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Cited by 59Educational Games and GamificationOnline Learning and AnalyticsArtificial Intelligence in Education

Artificial intelligence empowers gamification: Optimizing student engagement and learning outcomes in e-learning and moocs

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Bachiri, Younes-Aziz, Mouncif, Hicham, Bouikhalene, Belaid·2023·International Journal of Engineering Pedagogy (iJEP)
DOI 10.3991/ijep.v13i8.40853
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Abstract

In this era of Artificial Intelligence (AI) growth, characterized by advances in the Large Language Models (LLMs) used by ChatGPT and Bard, this study examines the effects of gamification and Automatic Question Generation (AQG) on student engagement and learning outcomes in the context of a Massive Open Online Course (MOOC). AQG, implemented via a Moodle plugin, transforms conventional assessments into an interactive, gamified experience, leveraging the “test effect” to improve learning outcomes. Research with 100 fifth-graders in a primary and secondary school shows that gamified assessments significantly boost student motivation and learning outcomes compared with traditional methods. The custom Moodle plugin facilitates the AQG process, generating contextually relevant and grammatically correct Multiple-Choice Questions (MCQs) from course content. The result is a dynamic, personalized assessment experience aimed at optimizing student retention. This paper concludes by discussing the implications of the study for educators and highlighting potential directions for future research.

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Crossref · 10.3991/ijep.v13i8.40853

Cites

34 of 34 references verified0 citation errors · 0 not checkable

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  • Studies in motivation and affect regulation use GenAI to stimulate students' interest and intrinsic motivation, strengthen their sense of classroom belonging, and support emotional well-being and persistence.

    “Studies in this strand use GenAI to stimulate students' interest and intrinsic motivation, strengthen their sense of classroom belonging, and support emotional well-being and persistence [106-108].”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [106]

    Section 4.2 reports that students found the AI-assisted gamified assessments engaging and were more motivated to do their best than students using traditional assessments. The paper does not establish intrinsic motivation, classroom belonging, emotional well-being, or persistence; Section 4.6 discusses adapting to students’ affective states only as future work. Page numbers were not provided in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • LLM-based automatic question generation dynamically constructs items, delivers immediate correctness judgments and targeted hints, and drives answer/feedback/revision loops.

    “LLM-based automatic question generation that dynamically constructs items, delivers immediate correctness judgments and targeted hints, and drives answer/feedback/revision loops; a light gamified interface (e.g., points, lives) is sometimes layered on only to sustain engagement [106].”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [106]

    Sections 3.1 and “Question generation” describe a text-to-text model that generates questions from course content, and section 2.5 says automatic question generation can give students immediate feedback. Section 3.2 describes points and lives as gamification features. The copy does not establish that the system gives immediate correctness judgments or targeted hints, or that students follow answer–feedback–revision loops; it also does not identify the question generator specifically as an LLM. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Numerous studies report higher enthusiasm, participation, curiosity, and satisfaction after AI use, alongside a general preference for AI tools as efficient and convenient.

    “Numerous studies report higher enthusiasm, participation, curiosity, and satisfaction after AI use [6,70,74,81,87,91,92,101,104,106,113,114,116,119,131,141,142], alongside a general preference for AI tools as efficient and convenient [99,111,124,127,135,140].”

    From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [106]

    Section 3.3 describes a study of fifth-graders using gamified, automatically generated assessments; Section 4.2 reports greater engagement and motivation than with traditional assessments. This supports part of the first, cited clause, but the paper does not establish higher participation, curiosity, or satisfaction after AI use generally. The separate claim about preferring AI tools is attached to other citations, not this one. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Cites

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1 of the 35 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

  1. J. T. Kim and W.-H. Lee, "Dynamical model and simulations for gamification of learn- ing," International Journal of Multimedia and Ubiquitous Engineering, vol. 8, no. 4, pp. 179-190, 2013.

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Authors
Bachiri, Y.-A., Mouncif, H., & Bouikhalene, B.
Affiliations
Université Sultan Moulay Slimane (MA)
ORCID
Venue
International Journal of Engineering Pedagogy (iJEP)
Publisher
International Association of Online Engineering (IAOE)
Published
2023-12-14
Volume
13
Issue
8
Pages
4-19
ISSN
2192-4880 (ISSN-L)
DOI
10.3991/ijep.v13i8.40853
OpenAlex
W4389765006
Cited by
59
References
35
Citations by year
2026: 19 · 2025: 27 · 2024: 11 · 2023: 2
FWCI
28.69
Type
journal article
Language
en
Open access
Licence
Locations
International Journal of Engineering Pedagogy (iJEP) (publishedVersion, cc-by)
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
gamification, automatic question generation, student engagement, learning outcomes, MOOCs, Moodle plugin, testing effect, student motivation, large language models, multiple-choice questions, online learning, individualized assessment

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