| Title: |
How Does Artificial Intelligence Compare to Human Feedback? A Meta-Analysis of Performance, Feedback Perception, and Learning Dispositions |
| Language: |
English |
| Authors: |
Rogers Kaliisa (ORCID 0000-0001-6528-8517); Kamila Misiejuk (ORCID 0000-0003-0761-8703); Sonsoles López-Pernas (ORCID 0000-0002-9621-1392); Mohammed Saqr (ORCID 0000-0001-5881-3109) |
| Source: |
Educational Psychology. 2026 46(1):80-111. |
| Availability: |
Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: |
Y |
| Page Count: |
32 |
| Publication Date: |
2026 |
| Document Type: |
Journal Articles; Information Analyses |
| Descriptors: |
Artificial Intelligence; Feedback (Response); Man Machine Systems; Meta Analysis; Learning Analytics; Effect Size; Empathy; Academic Achievement; Learner Engagement; Learning Processes; Student Attitudes; Outcomes of Education |
| DOI: |
10.1080/01443410.2025.2553639 |
| ISSN: |
0144-3410; 1469-5820 |
| Abstract: |
This exploratory meta-analysis synthesises current research on the effectiveness of Artificial Intelligence (AI)-generated feedback compared to traditional human-provided feedback. Drawing on 41 studies involving a total of 4813 students, the findings reveal no statistically significant differences in learning performance between students who received AI-generated feedback and those who received human-provided feedback. The pooled effect size was small and statistically insignificant (Hedge's g = 0.25, CI [-0.11; 0.60]), indicating that AI feedback is potentially as effective as human feedback. A separate meta-analysis focusing exclusively on studies in the domain of language and writing confirmed similar findings, with high heterogeneity persisting (I[superscript 2] = 95%). The study further explored differences in feedback perception and found a small, negative, and statistically insignificant effect size (Hedge's g = -0.20, CI [-0.67; 0.27]). The study advocates for a hybrid approach, leveraging the scalability of AI while retaining the deep, empathetic, and contextual features of human feedback. |
| Abstractor: |
As Provided |
| Entry Date: |
2026 |
| Accession Number: |
EJ1500976 |
| Database: |
ERIC |