This study investigated how a job seeker self-presentation affects recruiter’s hiring recommendations in an online communities and what categories of self-presentation contribute to fit perceptions for obtaining hiring recommendations. The study participants viewed potential candidates’ LinkedIn profiles and responded to questions regarding the argument quality and source credibility of their self-presentations, fit perceptions, and hiring recommendations. The results show that recruiters make inferences about job seekers’ person–job fit and person–organisation fit on the basis of argument quality in specific self-presentation categories, which in turn predict recruiters’ intentions to recommend job seekers for hiring. Although certain specific categories of self-presentation offering source credibility have positive associations with person–person (P–P) fit perception, there is a non-significant relationship between perceived P–P fit and hiring recommendations

With the development of artificial intelligence (AI), the automatic analysis of video interviews to recognize individual personality traits has become an active area of research and has applications in personality computing, human-computer interaction, and psychological assessment. Advances in computer vision and pattern recognition based on deep learning (DL) techniques have led to the establishment of convolutional neural network (CNN) models that can successfully recognize human nonverbal cues and attribute their personality traits with the use of a camera. In this study, an end-to-end AI interviewing system was developed using asynchronous video interview (AVI) processing and a TensorFlow AI engine to perform automatic personality recognition (APR) based on the features extracted from the AVIs and the true personality scores from the facial expressions and self-reported questionnaires of 120 real job applicants. The experimental results show that our AI-based interview agent can successfully recognize the "big five" traits of an interviewee at an accuracy between 90.9% and 97.4%. Our experiment also indicates that although the machine learning was conducted without large-scale data, the semisupervised DL approach performed surprisingly well with regard to automatic personality recognition despite the lack of labor-intensive manual annotation and labeling. The AI-based interview agent can supplement or replace existing self-reported personality assessment methods that job applicants may distort to achieve socially desirable effects.

The prediction of individual interpersonal communication skills and personality traits is a critical issue in both industrial and organizational psychology and affective computing. In this study, we invited 114 participants, including 57 interviewers and 57 interviewees, to collect the ground truth of interviewees’ communication skills and personality traits as perceived by real human interviewers in a structured behavioral interview setting. We develop an asynchronous video interview (AVI) platform with an artificial intelligence (AI) decision agent based on a TensorFlow convolutional neural network (CNN), called AVI-AI, that can be used to partially displace human raters’ work in the initial stage of employment screening and to successfully predict a job candidate’s communication skills and personality traits. The experimental results show that AVI-AI can predict not only a candidate’s interpersonal communication skills but also his or her openness, agreeableness, and neuroticism, as perceived by experienced human resource professionals. The interrater reliability values were all acceptable to support the ground truth assumption. However, our AVI-AI could not predict the conscientiousness and extraversion as perceived by the real human raters in this study.

In the rapidly evolving landscape of higher education and adult learning, asynchronous video-based online learning has not only become the new norm but has also emerged as the cornerstone of instructional delivery for Massive Open Online Courses (MOOCs). Despite its widespread adoption, this learning mode confronts a critical challenge: the inherent lack of social presence, posing a significant risk of diminishing learner affective engagement and, consequently, jeopardizing the efficacy of learning outcomes. Addressing this pressing issue, our study conducted a comprehensive analysis of 240 instructional videos from 240 distinct instructors on a MOOC platform, supplemented by 845 post-course learner feedback surveys from a diverse cohort of college students and adult learners. Using deep learning and statistical analysis, the research revealed that the on-screen presence of instructors does not inherently affect students’ affective engagement. The study revealed that learners’ affective engagement is affected by distinct combinations of the instructor’s facial and paraverbal expressions, including happiness, surprise, and anger, which vary depending on whether the instructor is visible. The discovery that vocal attractiveness is a pivotal element in enhancing learners’ affective engagement with instructional videos marks a paradigm shift in our understanding of digital andragogy and heutagogy. This study propels academic discourse by illuminating the critical role of instructor non-verbal cues in establishing social presence and facilitating emotional contagion within asynchronous video-based online learning but also provides educators and content creators with empirically-backed techniques to revolutionize video instruction and amplify affective engagement.