CoChat
Sign Up
Cited by 39Artificial Intelligence in EducationProblem and Project Based LearningEducation and Critical Thinking Development

The effects of critical thinking intervention on reliance behaviors, problem-solving quality, and creativity during human-Generative AI collaborative learning

Open access
Hou, Chenyu, Zhu, Gaoxia, Liu, Yanzhi, Sudarshan, Vidya, Chong, Josephine Leng Leng, Zhang, Fannie Yifan, Tan, Michael Yong Heng, Ong, Yew Soon·2026·Computers & Education
DOI 10.1016/j.compedu.2026.105576
Open this paper in CoChat — free account

Abstract

As Generative AI becomes increasingly used in various educational contexts, understanding how students engage with these tools during collaborative problem-solving is critical. While prior research suggests that critical thinking is essential in human-AI problem-solving, few studies have examined how instructional interventions, targeting critical thinking, might shape their reliance behaviors and collaborative outcomes. This study investigates the effects of a critical thinking intervention embedded in a problem-based learning (PBL) environment where students are engaged with Generative AI. The intervention combined strategies that foster critical thinking, including authentic instruction, structured dialogue, and AI-supported peer mentoring, aiming to promote students’ thoughtful engagement and improve problem-solving performance. Participants (N = 226) were assigned to experimental (with critical thinking interventions) or comparison (without critical thinking interventions) conditions. We used pre- and post-surveys to measure participants’ trust, critical thinking, and AI reliance behaviors, and group reports and chat histories to assess their problem-solving quality and creativity. Results revealed that the intervention did not produce significant improvement in self-reported critical thinking, possibly due to the short intervention duration. However, the intervention led to a marginal reduction in students’ thoughtless use of Generative AI and significantly reduced the…

Integrity signals

Registry

Crossref · 10.1016/j.compedu.2026.105576

Cites

11 of 11 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?

  • Written justification can complement broader instruction or training in critical thinking and AI literacy by prompting students to exercise critical evaluation during their interactions with a specific AI suggestion.

    “Rather than replacing broader instruction or training in critical thinking and AI literacy (e.g., Hou et al., 2026), written justification can complement these approaches by prompting students to exercise critical evaluation during their interactions with a specific AI suggestion.”

    From Think Before You Accept: Can Written Justification Reduce Uncritical Uptake of AI Writing Suggestions? (2026) · cites this paper as Hou et al., 2026

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Hou, C., Zhu, G., Liu, Y., Sudarshan, V., Chong, J. L. L., Zhang, F. Y., Tan, M. Y. H., & Ong, Y. S.
Affiliations
Nanyang Technological University (SG)
Corresponding author
Zhu, Gaoxia
ORCID
Venue
Computers & Education
Publisher
Elsevier BV
Published
2026-07
Volume
247
Pages
105576
ISSN
0360-1315 (ISSN-L)
DOI
10.1016/j.compedu.2026.105576
OpenAlex
W7124760248
Cited by
39
References
11
Citations by year
2026: 39
FWCI
174.96
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers & Education (publishedVersion, cc-by-nc-nd)
  • DR-NTU (Nanyang Technological University) (submittedVersion)
  • NIE Digital Repository (National Institute of Education, Nanyang Technological University) (submittedVersion)
Funders
  • Infocomm Media Development Authority
  • National Research Foundation of Singapore
  • Ministry of Education - Singapore (RG133/24, T2EP40124- 0004)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Education › Problem and Project Based Learning
  • Social Sciences › Education › Education and Critical Thinking Development
Keywords
generative AI, human-AI collaboration, AI reliance, problem-based learning, collaborative problem solving, creativity, trust in AI, peer mentoring, AI-generated content, student use of AI

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