Effects of AI-Generated Adaptive Feedback on Statistical Skills and Interest in Statistics: A Field Experiment in Higher Education
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| Title: | Effects of AI-Generated Adaptive Feedback on Statistical Skills and Interest in Statistics: A Field Experiment in Higher Education |
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| Language: | English |
| Authors: | Elisabeth Bauer (ORCID |
| Source: | British Journal of Educational Technology. 2025 56(5):1735-1757. |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 23 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Technology Uses in Education, Feedback (Response), Statistics Education, Skill Development, Higher Education, College Students, Natural Language Processing, Task Analysis |
| DOI: | 10.1111/bjet.13609 |
| ISSN: | 0007-1013 1467-8535 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1479894 |
| Database: | ERIC |
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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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| ISSN: | 0007-1013 1467-8535 |
| DOI: | 10.1111/bjet.13609 |
