Real-World Implementation and Integration of an Automatic Scoring System for Workplace Safety Courses in Italian

Nicola Arici (), Alfonso Emilio Gerevini, Matteo Olivato, Luca Putelli, Luca Sigalini and Ivan Serina ()
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Nicola Arici: Department of Information Engineering, University of Brescia, Via Branze 38, 25121 Brescia, Italy
Alfonso Emilio Gerevini: Department of Information Engineering, University of Brescia, Via Branze 38, 25121 Brescia, Italy
Matteo Olivato: Department of Information Engineering, University of Brescia, Via Branze 38, 25121 Brescia, Italy
Luca Putelli: Department of Information Engineering, University of Brescia, Via Branze 38, 25121 Brescia, Italy
Luca Sigalini: Mega Italia Media, Via Roncadelle 70A, 25030 Castel Mella, Italy
Ivan Serina: Department of Information Engineering, University of Brescia, Via Branze 38, 25121 Brescia, Italy

Future Internet, 2023, vol. 15, issue 8, 1-22

Abstract: Artificial Intelligence and Natural Language Processing techniques can have a very significant impact on the e-learning sector, with the introduction of chatbots, automatic correctors, or scoring systems. However, integrating such technologies into the business environment in an effective way is not a trivial operation, and it not only requires realising a model with good predictive performance, but also it requires the following: (i) a proper study of the task, (ii) a data collection process, (iii) a real-world evaluation of its utility. Moreover, it is also very important to build an entire IT infrastructure that connects the AI system with the company database, with the human employees, the users, etc. In this work, we present a real-world system, based on the state-of-the-art BERT model, which implements an automatic scoring system for open-ended questions written in Italian. More specifically, these questions pertain to the workplace safety courses which every worker must attend by law, often via e-learning platforms such as the one offered by Mega Italia Media. This article describes how our system has been designed, evaluated, and finally deployed for commercial use with complete integration with the other services provided by the company.

Keywords: natural language processing; System Integration; deep learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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