CoChat
Sign Up
PreprintCited by 268Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationArtificial Intelligence Applications

Engineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education

Open access
Qadir, Junaid·2022
DOI 10.36227/techrxiv.21789434.v1
Open this paper in CoChat — free account

Abstract

Engineering education is constantly evolving to keep up with the latest technological developments and meet the changing needs of the engineering industry. One promising development in this field is the use of generative artificial intelligence technology, such as the ChatGPT conversational agent. ChatGPT has the potential to offer personalized and effective learning experiences by providing students with customized feedback and explanations, as well as creating realistic virtual simulations for hands-on learning. However, it is important to also consider the limitations of this technology. ChatGPT and other generative AI systems are only as good as their training data and may perpetuate biases or even generate and spread misinformation. Additionally, the use of generative AI in education raises ethical concerns such as the potential for unethical or dishonest use by students and the potential unemployment of humans who are made redundant by technology. While the current state of generative AI technology represented by ChatGPT is impressive but flawed, it is only a preview of what is to come. It is important for engineering educators to understand the implications of this technology and study how to adapt the engineering education ecosystem to ensure that the next generation of engineers can take advantage of the benefits offered by generative AI while minimizing any negative consequences.

Integrity signals

Registry

Crossref · 10.36227/techrxiv.21789434.v1

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

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

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?

  • CGPA reflects academic performance and self-regulation, as high-performing students tend to engage with AI more responsibly and uphold integrity.

    “CGPA reflects academic performance and self-regulation, as high-performing students tend to engage with AI more responsibly and uphold integrity (Qadir, 2022;Vieriu & Petrea, 2025).”

    From Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education (2026) · cites this paper as Qadir, 2022

  • AI tools and resources may supplement traditional learning techniques by providing students with additional material and interactive experiences that improve their understanding of complex concepts.

    “AI tools and resources, such as virtual tutors, voice assistants [18], text-to-image generation (Stable Diffusion, Dall-e-2, Midjourney), text summarization (summarizer.org),AI video generation platforms (Synthesia or Elai.io),AI-enabled adaptive assessment [48], and smart content, may also supplement traditional learning techniques by providing students with additional material and interactive ex”

    From New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution (2023) · cites this paper as [34]

  • In its current state, ChatGPT has several serious inherent limitations, such as generating wrong answers and making-up articles that do not exist.

    “Despite the myriad of potential educational benefits, in its current state ChatGPT has been found to have several serious inherent limitations, such as generating wrong answers and making-up articles that do not exist.”

    From Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning (2023) · cites this paper as Qadir, 2022

  • An author asked ChatGPT to generate books and articles, and ChatGPT included a make-up article with full bibliographic details and a non-functional URL.

    “For example, an author asked ChatGPT to generate books and articles in a paper he is working on, ChatGPT included a make-up article which does not exist and even provided full bibliographic details of the article with a non-functional URL (Qadir, 2022).”

    From Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning (2023) · cites this paper as Qadir, 2022

  • ChatGPT has exhibited top performance in many application domains, such as coherent content and essay generation, chatbot responses, language translation, question answering, and programming code.

    “ChatGPT has exhibited top performance in many application domains, such as coherent content and essay generation, chatbot responses, language translation, question answering, and programming code [9,10].”

    From ChatGPT for Education and Research: Opportunities, Threats, and Strategies (2023) · cites this paper as [10]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Qadir, J.
Affiliations
Qatar University (QA)
Corresponding author
Qadir, Junaid
ORCID
Qadir, Junaid
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022-12-30
DOI
10.36227/techrxiv.21789434.v1
OpenAlex
W4313313935
Cited by
264
References
20
Citations by year
2026: 18 · 2025: 24 · 2024: 103 · 2023: 119
Type
posted content
Language
en
Open access
Licence
Locations
Figshare (submittedVersion, cc-by)
Funders
Qatar University
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
generative AI, ChatGPT, engineering education, artificial intelligence in education, personalized learning, virtual simulation, training data bias, misinformation, academic dishonesty, AI ethics, automation and unemployment

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