Publication Date
| In 2026 | 0 |
| Since 2025 | 1 |
| Since 2022 (last 5 years) | 1 |
| Since 2017 (last 10 years) | 1 |
| Since 2007 (last 20 years) | 1 |
Descriptor
| Artificial Intelligence | 1 |
| Automation | 1 |
| Data Collection | 1 |
| Evaluation | 1 |
| Feedback (Response) | 1 |
| Grading | 1 |
| Higher Education | 1 |
| Models | 1 |
| Preferences | 1 |
| Progress Monitoring | 1 |
| Teacher Attitudes | 1 |
| More ▼ | |
Source
| International Educational… | 1 |
Publication Type
| Reports - Research | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
| Higher Education | 1 |
| Postsecondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Juliette Woodrow; Sanmi Koyejo; Chris Piech – International Educational Data Mining Society, 2025
High-quality feedback requires understanding of a student's work, insights into what concepts would help them improve, and language that matches the preferences of the specific teaching team. While Large Language Models (LLMs) can generate coherent feedback, adapting these responses to align with specific teacher preferences remains an open…
Descriptors: Feedback (Response), Artificial Intelligence, Teacher Attitudes, Preferences

Peer reviewed
