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Cited by 33Intelligent Tutoring Systems and Adaptive LearningStatistics Education and MethodologiesStudent Assessment and Feedback

Effects of AI ‐generated adaptive feedback on statistical skills and interest in statistics: A field experiment in higher education

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Bauer, Elisabeth, Richters, Constanze, Pickal, Amadeus J., Klippert, Moritz, Sailer, Michael, Stadler, Matthias·2025·British Journal of Educational Technology
DOI 10.1111/bjet.13609
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Abstract

This study explores whether AI‐generated adaptive feedback or static feedback is favourable for student interest and performance outcomes in learning statistics in a digital learning environment. Previous studies have favoured adaptive feedback over static feedback for skill acquisition, however, without investigating the outcome of students' subject‐specific interest. This study randomly assigned 90 educational sciences students to four conditions in a 2 × 2 Solomon four‐group design, with one factor feedback type (adaptive vs. static) and, controlling for pretest sensitisation, another factor pretest participation (yes vs. no). Using a large language model, the adaptive feedback provided feedback messages tailored to students' responses for several tasks on reporting statistical results according to APA style, while static feedback offered a standardised expert solution. There was no evidence of pretest sensitisation and no significant effect of the feedback type on task performance. However, a significant medium‐sized effect of feedback type on interest was found, with lower interest observed in the adaptive condition than in the static condition. In highly structured learning tasks, AI‐generated adaptive feedback, compared with static feedback, may be non‐essential for learners' performance enhancement and less favourable for learners' interest, potentially due to its impact on learners' perceived autonomy and competence.

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  • genAI provides novel affordances previously limited by technological capabilities, such as adaptive and interactive feedback.

    “genAI provides novel affordances previously limited by technological capabilities, such as adaptive and interactive feedback (Bauer, Richters, et al., 2025;Neshaei et al., 2025).”

    From How do students regulate their learning with a genAI chatbot? (2026) · cites this paper as Bauer, Richters, et al., 2025

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Cited by

Sorted by citations

Authors
Bauer, E., Richters, C., Pickal, A. J., Klippert, M., Sailer, M., & Stadler, M.
Affiliations
  • University of Augsburg (DE)
  • LMU Klinikum (DE)
Corresponding author
Bauer, Elisabeth
ORCID
Venue
British Journal of Educational Technology
Publisher
Wiley
Published
2025-07-02
Volume
56
Issue
5
Pages
1735-1757
ISSN
  • 0007-1013 (ISSN-L)
  • 1467-8535
DOI
10.1111/bjet.13609
OpenAlex
W4411961348
Cited by
33
References
20
Citations by year
2026: 30 · 2025: 3
FWCI
13.35
Type
journal article
Language
en
Open access
Licence
Locations
British Journal of Educational Technology (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
  • Social Sciences › Education › Student Assessment and Feedback
Keywords
static feedback, statistics education, large language models, digital learning environments, higher education

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