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Cited by 32Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Healthcare and Education

Generative AI in the Classroom: Effects of Context-Personalized Learning Material and Tasks on Motivation and Performance

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Tasdelen, Osman, Bodemer, Daniel·2025·International Journal of Artificial Intelligence in Education
DOI 10.1007/s40593-025-00491-9
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Abstract

Maintaining student motivation is a persistent challenge in modern education, as traditional methods often fail to fully address students’ individual interests. While personalized approaches show promise, educators face substantial challenges in creating personalized educational content due to the significant resources required, often exceeding practical limitations. Context-personalization through Generative Artificial Intelligence (GAI) enables the real-time creation of diverse learning material and tasks tailored to each student’s unique interests, surpassing what educators can provide in practice. This paper reports on the development of such a GAI-based tool and its systematic investigation. A 2 $$\times $$ 2 mixed between-subjects design (N = 114) was conducted in primary school classrooms in Germany during regular math classes, with learning material (context-personalized vs. standard) and task type (context-personalized vs. standard) as factors. Results show that learning with context-personalized learning material and tasks led to higher intrinsic motivation, greater interest, and higher learning performance compared to standard learning material and tasks. The findings indicate that interests outside of typical school subjects can trigger situational interest and enhance intrinsic motivation in academic contexts. Furthermore, this paper highlights cutting-edge technologies, showcasing how Large Language Models, such as GPT-4, can place fractal text tasks in meaningful contexts in real time. This capability provides extensive opportunities to create learning material and tasks that foster intrinsic motivation, interest, and improve learning performance, effectively addressing the limitations previously faced by teachers.

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  • GenAI can expand the conceptual reach of the research experience by helping transform student interest into researchable inquiry.

    “This extends observations that GenAI-generated content aligned with the individual interests of learners can increase motivation, interest, and engagement (Bai et al. 2025; Tasdelen and Bodemer 2025) and suggests that GenAI can expand the conceptual reach of the research experience by helping transform student interest into researchable inquiry.”

    From Generative AI Expands the Intellectual Reach of Course Based Undergraduate Research Experiences (CUREs) (2026) · cites this paper as Tasdelen and Bodemer 2025

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Authors
Tasdelen, O., & Bodemer, D.
Affiliations
University of Duisburg-Essen (DE)
Corresponding author
Tasdelen, Osman
ORCID
Venue
International Journal of Artificial Intelligence in Education
Publisher
Elsevier BV
Published
2025-12
Volume
35
Issue
5
Pages
3049-3070
ISSN
1560-4292 (ISSN-L)
DOI
10.1007/s40593-025-00491-9
OpenAlex
W4412168386
Cited by
32
References
2
Citations by year
2026: 28 · 2025: 4
FWCI
20.57
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Artificial Intelligence in Education (publishedVersion, cc-by)
  • Universitätsbibliographie, Universität Duisburg-Essen (submittedVersion)
Funders
Universität Duisburg-Essen
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
generative AI in education, intrinsic motivation, situational interest, learning performance, primary school mathematics, large language models, GPT-4

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