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"GAN I hire you?" - A system for personalized virtual job interview training

  • Job interviews are usually high-stakes social situations where professional and behavioral skills are required for a satisfactory outcome. Professional job interview trainers give educative feedback about the shown behavior according to common standards. This feedback can be helpful concerning the improvement of behavioral skills needed for job interviews. A technological approach for generating such feedback might be a playful and low-key starting point for job interview training. Therefore, we extended an interactive virtual job interview training system with a Generative Adversarial Network (GAN)-based approach that first detects behavioral weaknesses and subsequently generates personalized feedback. To evaluate the usefulness of the generated feedback, we conducted a mixed-methods pilot study using mock-ups from the job interview training system. The overall study results indicate that the GAN-based generated behavioral feedback is helpful. Moreover, participants assessed that theJob interviews are usually high-stakes social situations where professional and behavioral skills are required for a satisfactory outcome. Professional job interview trainers give educative feedback about the shown behavior according to common standards. This feedback can be helpful concerning the improvement of behavioral skills needed for job interviews. A technological approach for generating such feedback might be a playful and low-key starting point for job interview training. Therefore, we extended an interactive virtual job interview training system with a Generative Adversarial Network (GAN)-based approach that first detects behavioral weaknesses and subsequently generates personalized feedback. To evaluate the usefulness of the generated feedback, we conducted a mixed-methods pilot study using mock-ups from the job interview training system. The overall study results indicate that the GAN-based generated behavioral feedback is helpful. Moreover, participants assessed that the feedback would improve their job interview performance.show moreshow less

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Metadaten
Author:Alexander HeimerlORCiDGND, Silvan MertesORCiDGND, Tanja Schneeberger, Tobias BaurORCiDGND, Ailin Liu, Linda Becker, Nicolas Rohleder, Patrick Gebhard, Elisabeth AndréORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1015607
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/101560
Parent Title (English):arXiv
Publisher:arXiv
Type:Preprint
Language:English
Date of Publication (online):2023/02/01
Year of first Publication:2022
Publishing Institution:Universität Augsburg
Release Date:2023/02/06
First Page:arXiv:2206.03869
DOI:https://doi.org/10.48550/arXiv.2206.03869
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Menschzentrierte Künstliche Intelligenz
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)