The Historical Development of Child-Oriented Intelligence Tests and Artificial Intelligence

Öz

This review study aims to examine the historical development of child-oriented intelligence tests and to evaluate the transformation of these instruments within the context of contemporary technological advancements. In this study, the theoretical foundations of intelligence and intelligence testing are initially addressed, followed by an examination of the emergence of psychometric assessment tools. The first section details the development processes, fields of application, and contributions to child assessment practices of foundational tests, particularly the Stanford-Binet Intelligence Scales and the Wechsler Intelligence Scale for Children (WISC), which are the primary instruments that come to mind regarding intelligence testing in the literature. Furthermore, other intelligence tests are briefly touched upon to provide a general framework. The second section comprehensively examines artificial intelligence (AI) applications within the scope of child-oriented assessments. In this regard, how AI is utilized in cognitive, emotional, and behavioral assessment processes, its developmental trajectory, the models and applications employed, and the efficacy of these technologies in diagnosis, classification, and intervention are analyzed based on the existing literature. In the conclusion and discussion section, the advantages and limitations of AI-based assessment tools are addressed, and implications for future research and practice are presented in line with current debates on the use of these technologies in the field of child intelligence assessment.

Anahtar Kelimeler

• Intelligence
• Child-Oriented Intelligence Tests
• WISC-V
• Standford-Binet
• Artificial Intelligence

Çocuk Odaklı Zeka Testlerinin Tarihsel Gelişimi ve Yapay Zeka

Öz

Bu derleme çalışmasının amacı, çocuk odaklı zekâ testlerinin tarihsel gelişim sürecini incelemek ve bu testlerin güncel teknolojik gelişmeler bağlamındaki dönüşümünü değerlendirmektir. Çalışmada öncelikle zekâ ve zekâ testi kavramlarının kuramsal temelleri ele alınmış, ardından psikometrik ölçme araçlarının ortaya çıkış süreci incelenmiştir. Birinci bölümde, literatürde zekâ testi denildiğinde akla ilk gelen araçlardan olan Stanford-Binet Zekâ Testi ve Wechsler Çocuklar İçin Zekâ Ölçeği (WISC) başta olmak üzere temel testlerin geliştirilme süreçleri, kullanım alanları ve çocuk değerlendirme pratiğine katkıları ayrıntılı biçimde ele alınmıştır. Ayrıca diğer zekâ testlerine genel bir çerçeve sunacak şekilde kısaca değinilmiştir. İkinci bölümde ise çocuk odaklı zekâ testleri bağlamında yapay zekâ uygulamaları kapsamlı biçimde incelenmiştir. Bu kapsamda yapay zekânın bilişsel, duygusal ve davranışsal değerlendirme süreçlerinde nasıl kullanıldığı, gelişim süreci, kullanılan model ve uygulamalar ile bu teknolojilerin tanılama, sınıflandırma, değerlendirme ve müdahale alanlarındaki etkililiği literatür temelinde analiz edilmiştir. Sonuç ve tartışma bölümünde, yapay zekâ tabanlı değerlendirme araçlarının sağladığı avantajlar ve barındırdığı sınırlılıklar ele alınmış; çocuklarda zekâ değerlendirmesi alanında bu teknolojilerin kullanımına ilişkin güncel tartışmalar doğrultusunda gelecekte yapılacak araştırmalar ve uygulamalar için çıkarımlar sunulmuştur.

Anahtar Kelimeler

• Zeka
• Çocuk Odaklı Zeka Testleri
• WISC-V
• Standford-Binet
• Yapay Zeka

Kaynakça

1. Akhtar, H. (2022). Measuring fluid reasoning and its cultural issues: A review in the Indonesian context. Buletin Psikologi, 30(2), 276–288. https://doi.org/10.22146/buletinpsikologi.74475
2. Alam, M. S., Rashid, M. M., Faizabadi, A. R., Mohd Zaki, H. F., Alam, T. E., Ali, M. S., Gupta, K. D., & Ahsan, M. M. (2023). Efficient deep learning-based data-centric approach for autism spectrum disorder diagnosis from facial images using explainable AI. Technologies, 11(5), Article 115.
3. Alberca-González, A., & Fernández-Jiménez, E. (2026). Artificial intelligence support for diagnosis of neurodevelopmental disorders during childhood: An umbrella review. Frontiers in Psychiatry, 17, Article 1697185.
4. Alekseeva, O. S., Rzhanova, I. E., Guskova, V. E., & Burdukova, Y. A. (2025). Wechsler Intelligence Scale for Children (WISC-V) as a diagnostic instrument for children with intellectual disabilities. Psychological-Educational Studies, 17(3), 42–58.
5. Amann, J., Vayena, E., Ormond, K. E., Frey, D., Madai, V. I., & Blasimme, A. (2023). Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke. PLoS ONE, 18(1).https://doi.org/10.1371/journal.pone.0279088
6. Aslan Genç, H., Markaroğlu, R., Muradoğlu, S., Necef, I., & Mutluer, T. (2023). Cognitive profiles of children with attention deficit hyperactivity disorder: Differences between those with and without comorbid psychiatric diagnoses. Turkish Journal of Clinical Psychiatry, 26, 272–281.
7. Atlam, E.-S., Masud, M., Rokaya, M., Meshref, H., Gad, I., & Almars, A. M. (2024). EASDM: Explainable Autism Spectrum Disorder Model based on deep learning. Journal of Disability Research, 3(1). https://doi.org/10.57197/jdr-2024-0003
8. Azka, F., & Hendrawan, D. (2023). Intelligence profile and executive function between children with typical development and children with high-functioning autism spectrum disorder. Jurnal Psikologi, 50(1), 83–103.

9. Bal Sezerel, B., Ateşgöz, N. N., & Kirişçi, N. (2023). Intelligence differences across years: A trend analysis. Journal of Theoretical Educational Sciences, 16(1), 107–126. https://doi.org/10.30831/akukeg.1099061
10. Band, S. S., Yarahmadi, A., Hsu, C.-C., Biyari, M., Sookhak, M., Ameri, R., Dehzangi, I., Chronopoulos, A. T., & Liang, H-W. (2023). Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods. Informatics in Medicine Unlocked, 40, Article 101286.
11. Bartholomaeus, V., Chronowski, N. H., Santiago, P. H. R., Kuring, J. K., & Sawyer, A. (2025). Equivalence of telehealth and face-to-face administration of the Wechsler Adult Intelligence Scale Fourth Edition (WAIS-IV). The Clinical Neuropsychologist, 39(5), 1073–1096.
12. Baum, K. T., Shear, P. K., Howe, S. R., & Bishop, S. L. (2015). A comparison of WISC-IV and SB-5 intelligence scores in adolescents with autism spectrum disorder. Autism: The International Journal of Research and Practice, 19(6), 736–745.
13. Becker, A., Daseking, M., & Kerner auch Koerner, J. (2021). Cognitive profiles in the WISC-V of children with ADHD and specific learning disorders. Sustainability, 13(17), Article 9948. https://doi.org/10.3390/su13179948
14. Bilsem Zekâ. (2026, 6 Nisan). TUZÖ nedir? 2026 örnek sorular, alt testler ve tam rehber. https://bilsemzekâ.com/turk-ulusal-zekâ-olcegi-tuzo/
15. Björngrim, S., van den Hurk, W., Betancort, M., Machado, A., & Lindau, M. (2019). Comparing traditional and digitized cognitive tests used in standard clinical evaluation - A study of the digital application Minnemera. Frontiers in Psychology, 10, Article 2327.
16. Bonvino, A., Cornacchia, E., Scaramuzzi, G. F., Gasparre, D., Manippa, V., Rivolta, D., & Taurisano, P. (2025). Digital tools for mild cognitive impairment: A systematic review and meta-analysis of diagnostic accuracy and methodological challenges. Neuropsychology Review. https://doi.org/10.1007/s11065-025-09680-8
17. Bulut Özek, M., & Gülmüş, M. (2025). Trends in artificial intelligence for autism spectrum disorder: A bibliometric analysis study. International Journal of Technology in Education, 8(4), 1048–1065. https://doi.org/10.46328/ijte.1222
18. Canivez, G. L., & Watkins, M. W. (2016). Review of the Wechsler Intelligence Scale for Children-Fifth Edition: Critique, commentary, and independent analyses. In A. S. Kaufman, S. E. Raiford, & D. L. Coalson (Eds.), Intelligent Testing with the WISC-V (pp. 683–702). Wiley.
19. Cao, M., Martin, E., & Li, X. (2023). Machine learning in attention-deficit/hyperactivity disorder: New approaches toward understanding the neural mechanisms. Translational Psychiatry, 13, Article 236.
20. Castro, A. de C. (2014). Mental test implementation in the National Technical School in the period between 1942 and 1959: An analysis from the questioning of the notions of center and periphery. Universitas Psychologica, 13(5), 1739–1756.
21. Chan, A. T. C., Ip, R. T. F., Tran, J. Y. S., Chan, J. Y. C., & Tsoi, K. K. F. (2024). Computerized cognitive training for memory functions in mild cognitive impairment or dementia: A systematic review and meta-analysis. Npj Digital Medicine, 7, Article 1. https://doi.org/10.1038/s41746-023-00987-5
22. Chan, J. Y. C., Bat, B. K. K., Wong, A., Chan, T. K., Huo, Z., Yip, B. H. K., Kowk, T. C. Y., & Tsoi, K. K. F. (2022). Evaluation of digital drawing tests and paper-and-pencil drawing tests for the screening of mild cognitive impairment and dementia: A systematic review and meta-analysis of diagnostic studies. Neuropsychology Review, 32, 566–576.
23. Chng, S. Y., Tern, M. J. W., Lee, Y. S., Cheng, L. T.-E., Kapur, J., Eriksson, J. G., Chong, Y. S., & Savulescu, J. (2025). Ethical considerations in AI for child health and recommendations for child-centered medical AI. Npj Digital Medicine, 8, Article 152.
24. Choun, S.-H., Lee, S.-W., Min, Y.-S., Park, E., Kim, J.-H., & Jung, T.-D. (2025). Intra- and inter-rater reliability analysis of MMSE-K and tablet PC-based MMSE-K kit in patients with neurologic disease. Healthcare, 13(23), Article 3015.
25. Cohen, R. J., & Swerdlik, M. E. (2013). Psikolojik test ve değerleme: Testlere ve ölçmeye giriş (E. Tavşancıl, Çev.). Nobel Akademik Yayıncılık. (Orijinal eserin yayın tarihi 1988).
26. Cornacchia, E., Bonvino, A., Scaramuzzi, G. F., Gasparre, D., Simeoli, R., Marocco, D., & Taurisano, P. (2025). Digital screening for early identification of cognitive impairment: A narrative review. Wiley Interdisciplinary Reviews: Cognitive Science, 16(4), Article e70009.
27. Coşkun, F., & Gülleroğlu, H. D. (2021). Yapay zekânın tarih içindeki gelişimi ve eğitimde kullanılması. Ankara Üniversitesi Eğitim Bilimleri Fakültesi Dergisi, 54(3), 947–966. https://doi.org/10.30964/auebfd.916220
28. Çak, H. T., Çengel Kültür, S. E., Gökler, B., Öktem, F., & Taşkıran, C. (2017). The behavior rating inventory of executive function and continuous performance test in preschoolers with attention deficit hyperactivity disorder. Psychiatry Investigation, 14(3), 260–270.
29. Çalıkoğlu, B. (2022). Zekâ Kuramları: Bazısı tutar bazısı tutmaz. Siirt Eğitim Dergisi, 2(2), 133–150. https://izlik.org/JA54TJ77WN D’Alfonso, S., Coghlan, S., Schmidt, S., & Mangelsdorf, S. (2025). Ethical dimensions of digital phenotyping within the context of mental healthcare. Journal of Technology in Behavioral Science, 10, 132–147. https://doi.org/10.1007/s41347-024-00423-9
30. Da Silveira, T. B. N., & Lopes, H. S. (2023). Intelligence across humans and machines: A joint perspective. Frontiers in Psychology, 14, Article 1209761. https://doi.org/10.3389/fpsyg.2023.1209761
31. Deary, I. J., & Sternberg, R. J. (2021). Ian Deary and Robert Sternberg answer five self-inflicted questions about human intelligence. Intelligence, 86, Article 101539. https://doi.org/10.1016/j.intell.2021.101539
32. Deckker, D., & Sumanasekara, S. (2025). Systematic review on AI in special education: Enhancing learning for neurodiverse students. EPRA International Journal of Multidisciplinary Research (IJMR), 11(2), 539–545. https://doi.org/10.36713/epra20360
33. Demir, H., & Gelbal, S. (2025). A systematic review on computerized adaptive testing. Erzincan Üniversitesi Eğitim Fakültesi Dergisi, 27(1), 137–150. https://doi.org/10.17556/erziefd.1577880
34. Devadas, R. S. (2025). AI augmented education for children with special needs: A scholarly perspective. World Journal of Advanced Engineering Technology and Sciences, 15(3), 2000–2008. https://doi.org/10.30574/wjaets.2025.15.3.1125
35. Dhar, D., Chowdhury, M., Farsi, S., Rahman, A., Dipto, A. S., & Sultana, S. (2025, November 20–22). Dyslexia, ADHD, dyscalculia, and dyspraxia prediction in children using machine learning and explainable AI. In S. Rangaswamy, Anjaneyappa, & J. Usha (Eds.), 2025 9th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS) (pp. 1–6). IEEE.
36. Egbuna, I. K., Otoibhili, P. A., Abdulkareem, R. O., Ogbozor, F. I., Oyegue, P. E., Azih, N. K., & Akomolafe, O. B. (2025). Explainable Artificial Intelligence (XAI) in diagnosing neurodevelopmental disorders: From black boxes to clinical transparency. International Journal of Biological and Pharmaceutical Sciences Archive, 10(1), 31–57.
37. El Hmimdi, A. E. E., & Kapoula, Z. (2025). Distinguishing dyslexia, attention deficit, and learning disorders: Insights from AI and eye movements. Bioengineering, 12(7), Article 737. https://doi.org/10.3390/bioengineering12070737
38. El Hmimdi, A. E. E., Kapoula, Z., & Sainte Fare Garnot, V. (2024). Deep learning-based detection of learning disorders on a large-scale dataset of eye movement records. BioMedInformatics, 4(1), 519–541. https://doi.org/10.3390/biomedinformatics4010029
39. Farmer, R. L., Lockwood, A. B., Floyd, R. G., & Sisco, A. D. (2025). AI in psychoeducational assessment: A study of report generation. PsyArXiv. https://doi.org/10.31234/osf.io/kn3p8_v1
40. Fokkema, M., Iliescu, D., Greiff, S., & Ziegler, M. (2022). Machine learning and prediction in psychological assessment. European Journal of Psychological Assessment, 38(3), 165–175. https://doi.org/10.1027/1015-5759/a000714
41. Foster, L. B., Perazzo, D. C., & Decker, S. L. (2025). Test review: Woodcock-Johnson V Tests of Cognitive Abilities. Journal of Psychoeducational Assessment, 44(2), 249–256. https://doi.org/10.1177/07342829251395781
42. Franklin, G., Stephens, R., Piracha, M., Tiosano, S., LeHouillier, F., Koppel, R., & Elkin, P. L. (2024). The sociodemographic biases in machine learning algorithms: A biomedical informatics perspective. Life, 14(6), Article 652. https://doi.org/10.3390/life14060652
43. Gassier-Bommart, C. (2022). Effets d’une praxie de l’éducabilité cognitive par l’orthopédagogue clinicien en France auprès des Elèves à Besoins Educatifs Particuliers: études de cas longitudinales sur le Haut Potentiel Intellectuel dyssynchrone. [Thèse de doktorat, Normandie Université]. HAL Theses.
44. Ghimire, S., Abdulla, S., Joseph, L. P., Prasad, S., Murphy, A., Devi, A., Barua, P. D., Deo, R. C., Acharya, R., & Yaseen, Z. M. (2024). Explainable artificial intelligence-machine learning models to estimate overall scores in tertiary preparatory general science course. Computers and Education: Artificial Intelligence, 7, Article 100331.
45. Giofrè, D., Allen, K., Toffalini, E., & Caviola, S. (2022). The impasse on gender differences in intelligence: A meta-analysis on WISC batteries. Educational Psychology Review, 34, 2543–2568. https://doi.org/10.1007/s10648-022-09705-1
46. Groth-Marnat, G., & Wright, A. J. (2016). Handbook of psychological assessment (6th ed.). Wiley. https://doi.org/10.1002/9781394258918
47. Gupta, K., Aly, A., Ifeachor, E., & Shankar, R. (2025). From predictions to explanations: Explainable AI for autism diagnosis and identification of critical brain regions. arXiv. https://doi.org/10.48550/arXiv.2509.10523
48. Hadar‐Shoval, D., Lvovsky, M., Asraf, K., Shimoni, Y., & Elyoseph, Z. (2025). The feasibility of large language models in verbal comprehension assessment: Mixed methods feasibility study. JMIR Formative Research, 9, Article e68347. https://doi.org/10.2196/68347
49. Hodson, A., Schmidt-Waselenchuk, K., Batson, J., Duckman, A., & Roberts, J. (2026). Integrating generative AI in educational psychology assessments: Preliminary ethical and practical guidelines. Canadian Journal of School Psychology, 41(1), 14–35.
50. Holtrop, D., & Bronzwaer, R. (2025). Validity at risk? The future of generative AI use by candidates in assessments. International Journal of Selection and Assessment, 34(1), 1–6. https://doi.org/10.1111/ijsa.70035
51. Hussein, E., Hussein, M., & Al-Hendawi, M. (2025). Investigation into the applications of artificial intelligence (AI) in special education: A literature review. Social Sciences, 14(5), Article 288. https://doi.org/10.3390/socsci14050288
52. Imran, M., & Almusharraf, N. (2024). Google Gemini as a next-generation AI educational tool: A review of emerging educational technology. Smart Learning Environments, 11, Article 22. https://doi.org/10.1186/s40561-024-00310-z
53. Jain, S., Jain, M., Daga, S., Roshni, & Bhardwaj, H. (2024). Harnessing machine intelligence to bridge the learning divide for students with disabilities. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 742–752.
54. Javed, A. R., Saadia, A., Mughal, H., Gadekallu, T. R., Rizwan, M., Maddikunta, P. K. R., Mahmud, M., Liyanage, M., & Hussain, A. (2023). Artificial intelligence for cognitive health assessment: State-of-the-art, open challenges and future directions. Cognitive Computation, 15, 1767–1812.
55. Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., & Siemens, G. (2023). Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review. Computers and Education Artificial Intelligence, 4, Article 100138. https://doi.org/10.1016/j.caeai.2023.100138
56. Joseph, J., Niemczak, C., Lichtenstein, J., Kobrina, A., Magohe, A., Leigh, S., Ealer, C., Fellows, A., Reike, C., Massawe, E., Gui, J., & Buckey, J. C. (2024). Central auditory test performance predicts future neurocognitive function in children living with and without HIV. Scientific Reports, 14(1), Article 2712.
57. Kaldaras, L., Akaeze, H. O., & Reckase, M. D. (2024). Developing valid assessments in the era of generative artificial intelligence. Frontiers in Education, 9, Article 1399377. https://doi.org/10.3389/feduc.2024.1399377
58. Karakaya, İ., Öç, U., & Gümüş, G. (2024). Üst düzey zihinsel becerilerin ölçülmesinde açık uçlu maddelerin yapay zekâ destekli uygulamalarda hazırlanması. Journal of Applied Measurement and Assessment, 1(2), 59–70.
59. Karasu, M., & Çoker, Ç. (2018). Askeri psikoloji ekseninde eleştirel bir inceleme: Psikoloji bilimi savaşların neresinde? In G. Tekdemir Yurtdaş & T. İkiz (Eds.), Savaş ve psikoloji: Psikoloji bilimi çerçevesinden Birinci Dünya Savaşı’na bir bakış (pp. 149–169). Hiperlink Yayınları.
60. Kılıçarslan, S., Bal Sezerel, B., & Sak, U. (2020). An investigation of the relationship between speed-based verbal reasoning subtest of Anadolu-Sak Intelligence Scale and perceptual speed tests. Talent, 10(2), 129–142. https://doi.org/10.46893/talent.844308
61. Kılınç, Ş., & Sözer, M. A. (2022). Fransa ve Türkiye’de özel yetenekli öğrencilerin eğitiminin karşılaştırmalı olarak incelenmesi. Eurasian Journal of Teacher Education, 3(3), 227–249. https://izlik.org/JA34DD28AN
62. Kim, S. Y., Youngstrom, E. A., Norris, M., Levine, A., Butter, E. M., & Stephenson, K. G. (2025). Identifying latent cognitive profiles in autism spectrum disorder and attention-deficit/hyperactivity disorder using the Stanford-Binet Intelligence Scales-5th Edition. Assessment, 33(6). https://doi.org/10.1177/10731911251369977
63. Konyalı, A., Naipoğlu, C., Güner, S., Bakkal, İ., & Çelik, A. R. (2025). Psikolojide yapay zekâ kullanımı ve uygulamaları. Journal of Kocaeli Health and Technology University, 3(1), 1–17. https://doi.org/10.66163/jokohtu.1641864
64. Kosinski, M. (2024). Evaluating large language models in theory of mind tasks. Proceedings of the National Academy of Sciences, 121(45), Article e2405460121. https://doi.org/10.1073/pnas.2405460121
65. Kshirsagar, R., Kothari, R., Lhase, P., & Sheikh, S. (2025). Explainable AI for clinical decision support: SHAP and Grad-CAM in text- and image-based medical predictions. International Journal For Multidisciplinary Research, 7(6), Article 250660343.
66. Kunda, M. (2019). AI and cognitive testing: A new conceptual framework and roadmap. Proceedings of the 41st Annual Meeting of the Cognitive Science Society, 2065–2070.
67. Kush, J. C. (2005). Review of the Stanford-Binet Intelligence Scales: Fifth Edition. The Sixteenth Mental Measurements Yearbook, 979–984.
68. Landers, R. N., & Behrend, T. S. (2023). Auditing the AI auditors: A framework for evaluating fairness and bias in high stakes AI predictive models. American Psychologist, 78(1), 36–49. https://doi.org/10.1037/amp0000972
69. Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K.-R. (2019). Unmasking Clever Hans predictors and assessing what machines really learn. Nature Communications, 10, Article 1096. https://doi.org/10.1038/s41467-019-08987-4
70. Lee, J., Hicke, Y., Yu, R., Brooks, C., & Kizilcec, R. F. (2024). The life cycle of large language models in education: A framework for understanding sources of bias. British Journal of Educational Technology, 55(5), 1982–2002. https://doi.org/10.1111/bjet.13505
71. Li, Y., Lin, X., Sha, Z., Jin, Z., & Li, X. (2025). AI psychometrics: Evaluating the psychological reasoning of large language models with psychometric validities. In T. X. Bui (Ed.), Proceedings of the Hawaii international conference on system sciences 2025 (HICSS-58) (pp. 5194–5202). AIS.
72. Liu, Y., He, K., Man, K., & Zhan, P. (2025). Exploring critical eye-tracking metrics for identifying cognitive strategies in Raven’s Advanced Progressive Matrices: A data-driven perspective. Journal of Intelligence, 13(2), Article 14. https://doi.org/10.3390/jintelligence13020014
73. Ma, S., & Jia, N. (2024). Measuring Raven’s Progressive Matrices combining eye-tracking technology and machine learning (ML) models. Journal of Intelligence, 12(11), Article 116. https://doi.org/10.3390/jintelligence12110116
74. Marnoufi, K., Touri, B., Bergadi, M., & Ghazlane, I. (2023). WAIS-IV, WISC-V, WPPSI-IV subtests and their relationship with CHC Theory. International Journal of Innovation and Economic Development, 9(4), 16–21. https://doi.org/10.18775/ijied.1849-7551-7020.2015.94.2002
75. McGill, R. J., & Dombrowski, S. C. (2019). Critically reflecting on the origins, evolution, and impact of the Cattell-Horn-Carroll model. Applied Measurement in Education, 32(3), 216–231. https://doi.org/10.1080/08957347.2019.1619561
76. McHenry, M. S., Mukherjee, D., Bhavnani, S., Kirolos, A., Piper, J., Crespo-Llado, M. M., & Gladstone, M. (2023). The current landscape and future of tablet-based cognitive assessments for children in low-resourced settings. PLOS Digital Health, 2(2). https://doi.org/10.1371/journal.pdig.0000196
77. Mehta, P., Chillarge, G. R., Sapkal, S. D., Shinde, G. R., & Kshirsagar, P. S. (2023). Inclusion of children with special needs in the educational system, artificial intelligence (AI). In A. Kumar, A. Nayyar, R. Sachan, & R. Jain (Eds.), AI-Assisted Special Education for Students with Exceptional Needs (pp. 156–185). IGI Global Scientific Publishing.
78. Mersha, M. A., Lam, K., Wood, J., AlShami, A. K., & Kalita, J. (2024). Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction. Neurocomputing, 599, Article 128111. https://doi.org/10.1016/j.neucom.2024.128111
79. Millî Eğitim Bakanlığı. (2023). Bilim ve Sanat Merkezleri yapay zekâ uygulamaları atölye programı. MEB Özel Eğitim ve Rehberlik Hizmetleri Genel Müdürlüğü.
80. Millî Eğitim Bakanlığı. (2025, 21 Ocak). İlk yerli ve milli zekâ testi Türk Ulusal Zekâ Ölçeği Bakan Tekin ve Bakan Kacır'ın katılımıyla tanıtıldı. https://www.meb.gov.tr/ilk-yerli-ve-milli-zekâ-testi-turk-ulusal-zekâ-olcegi-bakan-tekin-ve-bakan-kacirin-katilimiyla tanitildi/haber/36119/tr
81. Minnis, H., Vinciarelli, A., & Alsofyani, H. (2024). The use and potential of artificial intelligence for supporting clinical observation of child behaviour. Child and Adolescent Mental Health, 29(4), 340–344. https://doi.org/10.1111/camh.12714
82. Mohamed, S., Ben-Jaafar, A., Frimpong, M., Roy, S., Sanker, V., Nkrumah-Boateng, P. A., Imran, S., Mumeen, A. A., Mohamed, S., & Wireko, A. A. (2026). Applying artificial intelligence in neurodevelopmental disorders management and research. European Journal of Medical Research, 31(1), Article 185. https://doi.org/10.1186/s40001-025-03740-8
83. Mohd Yunus, S. Y., Hashim, N., Yusof, R., Aziz Fadzillah, N. A., & Abdul Rahman, N. (2025, October). AI in special education: Opportunities and challenges for autism and ADHD. APB Rembau e-Bulletin. https://ir.uitm.edu.my/id/eprint/127879
84. Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L., & PRISMA-P Group. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4, Article 1. https://doi.org/10.1186/2046-4053-4-1
85. Muralidharan, V., Burgart, A., Daneshjou, R., & Rose, S. (2023). Recommendations for the use of pediatric data in artificial intelligence and machine learning ACCEPT-AI. Npj Digital Medicine, 6, Article 166. https://doi.org/10.1038/s41746-023-00898-5
86. Nader, A-M., Jelenic, P., & Soulières, I. (2015). Discrepancy between WISC-III and WISC-IV cognitive profile in autism spectrum: What does it reveal about autistic cognition? PLoS ONE, 10(12), Article e0144645. https://doi.org/10.1371/journal.pone.0144645
87. Nikolić, M., & Cvijetić, M. (2023). The predictive influence of intelligence and communicative language ability on school achievement. Research in Pedagogy, 13(1), 131–145. https://doi.org/10.5937/istrped2301131n
88. Nurhasan, A. A., & Kasprowski, P. (2026). Application of eye movement analysis in medicine: A review across neurodevelopmental, neurological, and neurodegenerative disorders. Applied Sciences, 16(5), Article 2548. https://doi.org/10.3390/app16052548
89. Oladele, J. I., & Ndlovu, M. (2021). A review of standardised assessment development procedure and algorithms for computer adaptive testing: Applications and relevance for Fourth Industrial Revolution. International Journal of Learning Teaching and Educational Research, 20(5), 1–17.
90. Olawade, D. B., Wada, O. Z., Odetayo, A., David-Olawade, A. C., Asaolu, F., & Eberhardt, J. (2024). Enhancing mental health with Artificial Intelligence: Current trends and future prospects. Journal of Medicine, Surgery, and Public Health, 3, Article 100099. https://doi.org/10.1016/j.glmedi.2024.100099
91. Özberk, E. H., Ünsal Özberk, E. B., Uluç, S., & Öktem, F. (2021). Investigating invariant item ordering in intelligence tests: Mokken Scale Analysis of KBIT-2. International Journal of Assessment Tools in Education, 8(3), 714–728. https://doi.org/10.21449/ijate.858183
92. Pan, D., Xie, H., Zeng, Y., Zhou, Y., Lin, C., Ma, X., Ren, J., Jiao, Y., Wu, Y., Wei, W., & Xue, G. (2024). The development and validation of a tablet-based assessment battery of general cognitive ability. BMC Psychology, 12, Article 778. https://doi.org/10.1186/s40359-024-02283-7
93. Patel, H., Sheth, V., & Shah, M. (2025). An early detection of learning disabilities using machine learning: A comparative study of models, datasets, and diagnostic strategies. Intelligent Hospital, 1(2), Article 100022. https://doi.org/10.1016/j.inhs.2025.100022
94. Patil, A., & Singh, N. (2023). Improvements in artificial intelligence and its impact on child psychology. Preprints. https://doi.org/10.20944/preprints202307.1914.v1
95. Pauls, F., & Daseking, M. (2021). Revisiting the factor structure of the German WISC-V for clinical interpretability: An exploratory and confirmatory approach on the 10 primary subtests. Frontiers in Psychology, 12, Article 710929. https://doi.org/10.3389/fpsyg.2021.710929
96. Peterson, K. C., Peterson, M., & Carducci, B. J. (2020). Stanford-Binet Intelligence Scale. In B. J. Carducci, C. S. Nave & B. J. Carducci (Eds.), The Wiley encyclopedia of personality and individual differences (pp. 451–455). Wiley. https://doi.org/10.1002/9781118970843.ch145
97. Qin, H., Zhang, L., Wang, J., Yan, W., Wang, X., Qu, X., Peng, N., & Wang, L. (2025). Interpretable machine learning approaches for children’s ADHD detection using clinical assessment data: An online web application deployment. BMC Psychiatry, 25, Article 139.
98. Ramírez, M. A. N., Rodríguez, M. M., Salas, M. J. C., & Rincón, E. H. H. (2026). Artificial intelligence in diagnosis of pediatric neurodevelopmental disorders: A scoping review. World Journal of Pediatrics, 22, 315–329. https://doi.org/10.1007/s12519-025-00999-z
99. Rane, N., Choudhary, S., & Rane, J. (2024). Gemini versus ChatGPT: Applications, performance, architecture, capabilities, and implementation. Journal of Applied Artificial Intelligence, 5(1), 69–93. https://doi.org/10.48185/jaai.v5i1.1052
100. Rani, P., ur Rehman, K., Yadav, S. P., & Hussein, L. (2025). Deep learning and AI in behavioral analysis for revolutionizing mental healthcare. In A. Mishra, S. Yadav, M. Kumar, S. Biju, & G. Deka (Eds.), Demystifying the role of natural language processing (NLP) in mental health (pp. 263–282). IGI Global Scientific Publishing.
101. Reddy, M. (2025). The catastrophic paradox of human cognitive frameworks in large language model evaluation: A comprehensive empirical analysis of the CHC-LLM incompatibility. arXiv. https://doi.org/10.48550/arXiv.2511.18302
102. Russo, N., Kaplan‐Kahn, E. A., Wilson, J., Criss, A., & Burack, J. A. (2021). Choices, challenges, and constraints: A pragmatic examination of the limits of mental age matching in empirical research. Development and Psychopathology, 33(2), 727–738. https://doi.org/10.1017/s0954579420001480
103. Sadaf Mubashir, A., & Kainaat, R. (2025). Transforming psychometrics: Integrating technology and application in the 21st century. In M. C. Richaud, B. Mesurado, & V. N. Lemos (Eds.), Contemporary psychometrics: New developments and applications (pp. 1–21). IntechOpen.
104. Salima, M., M'hammed, S., Messaadia, M., & Benslimane, S. M. (2023). Machine learning for predicting personality traits from eye tracking. In 2023 International Conference on Decision Aid Sciences and Applications (DASA) (pp. 126–130). IEEE. https://doi.org/10.1109/DASA59624.2023.10286789
105. Salonen, J., Slama, S., Haavisto, A., & Rosenqvist, J. (2023). Comparison of WPPSI-IV and WISC-V cognitive profiles in 6-7-year-old Finland-Swedish children: Findings from the FinSwed study. Child Neuropsychology, 29(5), 687–709. https://doi.org/10.1080/09297049.2022.2112163
106. Shalileh, S., Ignatov, D. I., Lopukhina, A., & Dragoy, O. (2023). Identifying dyslexia in school pupils from eye movement and demographic data using artificial intelligence. PLoS ONE, 18(11). https://doi.org/10.1371/journal.pone.0292047
107. Shao, X., Skryagin, A., Stammer, W., Schramowski, P., & Kersting, K. (2021). Right for better reasons: Training differentiable models by constraining their influence functions. Proceedings of the AAAI Conference on Artificial Intelligence, 35(11), 9533–9540. https://doi.org/10.1609/aaai.v35i11.17148
108. Somma, F., Rega, A., & Gigliotta, O. (2019). Artificial intelligence-powered cognitive training applications for children with attention deficit hyperactivity disorder: A brief review. In CEUR Workshop Proceedings: Vol. 2524. Proceedings of the Second International Workshop on e-Health Pervasive Systems (EHPS 2019) (pp. 1–11).
109. Sun, L., Gibbons, C., Hernández‐Orallo, J., Wang, X., Jiang, L., Stillwell, D., Luo, F., & Xie, X. (2025). Beyond benchmarks: Evaluating generalist medical artificial intelligence with psychometrics. Journal of Medical Internet Research, 27. e70901. https://doi.org/10.2196/70901
110. Şahin, B., Usta, M. B., Önal, B. S., & Hoşoğlu, E. (2021). Evaluation of children with attention deficit hyperactivity disorder on the Wechsler Intelligence Scale for Children-IV. Çocuk Dergisi / Journal of Child, 21(2), 136–141. https://doi.org/10.26650/jchild.2021.932968
111. Şahin, H., & Zorlu, A. (2022). Türkiye’de özel yetenekli bireyleri tanılama süreci ve tanılamada kullanılan yöntemler. Milli Eğitim Dergisi, 51(233), 863–885. https://doi.org/10.37669/milliegitim.788454
112. Takayanagi, M., Kawasaki, Y., Shinomiya, M., Hiroshi, H., Okada, S., Ino, T., Sakai, K., Murakami, K., Ishida, R., Mizuno, K., & Niwa, S.-I. (2022). Review of cognitive characteristics of autism spectrum disorder using performance on six subtests on four versions of the Wechsler Intelligence Scale for Children. Journal of Autism and Developmental Disorders, 52, 240–253.
113. Tandon, A., Mazumdar, B. D., & Pal, M. K. (2024). Integrated intelligent computing models for cognitive-based neurological disease interpretation in children: A survey. EAI Endorsed Transactions on Pervasive Health and Technology, 10. https://doi.org/10.4108/eetpht.10.5541
114. Tınmaz, H. (2025). From transparency to Trust: A literature review on explainable AI in educational systems. Mersin Üniversitesi Eğitim Fakültesi Dergisi, 21(3), 999–1018.
115. Tornero-Costa, R., Martínez-Millana, A., Azzopardi‐Muscat, N., Lazeri, L., Traver, V., & Novillo-Ortiz, D. (2023). Methodological and quality flaws in the use of artificial intelligence in mental health research: Systematic review. JMIR Mental Health, 10. https://doi.org/10.2196/42045
116. Tuerk, C., Saha, T., Bouchard, M. F., & Booij, L. (2023). Computerized cognitive test batteries for children and adolescents—A scoping review of tools for lab- and web-based settings from 2000 to 2021. Archives of Clinical Neuropsychology, 38(8), 1683–1710.
117. Uluç, S., Öktem, F., Erden, G., Gençöz, T., & Sezgin, N. (2011). Wechsler Çocuklar için Zekâ Ölçeği-IV: Klinik bağlamda zekânın değerlendirilmesinde Türkiye için yeni bir dönem. Türk Psikoloji Yazıları, 14(28), 49–57.
118. Uluç, S., & Öktem, F. (2023). Kaufman Kısa Zekâ Testi İkinci Sürümünün (KBIT-2) ve Wechsler Çocuklar için Zekâ Ölçeğinin Dördüncü Sürümünün (WÇZÖ-IV) karşılaştırılması. Psikoloji Çalışmaları-Studies in Psychology, 43(1), 117–140.
119. Ülkü, L. (2024). Zekâ, gelişimi ve ölçeklerine dair. Uluslararası Sosyal Bilimler Akademik Araştırmalar Dergisi, 8(1), 88–102. https://doi.org/10.58201/utsobilder.1487718
120. Ünal, M., & Kandır, A. (2025). Digital Intelligence Adult Scale validity and reliability study. International Journal of Turkish Education Sciences, 13(1), 479–508. https://doi.org/10.46778/goputeb.1618778
121. Ünal, M., & Kandır, A. (2026). Digital Intelligence Child Scale validity and reliability study. Journal of Learning and Teaching in Digital Age, 1, 70–83. https://doi.org/10.53850/joltida.1721756
122. Ünay, E., Erdem, R., & Günal, Y. (2021). Geçici koruma statüsündeki özel gereksinimli öğrencilerin eğitsel tanılama süreci. Milli Eğitim Dergisi, 50(1), 661–982. https://doi.org/10.37669/milliegitim.959925
123. Valdiviezo Verdezoto, C. Y. (2025). Validez de la inteligencia artificial en la evaluación psicométrica. Revista Veritas de Difusão Cientifica, 5(3), 2773–2791. https://doi.org/10.61616/rvdc.v5i3.375
124. Verani, S., & Golden, C. (2021). A-153 neuropsychological effects assessed using WISC-IV and WISC-V. Archives of Clinical Neuropsychology, 36(6), 1207. https://doi.org/10.1093/arclin/acab062.171
125. Vianney Mitana, J. M., Muwagga, A. M., & Ssempala, C. (2019). Assessment for successful intelligence: A paradigm shift in classroom practice. International Journal of Educational Research Review, 4(1), 106–115. https://doi.org/10.24331/ijere.490162
126. Vimbi, V., Shaffi, N., & Mahmud, M. (2024). Interpreting artificial intelligence models: A systematic review on the application of LIME and SHAP in Alzheimer’s disease detection. Brain Informatics, 11, Article 10. https://doi.org/10.1186/s40708-024-00222-1
127. Wan, Y. N. (2024). Exploring interpersonal meanings of report cards and WISC-V scores: A case study of a gifted child. Theory and Practice in Language Studies, 14(6), 1664–1675. https://doi.org/10.17507/tpls.1406.07
128. Wang, W., Kofler, L., Lindgren, C., Lobel, M., Murphy, A., Tong, Q., & Pickering, K. (2023). AI for psychometrics: Validating machine learning models in measuring emotional intelligence with eye-tracking techniques. Journal of Intelligence, 11(9), Article 170.
129. Ward, K. E., Rothlisberg, B. A., McIntosh, D. E., & Bradley, J. M. (2011). Assessing the SB-V factor structure in a sample of preschool children. Psychology in the Schools, 48(5), 454–463. https://doi.org/10.1002/pits.20567
130. Wasserman, J. D. (2019). Deconstructing CHC. Applied Measurement in Education, 32(3), 249–268. https://doi.org/10.1080/08957347.2019.1619563
131. Williams, T. H., McIntosh, D. E., Dixon, F., Newton, J. H., & Youman, E. (2010). A confirmatory factor analysis of the Stanford-Binet Intelligence Scales, Fifth Edition, with a high-achieving sample. Psychology in the Schools, 47(10), 1071–1083. https://doi.org/10.1002/pits.20525
132. Yakut, A. D., & Kırca Demirbaga, K. (2025). Yapay zekâ ile özel eğitimde kapsayıcı uygulamalar. Ö. E. Akgün (Ed.), Eğitimde yapay zekâ teknolojileri içinde (ss. 13–51). İdeal Kültür Yayınları. https://doi.org/10.36657/ihu.yz.2025.1
133. Yan, L., Sha, L., Zhao, L., Li, Y., Martínez‐Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90–112. https://doi.org/10.1111/bjet.13370
134. Yenioğlu, S., Sayar, K., Köse, H., & Güner-Yıldız, N. (2019). Türkiye'de özel eğitim alanında değerlendirme süreci. Kastamonu Education Journal, 27(6), 2729–2746. https://doi.org/10.24106/kefdergi.3100
135. Yıldız, H., Tunaboylu, C., Ülkü, S., Giray, G., & Kelecioğlu, H. (2024). Investigation of measurement precision and test lengths in computerized adaptive tests in different conditions. Journal of Measurement and Evaluation in Education and Psychology, 15(1), 5–17.
136. Yiğit, İ. (2016). Üstün yetenekli çocukların değerlendirilmesinde Wechsler Çocuklar İçin Zekâ Ölçeği-IV (WÇZÖ-IV) formunun kullanımı. Turkiye Klinikleri Psychology - Special Topics, 1(1), 36–43.
137. Yonus, A. (2007). Kaufman Kısa Zekâ Testi’nin (Kaufman Brief Intelligence Test - K.BIT) 15–16 yaş Türk çocukları üzerinde geçerlik, güvenirlik ve ön norm çalışmaları. [Yayınlanmamış yüksek lisans tezi]. İstanbul Üniversitesi.
138. Yoo, J. H., Kang, C., Lim, J. S., Wang, B., Choi, C.-H., Hwang, H., Han, D. H., Kim, H., Cheon, H., & Kim, J.-W. (2024). Development of an innovative approach using portable eye tracking to assist ADHD screening: A machine learning study. Frontiers in Psychiatry, 15, Article 1337595.
139. Zhang, Y., & Ma, Z. (2026). Psychometric comparability of LLM-based digital twins. arXiv. https://doi.org/10.48550/arxiv.2601.14264
140. Zhuang, Y., Liu, Q., Bi, H., Huang, Z., Huang, W., Li, J., Yu, J., Liu, Z., Hu, Z., Hong, Y., Pardos, Z. A., Ma, H., Zhu, M., Wang, S., & Chen, E. (2026). Survey of computerized adaptive testing: A machine learning perspective. arXiv. https://doi.org/10.48550/arxiv.2404.00712