CoChat
Sign Up
Cited by 151Artificial Intelligence in EducationTechnology Adoption and User BehaviourDigital literacy in education

The DOI matches its Crossref record; 59 of the 62 references it cites were found in a registry or an index, and 2 are not checked yet.

RETRACTED: Factors influencing pre-service special education teachers’ intention toward AI in education: Digital literacy, teacher self-efficacy, perceived ease of use, and perceived usefulness

Open access
Yao, Ni, Wang, Qiong·2024·Heliyon
DOI 10.1016/j.heliyon.2024.e34894PMC PMC11325385
Open this paper in CoChat — free account

Abstract

The use of artificial intelligence in education (AIEd) has become increasingly significant globally. In China, there is a lack of research examining the behavioral intention toward AIEd among pre-service special education (SPED) teachers in terms of digital literacy and teacher self-efficacy. Building on the technology acceptance model, our study evaluated the aspects influencing pre-service special education teachers' intention toward AI in education. Data was gathered from 274 pre-service SPED teachers studying at a Chinese public normal university of special education and analyzed using structural equation modeling (SEM). The results show that digital literacy is associated with the perceived usefulness and ease of use of AIEd, which influences SPED teachers' intention to use AIEd. Additionally, digital literacy significantly impacts the self-efficacy of SPED teachers. Given these results, AI designers in special education should comprehend the effectiveness and usability of AIEd for fostering behavioral intention formation. Simultaneously, special educational programs that identify key content and activities for digital literacy training should be developed, and educators should attempt to execute the relevant pre-service training to enhance the intention of pre-service SPED teachers toward AIEd.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.heliyon.2024.e34894This paper is marked retracted.

Cites

59 of 61 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Technology acceptance model (TAM) (Venkatesh & Davis, 2000).
Fig. 1 Technology acceptance model (TAM) (Venkatesh & Davis, 2000).CC BY-NC · PubMed Central
Fig. 2 The research model.***p < 0.001, **p < 0.01, *p < 0.05.
Fig. 2 The research model.***p < 0.001, **p < 0.01, *p < 0.05.CC BY-NC · PubMed Central
Fig. 3 The research model with its standardized coefficients.
Fig. 3 The research model with its standardized coefficients.CC BY-NC · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 62 references

Research works printed but not linked: 4

  1. Exploring the Relationships between Reading Behavior Patterns and Learning Outcomes Based on Log Data from E-Books: A Human Factor Approach: Int. J. Hum. Comput. Interact.: Vol 35, No 4–5 - Get Access’. Accessed: July. 1, 2024. [Online]. Available: https://www.tandfonline.com/doi/full/10.1080/10447318.2018.1543077. · DOI
  2. Quaicoe, 2020, Teachers' digital literacy and digital activity as digital divide components among basic schools in Ghana, Educ. Inf. Technol., 25, 4077, 10.1007/s10639-020-10158-8 · DOI · Paper page
  3. Riina, 2022, DigComp 2.2: The Digital Competence Framework for Citizens - With new examples of knowledge, skills and attitudes, JRC Research Reports, 1
  4. Quaicoe, 2020, Teachers' digital literacy and digital activity as digital divide components among basic schools in Ghana, Educ. Inf. Technol., 25, 4077, 10.1007/s10639-020-10158-8 · DOI · Paper page

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Yao, N., & Wang, Q.
Affiliations
Nanjing Normal University of Special Education (CN)
Corresponding author
Yao, Ni
ORCID
Yao, Ni
Venue
Heliyon
Publisher
Elsevier BV
Published
2024-07
Volume
10
Issue
14
Pages
e34894
ISSN
2405-8440 (ISSN-L)
DOI
10.1016/j.heliyon.2024.e34894
PMC
PMC11325385
OpenAlex
W4400768008
Cited by
150
References
62
Citations by year
2026: 62 · 2025: 84 · 2024: 4
FWCI
56.40
Type
journal article
Language
en
Status
Retracted
Update notices
Open access
Licence
Locations
  • Heliyon (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Government of Jiangsu Province
  • Nanjing Normal University
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Education › Digital literacy in education
Keywords
artificial intelligence in education, pre-service special education teachers, digital literacy, teacher self-efficacy, perceived ease of use, perceived usefulness, technology acceptance model, special education teacher training, retracted article

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review