RLHR: A Framework for Driving Dynamically Adaptable Questionnaires and Profiling People Using Reinforcement Learning

Ciprian Paduraru, Catalina Patilea, Alin Stefanescu, Alin Stefanescu

2024

Abstract

In today’s corporate landscape, the creation of questionnaires, surveys or evaluation forms for employees is a widespread practice. These tools are regularly used to check various aspects such as motivation, opportunities for improvement, satisfaction levels and even potential cybersecurity risks. A common limitation lies in their generic nature: they often lack personalization and rely on predetermined questions. Our research focuses on improving this process by introducing AI agents based on reinforcement learning. These agents dynamically adapt the content of surveys to each person based on their unique personality traits. Our framework is open source and can be seamlessly integrated into various use cases in different industries or academic research. To evaluate the effectiveness of the approach, we tackle a real-life scenario: the detection of potentially inappropriate behavior in the workplace. In this context, the reinforcement learning-based AI agents function like human recruiters and create personalized surveys. The results are encouraging, as they show that our decision algorithms for content selection are very similar to those of recruiters. The open-source framework also includes tools for detailed post-analysis for further decision making and explanation of the results.

Paper Citation

in Harvard Style

Paduraru C., Patilea C. and Stefanescu A. (2024). RLHR: A Framework for Driving Dynamically Adaptable Questionnaires and Profiling People Using Reinforcement Learning. In Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-706-1, SciTePress, pages 359-366. DOI: 10.5220/0012752800003753

in Bibtex Style

@conference{icsoft24,
author={Ciprian Paduraru and Catalina Patilea and Alin Stefanescu},
title={RLHR: A Framework for Driving Dynamically Adaptable Questionnaires and Profiling People Using Reinforcement Learning},
booktitle={Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2024},
pages={359-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012752800003753},
isbn={978-989-758-706-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT
TI - RLHR: A Framework for Driving Dynamically Adaptable Questionnaires and Profiling People Using Reinforcement Learning
SN - 978-989-758-706-1
AU - Paduraru C.
AU - Patilea C.
AU - Stefanescu A.
PY - 2024
SP - 359
EP - 366
DO - 10.5220/0012752800003753
PB - SciTePress