Assessing personality traits and interview performance from asynchronous video interviews
Zhang,Tianyi Qi,Tianhua Koutsoumpis,Antonios Zong,Yuan Zheng,Wenming Oostrom,Janneke Holtrop,Djurre Luo,Zhaojie de Vries,Reinout E.
Zhang,Tianyi
Qi,Tianhua
Koutsoumpis,Antonios
Zong,Yuan
Zheng,Wenming
Oostrom,Janneke
Holtrop,Djurre
Luo,Zhaojie
de Vries,Reinout E.
Abstract
Asynchronous Video Interviews (AVIs) allow candidates to record responses to predefined questions using digital devices, offering both flexibility and remote accessibility. Assessing personality traits and interview performance via AVIs provides organizations with valuable insights into candidate profiles and facilitates the prediction of future job performance. However, prior benchmark challenges, whose datasets were predominantly sourced from social media, suffer from suboptimal construct and methodological validity, limiting their utility for model development and real-world applications. To address these limitations, we introduce the AVI Grand Challenge at ACM Multimedia 2025, featuring a novel dataset of mock AVIs comprising 3,876 videos from 646 participants in a simulated job application procedure. Interview questions were carefully designed to reflect real-world selection contexts and elicit personality expressions grounded in Trait Activation Theory. Personality traits and job competencies were annotated by trained evaluators and professional recruiters, ensuring both methodological rigor and ecological validity. The solutions and algorithms developed in this challenge are analyzed and summarized in this paper to foster the development of fair, reliable, and AI-driven hiring assessments.
Description
Date
2025
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Volume Title
Publisher
Association for Computing Machinery
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Keywords
Personality Assessment, Interview Performance, Asynchronous Video Interviews
Citation
Zhang, T, Qi, T, Koutsoumpis, A, Zong, Y, Zheng, W, Oostrom, J, Holtrop, D, Luo, Z & de Vries, R E 2025, Assessing personality traits and interview performance from asynchronous video interviews. in MM '25 : Proceedings of the 33rd ACM International Conference on Multimedia. Association for Computing Machinery, pp. 13895-13900, Proceedings of the 33rd ACM International Conference on Multimedia, 27/10/25. https://doi.org/10.1145/3746027.3762016