Intervention Fidelity for Enhanced Outcomes: A Systematic Review of Artificial Intelligence in Special Education

Abstract

Intervention fidelity is essential to achieving meaningful learning outcomes for students with disabilities. However, maintaining the fidelity of evidence-based interventions (EBIs) is difficult due to time constraints, lack of professional development, case loads, and varying student needs (Griffen et al., 2024). For students with disabilities, artificial intelligence (AI) has the potential to assist educators in intervention delivery (Mosher et al., 2026). However, AI’s use in intervention implementation remains underresearched. This systematic review examines whether artificial intelligence (AI) may support dimensions of intervention fidelity (i.e., adherence, dosage, and quality of delivery) in special education contexts. Across five empirical studies, AI-supported tools were associated with improved implementation monitoring and implementor fidelity under constrained conditions. Evidence linking fidelity gains to student outcomes remains indirect, as no included studies conducted formal mediation analyses. Findings should therefore be interpreted as suggestive rather than causal. Ethical, developmental, and feasibility considerations are discussed.

Keywords

intervention fidelity artificial intelligence literature review methodologies technology integration technology perspectives personalized learning technologies evidence based practices

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References

Alsolami

A. S.

(2025). The effectiveness of using artificial intelligence in improving academic skills of school-aged students with mild intellectual disabilities in Saudi Arabia. Research in Developmental Disabilities, 156(2), 104884. https://doi.org/10.1016/j.ridd.2024.104884

Goodman

K. E.

Kaminsky

Lessler

(2019). What is machine learning? A primer for the epidemiologist. American Journal of Epidemiology, 188(12), 2222–2239. https://doi.org/10.1093/aje/kwz189

Bos

S. E.

Powell

S. R.

Maddox

S. A.

Doabler

C. T.

(2023). A synthesis of the conceptualization and measurement of implementation fidelity in mathematics intervention research. Journal of Learning Disabilities, 56(2), 95–115. https://doi.org/10.1177/00222194211065498

Carroll

Patterson

Wood

Booth

Rick

Balain

(2007). A conceptual framework for implementation fidelity. Implementation Science, 2(1), 40. https://doi.org/10.1186/1748-5908-2-40

Chen

Lin

(2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510

Chiu

T. K. F.

Moorhouse

B. L.

Chai

C. S.

Ismailov

(2024). Teacher support, student needs satisfaction, and motivation when learning with AI chatbots. Interactive Learning Environments, 32(7), 3240–3256. https://doi.org/10.1080/10494820.2023.2172044

Choi

R. Y.

Coyner

A. S.

Kalpathy-Cramer

Chiang

M. F.

Campbell

J. P.

(2020). Introduction to machine learning, neural networks, and deep learning. Translational Vision Science & Technology, 9(2), 14. https://doi.org/10.1167/tvst.9.2.14

Collins

L. W.

Cook

S. E. C.

Cook

B. G.

(2025). Evidence-based practices in special education: A brief evolution and noteworthy practices. Teaching Exceptional Children, 58(2), 76–77. https://doi.org/10.1177/00400599251382310

Combs

K. M.

Buckley

P. R.

Lain

M. A.

Drewelow

K. M.

Urano

Kerns

S. E.

(2022). Influence of classroom-level factors on implementation fidelity during scale-up of evidence-based interventions. Prevention Science, 23(6), 969–981. https://doi.org/10.1007/s11121-022-01375-3

10.

Cook

B. G.

Odom

S. L.

(2013). Evidence-based practices and implementation science in special education. Exceptional Children, 79(2), 135–144. https://doi.org/10.1177/001440291307900201

11.

Cook

B. G.

Tankersley

Landrum

T. J.

(2009). Determining evidence-based practices in special education. Exceptional Children, 75(3), 365–383. https://doi.org/10.1177/001440290907500306

12.

Cote

(2021). What is predictive analytics? 3 examples of how it’s used. Harvard Business School Online. https://online.hbs.edu/blog/post/predictive-analytics.

13.

Creed

T. A.

Kuo

P. B.

Oziel

Reich

Thomas

O’Connor

Imel

Z. E.

Hirsch

Narayanan

Atkins

D. C.

(2022). Knowledge and attitudes toward an artificial intelligence-based fidelity measurement in community cognitive behavioral therapy supervision. Administration and Policy in Mental Health and Mental Health Services Research, 49(3), 343–356. https://doi.org/10.1007/s10488-021-01167-x

14.

Cumming

M. M.

Bettini

Chow

J. C.

(2023). High-quality systematic literature reviews in special education: Promoting coherence, contextualization, generativity, and transparency. Exceptional Children, 89(4), 412–431. https://doi.org/10.1177/00144029221146576

15.

Dane

A. V.

Schneider

B. H.

(1998). Program integrity in primary and early secondary prevention: Are implementation effects out of control? Clinical Psychology Review, 18(1), 23–45. https://doi.org/10.1016/s0272-7358(97)00043-3

16.

Dieker

L. A.

Hines

Wilkins

Hughes

Hawkins Scott

Smith

Ingraham

Ali

Zaugg

Shah

(2024). Using an AI agent to support teacher instruction and student learning. Journal of Special Education Preparation, 4(2), 78–88. https://doi.org/10.33043/d8xb94q7

17.

Durlak

J. A.

DuPre

E. P.

(2008). Implementation matters: A review of research on the influence of implementation on program outcomes and the factors affecting implementation. American Journal of Community Psychology, 41(3–4), 327–350. https://doi.org/10.1007/s10464-008-9165-0

18.

Fixsen

D. L.

Naoom

S. F.

Blase

K. A.

Friedman

R. M.

Wallace

(2005). Implementation research: A synthesis of the literature. University of South Florida, Louis de la Parte Florida Mental Health Institute, The National Implementation Research Network. https://nirn.fpg.unc.edu/resource/implementation-research-a-synthesis-of-the-literature/

19.

Fryer

L. K.

Nakao

Thompson

(2019). Chatbot learning partners: Connecting learning experiences, interest and competence. Computers in Human Behavior, 93, 279–289. https://doi.org/10.1016/j.chb.2018.12.023

20.

Gage

N. A.

Lewis

T. J.

Stichter

J. P.

(2012). Functional behavioral assessment-based interventions for students with or at risk for emotional and/or behavioral disorders in school: An HLM-meta-analysis. Behavioral Disorders, 37(2), 55–77. https://doi.org/10.1177/019874291203700201

21.

Gersten

Fuchs

L. S.

Compton

Coyne

Greenwood

Innocenti

M. S.

(2005). Quality indicators for group experimental and quasi-experimental research in special education. Exceptional Children, 71(2), 149–164. https://doi.org/10.1177/001440290507100202

22.

Goldman

Smith

Peyton

Carreon

Rowland

Mosher

(2026). Evaluating the use of ChatGPT in preservice special education courses for creating instructional handouts on disabilities. Learning Disabilities Quarterly. https://doi.org/10.1177/07319487261450954

23.

Gresham

F. M.

(2009). Evolution of the treatment integrity concept: Current status and future directions. School Psychology Review, 38(4), 533–540. https://doi.org/10.1080/2372966X.2009.12527793

24.

Griffen

Lorah

E. R.

Caldwell

Hantula

D. A.

Nosek

Tincani

Lemley

(2024). The effects of artificial intelligence on implementors’ fidelity of instructional strategies during handwashing acquisition in children with autism. Journal of Developmental and Physical Disabilities, 36(5), 793–819. https://doi.org/10.1007/s10882-023-09937-1

25.

Griffen

Lorah

E. R.

Holyfield

Caldwell

Nosek

(2025). Evaluating artificial intelligence on the efficacy of preference assessments for preservice speech-language pathologists. Journal of Developmental and Physical Disabilities, 37(4), 673–705. https://doi.org/10.1007/s10882-024-09976-2

26.

Hill

H. C.

Erickson

(2019). Using implementation fidelity to aid in interpreting program impacts: A brief review. Educational Researcher, 48(9), 590–598. https://doi.org/10.3102/0013189X19891436

27.

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

28.

Horner

R. H.

Carr

E. G.

Halle

McGee

Odom

Wolery

(2005). The use of single-subject research to identify evidence-based practice in special education. Exceptional Children, 71(2), 165–179. https://doi.org/10.1177/001440290507100203

29.

Kang

Cai

Tan

C. W.

Huang

Liu

(2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139–172. https://doi.org/10.1080/23270012.2020.1756939

30.

Katsarou

D. V.

Mantsos

Papadopoulou

Sofologi

Efthymiou

Vasileiou

Megari

Theodoratou

Kougioumtzis

G. A.

(2025). Exploring AI technology in grammar performance testing for children with learning disabilities. Education Sciences, 15(3), 351. https://doi.org/10.3390/educsci15030351

31.

King

Glazek

Ross

K. M.

Coghlan

Green

Feng

Lele

Bell

(2025). AI-integrated virtual reality training for teacher preparation on functional communication training: A randomized controlled trial. Virtual Reality, 29(3), 138. https://doi.org/10.1007/s10055-025-01212-2

32.

Krstić

Aleksić

Krstić

(2022). Artificial intelligence in education: A review. In Proceedings of the 9th International Scientific Conference Technics and Informatics in Education TIE 2022 (pp. 223–228). Faculty of Technical Sciences Čačak. https://doi.org/10.46793/TIE22.223K

33.

Laupichler

M. C.

Lindl

Spannagel

(2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3(4), 100115. https://doi.org/10.1016/j.caeai.2022.100115

34.

LeCun

Bengio

Hinton

(2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539

35.

Luckin

Holmes

Griffiths

Forcier

(2016). Intelligence unleashed: An argument for AI in education. Pearson.

36.

McKenna

J. W.

Flower

Ciullo

(2014). Measuring fidelity to improve intervention effectiveness. Intervention in School and Clinic, 50(1), 15–21. https://doi.org/10.1177/1053451214532348

37.

Mishra

Koehler

M. J.

(2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x

38.

Mosher

(2022). Technology tools available for implementing social skill instruction. TEACHING Exceptional Children. https://doi.org/10.1177/00400599211041738

39.

Mosher

Carreon

(2021). Teaching social skills to students with autism spectrum disorder through augmented, virtual, and mixed reality. Research in Learning Technology, 29. https://doi.org/10.25304/rlt.v29.2626

40.

Mosher

Carreon

Sullivan

(2020). A step-by-step process for selecting technology tools for students with ADHD. Journal of Special Education Technology. https://doi.org/10.1177/0162643420978570

41.

Mosher

Dieker

Turpin

(2026). Empowered by AI. Ingram Spark.

42.

Mosher

Dieker

Hines

(2024a). The past, present, and future use of artificial intelligence in teacher education. Journal of Special Education Preparation. https://doi.org/10.33043/8aa9855b

43.

Mosher

Frey

Carreon

Smith

Rowland

Lowrey

(2024b). The Technology Immersive Presence Scale (TIPS) Proof of Concept: Creating a Student Accessible Measure of Presence in Virtual Environments. Journal of Interactive Learning Research, 35(1), 101–133.

44.

Mosher

Lane

Rowland

Carreon

Smith

Frey

Sailor

Kingston

Jackson

Goldman

Ruhter

Williams

Bhattashali

(2024c). The social validity of video modeling versus virtual reality for improving students’ social communication. Issues and Trends in Learning Technology, 12(2). https://doi.org/10.2458/itlt.5830

45.

Mosher

Rowland

Dieker

(2025). Advancing military education, assessment, and communication through AI-enhanced extended reality simulations. Interservice/Industry Training, Simulation and Education (ITSEC) Proceedings, Orlando, FL, United States: NTSA., (25365). https://www.xcdsystem.com/iitsec/proceedings/index.cfm

46.

D. T. K.

Leung

J. K. L.

Chu

S. K. W.

(2024). Artificial intelligence (AI) literacy education in secondary schools: A systematic review. Interactive Learning Environments, 32(10), 6204–6224. https://doi.org/10.1080/10494820.2023.2255228

47.

Noell

G. H.

Gresham

F. M.

Gansle

K. A.

(2002). Does treatment integrity matter? A preliminary investigation of instructional implementation and mathematics performance. Journal of Behavioral Education, 11(1), 51–67. https://link.springer.com/article/10.1023/A:1014385321849

48.

Page

M. J.

McKenzie

J. E.

Bossuyt

P. M.

Boutron

Hoffmann

T. C.

Mulrow

C. D.

Shamseer

Tetzlaff

J. M.

Akl

E. A.

Brennan

S. E.

Chou

Glanville

Grimshaw

J. M.

Hróbjartsson

Lalu

M. M.

Loder

E. W.

Mayo-Wilson

McDonald

Moher

(2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71

49.

Roberts

G. J.

Dumas

D. G.

McNeish

Coté

(2022). Understanding the dynamics of dosage response: A nonlinear meta-analysis of recent reading interventions. Review of Educational Research, 92(2), 209–248. https://doi.org/10.3102/00346543211051423

50.

Rouse

A. G.

(2023). Fidelity of implementation. In De Smedt

Bouwer

Limpo

Graham

(Eds.), Conceptualizing, designing, implementing, and evaluating writing interventions (pp. 154–172). Brill. https://doi.org/10.1163/9789004546240_009

51.

Salas-Pilco

S. Z.

Xiao

Oshima

(2022). Artificial intelligence and new technologies in inclusive education for minority students: A systematic review. Sustainability, 14(20), Article 13572. https://doi.org/10.3390/su142013572

52.

Sallam

(2023). ChatGPT utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare, 11(6), 887. https://doi.org/10.3390/healthcare11060887

53.

Sanetti

L. M. H.

Cook

B. G.

Cook

(2021). Treatment fidelity: What it is and why it matters. Learning Disabilities Research & Practice, 36(1), 83–92. https://doi.org/10.1111/ldrp.12238

54.

Sanusi

I. T.

Oyelere

S. S.

Vartiainen

Suhonen

Tukiainen

(2023). A systematic review of teaching and learning machine learning in K-12 education. Education and Information Technologies, 28(5), 5967–5997. https://doi.org/10.1007/s10639-022-11416-7

55.

Shmueli

Koppius

O. R.

(2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553–572. https://doi.org/10.2307/23042796

56.

Smith

Cheatham

Mosher

(2020). Evidence-based practices to promote inclusion in today’s Catholic school. Journal of Catholic Education, 23(2), 111–134. https://doi.org/10.15365/joce.2302102020

57.

Strnadová

Danker

Dowse

Tso

(2024). Supporting students with disability to improve academic, social and emotional, and self-determination and life-skills outcomes: Umbrella review of evidence-based practices. International Journal of Inclusive Education, 28(14), 3606–3622. https://doi.org/10.1080/13603116.2023.2221239

58.

Szeliski

(2022). Computer vision: Algorithms and applications (2nd ed.). Springer. https://doi.org/10.1007/978-3-030-34372-9

59.

Taddy

(2018). The technological elements of artificial intelligence. In The economics of artificial intelligence: An agenda (pp. 61–87). University of Chicago Press.

60.

van Dijk

Lane

H. B.

Gage

N. A.

(2023). How do intervention studies measure the relation between implementation fidelity and students’ reading outcomes? A systematic review. The Elementary School Journal, 124(1), 56–84. https://doi.org/10.1086/725672

61.

Wainer

A. L.

Ingersoll

B. R.

(2013). Intervention fidelity: An essential component for understanding ASD parent training research and practice. Clinical Psychology: Science and Practice, 20(3), 335–357. https://doi.org/10.1111/cpsp.12045

62.

Yarlagadda

R. T.

(2018). The RPA and AI automation. International Journal of Creative Research Thoughts, 6(3), 365–373.

63.

Zhou

(2023). Integration of modern technologies in higher education on the example of artificial intelligence use. Education and Information Technologies, 28(4), 3893–3910. https://doi.org/10.1007/s10639-022-11309-9