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Generating personalized behavioral feedback for a virtual job interview training system through adversarial learning

Silvan Mertes; Alexander Heimerl; Tanja Schneeberger; Tobias Baur; Ailin Liu; Linda Becker; Nicolas Rohleder; Patrick Gebhard; Elisabeth André
In: Proceedings of the 23rd International Conference on Artificial Intelligence in Education. International Conference on Artificial Intelligence in Education (AIED-2022), Pages 679-684, ACM, 2022.


Job interviews are usually high-stakes social situations where professional and behavioral skills are required for a satisfactory outcome. In order to increase the chances of recruitment technological approaches have emerged to generate meaningful feedback for job candidates. 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.

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