CoChat
Sign Up
Cited by 78Intelligent Tutoring Systems and Adaptive LearningEducational Games and GamificationArtificial Intelligence in Education

Effect of an AI-based chatbot on students’ learning performance in alternate reality game-based museum learning

Liang, Hsin‐Yi, Hwang, Gwo‐Jen, Hsu, Tien‐Yu, Yeh, Jen‐Yuan·2024·British Journal of Educational Technology
DOI 10.1111/bjet.13448
Open this paper in CoChat — free account

Abstract

Abstract Recently, alternate reality games (ARGs) have been applied in museum learning to encourage learners' active engagement through playful problem‐solving activities. However, most learners have insufficient prior knowledge and metacognitive skills to complete the learning tasks in such games. To support learning with ARGs, there is a need to provide proper feedback so that the learners are capable of self‐regulated learning and solving the problems encountered during the learning process. Considering the influences of individual differences, this research developed a chatbot as a learning partner in ARG‐based learning to support students’ learning with adaptive feedback in a museum. A quasi‐experimental study was conducted to evaluate the effectiveness with and without the proposed approach. The results showed that the AI‐based chatbot approach could significantly improve learners' metacognition awareness, emotional engagement and behavioural engagement. Besides, it is helpful to facilitate students' double‐loop learning. Practitioner notes What is already known about this topic Alternate reality games (ARGs) enable students to interact with real‐world contexts. The complexities of real contexts and ambiguities of the ARGs could frustrate students and demotivate their engagement in problem‐solving activities. Conventional corrected feedback may fail to match individual students' requirements in solving encountered problems. What this paper adds An AI‐based chatbot was designed as a smart learning partner to support the students in solving encountered problems. The experimental results showed that the proposed approach improved students' metacognitive awareness and engagement. A drawing analysis and a behaviour sequential analysis were applied to further examine the students' perceptions and behavioural patterns. Implications for practice and/or policy It is potentially worth integrating AI‐based chatbots into game‐based learning to promote students' metacognitive awareness and engagement. AI‐based chatbots provide adaptive feedback to effectively facilitate students' self‐regulated learning and double‐loop learning.

Integrity signals

Registry

Crossref · 10.1111/bjet.13448

Cites

48 of 48 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Liang, H., Hwang, G., Hsu, T., & Yeh, J.
Affiliations
  • I-Shou University (TW)
  • National Taiwan University of Science and Technology (TW)
  • National Museum of Natural Science (TW)
Corresponding author
Hwang, Gwo‐Jen
ORCID
Venue
British Journal of Educational Technology
Publisher
Wiley
Published
2024-02-27
Volume
55
Issue
5
Pages
2315-2338
ISSN
  • 0007-1013 (ISSN-L)
  • 1467-8535
DOI
10.1111/bjet.13448
OpenAlex
W4392192640
Cited by
78
References
48
Citations by year
2026: 41 · 2025: 32 · 2024: 5
FWCI
11.49
Type
journal article
Language
en
Open access
closed
Licence
Locations
British Journal of Educational Technology (publishedVersion)
Funders
National Science and Technology Council
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
alternate reality games, museum learning, AI chatbots, adaptive feedback, metacognitive awareness, student self-regulation, double-loop learning, emotional engagement, behavioral engagement, game-based learning, individual differences, learning partners

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