The Use of Artificial Intelligence in Early Intervention
DOI:
https://doi.org/10.46328/ijtes.5389Keywords:
Artificial Intelligence, Early Childhood InterventionAbstract
The use of Artificial Intelligence (AI) is steadily increasing. This paper explores the extent to which AI is currently utilized in Early Childhood Intervention (ECI) services. Data were collected through an online survey conducted in German-speaking countries (Germany, Austria, Switzerland) and Turkey (n = 123). Results indicate that up to 50% of professionals in ECI services already use AI, predominantly ChatGPT. AI use is associated with younger age and professional background. Home visitors report less frequent use compared to professionals working in kindergarten settings. Non-users primarily cite a lack of information and general skepticism toward AI tools. Among users, AI is applied in methodological research, translation processes, and, to some extent, in planning interventions. Time efficiency is perceived as the main advantage of AI use; however, concerns remain regarding the validity of information and data protection. Overall, there is a clear demand for more training and well-defined guidelines concerning the handling of personal data when using AI in professional practice.
References
Ajuwon, O.A., Animashaun, E.S., Chiekezie, N. R. (2024). Integrating AI and technology in educational administration: Improving efficiency and educational quality, Open Access Research Journal of Science and Technology, 11(02), 116–127. https://doi.org/10.53022/oarjst.2024.11.2.0102
Alkan, A. (2024). The role of artificial intelligence in the education of students with special needs. International Journal of Technology in Education and Science, 8(4), 542-557. https://doi.org/10.46328/ijtes.569
Berliner Senatsvewaltung für Bildung, Jugend und Familie. (2024). Berliner Teilhabe und Förderplan für die Kindertagesbetreuung. Erhebungs-, Maßnahmen und Empfehlungsbögen. Berlin: Senatsverwaltung
Bostelmann, A. (2023) Künstliche Intelligenz und die Bildung. Das Kita-Handbuch. Verfügbar unter https://www.kindergartenpaedagogik.de/ [25.4.2025].
Cox, D.J., D'Ambrosio, D., Pagliaro, J. (2023). An Artificial Intelligence Driven System to Predict ASD Outcomes in ABA. https://doi.org/10.31219/osf.io/3t9zc
Devi, J. S., Sreedhar, B., P Arulprakash, P., Radhakrishnan,K. R. (2022). A path towards child-centric Artificial Intelligence based Education. International Journal of Early Childhood, 14(3), 9915-9922
Drigas, A., Ioannidou, R.E. (2012). Artificial intelligence in special education A decade review. International Journal of Engineering Education, 28(6), 1366-1372.
EDF (European Disability Forum (2024). The EU’s artificial intelligence policies. European Disability Forum. Verfügbar unter https://www.edf-feph.org/the-eus-artificial-intelligence-policies/ [25.4.2025].
Evangeline, B., Moorthy, A.D. (2023). A Comprehensive Review on Augmentative and Alternative Communication Tools for Speech-Impaired Individuals. In: Hewage, C., Nawaf, L., Kesswani, N. (eds) AI Applications in Cyber Security and Communication Networks. ICCS 2023. Lecture Notes in Networks and Systems, vol 1032. Springer, Singapore. https://doi.org/10.1007/978-981-97-3973-8_20
Ghafghazi, S., Carnett, A., Neely, L., Das, A., Rad, R. (2021). AI-augmented behavior analysis for children with developmental disabilities: building toward precision treatment. IEEE Systems, Man, and Cybernetics Magazine, 7(4), 4-12.
Hackbarth, K.R. (2024). Voices from Industry Revolutionizing Augmentative and Alternative Communication with Generative Artificial Intelligence. ATIA Assistive Technology Industry Association, 18, 100-123. https://doi.org/10.1145/3568294.3580161
Kojovic, N., Natraj, S., Mohanty, S. P., Maillart, T., Schaer, M. (2021). Using 2D video-based pose estimation for automated prediction of autism spectrum disorders in young children. Scientific Reports, 11, 15069.
Knaus, T. (2024). Künstliche Intelligenz und Pädagogik – ein Plädoyer für eine Perspektiverweiterung. Ludwigsburger Beiträge zur Medienpädagogik, 24, 1-34- https://doi.org/10.21240/lbzm/24/11
Krawitz, P.M., Ruder, A., Nihues, T. (2025). Schneller zur genetischen Diagnose bei Kindern mit auffälliger Fazies durch künstliche Intelligenz. Einsatz in der Kinderarztpraxis. Monatsschrift Kinderheilkunde, 173, 305-309.
Kumar, N. (2025). Real-Time User Behaviour Tracking for AI-Driven In-Session Product Recommendations and Insights. International Journal of Computer Engineering and Technology, 16(2), 137-146.
Land Kärnten (6.11.2023). Digitalisierung in Pflegeheimen: KI-gesteuerte Dokumentation entlastet Pflegekräfte. Verfügbar unter https://www.ktn.gv.at/Service/News?nid=36517 [25.4.2025].
Lai M., Lee J., Chiu S., Charm J., So, WY., Yuen, FP, Kwok C., Tsoi J., Lin, Y., Zee, B.(2020). A machine learning approach for retinal images analysis as an objective screening method for children with autism spectrum disorder. EClinicalMedicine, 5(28). https://doi.org/10.1016/j.eclinm.2020.100588.
Naranjos Velazquez, N. (2023). Nutzung von ChatGPT in Netzwerken Frühe Hilfen Impulspapier. Verfügbar unter:
https://rosdok.uni-rostock.de/file/rosdok_document_0000021649/rosdok_derivate_0000206592/Velazquez_Nutzung_2023.pdf [25.4.2025].
Pedro, F., Subosa, M., Rivas, A., Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. Verfügbar unter: https://unesdoc.unesco.org/ark:/48223/pf0000366994 [15.7.2025].
Pretis, M. (2022). Teilhabeziele planen, formulieren und überprüfen. München: Reinhardt.
Pretis, M., Todorova, K. (2025). KI unterstützte ICF Förderplanung. Erste Erfahrungen. Paper im Rahmen der ICF Anwenderkonfererenz Klagenfurt, 30.9.2025 [in press].
Reich, S., Zhang, D., Kulvicius, T., Bölte, S., Nielsen-Saines, K., Pokorny .B., Peharz, R., Poustka, L., Wörgötter F., Einspieler, Ch., Marschik, P.B. (2021). Novel AI driven approach to classify infant motor functions. Scientific Reports, 11, 9888.
Stanley, J., Rabot,, E., Reddy, S., Belilovsky, E., Mottron, L., Bzdok, D. (2025). Large language models deconstruct the clinical intuition behind diagnosing autism. Cell, 188(8), 2235-2248.
Stait, N.H. (1972). Management training and the smaller company: SWOT analysis. Industrial and Commercial Training, 4(7), 325–330. doi:10.1108/eb003232
Statistik Austria (2025). Rund ein Viertel der Bevölkerung schätzt ihr Wissen über KI als gut ein Jüngere und Hochgebildete stehen KI-Anwendungen positiver gegenüber. Pressemitteilung, 13, 605-098/25 Verfügbar unter: https://www.statistik.at/fileadmin/announcement/2025/05/20250515KI_Analyse.pdf [25.4.2025].
Tariq, Q., Daniels, J., Schwartz, J.N., Washington, P., Kalantarian, H., Wall, D.P. (2018). Mobile detection of autism through machine learning on home video: A development and prospective validation study. PLoS Med, 15(11): e1002705. https://doi.org/10.1371/journal.pmed.1002705
Thiessen, R., Dabiri, M., Geiskkovitch, D.Y, Ripat, J.,Young, J.E. (2023). Social Robots to Encourage Play for Children with Disabilities: Learning Perceived Requirements and Barriers from Family Units. https://doi.org/10.1145/3568294.3580161
Wei,Q., Cao, H., Shi,Y., Xu, X., Li, T. (2023). Machine learning based on eye-tracking data to identify Autism Spectrum Disorder: A systematic review and meta-analysis. Journal of Biomedical Informatics, 137. https://doi.org/10.1016/j.jbi.2022.104254
World Medical Association (1991). Declaration of Helsinki. Law Med Health Care, 19(3-4), 264-5.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Technology in Education and Science
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
