Cited by 1Artificial Intelligence in EducationManagement and Marketing EducationArtificial Intelligence in Healthcare and Education

The DOI matches its DataCite record.

Interview with ChatGPT 3.5 on the Integration of GenAI tools into Experiential Learning for Authentic Assessment, an Example of Lean Healthcare in Operations Management Education

Open access
Michel-Villarreal, Rosario·2023·Mendeley Data

Abstract

This interview delves into the integration of GenAI tools into Experiential Learning activities for Authentic Assessment in a Lean Healthcare context, as explained by Chat GPT 3.5.

Overview

Integrity signals

Registry

DataCite · 10.17632/wwbtfwwfwn.1

Cites

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

Still being checked.

Cited by

Sorted by citations

See all 1

Metadata

Authors
Michel-Villarreal, R.
Venue
Mendeley Data
Publisher
Mendeley Data
Published
2023-11-13
OpenAlex
W6887874678
Cited by
1
References
0
Citations by year
2024: 1
Type
journal article
Language
en
Open access
Locations
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • Mendeley Data (cc-by)
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Social Sciences › Education › Management and Marketing Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
generative AI, ChatGPT-3.5, experiential learning, authentic assessment, lean healthcare, operations management education, AI integration in education

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