Adaptive AI-Based Interview Training System with Emotion-Aware Feedback and Performance Tracking

The objective of this study was to create an Adaptive AI-Based Interview Training System with Emotion-Aware Eval-uation and Performance Monitoring that would help improve student and job-seeker preparation for interviews. The pro-posed system combines sophisticated technologies including speech-to-text conversion [20], [21], large language models [17], [19], emotion detection [22], [24], and adaptive learning 10] to provide students with a realistic, intelligent, and per-sonalized interview training experience. By using multiple AI-driven subsystems in a single framework, the proposed system addresses the weaknesses of traditional and other existing interview preparation systems. The proposed system successfully addresses major diffi-culties experienced by current interview preparation systems. Unlike traditional systems which use fixed sets of questions for each user, the adaptive learning feature [10] of this system modifies the level of difficulty of an interview based upon the performance of the user and the emotional state of the user. This allows for a customized learning experience for each user. By adding an emotion detection module [23], [24], this system is able to evaluate nonverbal signals such as confidence, stress, and anxiety and thus provide much more complete and meaningful evaluation than has previously been the case. A performance tracking module [27] builds on the existing system by retaining historical user interaction data and en-abling a longitudinal analysis of improvements. Users are able to see their progress over multiple sessions and identify their strengths and weaknesses; thus helping them to make informed efforts to develop certain skills. Furthermore, when all three features are combined (adaptive learning [10], emotion-aware feedback [24], [25] and performance tracking [27]), they greatly enhance the effectiveness of the proposed system compared with traditional interview preparation methods. Experiments show that the proposed system significantly improves user performance by producing higher accuracy of answers, greater response confidence and developing com-munication skills. The improvements in evaluation metrics indicate the successful integration of multi-modal analysis [25] and adaptive mechanisms [10]. Improved user engagement is also achieved through the development of an interactive and realistic interview simulation environment. While the proposed system has many advantages, it does have limitations. First, emotion detection accuracy is depen-dent on the reliability of facial recognition [23] and speech analysis models [25], which can be adversely affected by the environmental conditions of lighting, ambient noise and the quality of the camera equipment. Lastly, the need to process multiple modes of data in real-time may also create additional computational load, which could negatively affect the system’s performance in environments where computing resources are limited. In order to overcome these limitations, future work will focus on enhancing the performance and dependability of the emotion recognition approaches by utilizing state-of-the-art deep learning techniques [24]. This could be done with the intent of improving the overall functionality of the overall system by including additional multi-modal behavioural anal-ysis of users, such as through body language and gestures [25], to provide a comprehensive assessment of how well users performed during an interview scenario. Additionally, lighter and optimized models (to facilitate real-time application of the models on low-resource equipment will be developed as well. The proposed system could also be expanded to allow for domain-specific interview preparation, including technical, managerial, and behavioural interviews. Furthermore, the in-corporation of multilingual functionality would increase the accessibility and usability of the system across a wide variety of users [15]. The incorporation of emerging technologies such as AR/VR [6] in future implementations will provide an immersive interview training experience and could also pos-sibly lead to the creation of hybrid technologies that improve the overall functionality of the system as advancements occur within the technology. The Adaptive AI-based Interview Training System is a comprehensive, scalable, and intelligent solution for today’s interview preparation needs. By leveraging the benefits gained from utilizing AI [29] and adaptive learning [10], this system offers great opportunities to improve employability skills, increase user self-assurance, and create a link between what students learn at college or university and what is required in the workforce.

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