Development and Fine-tuning of Large Language Models to Optimize and Automatically Assign Cases at the Service Desk
Year: 2025
Type: article
Abstract: This study presents the development and fine-tuning of an Artificial Intelligence (AI) model based on the BERT model to automate the manual case assignment process at the IT service desk. The manual case assignment process in the university has proven inefficient and error-prone, negatively impacting service quality and user satisfaction. Integrating BERT, a specialized transformer for Natural Language Processing (NLP) and Machine Learning (ML) tasks, has enabled high-precision and efficient automatic case classification. This study used a dataset composed of 33,000 original records and 77,000 artificially generated records. The results demonstrated a marked improvement in the speed and precision of case assignment, allowing human resources to concentrate on other specific tasks. This article provides practical guidance for developing and tuning AI models in IT service management, highlighting the operational benefits and implications of digital transformation in academic institutions.
Source: Intelligenza Artificiale
Institutions: University of Cuenca, Universidad Católica de Cuenca
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4408802544