Why explainable AI may not be enough: predictions and mispredictions in decision making in education
Abstract In learning analytics and in education at large, AI explanations are always computed from aggregate data of all the students to offer the “average” picture. Whereas the average may work for most students, it does not reflect or capture the individual differences or the variability among stu...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-11-01
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| Series: | Smart Learning Environments |
| Online Access: | https://doi.org/10.1186/s40561-024-00343-4 |
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