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

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, wi...

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Published inBritish journal of educational technology Vol. 56; no. 5; pp. 1735 - 1757
Main Authors Bauer, Elisabeth, Richters, Constanze, Pickal, Amadeus J., Klippert, Moritz, Sailer, Michael, Stadler, Matthias
Format Journal Article
LanguageEnglish
Published Coventry Blackwell Publishing Ltd 01.09.2025
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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.
AbstractList 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.
Author Klippert, Moritz
Stadler, Matthias
Pickal, Amadeus J.
Sailer, Michael
Richters, Constanze
Bauer, Elisabeth
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Snippet This study explores whether AI‐generated adaptive feedback or static feedback is favourable for student interest and performance outcomes in learning...
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SubjectTerms adaptivity
artificial intelligence
Cognitive tasks
Design factors
Feedback
higher education
interest
Large language models
natural language processing
Performance enhancement
Pretesting
Skill Development
statistical skills
Student Interests
Students
Title Effects of AI‐generated adaptive feedback on statistical skills and interest in statistics: A field experiment in higher education
URI https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fbjet.13609
https://www.proquest.com/docview/3238244770
Volume 56
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