"GAN I hire you?" -- A System for Personalized Virtual Job Interview Training

TL;DR

This paper presents a GAN-based system that detects behavioral weaknesses in virtual job interviews and generates personalized feedback, improving interview training effectiveness.

Contribution

It introduces a novel GAN approach for personalized behavioral feedback in virtual interview training, enhancing existing methods.

Findings

GAN-generated feedback is perceived as helpful by users.

Participants believe the feedback can improve interview performance.

The system effectively detects behavioral weaknesses.

Abstract

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 the feedback would improve their job interview performance.