Chinese Special Education Teachers’ Experiences With Generative AI: Differences in Perceptions,Use,and TAM Pathways for IEP Writing and Lesson Planning

Abstract

This study investigates how special education teachers perceive and use GenAI for writing Individualized Education Plans (IEPs) and lesson plans. We surveyed 277 Chinese special education teachers and conducted follow-up interviews with 10 participants. Our results show that teachers rated GenAI lower for IEP writing compared to lesson plan writing across all dimensions of the Technology Acceptance Model (TAM). For lesson planning, all TAM pathways—Perceived Ease of Use (PEU) to Perceived Usefulness (PU), PEU to Intention to Use (IU), PU to IU, and IU to Actual Usage (AU)—were significant, indicating a smooth progression from perceptions to use. However, for IEP writing, the IU to AU pathway was not significant. Interviews highlighted the influence of task characteristics on teachers’ perceptions and use of GenAI.

Keywords

generative artificial intelligence technology acceptance model special education teachers individualized education plans lesson plans

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References

Ali

Warraich

N. F.

(2024). Meta-analysis of technology acceptance for Mobile and digital libraries in academic settings using technology acceptance model (TAM). In: Global knowledge, memory and communication. Advance online publication. https://doi.org/10.1108/GKMC-09-2023-0360

Banh

Strobel

(2023). Generative artificial intelligence. Electronic Markets, 33(1), 63. https://doi.org/10.1007/s12525-023-00680-1

Bentler

P. M.

(1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238

Braun

Clarke

(2021). Thematic analysis: A practical guide. Sage.

Brislin

R. W.

(1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301

Brunsting

N. C.

Sreckovic

M. A.

Lane

K. L.

(2014). Special education teacher burnout: A synthesis of research from 1979 to 2013. Education & Treatment of Children, 37(4) 681–711. https://doi.org/10.1353/etc.2014.0032. https://www.jstor.org/stable/44683943

Chen

F. F.

(2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. https://doi.org/10.1080/10705510701301834

Chin

W. W.

(1998). The partial least squares approach to structural equation modeling. In Marcoulides

G. A.

(Ed.), Modern methods for business research. Lawrence Erlbaum Associates. https://doi.org/10.4324/9781410604385-10

Collie

R. J.

Martin

A. J.

(2024). Teachers’ motivation and engagement to harness generative AI for teaching and learning: The role of contextual, occupational, and background factors. Computers and Education: Artificial Intelligence, 6(2), Article 100224. https://doi.org/10.1016/j.caeai.2024.100224

10.

Creswell

J. W.

Plano Clark

V. L.

(2017). Designing and conducting mixed methods research (3rd ed.). Sage Publications.

11.

Cui

Lei

Zhang

(2024)培智学校劳动技能课程“从课标到课堂”的转化路径探析”[An analysis of the transformation path of labor skills courses in special education schools: From curriculum standards to the classroom] (1, pp. 25–32). Modern Special Education.

12.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

13.

Diehm

(2017). Writing measurable and academically relevant IEP goals with 80% accuracy over three consecutive trials. Perspectives of the ASHA Special Interest Groups, 2(16), 34–44. https://doi.org/10.1044/persp2.SIG16.34

14.

Dowd

Langran

(2024). Enhancing special education lesson planning: Harnessing the potential of ChatGPT for in-service and pre-service teachers. In Society for information technology & teacher education international conference (pp. 728–737). Association for the Advancement of Computing in Education (AACE).

15.

F.-E.

Sui

C.-L.

(2021). 近十年我国特殊儿童基础教育发展研究——基于2010—2019年教育部统计数据 [research on the development of basic education for special children in China in the past decade: Based on the ministry of Education’s statistical data from 2010–2019]. Journal of Suihua University, 41(4), 133–137.

16.

Farrokhnia

Banihashem

S. K.

Noroozi

Wals

(2024). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in education and teaching. international, 61(3), 460–474. https://doi.org/10.1080/14703297.2023.2195846

17.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312

18.

Fusch

P. I.

Ness

L. R.

(2015). Are we there yet? Data saturation in qualitative research. Qualitative Report, 20(9), 1408–1416. https://doi.org/10.46743/2160-3715/2015.2281

19.

Gefen

Karahanna

Straub

D. W.

(2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519

20.

Goldman

S. R.

Taylor

Carreon

Smith

S. J.

(2024). Using AI to support special education teacher workload. Journal of Special Education Technology, 39(3), 434–447. https://doi.org/10.1177/01626434241257240

21.

Granić

(2022). Educational technology adoption: A systematic review. Education and Information Technologies, 27(7), 9725–9744. https://doi.org/10.1007/s10639-022-10951-7

22.

Granić

Marangunić

(2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864

23.

Hair

J. F.

Jr. Sarstedt

Hopkins

Kuppelwieser

V. G.

(2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128

24.

Hashem

Ali

El Zein

Fidalgo

Khurma

O. A.

(2024). AI to the rescue: Exploring the potential of ChatGPT as a teacher ally for workload relief and burnout prevention. Research and Practice in Technology Enhanced Learning, 19, Article 23. https://doi.org/10.58459/rptel.2024.19023

25.

Howorth

S. K.

Marino

M. T.

Flanagan

Cuba

M. J.

Lemke

(2024). Integrating emerging technologies to enhance special education teacher preparation. Journal of Research in Innovative Teaching & Learning. https://doi.org/10.1108/JRIT-08-2024-0208 (ahead-of-print).

26.

Bentler

P. M.

(1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037/1082-989X.3.4.424

27.

Individuals with Disabilities Education Act , 20 U.S.C. § 1400 (2004).

28.

Iqbal

M. H.

Siddiqie

S. A.

Mazid

M. A.

(2021). Rethinking theories of lesson plan for effective teaching and learning. Social Sciences & Humanities Open, 4(1), Article 100172. https://doi.org/10.1016/j.ssaho.2021.100172

29.

Johnson

Semmelroth

C. L.

(2014). Special education teacher evaluation: Why it matters, what makes it challenging, and how to address these challenges. Assessment for Effective Intervention, 39(2), 71–82. https://doi.org/10.1177/153450841351331

30.

Landis

J. R.

Koch

G. G.

(1977). An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics, 33(2), 363–374.

31.

(2019). “四川省特殊教育学校拟定和实施个别化教育计划现状的调查研究” [A survey on the current status of the formulation and implementation of individualized education plans in special education schools in Sichuan Province]. Journal of Suihua University, 4, 124–129.

32.

Lin

Cui

N. S.

Wang

K. Y.

(2025). Exploring the impact of chatbot design on student engagement and cognitive load: A case study on error-analysis chatbots. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1–7). ACM. https://doi.org/10.1145/3706599.3719862

33.

Lin

Han

Qiu

(2025). Generative AI in special education: Teachers’ insights on instructional enrichment vs. accommodations. In In proceedings of the extended abstracts of the CHI conference on human factors in computing systems (pp. 1–6). ACM. https://doi.org/10.1145/3706599.3719972

34.

MacCallum

R. C.

Browne

M. W.

Sugawara

H. M.

(1996). Power analysis and determination of sample size for covariance structure modelling. Psychological Methods, 1(2), 130–149. https://doi.org/10.1037/1082-989X.1.2.130

35.

Manley

S. C.

Levy

S. M.

(1981). The IEP organizer: A strategy for turning IEP's into daily lesson plans. Teaching Exceptional Children, 14(2), 70–72. https://doi.org/10.1177/004005998101400207

36.

Marino

M. T.

Vasquez

Dieker

Basham

Blackorby

(2023). The future of artificial intelligence in special education technology. Journal of Special Education Technology, 38(3), 404–416. https://doi.org/10.1177/01626434231165977

37.

Marsh

J. A.

(2019). Special education trends and variations from the United States and United Kingdom (England), 1984 to 2014. International Journal of School & Educational Psychology, 7(4), 229–239. https://doi.org/10.1080/21683603.2018.1429335

38.

Ministry of Education of the People’s Republic of China . (2022). 十四五特殊教育发展提升行动计划 14th Five-Year Special Education Development and Enhancement Action Plan. Beijing: Ministry of Education. Retrieved from: https://www.moe.gov.cn/srcsite/A06/s3331/202201/t20220125_598022.html

39.

Rakap

Balikci

(2024). Enhancing iep goal development for preschoolers with autism: A preliminary study on chatgpt integration. Journal of Autism and Developmental Disorders, 1–6. https://doi.org/10.1007/s10803-024-06343-0

40.

Sahin

(2012). The development of individualized educational programs in Turkey IEP applications. Procedia-Social and Behavioral Sciences, 46, 5030–5034. https://doi.org/10.1016/j.sbspro.2012.06.381

41.

Sarstedt

Ringle

C. M.

Henseler

Hair

J. F.

(2014). On the emancipation of PLS-SEM: A commentary on rigdon (2012). Long Range Planning, 47(3), 154–160. https://doi.org/10.1016/j.lrp.2014.02.007

42.

Scherer

Siddiq

Tondeur

(2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009

43.

Shenzhen Municipal Education Bureau . (2023). “深圳市特殊教育发展提升行动计划(2023-2025)”[Action plan for the development and enhancement of special education in shenzhen (2023–2025)]. Shenzhen Municipal Education Bureau.

44.

J. J.

Lin

Cui

Zhou

Wang

K. Y.

(2025). Digital safety for children with intellectual disabilities when using mobile devices from parents’ and teachers’ perspectives. In Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security. ACM. https://doi.org/10.1145/3719027.3765101

45.

Siegel

(2023). The complete IEP guide. How to advocate for special education services for your child (11th ed.). Nolo.

46.

Siyam

(2019). Factors impacting special education teachers’ acceptance and actual use of technology. Education and Information Technologies, 24(3), 2035–2057. https://doi.org/10.1007/s10639-018-09859-y

47.

Sonderen

E. V.

Sanderman

Coyne

J. C.

(2013). Ineffectiveness of reverse wording of questionnaire items: Let’s learn from cows in the rain. PLoS One, 8(7), Article e68967. https://doi.org/10.1371/journal.pone.0068967

48.

Sun

Zhang

(2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies, 64(2), 53–78. https://doi.org/10.1016/j.ijhcs.2005.04.013

49.

Tabachnick

B. G.

Fidell

L. S.

(2007). Using multivariate statistics (5th ed.). Allyn & Bacon.

50.

Tucker

L. R.

Lewis

(1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1–10. https://doi.org/10.1007/BF02291170

51.

Vandenberg

R. J.

Lance

C. E.

(2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70. https://doi.org/10.1177/109442810031002

52.

Venkatesh

(2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. https://doi.org/10.1287/isre.11.4.342.11872

53.

Venkatesh

Bala

(2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x

54.

Wang

Tlili

Khribi

M. K.

C. K.

Huang

(2025). Generative artificial intelligence in special education: A systematic review through the lens of the mediated-action model. Information Development, 02666669251335655. https://doi.org/10.1177/02666669251335655.

55.

Xin

W.-H.

Cao

S.-Q.

(2016). 培智学校个别化教育计划: 制定、实施及困难——基于对杭州市部分培智学校的调查 [Individualized education plans in special schools: Development, implementation, and challenges—Based on a survey of several special schools in Hangzhou]. Chinese Journal of Special Education, (4), 18–26.

56.

Yang

Zhu

(2025) “培智学校教师教科书使用情况的调查研究.教育理论与实践”[A study on the use of textbooks by teachers in special education schools] (1, pp. 25–32). Modern Special Education.

57.

Yuan

Hart Barnett

J. E.

(2024). Developing quality IEP goals in the age of artificial intelligence. In: Teaching exceptional children. Advance online publication. https://doi.org/10.1177/00400599241239311

58.

Zhai

(2024). Transforming teachers’ roles and agencies in the era of generative AI: Perceptions, acceptance, knowledge, and practices. Journal of Science Education and Technology, 34(6), 1–11. https://doi.org/10.1007/s10956-024-10174-0

59.

Zhang

(2024). 数字化技术赋能特殊教育教学研究 [Research on empowering special education teaching with digital technology]. In Proceedings of the 2024 Guangdong province special education academic conference (pp. 911–917). Gaozhou Special Education School of Guangdong Province.

60.

Zhao

Hang

(2024). “论普惠发展要求下特殊教育学校发展转型与职能升级”[On the transformation and functional upgrading of special education schools under the requirements of inclusive development]. Journal of Language and Education Research, 3, 23–32.

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