Cited by 78Artificial Intelligence in EducationAcademic integrity and plagiarismEthics and Social Impacts of AI

Faculty members’ use of artificial intelligence to grade student papers: a case of implications

Open access
Kumar, Rahul·2023·International Journal for Educational Integrity

Abstract

This paper presents the case of an adjunct university professor to illustrate the dilemma of using artificial intelligence (AI) technology to grade student papers. The hypothetical case discusses the benefits of using a commercial AI service to grade student papers—including discretion, convenience, pedagogical merits of consistent feedback for students, and advances made in the field that yield high-quality work—all of which are achieved quickly. Arguments against using AI to grade student papers involve cost, privacy, legality, and ethics. The paper discusses career implications for faculty members in both situations and concludes with implications for researchers within the discourse on academic integrity.

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 · 10.1007/s40979-023-00130-7

Cites

10 of 10 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?

  • Many teachers may be hesitant to embrace GenAI tools, such as ChatGPT, due to concerns related to cost, privacy, and legality, in addition to a naivety as to how these tools can be appropriately used for educational purposes.

    “Many may be hesitant to embrace GenAI tools, such as ChatGPT, due to concerns related to cost, privacy, and legality (Kumar, 2023), in addition to a naivety as to how these tools can be appropriately used for educational purposes.”

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

Research works printed but not linked: 3

  1. Al-Bashir MM, Kabir RM, Rahman I (2016) The value and effectiveness of feedback in improving students' learning and professionalizing teaching in higher education. J Educ Pract 7(16):38-41 https:// iiste. org/ Journ als/ index. php/ JEP/ artic le/ view/ 31077/ 31909
  2. Pressey SL (1926) A simple apparatus which gives tests and scores-and teaches. Sch Soc 23(586):373-376
  3. Ross WD (1930) The right and the good. Clarendon Press

Cited by

Sorted by citations

Authors
Kumar, R.
Affiliations
Brock University (CA)
Corresponding author
Kumar, Rahul
Venue
International Journal for Educational Integrity
Published
2023-05-15
Volume
19
Issue
1
ISSN
1833-2595 (ISSN-L)
OpenAlex
W4376611400
Cited by
78
References
10
Citations by year
2026: 22 · 2025: 26 · 2024: 24 · 2023: 6
FWCI
18.31
Type
journal article
Language
en
Open access
Locations
  • International Journal for Educational Integrity (publishedVersion, cc-by)
  • Brock University Digital Repository (Brock University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Brock University Digital Repository (Brock University) (submittedVersion, cc-by-nd)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
artificial intelligence, automated grading, student papers, academic integrity, university faculty, adjunct faculty, privacy, legal issues, cost, feedback consistency

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