Automated Feedback Generation

Automated Feedback Generation is an artificial intelligence-driven educational technology that systematically produces individualized, actionable responses to learner inputs by analyzing performance data, thereby distinguishing itself from manual feedback through its computational autonomy and scalability in instructional settings.

560

Publications

30.3K

Citations

1.7K

Authors

506

Institutions

Publications per year

2017–2026

358

Authors

1.7K

  1. SG
    Sumit Gulwani

    Microsoft (United States), United States

    7 publications · 758 citations

    8

    h-index

  2. DG
    Dragan Gašević

    Monash University, Australia

    7 publications · 1.6K citations

    7

    h-index

  3. GS
    Gustavo Soares

    Universidade Federal de Campina Grande, Brazil

    6 publications · 249 citations

    7

    h-index

  4. KR
    Kenneth R. Koedinger

    Carnegie Mellon University, United States

    5 publications · 504 citations

    6

    h-index

  5. JJ
    Johan Jeuring

    Utrecht University, Netherlands

    6 publications · 509 citations

    6

    h-index

1–5 of 1.7K

Venues

  1. arXiv (Cornell University)

    Repository · 23 publications · 1.2K citations

    18

    h-index

  2. 12

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 336 citations

    8

    h-index

  4. IEEE Transactions on Learning Technologies

    6 publications · 307 citations

    8

    h-index

  5. ACM SIGCSE Bulletin

    6 publications · 269 citations

    6

    h-index

1–5