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
Children
Automated Comment GenerationAutomated Hints GenerationAutomated Writing EvaluationData-driven Feedback GenerationExplainable Feedback GenerationFeedback Generation For ProgrammingFeedback Generation For WritingLearning Analytics FeedbackMultimodal Feedback GenerationNeural Feedback Generation
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506
University of California, Berkeley
Berkeley, United States
14 h-index
46 publications
1.9K citations
Microsoft (United States)
Redmond, United States
13 h-index
25 publications
1.9K citations
Carnegie Mellon University
Pittsburgh, United States
11 h-index
32 publications
2.2K citations
Stanford University
Stanford, United States
11 h-index
42 publications
3.3K citations

Monash University
Melbourne, Australia
10 h-index
47 publications
10.7K citations

Open University of the Netherlands
Heerlen, The Netherlands
10 h-index
23 publications
1.7K citations
North Carolina State University
Raleigh, United States
9 h-index
29 publications
974 citations
University of California San Diego
San Diego, United States
9 h-index
12 publications
475 citations
1–8 of 506
Repository · 23 publications · 1.2K citations
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12 publications · 812 citations
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Conference · 8 publications · 336 citations
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6 publications · 307 citations
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6 publications · 269 citations
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