CoChat
Sign Up
Cited by 397Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationIntelligent Tutoring Systems and Adaptive Learning

The DOI matches its Crossref record; 107 of the 108 references it cites were found in a registry or an index.

The influence of ChatGPT on student engagement: A systematic review and future research agenda

Open access
Lo, Chung Kwan, Hew, Khe Foon, Jong, Morris Siu-yung·2024·Computers & Education
DOI 10.1016/j.compedu.2024.105100
Open this paper in CoChat — free account

Abstract

ChatGPT, a state-of-the-art artificial intelligence (AI) chatbot, has gained considerable attention as a transformative yet controversial tool for enhancing teaching and learning experiences. Several reviews and numerous articles have been written about harnessing ChatGPT in education since its release on November 30, 2022. Besides summarising its strengths, weaknesses, opportunities, and threats (SWOT) as identified in previous systematic reviews of ChatGPT research, this systematic review aims to develop a new understanding of its influence on student engagement by synthesising the existing related research using a three-dimensional framework comprising behavioural, emotional, and cognitive aspects. We searched relevant databases and included 72 empirical studies published within one year of ChatGPT's initial release. The findings reveal robust but narrowly focused evidence related to behavioural engagement (i.e., work with ChatGPT) and disengagement (i.e., academic dishonesty). The evidence related to the emotional aspect is mixed, with instances of both engagement (e.g., satisfaction and interest/fun) and disengagement (e.g., disappointment and worry/anxiety). There is broad but weak evidence regarding cognitive engagement (e.g., increased understanding and positive self-perception) and disengagement (e.g., reduced critical thinking and overreliance). Our review uncovers several under-explored indicators of student engagement, pointing to the need for further research. Specifically, future studies could focus on students' study habits and attendance (behavioural engagement), social interaction (emotional engagement), and self-regulation and critical thinking (cognitive engagement) in ChatGPT-supported learning environments.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.compedu.2024.105100

Cites

107 of 107 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

Flagged first, then by citations

Show all 108 references

1 of the 108 references couldn’t be matched to a known paper.

Research works printed but not linked: 7

  1. Alqasham, 2023, ChatGPT in the Saudi EFL classroom: A study of learner usage patterns and possibilities in learning optimization, Migration Letters, 20, 1251
  2. Bašić, 2023, ChatGPT-3.5 as writing assistance in students' essays, Humanities and Social Sciences Communications, 10, 750, 10.1057/s41599-023-02269-7 · DOI · Paper page
  3. Javier, 2023, Developing secondary school English language learners' productive and critical use of ChatGPT, TESOL Journal
  4. Limo, 2023, Personalized tutoring: ChatGPT as a virtual tutor for personalized learning experiences, Social Space, 23, 293
  5. Muñoz, 2023, Examining the impacts of ChatGPT on student motivation and engagement, Social Space, 23, 1
  6. 2022, Introducing ChatGPT
  7. Zhang, 2023, A systematic review of ChatGPT use in K-12 education, European Journal of Education

Cited by

Sorted by citations

Items 301–397 of 397

Related

See all

Metadata

Authors
Lo, C. K., Hew, K. F., & Jong, M. S.-Y.
Affiliations
  • Education University of Hong Kong (HK)
  • University of Hong Kong (HK)
  • Chinese University of Hong Kong (HK)
Corresponding author
Lo, Chung Kwan
ORCID
Venue
Computers & Education
Publisher
Elsevier BV
Published
2024-10
Volume
219
Pages
105100
ISSN
0360-1315 (ISSN-L)
DOI
10.1016/j.compedu.2024.105100
OpenAlex
W4399455976
Semantic Scholar
e407f4afa951efeb990ac543e22a83d48fd0a915
Cited by
392
References
108
Citations by year
2026: 190 · 2025: 176 · 2024: 26
FWCI
23.31
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers & Education (publishedVersion, cc-by-nc)
  • The HKU Scholars Hub (University of Hong Kong) (submittedVersion, cc-by-nc-nd)
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, student engagement, AI chatbots in education, academic dishonesty, behavioral engagement, emotional engagement, cognitive engagement, student satisfaction, critical thinking, overreliance on AI, self-regulation, study habits, student attendance, social interaction

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