Comparison of Artificial Intelligence Methods to Enhance an Automated Peer-Evaluation Suite

Abstract

A Department of Defense strategic focus area for artificial intelligence is the better allocation of personnel resources. The current peer-evaluation system at the Marine Officer Candidates School could benefit from artificial intelligence methods to partially automate the process. The school identifies performance trends by summarizing peer inputs and providing useful feedback to candidates to improve performance. This thesis used data from a recent training company and applied natural-language processing to preprocess peer inputs, identified phrases most helpful in predicting overall performance, extracted the best sentences for characterizing a candidate, and assembled draft counseling documents that required minimal revision by staff. Experiments with a prototype of our methods on a sample of real peer evaluations and summary counseling documents showed good though not perfect performance.

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