Cited by 169Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationTechnology-Enhanced Education Studies

EXPLORING TEACHERS’ ATTITUDES TOWARDS USING CHATGPT

Open access
Iqbal, Nayab, Ahmed, Hassaan, Azhar, Kaukab Abid·2022·Global Journal for Management and Administrative Sciences

Abstract

ChatGPT has the potential to revolutionize education. By allowing machine learning and natural language processing (NLP) algorithms to interact with students in real-time, ChatGPT can provide students with personalized and interactive learning experiences that are tailored specifically for their individual needs. This study uses Technology Acceptance Model to explore the attitudes of faculty members towards using ChatGPT. Semi-structured interviews with 20 faculty members of a private university in Pakistan were conducted. The responses from the interviews indicate that university faculty are generally cautious in their approach to using ChatGPT. The faculty members had a negative perception and attitude towards using ChatGPT. Potential risks such as cheating and plagiarism were cited as major concerns, while potential benefits such as ease in lesson planning and assessment were also noted. The results of this study suggest that university faculty need more information and education about ChatGPT in order to make informed decisions about its use. Additionally, future research should focus on student perceptions and the use of quantitative methods to gain further insight into how university faculty are using ChatGPT for teaching and learning purposes.

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 · 10.46568/gjmas.v3i4.163

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

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?

  • The review found that teachers without the requisite skills to utilize ChatGPT realized that it did not contribute positively to their work and could potentially have adverse effects.

    “the review also found that teachers without the requisite skills to utilize ChatGPT realized that it did not contribute positively to their work and could potentially have adverse effects [37]”

Self-check

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

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

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Iqbal, N., Ahmed, H., & Azhar, K. A.
Affiliations
  • National University of Malaysia (MY)
  • Salim Habib University (PK)
Venue
Global Journal for Management and Administrative Sciences
Published
2022-12-31
Volume
3
Issue
4
Pages
97-111
ISSN
  • 2788-4821 (ISSN-L)
  • 2788-483X
OpenAlex
W4375930474
Cited by
169
References
17
Citations by year
2026: 26 · 2025: 64 · 2024: 69 · 2023: 10
FWCI
35.38
Type
journal article
Language
en
Open access
Locations
Global Journal for Management and Administrative Sciences (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Education › Technology-Enhanced Education Studies
Keywords
ChatGPT, teacher attitudes, university faculty, technology acceptance model, generative AI in education, academic integrity, cheating, plagiarism, lesson planning, assessment, faculty training

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