Crack It Up: Practice-Based Application for Interview Rounds

International Journal of Research (IJR) e-ISSN: 2348-6848 p-ISSN: 2348-795X Vol. 12 Issue 11 November 2025 Received: 24 October 2025 209 Revised: 8 November 2025 Accepted: 18 November 2025 Copyright  authors 2025 DOI: HTTPS://DOI.ORG/10.5281/ZENODO.17622927 Crack It Up: Practice-Based Application for Interview Rounds Tanvi Harde ([email protected]), Shweta Bisen ([email protected]), Vaishnavi Pawar ([email protected]), Suhani Babhulkar ([email protected]), Satyam Kirekar (sa[email protected]). Swati Kosankar ([email protected]) ABSTRACT Objectives: To develop an AI-powered IOS application that simulates realworld interview scenarios and enhances interview preparedness by providing real-time feedback on verbal and non-verbal communication. The purpose of this thesis is to demonstrate the development and implementation of "Crack It Up: Practice Based Application For Interview Round," an innovative application for iOS, for students to enhance the essential skills for gaining employment. The application focuses on students' preparatory practices for assessments on campus and beyond, for example, aptitude assessments, coding tests, and even training experiences. The process was designed in the programming language Swift in an Xcode environment so students can interactively practice multiple-choice questions in real time while measuring their progress and performance within areas of their academic and professional disciplines. Overall, this project aims to create pathways to learning that connect the traditional academic learning environment to job market demand, while providing a personalized, approachable, and timely resource that supports students in preparing for competition in preparation for testing and interviews. Each student would be able to practice questions about aptitude or coding that have been contextualized in their respective disciplines. Thus, while the app addresses a narrow scope of preparation, the app can be designed and utilized at scale across all academic disciplines. Moreover, the present study points to future improvements that will entail developing a virtual AI-based personalized interviewer that can hold dynamic mock interviews, offer personalized findings, and adjust questioning dynamically based upon both the students' skills and career goals. This will act as a tool for continual building of the students' confidence and skills with respect to interviewing in real world contexts. Overall, the study offers a contribution to the educational technology for employability of students which will (if build) serve in scalable, adaptable, and student-sustaining way to prepare students for future employment assessments. The study indicates the potential of International Journal of Research (IJR) e-ISSN: 2348-6848 p-ISSN: 2348-795X Vol. 12 Issue 11 November 2025 Received: 24 October 2025 210 Revised: 8 November 2025 Accepted: 18 November 2025 Copyright  authors 2025 DOI: HTTPS://DOI.ORG/10.5281/ZENODO.17622927 educational technology (via combinations of mobile technology and artificial intelligence) to improve graduate attributes associated with employability in a competitive labor market. INTRODUCTION This is a one-stop iOS app, built using Swift with Xcode that provides students with everything they need to help them study effectively for their coding tests, aptitude tests, and interviews. The app features user registration, an easy-to-navigate dashboard, functionality to track student preformance, and reports of individual students' progress including questions that adapt based on a student's confidence and competency level. The app was deliberately designed to provide consistent accessibility across multiple devices and to provide an engaging practice service, helping all students acquire employability skills prior to graduating. Many job seekers rely on traditional preparation methods such as question banks, coding exercises, and mock interviews. However, these approaches often fall short in providing a truly personalized and immersive experience. They fail to address behavioral aspects, which are just as critical as technical skills in making a lasting impression on interviewers. To bridge this gap, we introduce an AI-powered interview preparation tool designed to simulate real-world interview scenarios, offering a customized and comprehensive approach to readiness. Beyond technical evaluation, our AI tool incorporates behavioral analysis to assess a candidate’s confidence, honesty, and communication skills. By leveraging eye-tracking technology and tone assessment, it provides insights into how candidates present themselves, helping them refine their body language, speech clarity, and overall presence. At the end of each session, candidates receive a detailed performance report highlighting their strengths and pinpointing areas for improvement. This feedback empowers users to refine their skills, build confidence, and develop strategies to tackle challenging interview scenarios effectively. By offering a holistic approach to interview preparation, this AI driven tool not only helps candidates excel in their interviews but also equips them with the communication and problem-solving skills necessary for long-term professional success.[1] The Placement Preparation System addresses this need by offering personalized learning paths, real-time practice assessments in both aptitude and technical domains, constructive feedback mechanisms, and comprehensive interview preparation encompassing both technical and HR aspects. Recognizing the significance of coding skills, the system incorporates coding practice modules.[3]. Traditional methods of interview preparation, such as mock interviews and feedback sessions, often fall short in offering timely, objective, and comprehensive evaluations. These methods typically lack real-time, personalized feedback, especially on nonverbal communication cues such as facial expressions, eye contact, and vocal tone, which are crucial to a candidate’s perceived confidence and effectiveness.[4] Traditional methods often lack personalization and real-time interaction, leaving candidates underprepared. Next Interview is an AI-powered web application designed to bridge this gap by offering a realistic, personalized interview experience. VAPI enables voice-based mock interviews, simulating real-world interview dynamics. Users can define job roles, experience levels, and interview preferences for a tailored experience. The system then conducts a mock interview using AI voice assistants. Afterward, users receive comprehensive feedback generated by AI. Next Interview enhances confidence, communication skills, and readiness, International Journal of Research (IJR) e-ISSN: 2348-6848 p-ISSN: 2348-795X Vol. 12 Issue 11 November 2025 Received: 24 October 2025 211 Revised: 8 November 2025 Accepted: 18 November 2025 Copyright  authors 2025 DOI: HTTPS://DOI.ORG/10.5281/ZENODO.17622927 making it a practical tool for job seekers.[5] Some companies also take hiring rounds via video interviews and conference calls, where confident and well-defined body language is essential. However, existing interview preparation platforms often rely on text-based methods, requiring manual intervention and limited efficiency. While they may offer feedback solely on the correctness of answers, they often overlook the nuances of a candidate’s gestures. The mock interview module is designed to equip students with the skills and confidence needed to thrive in real-world job interviews.[6] System/Application Design FRONTEND: Frontend Components: 1. Storyboard: Swift Storyboard is a visual design tool used to help design iOS app interfaces and handle navigation between screens all contained in a single file. Developers can dragand-drop UI elements such as buttons and labels onto scenes (each of which is treated as a view controller) and connect scenes with segues. These drag-and-drop elements provide for transitions to follow the established flow of the application. Storyboards provide a means to see a bird's-eye view of the entire app structure and flow, to ultimately make managing navigation simpler. Storyboards also support adaptive layouts which can respond to the guidelines set with Auto Layout and Size Classes. They can also allow data to be passed from one screen to the next via prepareForSegue. Storyboards can be a much more visually appealing option for people to use when designing apps, making the storyboard particularly applicable to developers who favor a graphical workflow. However, sometimes Storyboards can present issues with maintainability and version control issues if the project becomes very large. Still, storyboards remain a very popular development option for iOS and can even work with SwiftUI later in a hybrid design project to take advantage of both approaches. 2. UIKIT: UIKit is a highly regarded framework by Apple for the development of graphical, event-driven user interfaces in iOS applications. UIKit provides a standard set of UI components, including windows and views, buttons and labels, and a set of gesture recognizers that handle touch events, animations, and views in transit dealing with the user. UIKit can integrate and work seamlessly with Swift and Objective-C interfaces that enable the developer to create and update UI components in code. In their most recent update included with iOS 26 (2025) Framework update includes – new updateProperties for easier and better performance updates to a user interface, improved handling of animations and animations in transition, views responsive to menu bars, code that works alongside SwiftUI scenes through a UIHostingSceneDelegate, richer HDR colors render, and UIScene lifecycle support is needed to build multi-window support in the application. UIKit continues to be a required and most used UI framework combined with swiftUI to International Journal of Research (IJR) e-ISSN: 2348-6848 p-ISSN: 2348-795X Vol. 12 Issue 11 November 2025 Received: 24 October 2025 212 Revised: 8 November 2025 Accepted: 18 November 2025 Copyright  authors 2025 DOI: HTTPS://DOI.ORG/10.5281/ZENODO.17622927 combined the best of both worlds declarative vs. imperative UI development of modern iOS applications. 3. SWIFTUI(INTERFACE): SwiftUI is Apple's contemporary and declarative framework for developing user interfaces for all Apple platforms. It simplifies the development process by allowing the developer to define what the user interface is to do, minimizing code complexity, and promoting real-time previews for accelerating development. SwiftUI is fully interoperable with Swift, runs on multiple Apple platforms, and has several features, including the Liquid Glass design system, advanced 3D charts, rich text editing, and RealityKit, for immersive experiences. Because its state-driven design is designed for the interactive, data-driven app, for instance, an AI interviewer for testing aptitude, training, and coding practice MCQs, it dynamically processes user input and backend feedback on the user interface. Overall, SwiftUI is a model of scalable, high-performance app development leveraging clean syntax, making it ideal for creating iOS projects that demand a fluid and responsive user interface. 4. SWIFT DATA: Developers can leverage Firebase Firestore for Swift data handling and backend management of data in iOS applications yet another modern scalable methodology for building backend solutions with data, for example for AI interviewer projects, and aptitude, training, and coding MCQ practice rounds will greatly benefit from this solution. Firestore is a cloud-hosted NoSQL database which stores data in flexible documents and collections with great real-time sync, offline capabilities (local persistence). Swift's Codable protocol works great with Firestore, making the mapping between Firestore documents and Swift data models easier and meaningful, making code simple and less error prone. Almost effortless coding for fetching, updating, and syncing data between the app UI and the backend can be accomplished with clean, type-safe code. Firestore provides superior scalability and real-time sync, allowing the creation of interactive, responsive iOS applications with support for dynamically tracking training and practice application user progress. Given the modern day demands placed on user interface and UX design, whatever anything with Swift and Firebase Firestore equest an handling user data will provide reability and responsiveness that is critical to modern iOS development framework applications. International Journal of Research (IJR) e-ISSN: 2348-6848 p-ISSN: 2348-795X Vol. 12 Issue 11 November 2025 Received: 24 October 2025 213 Revised: 8 November 2025 Accepted: 18 November 2025 Copyright  authors 2025 DOI: HTTPS://DOI.ORG/10.5281/ZENODO.17622927 Frontend Design: Registration/Login Page Design: The Registration/Login page provides a secure and efficient way for users to create accounts or access existing ones using traditional authentication methods. The registration section includes fields for username, email, password, and confirm password, with real-time validation for proper email format, matching passwords, and strong password rules. All inputs are masked for privacy, and data is safely sent to the backend with error messages for issues like duplicate emails. Users can easily switch between registration and login views. The login section requires email and password, checks for empty fields, verifies credentials with the backend, and displays clear error messages for invalid details. The interface is clean, simple, and focused on a smooth user experience, with an optional link to reset forgotten passwords. Profile Page: The Profile Page acts as a personalized hub where users can view and manage their account details while tracking activity and progress within the app. It displays key information such as name, email, and profile photo, along with comprehensive records of practice attempts, interview sessions, progress statistics, and feedback summaries. Users can securely update personal details through an intuitive edit mode that reduces accidental changes. The layout is organized into sections like Personal Info, Performance History, and Settings for better clarity and usability. Strong security features, including password management and privacy controls, protect user data, while the accessible design promotes engagement and longterm user retention. Home page: The Home Page serves as the central dashboard, providing clear navigation to the app’s main features while maintaining simplicityand ease of use. It includes four primary buttons directing users to Aptitude Practice, Technical Quizzes, Coding Challenges, and AIPowered Interview Rounds. Designed for mobile accessibility, buttons are positioned for easy thumb reach with bottom navigation for quick interaction. The clean layout and intuitive icons ensure effortless navigation and minimize distractions, allowing users to begin practice or interviews seamlessly. Its responsive design adapts smoothly to various iOS screen sizes and orientations, while the layout promotes user engagement by keeping key features easily accessible, supporting a streamlined user journey aligned with modern mobile design standards. Progress Page: The Progress Page helps users track their learning and interview preparation through clear visuals and feedback. It includes progress bars, charts, and graphs showing completed and pending tasks across aptitude, technical, coding, and interview modules. Users can view scores, attempt histories, and success rates to identify strengths and areas for improvement. Trend analysis highlights progress over time, while real-time updates offer instant feedback. With a clean, responsive design, easy navigation, and motivational badges, the page promotes continuous learning and self-assessment. International Journal of Research (IJR) e-ISSN: 2348-6848 p-ISSN: 2348-795X Vol. 12 Issue 11 November 2025 Received: 24 October 2025 214 Revised: 8 November 2025 Accepted: 18 November 2025 Copyright  authors 2025 DOI: HTTPS://DOI.ORG/10.5281/ZENODO.17622927 BACKEND 1. Firebase core: Firebase Core serves as the foundational layer of the Firebase Platform. Created by Google, Firebase is a Backend-as-a-Service (BaaS) to assist developers in building and scaling apps for the web and mobile devices in a fast manner. Firebase Core is the basic library that makes your app connect to Firebase backend services – making features such as authentication, real-time databases, cloud messaging, and analytics operational. Firebase Core handles the initialization of the app and establishes a secure connection to Google cloud services so your communication with Firebase will work with Android, iOS, web, and Flutter. Firebase Core allows developers to have a modular library so developers have the ability to only import Firebase services they are using and reduce both application performance and application size. Firebase Core allows developers to take advantage of a high functioning backend and not have to manage server-side/subscriber as part of an application. Firebase Core will give developers smooth real-time synchronization of data, authentication of users, and push notifications. Firebase Core is a key library/service for all integration of Firebase and so it is an essential library for any development purpose for a Firebase application. 2. Firebase Auth: Firebase Authentication is a secure authentication service and offers an easy-to-use sign-in service as part of Google Firebase. Sign-in providers support a range of web or mobile application sign-in methods, such as email or phone sign-in and popular identity providers such as Google or Facebook. Software development kits (SDKs) and user interface (UI) libraries provide out-of-the-box and configurable authentication capabilities that include sign-up, sign-in, password reset, and account management solutions. Firebase Authentication generates authentication tokens, offers multi-factor authentication, provides an anonymous user option, and supports OAuth 2.0 and OpenID Connect standards if better security options are desired. An optional upgrade feature called Identity Platform adds enterprise capability features, such as multi-tenancy, audit logging, and enhanced identity management. 3. Firebase Firestore: Firestore is a document cloud database that is both flexible and scalable provided by Firebase for building mobile, web and server apps. Firestore stores your data in documents and collections that can contain nested subcollections. Firestore includes real-time synchronization across multiple devices along with cached data for offline usage. Firestore includes a framework for complex queries, transaction support that is ACID compliant, and reasonable availability with multi-region replication and security rules. Firestore can be used with Firebase Authentication and Cloud Functions to manage a backend service without a server and without managing a backend service yourself. Its International Journal of Research (IJR) e-ISSN: 2348-6848 p-ISSN: 2348-795X Vol. 12 Issue 11 November 2025 Received: 24 October 2025 215 Revised: 8 November 2025 Accepted: 18 November 2025 Copyright  authors 2025 DOI: HTTPS://DOI.ORG/10.5281/ZENODO.17622927 pay-as-you-grow pricing might appeal to any developer that wants to build an application that needs scaling, collaboration and real-time capabilities. 4. Authentication: Firebase Authentication is a user-friendly and secure way for users to access mobile and web applications with their Google accounts, so they do not need to worry about remembering a new username and password. The developers can turn on the Google sign-in option in the Firebase console very quickly and then follow the documentation to set up the Firebase SDK. After the SDK has been set up, developers can utilize the GoogleAuthProvider classes and call either signInWithPopup or signInWithRedirect to kick off the sign-in process. Once it is done, Firebase will issue the user an ID token (JWT) as a means to secure the session and allow entrance to the resource. Firebase Authentication with Google can be used in Android, iOS, and web apps. Moreover, developers do not have to handle sign-in error messages since Firebase Authentication will deal with that. Firebase Authentication employs the OAuth 2.0 work flow for secure authentication. The Google Sign-In library allows developers to have a greater influence on the sign-in process. For example, a developer may want to capture the Google ID token from the flow and then manually swap it for Firebase credentials. FUTURE SCOPE: BOT MODULE 1. OpenAI: OpenAI is a leading artificial intelligence research and deployment company focused on developing advanced AI models that safely benefit humanity. It creates stateof-the-art technologies in natural language processing, image generation, speech recognition, and coding assistance, harnessing deep learning, neural networks, and reinforcement learning. OpenAI’s flagship models include the GPT series, which excel at understanding and generating human-like text, enabling applications like chatbots, virtual assistants, content creation, and problem solving. The company offers its AI capabilities through APIs that businesses and developers can integrate into applications, fostering innovation across industries such as healthcare, finance, education, and customer service. OpenAI emphasizes ethical AI development, transparency, safety, and widespread accessibility, continuously refining its models with human feedback and real-world usage data. Its research pushes boundaries in AI capabilities while addressing risk mitigation and alignment with human values. OpenAI also invests heavily in infrastructure and compute power, ensuring scalable and efficient AI deployment worldwide, solidifying its role as a pioneer in the AI industry. International Journal of Research (IJR) e-ISSN: 2348-6848 p-ISSN: 2348-795X Vol. 12 Issue 11 November 2025 Received: 24 October 2025 216 Revised: 8 November 2025 Accepted: 18 November 2025 Copyright  authors 2025 DOI: HTTPS://DOI.ORG/10.5281/ZENODO.17622927 2. AVFoundation (AVSpeechSynthesizer, AVAudioEngine, SFSpeechRecognizer): AVFoundation is a powerful Apple framework for working with time-based audiovisual media across iOS, macOS, tvOS, and watchOS. It provides extensive APIs for playing, recording, editing, and processing audio and video. The AVSpeechSynthesizer class within AVFoundation enables text-to-speech functionality, allowing apps to convert written text into spoken audio using various voice options. The AVAudioEngine is a robust audio processing framework that offers low-level control for audio playback, mixing, and audio signal processing with customizable audio nodes and effects. It is designed for complex audio tasks like real-time audio manipulation and recording. Meanwhile, the Speech Framework (often referenced as Speech Recognizer or SFSpeechRecognizer) complements AVFoundation by providing speech recognition capabilities. It allows apps to convert spoken language into text using on-device or server-based recognition, supporting tasks like voice commands and transcription. Together, AVFoundation's audio engine and speech synthesizer, along with the Speech Framework's speech recognizer, offer a comprehensive suite for handling audio input, output, and speech interactions in Apple platforms, enabling developers to create rich multimedia and voice-enabled applications. LITERATURE REVIEW 1. AI Interviewer Using Genrerative AI: This document highlights several key studies relevant to the development of an AI-powered interview preparation tool. It discusses the role of OpenCV in real-time computer vision applications, including facial recognition and behavioral analysis, emphasizing its impact across diverse industries (Bradski). Research on nonverbal behaviors in job interviews using machine learning demonstrates the importance of facial expressions, eye movements, and speech patterns in hiring decisions, underscoring the value of AI for analyzing these cues (Nguyen et al.). Additionally, speech feature extraction techniques are reviewed for their applications in voice authentication and communication systems (Narang & Gupta). Security aspects of user authentication methods are also considered (Davis et al.). Advances in AI frameworks like TensorFlow and containerization through Docker are discussed for their roles in scalable and reproducible AI model development (Abadi et al., Boettiger). Recent studies on AI-driven behavioral analysis further validate the use of deep learning to assess candidate emotions and stress levels during interviews, enhancing the objectivity and effectiveness of hiring evaluations (Patel et al.). Finally, research on AI-powered coding platforms reveals how real-time feedback on coding enhances technical interview preparation by simulating authentic assessments (Sharma et al.). Together, these works provide a comprehensive foundation supporting the development of a sophisticated, AI-integrated interview simulator that combines resume parsing, personalized question generation, real-time coding challenges, behavior monitoring, and performance feedback. This literature reflects an interdisciplinary approach, pooling insights from computer vision, natural language processing, machine learning, and software development to improve interview readiness and candidate assessment. International Journal of Research (IJR) e-ISSN: 2348-6848 p-ISSN: 2348-795X Vol. 12 Issue 11 November 2025 Received: 24 October 2025 217 Revised: 8 November 2025 Accepted: 18 November 2025 Copyright  authors 2025 DOI: HTTPS://DOI.ORG/10.5281/ZENODO.17622927 2.Hi. I’m Molly, Your Virtual Interviewer!: It focuses on the intersection of AI-powered asynchronous video interviews (AVIs) and the impact of race and gender in virtual interviewer experiences. It traces historical and contemporary research on recruitment interview biases, stereotype threats, and demographic influences on interview outcomes, emphasizing how AVIs offer scalable, standardized alternatives to traditional interviews but face challenges in social presence, fairness, and privacy perceptions. The review also covers technical advances in avatar realism, conversational agents, and dialogue management, alongside studies on algorithmic fairness, applicant reactions, and intervention strategies to reduce bias in AI hiring systems. Collectively, this scholarly foundation underscores the necessity to explore how demographic identities of both AI agents and users shape perceptions of fairness, social presence, emotional response, and impression management during AVIs, to inform equitable design and implementation in recruitment technology. This provides the basis for the study’s empirical exploration of gender and race effects within AIdriven interview systems. 3.Enhancing Student Placement Preparation Through Web Application: This paper reviews existing placement preparation platforms and studies guiding the creation of a comprehensive web application for student job readiness. Platforms like Bakshi (2021), PrepInsta, GeekForGeeks, and HackerRank offer aptitude tests, coding challenges, mock interviews, and skill development, excelling in content variety but lacking personalization or holistic coverage. Prior research emphasizes integrating aptitude, technical, and interview preparation with resume support (Castillo, 2013; Khatter & Jain, 2020) and highlights the benefits of alumni mentorship and collaborative, feedback-driven learning (Kumar, 2023; Amrithesh, 2022). Studies also advocate real-world experience in curricula (Neill & Mulholland, 2003) and predictive placement models using academic data (Jadhav, 2022). Overall, the literature identifies a gap in end-to-end, personalized, and integrated solutions, motivating the development of a dynamic platform connecting students, teachers, alumni, and placement cells to bridge the academic–industry gap. 4.Indian Journal of Science and Technology: This paper underscores the growing importance of AI-driven interview preparation tools that integrate real-time verbal and non-verbal behavior analysis to enhance candidate readiness. It highlights previous works such as Khapekar et al.’s voice and emotion analysis system, Suguna et al.’s Gen-AI virtual selfpractice tool, and Rai et al.’s AI-driven mock interview platform, noting their individual strengths and limitations—primarily the lack of a holistic system combining facial expression tracking, voice assessment, and dynamic question generation. The review emphasizes the need for multimodal feedback, including eye contact and facial expression analysis alongside speech evaluation, which are vital for realistic interview simulation but are often absent in existing systems. Advances in machine learning models, Google’s ML Kit for facial analysis, Teachable Machine for voice analysis, and conversational AI like ChatGPT offer promising avenues to fill these gaps. This literature foundation justifies the study’s development of a unified mobile application that delivers an immersive, adaptive, real-time interview experience by bridging verbal and non-verbal feedback with interactive AI questioning, addressing shortcomings of prior AI tools and traditional mock interviews comprehensively.