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 in | British journal of educational technology Vol. 56; no. 5; pp. 1735 - 1757 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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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. |
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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 |
Author_xml | – sequence: 1 givenname: Elisabeth orcidid: 0000-0003-4078-0999 surname: Bauer fullname: Bauer, Elisabeth email: elisabeth.bauer@uni‐a.de organization: University of Augsburg – sequence: 2 givenname: Constanze surname: Richters fullname: Richters, Constanze organization: Institute of Medical Education, LMU University Hospital, LMU Munich – sequence: 3 givenname: Amadeus J. orcidid: 0000-0001-5897-3153 surname: Pickal fullname: Pickal, Amadeus J. organization: University of Augsburg – sequence: 4 givenname: Moritz surname: Klippert fullname: Klippert, Moritz organization: University of Augsburg – sequence: 5 givenname: Michael surname: Sailer fullname: Sailer, Michael organization: University of Augsburg – sequence: 6 givenname: Matthias surname: Stadler fullname: Stadler, Matthias organization: Institute of Medical Education, LMU University Hospital, LMU Munich |
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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 |
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