Engineering an Intelligent Essay Scoring and Feedback System: An Experience Report

• URL: http://arxiv.org/abs/2103.13590v1
• Date: Thu, 25 Mar 2021 03:46:05 GMT
• Title: Engineering an Intelligent Essay Scoring and Feedback System: An Experience Report
• Authors: Akriti Chadda, Kelly Song, Raman Chandrasekar, Ian Gorton
• Abstract summary: We describe an exploratory system for assessing the quality of essays supplied by customers of a specialized recruitment support service. The problem domain is challenging because the open-ended customer-supplied source text has considerable scope for ambiguity and error. There is also a need to incorporate specialized business domain knowledge into the intelligent processing systems.
• Score: 1.5168188294440734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial Intelligence (AI) / Machine Learning (ML)-based systems are widely sought-after commercial solutions that can automate and augment core business services. Intelligent systems can improve the quality of services offered and support scalability through automation. In this paper we describe our experience in engineering an exploratory system for assessing the quality of essays supplied by customers of a specialized recruitment support service. The problem domain is challenging because the open-ended customer-supplied source text has considerable scope for ambiguity and error, making models for analysis hard to build. There is also a need to incorporate specialized business domain knowledge into the intelligent processing systems. To address these challenges, we experimented with and exploited a number of cloud-based machine learning models and composed them into an application-specific processing pipeline. This design allows for modification of the underlying algorithms as more data and improved techniques become available. We describe our design, and the main challenges we faced, namely keeping a check on the quality control of the models, testing the software and deploying the computationally expensive ML models on the cloud.

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