Personalized AI Mock Interview Platform

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 06 | Jun 2025

p-ISSN: 2395-0072

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Personalized AI Mock Interview Platform Devika S.S.1, Najah Najm Ahmed2, Nicole Colin3, Sneha P.4, Prof. Arya A. R.5 1B. Tech student, Department of Computer Science and Engineering, Rajadhani Institute of Engineering and

Technology, Kerala, India

2B. Tech student, Department of Computer Science and Engineering, Rajadhani Institute of Engineering and

Technology, Kerala, India

3B. Tech student, Department of Computer Science and Engineering, Rajadhani Institute of Engineering and

Technology, Kerala, India

4B. Tech student, Department of Computer Science and Engineering, Rajadhani Institute of Engineering and

Technology, Kerala, India

5Professor, Department of Computer Science and Engineering, Rajadhani Institute of Engineering and Technology,

Kerala, India ---------------------------------------------------------------------***--------------------------------------------------------------------Our project directly addresses this pressing need by Abstract - In today’s competitive technological landscape, excelling in job interviews is paramount for securing desired positions. Despite a wide array of existing resources, many candidates struggle to find interview preparation materials that precisely align with specific demands of their target roles.

This often puts even highly talented individuals at a disadvantage. To address this critical need, this project introduces a personalized AI-powered mock interview platform. This innovative application customizes interview questions to the unique requirements of each job position, offering users a realistic and targeted practice experience.

Additionally, the platform provides comprehensive performance analytics on user responses and presents optimal answer exemplars for each question. Recognizing the crucial importance of interview preparation, particularly for students and early-career professionals, this platform aims to bridge the gap between potential and performance, empowering users to refine their skills, boost confidence, and ultimately achieve career success.

developing a groundbreaking personalized AI-driven mock interview platform. This innovative application is meticulously designed to offer a highly tailored and realistic practice experience by generating interview questions that precisely match the unique requirements of each specific job position. Beyond questions the platform provides invaluable analytical feedback on user responses, along with optimal answer exemplars, enabling a clear understanding of areas for improvement. We recognize the paramount importance for thorough interview preparation, especially for students and early-career professionals who are just beginning their career journeys. By offering a comprehensive and uniquely personalized preparation experience, our platform aims to effectively bridge the existing gap between a candidate’s inherent potential and their actual interview performance.

Through its features, the platform empowers users to significantly refine their communication and technical skills, substantially boost their confidence, and ultimately achieve success in their chosen career pursuits.

Key Words: artificial intelligence, automated feedback, interview preparation, mock interview, performance analysis …

2.EXISTING SYSTEM 2.1 Pramp by Exponent

1.INTRODUCTION

This platform offers a highly realistic interview experience by providing live practice sessions with peers, which can be significantly more effective than solo preparation. A key advantage is its inherent flexibility, allowing users to select specific industries or types of interviews, thereby customizing their practice sessions to align precisely with their individual career goals. Furthermore, the system likely includes robust guidance for improvement, potentially in the form of direct feedback from peers or prompts designed to elicit better responses, which is crucial for users to refine their interview skills continuously. However, the effectiveness of this peer-based practice is inherently tied to the quality of the participating peer network, as their expertise and professionalism directly influence the learning experience. Additionally, the platform appears to be primarily

Securing a job in the fast-paced and competitive world of technology is increasingly dependent on a candidate’s ability to perform exceptionally well in interviews. As the demand for skilled professionals continues its upward trend, so does the necessity for effective interview preparation tools. While the market offers a plethora of resources, a significant challenge for many aspiring professionals is the scarcity of practice materials that accurately reflect the specific demands and nuances of their desired job roles. This crucial gap in effective preparation resources can unfortunately leave even the most qualified individuals at a disadvantage during critical interview processes.

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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 06 | Jun 2025

p-ISSN: 2395-0072

www.irjet.net

focused on interview skills, which might mean it offers limited support for other vital career development aspects, such as resume building or networking. Lastly, a significant change for existing users is the transition of new sessions to “Exponent Practice”, which could involve a learning curve or a period of adjustment for those accustomed to the previous interface or workflow.

interview skills and confidence. Its robust system architecture integrates several key components to deliver a tailored and effective practice experience.

3.1 User Access Control This crucial initial component manages user access and security. The platform incorporates a secure, user-friendly interface powered by an enterprise-grade identity management system for both new user onboarding and preventing unauthorized access. Upon successful registration, users receive a confirmation and are directed to their personalized dashboard. Similarly, for returning users, the identity management system's robust authentication protocols validate credentials and grant authorized access, ensuring data integrity and a hassle-free login experience.

This typically involves OAuth 2.0 or OpenID Connect flows for secure token-based authentication.

2.2 SmallTalk2Me This platform is powered by an AI-driven natural language processing (NLP) technology, offering a personalized mock interview simulator that provides users with an authentic practice experience. It places a strong emphasis on boosting confidence in English communication, which is a critical skill for excelling in interviews, particularly for non-native speakers. The simulator provides valuable feedback on sentence structure, guiding users to improve their complex sentence construction and overall language fluency.

Additionally, it includes recruiter suggestions, offering tailored tips designed to make answers more effective and polished. However, a potential limitation is its primary focus on English communication, which might lead to overlooking other crucial aspects of interview preparation, such as body language or technical skills. The feedback provided on language might also be limited by the scope of its current AI models, meaning it may not always capture the nuances of real recruiter evaluations. Furthermore, users who are not entirely comfortable with AI-powered interactive tools might find it challenging to adapt to this simulator’s interface and functionality.

3.2 User Activity Center Serving as the central hub for user activities, the dashboard is designed for intuitive navigation and accessibility to all platform features. It provides users with various options, including initiating a new mock interview, revisiting previous sessions, and accessing performance analytics. Users can also manage their profiles and job description directly from this page, allowing for a customized preparation experience. The dashboard acts as a unified control panel for users to track and manage their interview preparation journey effectively.

3.3 Customized Question Creation

2.3 BARCLAYS LifeSkills

This core AI-powered feature creates personalized interview questions for users. Users input details such as the job title, a brief job description, and relevant skills. The platform’s intelligent algorithmic pipeline then processes this information using advanced natural language understanding (NLU) techniques to extract key entities and concepts. Based on this contextual understanding, it generates five rolespecific questions, aiming to simulate a realistic interview scenario. Additionally, a speaker icon is included, leveraging text-to-speech (TTS) synthesis, to enable users to hear the questions for enhanced comprehension. A leading generative AI model is specifically utilized for generating both the questions and their corresponding optimal answer exemplars.

This tool is specifically designed to help users prepare for job interviews across a variety of industries. It offers flexible options for providing answers, allowing users to either record their responses verbally or type them out, thereby catering to different comfort levels and preferences. The primary aim of the tool is to significantly improve user’s interview skills by enabling them to refine their responses through practice. It is likely to be particularly beneficial for young people, especially students, given its apparent accessibility for novice users in the interview preparation process. However, a potential limitation is that the specific range of industries covered is not explicitly mentioned, meaning it might not cater comprehensively to every specialized job sector. Furthermore, it is possible that the full feature set may only become accessible after users complete a sign-up or login process. Ultimately, the overall impact of the tool on a user’s interview performance may vary considerably depending on how diligently it is utilized and the individual’s existing level of preparation.

3.4 Candidate Response Management This component handles the capture and conversion of user responses during a mock interview. User responses are recorded via an integrated webcam and microphone, capturing both audio and video streams. These recorded responses are then processed through an automated speech recognition (ASR) engine to be accurately converted into text format. The converted textual transcripts, along with metadata such as timestamping and audio/video file

3. PROPOSED SYSTEM The Personalized AI Mock Interview Platform is a comprehensive solution designed to enhance job seeker’s

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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 06 | Jun 2025

p-ISSN: 2395-0072

www.irjet.net

4.1 Landing Page

references, are securely stored for subsequent analysis by the performance evaluation module.

The Landing Page serves as the initial public face of the platform, designed to capture the interest of potential users.

It provides a compelling overview of the platform’s core value proposition, highlighting how it offers personalized, AIdriven mock interviews to enhance confidence and readiness.

The landing page features a clean and modern layout with ample visual hierarchy, large fonts, and centered content that immediately catches the eye and invites users to explore the platform. Its design reflects user-centric aesthetics with an emphasis on simplicity, encouraging seamless navigation.

The landing page typically includes prominent call-to-actions (CTAs) for users to either sign up or sign in, guiding them towards deeper engagement with the application.

3.5 Performance Analysis and Guidance After a mock interview, users are directed to a dedicated post-interview analysis page. Here, the platform provides a comprehensive computational analysis of their performance, including detailed performance metrics on their responses and actionable suggestions for improvement. This analysis involves comparing the user's transcribed responses against pre-defined ideal answer criteria and industry best practices using AI-driven comparative analytics. Crucially, the suggested answers to the questions are also displayed, enabling users to compare their responses and learn effectively. This feature also stores historical performance reports, allowing users to track their progress over time, thus supporting continuous learning and boosting confidence in their interview skills. A sophisticated AI reasoning engine is utilized to analyze the recorded data, identify semantic gaps, assess verbal fluency, and provide this detailed performance review, including suggested answers.

3.6 Information Repository System The backend of the platform relies on a robust relational database management system (RDBMS) for data persistence, often combined with an object-relational mapping (ORM) framework. This combination provides a structured and efficient way to handle data operations, including the secure storage of user profiles, interview question banks, recorded responses, and analytical reports. RESTful APIs are employed to ensure a smooth, standardized data flow and communication between the front-end user interface and the backend data repository.

Fig -1: Landing Page

4.2 Sign-In Page The Sign-In Page provides a secure access point for returning users. Users can seamlessly log in using their registered credentials (email and a cryptographically secured password or multi-factor authentication code). Powered by the platform’s secure authentication subsystem, this page ensures quick, secure validation of credentials, granting authorized access to their personalized dashboard and previously saved data, ensuring a smooth continuation of their interview preparation journey. This page adopts a minimalistic aesthetic with a centered login modal, balanced spacing, and consistent typography, creating a sleek and professional impression for returning users. Crucially, the interface maintains design consistency with the overall platform, enhancing usability and providing a familiar experience for all users.

3.7 Interactive User Experience Creation The user interface (UI) of the platform is built using modern, component-based web frameworks, ensuring a seamless and highly responsive user experience across various devices.

The identity management system is seamlessly integrated into the front-end architecture to securely manage user registration and login workflows. Key components such as the dashboard and the virtual interview room are designed with a user-centric design methodology, prioritizing intuitive navigation, accessibility, and optimal interactive performance for all features. This involves leveraging asynchronous data fetching and state management patterns for a fluid user experience.

4. RESULTANT APPLICATION The Personalized AI Mock Interview Platform is meticulously designed as a single-page web application (SPA), offering a structured and interactive journey for users preparing for job interviews. Each page and feature is crafted to contribute to a seamless and effective practice experience.

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International Research Journal of Engineering and Technology (IRJET)

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Volume: 12 Issue: 06 | Jun 2025

p-ISSN: 2395-0072

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Fig -2: Sign-In Page (email)

Fig -4: Sign-Up Page

4.4 Control Panel Once authenticated, this centralized user dashboard serves as the command center for all user activities. It offers a clean and intuitive interface, providing quick access to all major functionalities. The control panel showcases a visually organized interface with clearly separated sections, iconbased navigational elements, and a card-style layout that enhances accessibility for a smooth user journey. From here, users can seamlessly initiate new mock interview sessions, review their comprehensive history of past interviews, and access detailed performance analytics reports. The control panel also allows for easy profile management and efficient updating of job descriptions, offering a truly personalized overview of their progress and available options.

Fig -3: Sign-In Page (code)

4.3 Sign-Up Page

4.5 Interview Customization Page

For new users, the sign-up page is the gateway to the platform’s features. It presents a secure, user-friendly interface powered by the integrated authentication service, meticulously designed to be intuitive and straightforward.

The sign-up page is visually welcoming with neatly aligned input fields, soft color tones, and a balanced layout that provides a calm and user-friendly first-time experience. This page thoughtfully guides users to create an account by providing essential details (their email address and a securely hashed password). The design prioritizes user experience (UX) with an emphasis on simplicity, ensuring a seamless and reassuring onboarding process, while also clearly emphasizing the secure handling of personal data and setting the robust foundation for a truly personalized user experience.

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This dedicated page is crucial for personalizing the mock interview experience. With a form-based layout framed in generous spacing and clean input blocks, this page looks structured and interactive, encouraging users to fill in details without feeling overwhelmed. Users are prompted to provide specific details about the job they are applying for, including the job title, a brief description of the role, and relevant skills.

This input is vital as it directly feeds into the AI question generation algorithm, facilitating the creation of highly customized and relevant interview questions.

4.6 Interview Question Synthesis Upon submitting the job description, the platform processes the input using its sophisticated AI-based NLU algorithm.

This feature dynamically generates five role-specific interview questions that accurately simulate a real-world interview scenario. An integrated speaker icon allows users to listen to the questions aloud, leveraging advanced text-tospeech (TTS) models to enhance comprehension and prepare for auditory processing during an actual interview. The intelligent question generation engine, powered by a large language model (LLM), plays a key role in synthesizing tailored questions by understanding the nuances of the job description.

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International Research Journal of Engineering and Technology (IRJET)

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Volume: 12 Issue: 06 | Jun 2025

p-ISSN: 2395-0072

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4.7 Virtual Interview Room

displayed, enabling users to compare their responses and learn effectively. The platform’s analytical AI, leveraging semantic analysis and comparative scoring algorithms, is instrumental in processing the recorded data and generating this insightful, detailed performance analysis. This page also serves as a persistent repository for past performance reports, allowing users to track their progress and identify trends in their skill development over time.

This is where the actual mock interview session takes place.

This page offers a visually balanced introduction with a clean header, instructional text, and intuitive design elements that gently guide users into the interview session. Designed like a virtual interview room, this page uses a split-screen layout with prominent questions, a dedicated video input pane for user recording, and a bold visual hierarchy that mimics a real-time experience. On this page, users encounter the generated questions one by one. The users can record their responses via their webcam and microphone. The page presents a content-rich, scrollable interface with a neat card or sectioned layout, visually organizing supplementary resources for easy scanning. This page is engineered to mimic a real interview environment, allowing users to practice their verbal and non-verbal communication, as well as their response formulation strategies.

Fig -6: Performance Analysis Page

5. CONCLUSION In sum, the Personalized AI Mock Interview Platform stands as a transformative solution, meticulously crafted to bridge the critical chasm between a candidate’s inherent potential and the demanding realities of a job interview. By ingeniously leveraging cutting-edge technologies such as dynamic frontend frameworks, the intelligent backbone of advanced AI algorithms for natural language processing and generation, and the robust data management provided by a scalable structured database system, this platform delivers an unparalleled, hyper-personalized experience. It meticulously tailors interview questions and provides insightful, datadriven performance analytics, uniquely adapted to individual job roles and each user’s evolving proficiency.

Fig -5: Virtual Interview Room

4.8 Session Wrap-Up After completing all the interview questions or choosing to conclude the session, the "Session Wrap-Up" functionality finalizes the mock interview. A well-structured, minimal end screen uses centered text, subtle colors, and clear call-toaction buttons to neatly wrap up the session and direct the user forward. This action triggers the system to process the recorded responses for analysis, initiating the backend processing pipeline for performance assessment and preparation of the comprehensive feedback report. It marks the transition from the interactive interview phase to the analytical evaluation phase.

6. FUTURE WORKS The future scope of the “Personalized AI Mock Interview Platform” extends beyond its current functionalities, aiming to broaden its accessibility, features, and utility in both individual and corporate settings.

6.1 Global Audience Accessibility

4.9 Performance Analysis and Guidance Page

A key future direction involves making the platform accessible to a global audience. This would likely entail comprehensive language localization, cultural adaptations in interview practices, and potentially diverse question sets to cater to international job markets, possibly leveraging multilingual LLMs and cross-cultural linguistic models.

This is where users receive a comprehensive analytical breakdown of their performance. This section provides detailed insights into their responses, including data-driven suggestions for improvement and identified areas of strength.

The page combines data visualization with design clarity, using sectioned cards, highlighted performance metrics, and scrollable lists in a polished, report-like format. Crucially, the optimal answer exemplars to the questions are also

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6.2 AI-Driven Career Counseling The platform plans to incorporate AI-driven career counseling features. This would go beyond just interview preparation, offering personalized guidance on career paths, skill development recommendations, and potentially mentorship connections, all powered by AI analysis of user skill profiles, interview performance data, and real-time labor market trends.

6.3 Industry-Specific Mock Interview for Broader Professions To enhance its relevance across the job market, the platform intends to expand its industry-specific mock interview offerings. This means catering to a wider range of professions beyond its initial focus, developing specialized domain-specific question banks and tailored feedback mechanisms for fields like healthcare, finance, arts, and more, requiring the fine-tuning of AI models on specialized datasets.

6.4 Real-Time Candidate Evaluation Tool for Hiring Processes A significant future development includes partnering with organizations and recruiters. This collaboration would enable the platform to serve as a pre-screening and real-time candidate evaluation tool in actual hiring processes. Such a feature would strengthen its utility not only for job seekers but also for companies seeking efficient and objective preemployment assessment solutions. This integration would position the platform as a valuable asset in both individual career development and corporate talent acquisition strategies, potentially involving API integrations with applicant tracking systems (ATS).

REFERENCES [1]

Ninak Chavan “AI Based Mock Interview System” International Research Journal of Modernization in Engineering Technology and Science (IRJMETS), October 2024.

[2]

Puttamma T “MOCK INTERVIEW SYSTEM using AI, ML and IMAGE PROCESSING” International Journal of Scientific Research in Engineering and Management (IJSREM), July 2024.

[3]

Shashank Rai “Skillup Bot: An AI Driven Mock Interview Platform” International Research Journal of Engineering and Technology (IRJET), April 2024.

[4]

Yi-Chi Chou “An AI Mock-interview Platform for Interview Performance Analysis” 10th International Conference on Information and Education Technology (ICIET), 2022.

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