An AI-Driven Multimodal Framework for Interview Readiness Assessment and Skill Enhancement

Abstract

In today’s competitive job market, candidates often struggle with structured preparation, resume optimization, and real-world interview exposure. This paper presents an adaptive multimodal Artificial Intelligence (AI) framework designed to enhance interview readiness through integrated modules for skill assessment, resume analysis, and interview simulation. The system leverages Machine Learning (ML) and Natural Language Processing (NLP) techniques to provide personalized feedback and performance evaluation. The proposed framework consists of three core modules: an aptitude and Data Structures & Algorithms (DSA) practice system, an AI-powered resume analyzer, and an AI-driven interview simulator. Built using the MERN stack (MongoDB, Express.js, React.js, Node.js), the system ensures scalability and real-time interaction. Experimental usage indicates improved candidate preparedness, confidence, and alignment with job requirements. The framework bridges the gap between academic learning and industry expectations.

Introduction

With increasing demands in recruitment, candidates must demonstrate not only technical skills but also communication and presentation abilities. Traditional preparation methods are limited because they lack personalization and real-time feedback.

To address this, the proposed system integrates three core features:

• Skill evaluation through aptitude and DSA practice
• Resume optimization using NLP techniques
• AI-based mock interview simulation

This combination helps users identify strengths, improve weaknesses, and gain structured interview experience.

The literature review shows that existing solutions focus on individual areas such as interview experience sharing, AI mock interviews, or resume screening for recruiters. However, they do not provide a unified platform for candidates that combines all key preparation aspects.

Conclusion

The Adaptive multimodal Artificial Intelligence Framework for the Interview Readiness Prediction and Skills Enhancement offer an innovative and efficient solution for candidates preparing for interviews. By leveraging Machine Learning and Artificial Intelligence, the platform provides personalized learning, real-time feedback, and adaptive assessments to enhance users\' skills. The MERN stack (MongoDB, Express.js, React.js, Node.js) ensures a scalable, responsive, and high-performance application, making it accessible across devices. With AI-driven insights, automated evaluations, and an interactive user experience, this system empowers users to improve their problem-solving abilities and succeed in technical and behavioral interviews. In summary, this project bridges the gap between candidates and recruiters, offering a smart, data-driven approach to interview preparation while utilizing modern web technologies for an optimized user experience.

References

[1] Cormen, T.H., et al., Introduction to Algorithms is a fundamental resource for understanding algorithms and data structures. [2] McDowell, G. L., Cracking the Coding Interview serves as an essential guide for technical interview preparation. [3] Aho, A.V., et al., The Design and Analysis of Computer Algorithms explore advanced concepts in algorithm design and analysis. [4] Russell, S., & Norvig, Artificial Intelligence: A Modern Approach provides a comprehensive overview of artificial intelligence principles and practices. [5] Goodfellow, I., et al., Deep Learning is a cornerstone text for studying deep learning technologies and methodologies. [6] Ma, L., et al., \"Research and Development of Mobile Application for Android Platform\" discusses the development processes for Android mobile applications. [7] Smith, J., & Johnson, R., \"Algorithm Efficiency and Performance Analysis\" examines techniques for optimizing algorithm efficiency and performance. [8] Kumar, S., & Sharma, A., \"AI-based Resume Screening and Optimization\" explores innovative approaches for enhancing AI-driven resume screening systems. [9] Priyanka, L., et al., \"Smart Shopping: Location-Based Android App\" focuses on location-based Android application development for smart shopping experiences. [10] GeeksforGeeks is a popular online platform offering tutorials and resources on data structures and algorithms. [11] LeetCode is widely recognized for its collection of coding problems designed to improve programming skills. [12] Stack Overflow provides a platform for debugging and collaborative problem- solving in software development. [13] TensorFlow is an open-source library widely used for machine learning and AI applications. [14] OpenAI API is a tool enabling developers to integrate AI-powered functionalities into applications. [15] Android Developers Guide serves as an official reference for developing and deploying Android applications. [16] Sangita Lade, Mihir Gajbhiye, Waidehi Gautam, Vidya Gaikwad, Mahesh Dase, “Interview Experience Portal Using MERN Stack” [17] Mann Monpara, Shruti Amrutkar, Kunal Puri, Gajanan Rathod, Prof. N. H. Deshpande, “A Real-Time Web Platform to Help in Student Interview Preparation using AI”. [18] Wikipedia offers general conceptual overviews across a wide range of topics, including AI and technology. [19] Tutorials Point provides practical tutorials and resources

Copyright

Copyright © 2026 Dr. N. Srinadh Reddy, Chava Sai Mrudhula. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.