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Cited by 85Artificial Intelligence in EducationEducational Games and GamificationIntelligent Tutoring Systems and Adaptive Learning

Leveraging the Potential of Large Language Models in Education Through Playful and Game-Based Learning

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Huber, Stefan E., Kiili, Kristian, Nebel, Steve, Ryan, Richard M., Sailer, Michael, Ninaus, Manuel·2024·Educational Psychology Review
DOI 10.1007/s10648-024-09868-z
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Abstract

Abstract This perspective piece explores the transformative potential and associated challenges of large language models (LLMs) in education and how those challenges might be addressed utilizing playful and game-based learning. While providing many opportunities, the stochastic elements incorporated in how present LLMs process text, requires domain expertise for a critical evaluation and responsible use of the generated output. Yet, due to their low opportunity cost, LLMs in education may pose some risk of over-reliance, potentially and unintendedly limiting the development of such expertise. Education is thus faced with the challenge of preserving reliable expertise development while not losing out on emergent opportunities. To address this challenge, we first propose a playful approach focusing on skill practice and human judgment. Drawing from game-based learning research, we then go beyond this playful account by reflecting on the potential of well-designed games to foster a willingness to practice, and thus nurturing domain-specific expertise. We finally give some perspective on how a new pedagogy of learning with AI might utilize LLMs for learning by generating games and gamifying learning materials, leveraging the full potential of human-AI interaction in education.

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  • A major concern associated with inadequate use or implementation of AI in learning contexts is its potential to undermine the acquisition of knowledge and skills.

    “A major concern associated with an inadequate use or implementation of AI in learning contexts is its potential to undermine the acquisition of knowledge and skills (Huber et al., 2024;Kasneci et al., 2023).”

    From Looking Beyond the Hype: Understanding the Effects of AI on Learning (2025) · cites this paper as Huber et al., 2024

  • Generative AI can support preparation and implementation of instructional approaches, such as problem-based learning, scenario-based learning, and game-based learning, enhancing cognitive engagement.

    “Further, generative AI can support the preparation and implementation of instructional approaches, such as problem-based learning, scenario-based learning, and game-based learning, enhancing cognitive engagement (Huber et al., 2024;Kasneci et al., 2023).”

    From Looking Beyond the Hype: Understanding the Effects of AI on Learning (2025) · cites this paper as Huber et al., 2024

  • Generative AI can assist learners in creating text elements, visuals, or code, for example, to design games and other creative outputs.

    “Generative AI can assist learners in creating text elements, visuals, or code, for example, to design games and other creative outputs (Huber et al., 2024).”

    From Looking Beyond the Hype: Understanding the Effects of AI on Learning (2025) · cites this paper as Huber et al., 2024

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Authors
Huber, S. E., Kiili, K., Nebel, S., Ryan, R. M., Sailer, M., & Ninaus, M.
Affiliations
  • University of Graz (AT)
  • Tampere University (FI)
  • University of Potsdam (DE)
  • Ewha Womans University (KR)
  • University of Augsburg (DE)
Corresponding author
Ninaus, Manuel
ORCID
Venue
Educational Psychology Review
Publisher
Springer Science and Business Media LLC
Published
2024-02-27
Volume
36
Issue
1
ISSN
  • 1040-726X (ISSN-L)
  • 1573-336X
DOI
10.1007/s10648-024-09868-z
OpenAlex
W4392196220
Cited by
85
References
19
Citations by year
2026: 34 · 2025: 41 · 2024: 10
FWCI
32.06
Type
journal article
Language
en
Open access
Licence
Locations
  • Educational Psychology Review (publishedVersion, cc-by)
  • OPUS (Augsburg University) (submittedVersion, cc-by)
  • Tampere University Institutional Repository (Tampere University) (submittedVersion, cc-by)
Funders
  • Strategic Research Council (335625, 358250)
  • University of Graz
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
large language models, game-based learning, playful learning, domain knowledge, overreliance on AI, human judgment, skill practice, gamification, AI-assisted learning, human-AI interaction, critical evaluation of AI-generated content

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 ›

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