AI enhancing knowledge exchange about university buildings

Abstract

Efficient knowledge exchange is important for organizational success, especially in the multifaceted real estate industry. As the industry navigates rapid changes, complexity, and diverse stakeholders, the ability to share information, expertise, and insights is crucial. When managing big real estate portfolios, in the case of universities, the exchange of knowledge about real estate can play a beneficial role for the management of the buildings. However, barriers such as the lack of shared databases and repositories, inconsistencies in knowledge sharing tools, and limited technological utilization hinder the effective exchange of knowledge and collaboration between universities. Existing literature recognizes the potential benefits of AI in everyday use and the importance of tools in overcoming challenges regarding knowledge exchange. But the optimal contribution of AI remains an area requiring further research. Therefore, this research explores how Artificial Intelligence, through the creation of a project database and Tailored GPT model, can address these barriers and enhance knowledge exchange about university real estate buildings.

This research aims to address this gap by exploring the effectiveness of AI enhancing knowledge exchange about university buildings. Drawing on theoretical frameworks and empirical evidence, the research seeks to investigate how a knowledge database can be created for university real estate with the help of AI. Therefore, the goal of this research is how the exchange of knowledge between Dutch universities can be enhanced by the quick and effortless creation of a centralized AI-driven knowledge database for university real estate projects. The research uses a mixed-method approach, combining qualitative and quantitative analyses. The quantitative method involves the theoretical background and creation of the knowledge database using four steps: finding, collecting, creating an overview, and analyzing and identifying. The qualitative method involves interviews with campus managers, in which the created knowledge database will be explained and in which the campus managers can give their feedback. In the synthesis, the results from the theoretical and empirical research will be demonstrated in an expert panel. In the expert panel the project database is evaluated and the Tailored GPT is tested by people working in campus management to get a final understanding on how the database and GPT can be further improved. Resulting in a database and GPT model that can be used by people working in campus management as a stepping stool for the enhancement of the exchange of knowledge.