Can Candidates Perform Better? Examining the Effects of Training on Performance in AI-Based Interviews

DOI

10.2991/978-94-6463-845-5_78How to use a DOI?

Keywords

AI Interview; Training; AI Interview Performance

Abstract

While AI interviews provide considerable convenience for employers in selecting candidates, studies have shown that AI interviews also face challenges such as poor candidate performance and resulting validity concerns. Therefore, previous research has suggested offering training to candidates to alleviate these concerns. However, given the inherent differences in evaluation mechanisms between AI and traditional interviews, little is known about whether training can improve candidates’ performance in AI interviews and the underlying mechanisms involved. This study recruited 120 undergraduate students, randomly assigned them to training and no-training groups, and conducted a between-subjects simulated interview experiment, aiming to examine how AI interview training affects interview performance, the underlying mediating mechanisms, and relevant boundary conditions. Results showed a positive relationship between AI interview training and interview performance. Mediation analysis revealed that trained participants reported higher self-efficacy of human-AI interaction, which in turn led to better AI interview performance. Moderation analysis showed that the effect of AI interview training on self-efficacy of human-AI interaction was stronger for participants with higher levels of AI resistance.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bingqian Liang
AU  - Yi Shi
AU  - Weiwei Huo
AU  - Xiaolei Lucas Fang
PY  - 2025
DA  - 2025/09/16
TI  - Can Candidates Perform Better? Examining the Effects of Training on Performance in AI-Based Interviews
BT  - Proceedings of the 2025 6th International Conference on Management Science and Engineering Management (ICMSEM 2025)
PB  - Atlantis Press
SP  - 774
EP  - 784
SN  - 2667-1271
UR  - https://doi.org/10.2991/978-94-6463-845-5_78
DO  - 10.2991/978-94-6463-845-5_78
ID  - Liang2025
ER  -