Authors:
Mykhailo Paslavskyi, Mohamed Solomon, Mohammad Ahmad, J. Angelin Jeba, Maruf Farhan, Sheila Agnes Vidot
Addresses:
Department of Computer Science, Ukrainian National Forestry University, Lviv, Lviv Oblast, Ukraine. Department of Business Technology, Canadian International College, New Cairo, Cairo Governorate, Egypt. School of Computer Science and Technology, University of Bedfordshire, Bedford, Bedfordshire, United Kingdom. Department of Electronics and Communication Engineering, S.A. Engineering College, Chennai, Tamil Nadu, India. Department of STEM, University of Sussex International Study Centre, Brighton, East Sussex, United Kingdom. Department of Medical Consultants, IPS Health, Mahe, Seychelles.
Abstract:
The job interview process is a critical yet often challenging barrier for candidates, where success hinges not only on technical knowledge but also on effective communication, emotional intelligence, and non-verbal cues. Traditional preparation methods, such as mock interviews with peers, lack objective, data-driven feedback. This paper presents an AI-based smart interview practice system designed to provide a holistic, automated, and personalised interview training platform. The proposed system leverages advances in artificial intelligence to analyse multiple facets of a candidate's performance in real time. It utilises computer vision and facial expression recognition to assess non-verbal cues, identifying emotions such as confidence, nervousness, and engagement. Simultaneously, it employs natural language processing and speech analysis to evaluate verbal responses, focusing on linguistic content, clarity, fluency, tone, and pace. By integrating these multimodal data streams, the system generates comprehensive, real-time feedback for the user. This feedback highlights key strengths and pinpoints areas for improvement, such as filler words, inconsistent emotional states, and unresponsive body language. The primary objective is to create an accessible, low-pressure environment where candidates can practice and refine their interview skills iteratively, thereby reducing anxiety and improving performance. By bridging the gap between self-perception and actual presentation, this intelligent system aims to democratise high-quality interview coaching, ultimately enhancing career preparedness and increasing the likelihood of success in real-world interview scenarios.
Keywords: Job Interview; Challenging Barrier; Interview Practice System; Candidate's Performance; Natural Language Processing; Speech Analysis; Multimodal Data Streams; Emotional Intelligence.
Received on: 19/01/2025, Revised on: 12/03/2025, Accepted on: 29/05/2025, Published on: 05/06/2026
DOI: 10.64091/ATICL.2026.000327
AVE Trends in Intelligent Computer Letters, 2026 Vol. 2 No. 2 , Pages: 82-99