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The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues
Tack, Anaïs; Piech, Chris – International Educational Data Mining Society, 2022
How can we test whether state-of-the-art generative models, such as Blender and GPT-3, are good AI teachers, capable of replying to a student in an educational dialogue? Designing an AI teacher test is challenging: although evaluation methods are much-needed, there is no off-the-shelf solution to measuring pedagogical ability. This paper reports…
Descriptors: Artificial Intelligence, Dialogs (Language), Bayesian Statistics, Decision Making
Cook, Robert J.; Durning, Steven J. – AERA Online Paper Repository, 2016
In an effort to better align item development to goals of assessing higher-order tasks and decision making, complex decision trees were developed to follow clinical reasoning scripts and used as models on which multiple-choice questions could be built. This approach is compatible with best-practice assessment frameworks like Evidence Centered…
Descriptors: Multiple Choice Tests, Decision Making, Models, Task Analysis

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