AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2023
Si, J.; Song, J.; Woo, M.; Kim, D.; Lee, Y.; Kim, S.
Generative AI Models for Virtual Interviewers: Applicability and Performance Comparison Proceedings Article
In: IET. Conf. Proc., pp. 27–28, Institution of Engineering and Technology, 2023, ISBN: 27324494 (ISSN).
Abstract | Links | BibTeX | Tags: 3D Generation, College admissions, Digital elevation model, Effective practices, Generative AI, Job hunting, Metaverse, Metaverses, Performance, Performance comparison, Virtual environments, Virtual Interview, Virtual Reality
@inproceedings{si_generative_2023,
title = {Generative AI Models for Virtual Interviewers: Applicability and Performance Comparison},
author = {J. Si and J. Song and M. Woo and D. Kim and Y. Lee and S. Kim},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203492324&doi=10.1049%2ficp.2024.0193&partnerID=40&md5=84eb48f6b51c941da9c77fa3aba46262},
doi = {10.1049/icp.2024.0193},
isbn = {27324494 (ISSN)},
year = {2023},
date = {2023-01-01},
booktitle = {IET. Conf. Proc.},
volume = {2023},
pages = {27–28},
publisher = {Institution of Engineering and Technology},
abstract = {Interviewing processes are considered crucial steps in job hunting or college admissions, and effective practice plays a significant role in successfully navigating these stages. Although various platforms have recently emerged for practicing virtual interviews, they often lack the tension and realism of actual interviews due to repetitive and formal content. This study aims to analyze and compare the performance of different generative AI models for creating a diverse set of virtual interviewers. Specifically, we examine the characteristics and applicability of each model, as well as the differences and advantages between them, and evaluate the performance of the generated virtual interviewers. Through this analysis, we aim to propose solutions for enhancing the practicality and efficiency of virtual interviews. © The Institution of Engineering & Technology 2023.},
keywords = {3D Generation, College admissions, Digital elevation model, Effective practices, Generative AI, Job hunting, Metaverse, Metaverses, Performance, Performance comparison, Virtual environments, Virtual Interview, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Interviewing processes are considered crucial steps in job hunting or college admissions, and effective practice plays a significant role in successfully navigating these stages. Although various platforms have recently emerged for practicing virtual interviews, they often lack the tension and realism of actual interviews due to repetitive and formal content. This study aims to analyze and compare the performance of different generative AI models for creating a diverse set of virtual interviewers. Specifically, we examine the characteristics and applicability of each model, as well as the differences and advantages between them, and evaluate the performance of the generated virtual interviewers. Through this analysis, we aim to propose solutions for enhancing the practicality and efficiency of virtual interviews. © The Institution of Engineering & Technology 2023.