A Systematic Literature Review of Automated Feedback Generation in Education
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| Title: | A Systematic Literature Review of Automated Feedback Generation in Education |
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| Language: | English |
| Authors: | Yajie Song (ORCID |
| Source: | International Journal of Technology in Education. 2026 9(2):512-556. |
| Availability: | International Society for Technology, Education, and Science. ISTES Organization, Monument, CO 80132. e-mail: istesorganization@gmail.com; e-mail: ijteoffice@gmail.com; Web site: https://www.ijte.net/index.php/ijte/about |
| Peer Reviewed: | Y |
| Page Count: | 45 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Information Analyses |
| Descriptors: | Literature Reviews, Automation, Feedback (Response), Artificial Intelligence, Error Correction, Technology Uses in Education, Educational Technology, Educational Environment, Ethics, Context Effect, Student Evaluation |
| ISSN: | 2689-2758 |
| Abstract: | Feedback that is individualized and immediate is essential to improving learning outcomes but providing it to every learner is difficult. Automatic feedback generation (AFG) aims to alleviate this problem, especially with technology-enhanced learning environments. This systematic literature review of AFG in education, following the PRISMA framework, examines 34 peer-reviewed publications. The findings revealed that the reviewed studies (1) gained momentum after 2019; (2) often used secondary cognitive data to evaluate AFG approaches; (3) mainly targeted computer science domain; (4) frequently combined multiple methods to generate feedback; (5) employed multiple performance evaluations; and (6) mostly provided written feedback aimed at correcting student errors. This review also highlighted several gaps, including the lack of (1) in-depth cognitive and affective data from user studies to evaluate feedback and understand how students interpret it; (2) research on feedback use and strategies to close feedback loop; (3) AFG systems for ill-defined domains with strong transferability; (4) elaborated feedback that scaffolds problem-solving rather than giving answers; (5) feedback using multiple modalities and valences; and (6) integration of learning theories in AFG design. This review advances understanding of current AFG practices, evaluates and extends conceptual frameworks of AFG, and provides insights for future AFG design and evaluation. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1506278 |
| Database: | ERIC |
| Abstract: | Feedback that is individualized and immediate is essential to improving learning outcomes but providing it to every learner is difficult. Automatic feedback generation (AFG) aims to alleviate this problem, especially with technology-enhanced learning environments. This systematic literature review of AFG in education, following the PRISMA framework, examines 34 peer-reviewed publications. The findings revealed that the reviewed studies (1) gained momentum after 2019; (2) often used secondary cognitive data to evaluate AFG approaches; (3) mainly targeted computer science domain; (4) frequently combined multiple methods to generate feedback; (5) employed multiple performance evaluations; and (6) mostly provided written feedback aimed at correcting student errors. This review also highlighted several gaps, including the lack of (1) in-depth cognitive and affective data from user studies to evaluate feedback and understand how students interpret it; (2) research on feedback use and strategies to close feedback loop; (3) AFG systems for ill-defined domains with strong transferability; (4) elaborated feedback that scaffolds problem-solving rather than giving answers; (5) feedback using multiple modalities and valences; and (6) integration of learning theories in AFG design. This review advances understanding of current AFG practices, evaluates and extends conceptual frameworks of AFG, and provides insights for future AFG design and evaluation. |
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| ISSN: | 2689-2758 |
