CoChat
Sign Up
Cited by 344AI in Service InteractionsTechnology Adoption and User BehaviourArtificial Intelligence in Education

The DOI matches its Crossref record, with a small difference in title or year; 55 of the 57 references it cites were found in a registry or an index, and 2 are not checked yet.

Teachers’ attitudes towards chatbots in education: a technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics

Open access
Chocarro, Raquel, Cortiñas, Mónica, Marcos-Matás, Gustavo·2023·Educational Studies
DOI 10.1080/03055698.2020.1850426
Open this paper in CoChat — free accountOpen PDF

Abstract

The appearance of Artificial Intelligence implementations, such as text-based virtual assistants (chatbots) in education is relatively new. These implementations can be useful for helping teachers and students to solve both educational questions and routine tasks. This paper examines the factors that explain teachers’ acceptance of chatbots through the dimensions of the Technology Acceptance Model (perceived usefulness and perceived ease of use), its conversational design (use of social language and proactiveness), and the teachers’ age and digital skills. The data collection process included a pre-test and an online survey with four different types of chatbots. We analyse 225 responses of primary and secondary education teachers. The results show that the perceived easiness and perceived usefulness leads to greater acceptance of chatbots. As for the chatbots’ features, formal language by a chatbot leads to a higher intention of using them. These results can help in chatbot design and communication decisions, improving the acceptance of the educational community.

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 · 10.1080/03055698.2020.1850426

Cites

55 of 57 references verified0 citation errors · 0 not checkable

Statistics

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

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 55 references

Research works printed but not linked: 6

  1. Baron N. S., 2003, The Stanford Handbook for Language Engineers, e dited by A. Farghali,, 59
  2. Bii P., 2013, Educational Research, 4, 218
  3. Gulz A., 2004, International Journal of Artificial Intelligence in Education, 14, 313
  4. Heller B., 2005, EdMedia+ Innovate Learning, 3913
  5. 10.2760/159770 · DOI
  6. Xiao J., 2003, Proccedings of Interact 2003, 383

Cited by

Sorted by citations

Items 201–300 of 344

Related

See all

Metadata

Authors
Chocarro, R., Cortiñas, M., & Marcos-Matás, G.
Affiliations
  • Universidad Pública de Navarra (UPNA) (ES)
  • Departamento Gestión de Empresas, Universidad Pública de Navarra, Pamplona, Spain
Corresponding author
Cortiñas, Mónica
ORCID
Venue
Educational Studies
Publisher
Informa UK Limited
Published
2021-02-04
Volume
49
Issue
2
Pages
295-313
ISSN
  • 0305-5698 (ISSN-L)
  • 1465-3400
DOI
10.1080/03055698.2020.1850426
OpenAlex
W3126219458
Cited by
343
References
55
Citations by year
2026: 49 · 2025: 93 · 2024: 115 · 2023: 56 · 2022: 23 · 2021: 7
FWCI
17.76
Type
journal article
Language
en
Open access
Licence
other-oa
Locations
  • Educational Studies (publishedVersion)
  • Academica-e (Universidad Pública de Navarra) (submittedVersion, other-oa)
Topics
  • Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Education › Artificial Intelligence in Education
Keywords
chatbots, teacher attitudes, technology acceptance model, perceived usefulness, perceived ease of use, social language, conversational design, chatbot design, digital skills, age differences, basic education teachers, intention to use

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