesi açısından önemli fırsatlar sunduğu; ancak etik, veri güvenliği, bilimsel kanıt ve gelişimsel uygunluk boyutlarında dikkatle değerlendirilmesi gerektiği saptanmıştır. Bu doğrultuda, çocuklara yönelik yapay zekâ uygulamalarının yalnızca teknolojik yenilik açısından değil, pedagojik ve gelişimsel ilkeler çerçevesinde bütüncül olarak ele alınması önerilmektedir.

🔖

Cite This Article

Gökaslan, F. G., Keser, H., Şirin, Ş., Aldırmaz, K. (2026). Evaluation of Artificial Intelligence-Based Early Intervention and Education Applications. İstanbul Gelişim Üniversitesi Sağlık Bilimleri Dergisi, Sayı: 29 Özel Sayı, 80–95. https://doi.org/10.38079/igusabder.1911793

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (51)

  1. Holmes W, Bialik M, Fadel C. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston, MA: Center for Curriculum Redesign; 2019.
  2. UNESCO. AI and Education: Guidance for Policy-Makers. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386012. Accessed January 7, 2026.
  3. Zawacki-Richter O, Marín VI, Bond M, Gouverneur F. Systematic review of research on artificial intelligence applications in higher education: where are the educators? Int J Educ Technol High Educ. 2019;16(1):1-27.
  4. OECD. AI and the Future of Skills, Volume 1: Capabilities and Assessments. OECD Publishing; 2021. doi: 10.1787/5ee71f34-en.
  5. Woolf N, Quinn J. Students’ perceptions of instructional design implementation in a contextualized learning activity. Educ Technol Res Dev. 2009;57(1):25-43.
  6. Luckin R, Holmes W, Griffiths M, Forcier LB. Intelligence Unleashed: An Argument for AI in Education. London: Pearson; 2016.
  7. Cassell J. Embodied conversational agents: representation and intelligence in user interfaces. AI Mag. 2000;22(4):67-83. doi: 10.1609/aimag.v22i4.1593.
  8. Sagar M. BabyX Project. Commercial project. Soul Machines; 2013.
  9. Nye BD, Graesser AC, Hu X. AutoTutor and family: a review of 17 years of natural language tutoring. Int J Artif Intell Educ. 2014;24(4):427-469.
  10. Tartaro A, Cassell J. Playing with virtual peers: bootstrapping contingent discourse in children with autism. In: Proceedings of the 8th International Conference on Intelligent Virtual Agents (IVA 2008). Springer; 2008:294-300. doi: 10.1007/978-3-540-85483-8_30.
  11. Mostow J, Aist G, Burkhead P, et al. Evaluation of an automated reading tutor that listens: comparison to human tutoring and classroom instruction. J Educ Comput Res. 2003;29(1):61-117. doi: 10.2190/06AX-QW99-EQ5G-RDCF.
  12. Farhood H, Nyden M, Beheshti A, Muller S. Artificial intelligence-based personalised learning in education: a systematic literature review. Discover Artificial Intelligence. 2025;5(1):331. doi: 10.1007/s44163-025-00598-x
  13. Zero To Three. Using Technology to Support Young Children’s Learning. Zero To Three. https://www.zerotothree.org/resource/using-technology-to-support-young-childrens-learning. Accessed December 24, 2025.
  14. Qin L, Wang H, Ning W, Cui M, Wang Q. New advances in the diagnosis and treatment of autism spectrum disorders. Eur J Med Res. 2024;29(1):322.
  15. Lai T, Xie C, Ruan M, Wang Z, Lu H, Fu S. Influence of artificial intelligence in education on adolescents’ social adaptability: the mediatory role of social support. PLoS One. 2023;18(3). doi: 10.1371/journal.pone.0283170.
  16. UNICEF. Policy Guidance on AI for Children. UNICEF. https://www.unicef.org/globalinsight/reports/policy-guidance-ai-children. Accessed January 2026.
  17. Resnick M, Maloney J, Monroy-Hernández A, et al. Scratch: programming for all. Commun ACM. 2009;52(11):60-67. doi: 10.1145/1592761.1592779.
  18. Miles MB, Huberman AM. Qualitative Data Analysis: An Expanded Sourcebook. 2nd ed. SAGE Publications; 1994.
  19. Duolingo ABC. https://abc.duolingo.com/. Accessed July 19, 2025.
  20. DreamBox. https://www.dreambox.com/. Accessed July 19, 2025.
  21. Khanmigo. https://www.khanmigo.ai/. Accessed July 19, 2025.
  22. Suufle. https://suufle.com/. Accessed August 10, 2025.
  23. TADİS. https://www.TADİS.com.tr/. Accessed August 10, 2025.
  24. Korkmaz B. Dil ve Beyin: Çocuklarda Dil ve Konuşma Bozuklukları. Yüce Yayınları; 2005:3.
  25. Güngören F. Chomsky. In: Özyürek A, ed. Kuramcıların Perspektifinden Gelişim Kuramlarına Bir Bakış. Nobel; 2024:171.
  26. Şahin S. Bilişsel gelişim. In: Metin NB, ed. Doğum Öncesi Dönemden Ergenliğe Çocuk Gelişimi. Pegem; 2021:91.
  27. González-Fernández B, de la Viña I. The effectiveness of app-based and classroom-based instruction on L2 learning and motivation. Lang Learn Technol. 2025;29(1):1-18.
  28. Smith B, Jiang X, Peters R. The effectiveness of Duolingo in developing receptive and productive language knowledge and proficiency. Lang Learn Technol. 2024;28(1).
  29. Kittredge AK, Hopman EW, Reuveni B, Dionne D, Freeman C, Jiang X. Mobile language app learners’ self-efficacy increases after using generative AI. Front Educ. 2025;10:1499497.
  30. Sudina E, Plonsky L. The effects of frequency, duration, and intensity on L2 learning through Duolingo: a natural experiment. J Second Lang Stud. 2024;7(1):1-43.
  31. Şirin Ş. Bandura. In: Özyürek A, ed. Kuramcıların Perspektifinden Gelişim Kuramlarına Bir Bakış. Nobel; 2024:81.
  32. Channa MA, Nordin ZS, Siming IA, Chandio AA, Koondher MA. Developing reading comprehension through metacognitive strategies: a review of previous studies. Engl Lang Teach. 2015;8(8):181-186.
  33. Berk L. Bebekler ve Çocuklar. Nobel. 2020:442-443.
  34. Oreopoulos P, Gibbs C, Jensen M, Price J. Teaching Teachers to Use Computer Assisted Learning Effectively: Experimental and Quasi-Experimental Evidence. National Bureau of Economic Research; 2024. Working Paper 32388. doi: 10.3386/w32388.
  35. Ferman B, Finamor L, Lima L. Are Public Schools Ready to Integrate Math Classes With Khan Academy? University Library of Munich; 2019.
  36. Khan Academy. How Oasis Elementary South Scaled Math Mastery With Khan Academy Districts. https://www.kisa.link/XAqSs. Accessed February 14, 2026.
  37. Khan Academy. Newark Public Schools. https://www.kisa.link/SYZYo. Accessed February 14, 2026.
  38. Khan Academy. Cold Spring. https://www.kisa.link/pbnpL. Accessed February 14, 2026.
  39. Khan Academy. How Oasis Elementary South Scaled Math Mastery With Khan Academy Districts. https://www.kisa.link/XAqSs. Accessed February 14, 2026.
  40. Doğan Ö, Şengül EA. Büyüme ve gelişme. In: Metin NB, ed. Doğum Öncesi Dönemden Ergenliğe Çocuk Gelişimi. Pegem; 2021:8.
  41. Harvard University, Center for Education Policy Research. DreamBox Learning Achievement Growth in the Howard County Public School System and Rocketship Education. https://www.kisa.link/gDATs.
  42. LearnPlatform. DreamBox Math Drives Higher Achievement for K-8 Students in Large Arizona District. https://www.kisa.link/YXFtZ. Accessed August 18, 2025.
  43. LearnPlatform by Instructure. DreamBox Math (2021-22). https://www.kisa.link/HPGwa. Accessed February 13, 2026.
  44. Discovery Education. Reading Motivation and Reading Success: A Two-Way Street. https://www.kisa.link/OvHvg. Accessed August 18, 2025.
  45. Neumann MM, Neumann DL. The use of touch-screen tablets at home and pre-school to foster emergent literacy. J Early Child Lit. 2017;17(2):203-220.
  46. Kucirkova N, Sheehy K, Messer D, Flewitt R. Digital Literacy in Early Childhood Education: Theoretical Perspectives and Practical Applications. Routledge; 2020.
  47. Bektaş E, Görgü E. Türk alternatif iletişim destek sistemlerinin engelli bireylerin dil gelişimi üzerindeki etkisi. J Civ Educ Res. 2025;9(2):55-65.
  48. Wang S. Artificial intelligence in education: a systematic literature review. Comput Educ. 2024;198:104804. doi: 10.1016/j.compedu.2024.104804.
  49. OECD. The Potential Impact of Artificial Intelligence on Equity and Inclusion in Education. OECD Publishing; 2024. doi: 10.1787/15df715b-en.
  50. UNICEF Innocenti. Policy Guidance on Artificial Intelligence for Children. UNICEF Innocenti; 2021.
  51. Personal Data Protection Authority (KVKK). Law No. 6698 on the Protection of Personal Data. Official Gazette. January 7, 2016.