Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 2Online Learning and AnalyticsArtificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive Learning

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

Use of Artificial Intelligence to Grade Student Discussion Boards: An Exploratory Study

Rutner, Stephen, Scott, Rebecca·2022·Information Systems Education Journal
DOI 10.62273/uqpe3732
Open this paper in CoChat — free account

Abstract

There appears to be an increasing acceptance of Artificial Intelligence (AI) across society. As people become more comfortable with AI’s use in advertising, basic services and other areas of day -to-day life, the question arises will students also be willing to accept AI in learning situations. Furthermore, what are the impacts on both the student learning and acceptance as well as the effect on the instructor or professor. This paper presents the initial findings of the use of AI in grading students’ dis cussion boards. It presents an initial model of student expectations, discusses potential benefits and drawbacks of AI and presents initial findings from a limited number of classes using AI grading

Overview

Integrity signals

Registry

Crossref · 10.62273/uqpe3732

Cites

19 of 19 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 19 references

Research works printed but not linked: 5

  1. Bridgeman, Brent & Thomas Quinlan (2009). "Differences between Humans and Computers for Evaluating and Teaching writing." In Proceedings of 2009 International Conference on ELT Technological Enterprises, pp, 3-24.
  2. Gill, Grandon (2006). "Asynchronous Discussion Groups: A Use-based Taxonomy with Examples." Journal of Information Systems Education, v. 17, n. 4, p. 373-383.
  3. Hinton, Leone and Julie Bradshaw (2004). "Benefits of an Online Discussion List in a Traditional Distance Education Course," Turkish Online Journal of Distance Education, vol. 5, no.3, pp. 8-16.
  4. Pecka, Shannon, Kendra Schmid and Bunny Pozehl (2014). "Psychometric Testing of the Pecka Grading Rubric for Evaluating HigherOrder Thinking in Distance Learning." AANA Journal, 82(6), 449-456.
  5. Yeh, Y., Liou, H. C., & Yu, Y. T. (2007). "The Influence of Automatic Essay Evaluation and Bilingual Concordance on EFL Students' Writing." English Teaching and Learning, 31(1), 117-160.

Cited by

See all 2

Metadata

Authors
Rutner, S., & Scott, R.
Venue
Information Systems Education Journal
Publisher
Information Systems & Computing Academic Professionals Education Special Interest Group
Published
2022
Volume
20
Issue
4
Pages
4-18
ISSN
1545-679X (ISSN-L)
DOI
10.62273/uqpe3732
OpenAlex
W7164413506
Cited by
2
References
19
Citations by year
2026: 2
FWCI
0.00
Type
journal article
Language
en
Open access
closed
Locations
Information Systems Education Journal (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
artificial intelligence, automated grading, online forums, virtual learning environments, educational technology

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Use of Artificial Intelligence to Grade Student Discussion Boards: An Exploratory Study. https://app.cochat.ai/papers/10.62273/uqpe3732
The paper
Rutner, S., & Scott, R. (2022). Use of Artificial Intelligence to Grade Student Discussion Boards: An Exploratory Study. Information Systems Education Journal. https://doi.org/10.62273/uqpe3732

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.