EFL College Students’ Identity Construction in AI-Powered Video Job Interviews

Abstract

The proliferation of technological advancements has transformed the modality of job interviews, leading to the emergence of AI (artificial intelligence)-enabled video interviews. This interview format has gained increasing popularity in the industry sector due to its flexibility, convenience, and efficiency in recruiting diverse and global talent during the hiring process. Conducting an effective job interview now requires sophisticated multimodal skills to coordinate rhetorical conventions, linguistic knowledge, and other semiotic resources in order to project one's intended professional identity in response to job advertisements. However, little empirical research on how EFL college senior students, who are novices in their future profession, negotiate their identity in a new job-hunting genre, namely video interviews.

To address this research gap, this project adopts a discursive approach, supplemented by a semi-structured interview method. The data sources include video samples of six participants' job interviews recorded through the InterviewStream platform, as well as post-task follow-up interviews. The discursive approach draws upon a systemic functional approach to Multimodal Discourse Analysis (SF-MDA), and it is complemented by semi-structured interviews with all participants.

The findings indicate that most students are able to present a professional identity relevant to their intended job by drawing on a wide array of working and learning experiences, as well as their achievements. This identity is mainly crafted through the use of typical verbs and is reinforced through the application of appraisal features, which promote the profile of the applicant as professionally competent. In terms of multimodal actions, there are significant variations among the participants' performances. They primarily focus on maintaining eye contact and facial expressions while downplaying other nonverbal cues. Additionally, they acknowledge four major challenges in AI-enabled video interviews: crafting applicable answers to interview questions, adapting to the interface design of the video interview platform, memorizing prepared answers, and managing kinesic performance.

Keywords

AI-Powered interview, job interview, identity construction, Multimodal analysis, SFL