“Nothing Can Replace Authentic Human Interaction”: EI/ECSE Professionals’ Perception of AI

Abstract

Artificial intelligence (AI) is becoming increasingly integrated into everyday life, helping to automate routine tasks and freeing up individuals to focus on more complex, creative, or interpersonal responsibilities. Yet, the use of AI presents unique concerns related to privacy and bias. While AI developments have become widespread in K–12 and higher education settings, far less is understood about their use in early intervention (EI) and early childhood special education (ECSE). To fill this gap in the literature, we conducted an exploratory multi-method study to examine how EI/ECSE professionals perceive and utilize AI in their practice. Results from 68 participants provided unique preliminary insights into the knowledge, use, attitudes, needs, benefits, and concerns of AI by EI/ECSE professionals. Our findings reveal a complex landscape of attitudes toward AI among EI/ECSE professionals, characterized by cautious optimism coupled with significant knowledge gaps and professional development needs. Limitations, implications for practice, and future research directions are discussed.

Keywords

artificial intelligence professional development EI/ECSE professionals

Get full access to this article

View all access options for this article.

References

Braun

Clarke

(2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

Council for Exceptional Children. (2020). The early interventionist/early childhood special educator (EI/ECSE) standards. https://exceptionalchildren.org/standards/initial-practice-based-standards-early-interventionists-early-childhood-special-educators

Cox

D. J.

Jennings

A. M

. (2024). The promises and possibilities of artificial intelligence in the delivery of behavior analytic services. Behavior Analysis in Practice, 17(1), 123–136. https://doi.org/10.1007/s40617-023-00864-3

DEC Recommended Practices. (2014). Division for early childhood recommended practices in early intervention/early childhood special education. https://www.dec-sped.org/dec-recommended-practices

Family Educational Rights and Privacy Act (FERPA). (1974). 20 U.S.C. § 1232g.

Fitas

(2025). Inclusive education with AI: Supporting special needs and tackling language barriers. AI and Ethics, 5, 5729–5757. https://doi.org/10.1007/s43681-025-00824-3

Haenlein

Kaplan

. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925

Hopcan

Polat

Ozturk

M. E.

Ozturk

(2023). Artificial intelligence in special education: A systematic review. Interactive Learning Environments, 31(10), 7335–7353. https://doi.org/10.1080/10494820.2022.2067186

10.

Huang

Bao

Wang

Wei

. (2024). Exploring AI tools in early childhood education: Usage patterns, functions, and developmental outcomes. In Education and human development. IntechOpen. https://doi.org/10.5772/intechopen.1007116

11.

Hussein

Al-Hendawi

(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

12.

Johnston

Jones

Manley

(2018). Confounding and collinearity in regression analysis: A cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour. Quality & Quantity, 52(4), 1957–1976. https://doi.org/10.1007/s11135-017-0584-6

13.

Johnston

Jameson

J. M.

O’Keeffe

B. V.

Raines

(2024). Teaching in the era of Artificial Intelligence: Reimagining activities and assignments in preservice special education teacher education programs. Journal of Special Education Preparation, 4(2), 38–50. https://doi.org/10.33043/5z5b435y

14.

Laupichler

M. C.

Aster

Schirch

Raupach

(2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, Article 100101. https://doi.org/10.1016/j.caeai.2022.100101.

15.

Lawrence

Dodsworth

Massou

Sherlaw-Johnson

Ramsay

A. I. G.

Walton

O’Regan

Gleeson

Crellin

Herbert

P. L.

Elphinstone

Mehta

Lloyd

Halliday

Morris

Fulop

N. J.

(2025). Artificial intelligence for diagnostics in radiology practice: A rapid systematic scoping review. EClinicalMedicine, 83, Article 103228. https://doi.org/10.1016/j.eclinm.2025.103228

16.

Meadan

Alqunaysi

Martin Loya

Xia

Xiong

. (in press). Artificial intelligence in practice: BCBAs’ attitudes, concerns, and needs. Education and Treatment of Children.

17.

Montenegro-Rueda

Fernández-Cerero

Fernández-Batanero

J. M.

López-Meneses

(2023). Impact of the implementation of ChatGPT in Education: A systematic review. Computers, 12(8), Article 153. https://doi.org/10.3390/computers12080153

18.

Morgan

D. L.

(2023). Exploring the use of artificial intelligence for qualitative data analysis: The case of ChatGPT. International Journal of Qualitative Methods, 22, 1–10.

19.

Morse

J. M.

(2003). Principles of mixed methods and multi-method research design. In Teddlie

Tashakkori

(Eds.), Handbook of mixed methods in social and behavioral research (pp. 189–208). Sage Publication.

20.

National Science and Technology Council. (2023). The national artificial intelligence research and development strategic plan: 2023 update. Office of Science and Technology Policy. https://www.nitrd.gov/pubs/national_ai_rd_strategic_plan.pdf

21.

Posit Team. (2025). RStudio: Integrated development environment for R. Posit Software, PBC. http://www.posit.co/

22.

Prescott

M. R.

Yeager

Ham

Rivera Saldana

C. D.

Serrano

Narez

Paltin

Delgado

Moore

D. J.

Montoya

(2024). Comparing the efficacy and efficiency of human and generative AI: Qualitative thematic analyses. JMIR AI, 3, Article e54482. https://doi.org/10.2196/54482

23.

Rakap

(2024). Chatting with GPT: Enhancing individualized education program goal development for novice special education teachers. Journal of Special Education Technology, 39(3), 339–348. https://doi.org/10.1177/01626434231211295

24.

Rogers

E. M.

(2003). Diffusion of innovations (5th ed.). Free Press.

25.

Yang

(2022). Artificial intelligence in early childhood education: A scoping review. Computers and Education Artificial Intelligence, 3, Article 100049. https://doi.org/10.1016/j.caeai.2022.100049

26.

Waterfield

D. A.

Coleman

O. F.

Welker

N. P.

Kennedy

M. J.

McDonald

S. D.

Cook

B. G.

(2025). IEPs in the age of AI: Examining IEP goals written with and without ChatGPT. Journal of Special Education Technology, 41(1), 57–71. https://doi.org/10.1177/01626434251324592

27.

Wheldon

C. W.

McKee

R. W.

(2025). AI-empowered qualitative data analysis: Training future public health researchers. CommonHealth, 6(1), 1–46. https://doi.org/10.15367/vd6f8w75

28.

Liu

Lan

(2024). The key artificial intelligence technologies in early childhood education: A review. Artificial Intelligence Review, 57, Article 12. https://doi.org/10.1007/s10462-023-10637-7

29.

Yim

I. H. Y.

Wegerif

(2024). Teachers’ perceptions, attitudes, and acceptance of artificial intelligence (AI) educational learning tools: An exploratory study on AI literacy for young students. Future in Educational Research, 2(4), 318–345. https://doi.org/10.1002/fer3.65