CoChat
Sign Up
Cited by 536Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationIntelligent Tutoring Systems and Adaptive Learning

The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.

Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system

Open access
Javaid, Mohd, Haleem, Abid, Singh, Ravi Pratap, Khan, Shahbaz, Khan, Ibrahim Haleem·2023·BenchCouncil Transactions on Benchmarks, Standards and Evaluations
DOI 10.1016/j.tbench.2023.100115
Open this paper in CoChat — free account

Abstract

Artificial Intelligence (AI)-based ChatGPT developed by OpenAI is now widely accepted in several fields, including education. Students can learn about ideas and theories by using this technology while generating content with it. ChatGPT is built on State of the Art (SOA), like Deep Learning (DL), Natural Language Processing (NLP), and Machine Learning (ML), an extrapolation of a class of ML-NLP models known as Large Language Model (LLMs). It may be used to automate test and assignment grading, giving instructors more time to concentrate on instruction. This technology can be utilised to customise learning for kids, enabling them to focus more intently on the subject matter and critical thinking ChatGPT is an excellent tool for language lessons since it can translate text from one language to another. It may provide lists of vocabulary terms and meanings, assisting students in developing their language proficiency with resources. Personalised learning opportunities are one of ChatGPT’s significant applications in the classroom. This might include creating educational resources and content tailored to a student’s unique interests, skills, and learning goals. This paper discusses the need for ChatGPT and the significant features of ChatGPT in the education system. Further, it identifies and discusses the significant applications of ChatGPT in education. Using ChatGPT, educators may design lessons and instructional materials specific to each student’s requirements and skills based on current trends. Students may work at their speed and concentrate on the areas where they need the most support, resulting in a more effective and efficient learning environment. Both instructors and students may profit significantly from using ChatGPT in the classroom. Instructors may save time on numerous duties by using this technology. In future, ChatGPT will become a powerful tool for enhancing students’ and teachers’ experience.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.tbench.2023.100115

Cites

78 of 78 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

Show all 74 references

Research works printed but not linked: 9

  1. Gilson, 2023, How does CHATGPT perform on the United States medical licensing examination? The implications of large language models for medical education and knowledge assessment, JMIR Med. Educ., 9
  2. Rathore, 2023, Future of AI & generation alpha: ChatGPT beyond boundaries, Eduzone: Int. Peer Rev./Refer. Multidiscip. J., 12, 63
  3. Bommineni, 2023, Performance of ChatGPT on the MCAT: The road to personalised and equitable premedical learning, MedRxiv
  4. Teubner, 2023, Welcome to the era of ChatGPT others, the prospects of large language models, Bus. Inf. Syst. Eng., 1
  5. Singh, 2023, ChatGPT: A new approach to revolutionise organisations, Int. J. New Media Stud. (IJNMS), 10, 57
  6. U. Bukar, M.S. Sayeed, S.F.A. Razak, S. Yogarayan, O.A. Amodu, Text analysis of chatGPT as a tool for academic progress or exploitation. Available at SSRN 4381394.
  7. Rathore, 2023, Future of textile: Sustainable manufacturing & prediction via ChatGPT, Eduzone: Int. Peer Rev./Refer. Multidiscip. J., 12, 52
  8. Opara, 2023, ChatGPT for teaching, learning and research: Prospects and challenges. Opara emmanuel chinonso, adalikwu mfon-ette theresa, tolorunleke caroline aduke 2023. ChatGPT for teaching, learning and research: Prospects and challenges, Glob. Acad. J. Humanit. Soc. Sci., 5
  9. Antaki, 2023, Evaluating the performance of chatbot in ophthalmology: An analysis of its successes and shortcomings, MedRxiv

Cited by

Sorted by citations

Items 401–500 of 536

Related

See all

Metadata

Authors
Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Khan, I. H.
Affiliations
  • Jamia Millia Islamia (IN)
  • National Institute of Technology Kurukshetra (IN)
  • GLA University (IN)
  • Northeastern University (US)
Corresponding author
Javaid, Mohd
ORCID
Venue
BenchCouncil Transactions on Benchmarks, Standards and Evaluations
Publisher
BenchCouncil Press
Published
2023-06
Volume
3
Issue
2
Pages
100115
ISSN
2772-4859 (ISSN-L)
DOI
10.1016/j.tbench.2023.100115
OpenAlex
W4378473491
Cited by
535
References
74
Citations by year
2026: 73 · 2025: 183 · 2024: 222 · 2023: 57
FWCI
125.56
Type
journal article
Language
en
Open access
Licence
Locations
  • BenchCouncil Transactions on Benchmarks Standards and Evaluations (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
ChatGPT, large language models, artificial intelligence in education, automated grading, personalized learning, language translation, educational content development, teacher workload reduction, classroom application

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