Automated Corrective Feedback

Automated Corrective Feedback is a form of corrective feedback generated by computational systems rather than human agents, distinguished by its reliance on algorithms and artificial intelligence to identify, analyze, and address learner errors in academic or instructional contexts.

114

Publications

5.7K

Citations

347

Authors

170

Institutions

Publications per year

2017–2026

67

Authors

347

  1. SB
    Simon Buckingham Shum

    University of Technology Sydney, Australia

    4 publications · 132 citations

    4

    h-index

  2. RM
    Roberto Martínez‐Maldonado

    Monash University, Australia

    3 publications · 91 citations

    3

    h-index

  3. KB
    Kevin Buffardi

    California State University, Chico, United States

    2 publications · 42 citations

    2

    h-index

  4. LP
    Liangming Pan

    National University of Singapore, Singapore

    2 publications · 87 citations

    2

    h-index

  5. DN
    Dana Niles

    Children's Hospital of Philadelphia, United States

    2 publications · 215 citations

    2

    h-index

1–5 of 347

Venues

  1. Computers & Education

    4 publications · 289 citations

    4

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 155 citations

    3

    h-index

  3. ReCALL

    2 publications · 76 citations

    2

    h-index

  4. RELC Journal

    2 publications · 139 citations

    2

    h-index

  5. Computer Assisted Language Learning

    2 publications · 100 citations

    2

    h-index

1–5