INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026  
Preplyte: An Integrated AI-Powered Placement Preparation and  
Simulation Platform for Student and Institutions  
1 Prof. Jayshree Pawar, 2 Swaranjith Satyanarayana Gudelli, 3 Shravan Vijay Chavan, 4 Sarvesh Balasaheb  
Bhoite  
1,2 Dept. of Information Technology, Vasantdada Patil Pratishthan’s College of Engineering and Visual  
Arts, Sion, Mumbai  
3,4 Dept. of Information Technology Vasantdada Patil Pratishthan’s College of Engineering and Visual  
Arts, Sion, Mumbai  
DOI : https://doi.org/10.51583/IJLTEMAS.2026.150100044  
Received: 16 January 2026; Accepted: 23 January 2026; Published: 29 January 2026  
ABSTRACT  
Campus placement is a critical milestone in a student’s academic journey, as it significantly influences their  
professional future. To address the challenges of fragmented and unstructured placement preparation, this paper  
presents Preplyte, an AI-powered placement preparation and simulation platform. The platform provides  
structured training for aptitude assessments, coding evaluations, interview preparation, and soft skill  
development within a single integrated system. Preplyte offers company-specific preparation processes,  
personalized practice modules, and continuous feedback to improve student confidence and performance. AI-  
driven mock interviews are conducted based on student resumes, while an integrated resume builder with ATS  
scoring helps optimize resumes for real recruitment systems. In addition, the platform enables educational  
institutions to track student progress, analyze performance, and provide targeted guidance. By combining  
intelligent automation with realistic placement simulations, Preplyte aims to enhance placement readiness and  
reduce failure rates in campus recruitment processes.  
INTRODUCTION  
Campus placement is a critical phase in a student’s academic journey, as it plays a major role in shaping their  
professional future. However, many students face difficulties during placement preparation due to the use of  
multiple unconnected platforms for aptitude practice, coding assessments, interview preparation, and resume  
building. This fragmented approach often leads to unstructured learning, limited feedback, and inadequate  
exposure to real recruitment processes.  
To address these challenges, this paper introduces Preplyte, an integrated AI-powered placement preparation  
and simulation platform designed to align student preparation with industry expectations. The platform provides  
structured training for aptitude tests, coding rounds, interview preparation, and soft skill development within a  
single unified system.  
Preplyte offers instant performance feedback, personalized practice modules, and company-specific preparation  
workflows that enable students to improve efficiency and confidence across multiple attempts. The platform also  
includes AI-assisted resume building with Applicant Tracking System (ATS) scoring, allowing students to  
optimize their resumes for real-world hiring systems.  
In addition to student-focused features, Preplyte supports educational institutions by providing dashboards and  
analytics to monitor student progress, identify skill gaps, and guide training efforts effectively. Through  
intelligent automation and structured feedback, the platform aims to enhance placement readiness and improve  
overall success rates in campus recruitment.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026  
RESEARCH GOALS AND OBJECTIVES:  
The primary goal of this research is to design and develop an integrated, AI-powered placement preparation  
platform that supports structured skill development and aligns student training with real-world recruitment  
processes.  
The research aims to leverage artificial intelligence to personalize learning tasks based on individual student  
profiles and resumes, while also enabling educational institutions to monitor student performance, analyze  
preparation progress, and provide targeted guidance.  
Overall, this research focuses on improving the effectiveness of campus placement drives by introducing a  
structured, data-driven, and skill-oriented preparation framework that enhances student readiness and placement  
outcomes.  
RESEARCH OBJECTIVE:  
The objectives of this research are as follows:  
1. To design and develop an integrated, user-centric placement preparation platform by analysing existing  
systems and addressing limitations related to fragmentation and lack of integration.  
2. To implement AI-based assessment and feedback mechanisms for evaluating student performance in  
aptitude tests, coding assessments, interviews, and resume analysis.  
3. To simulate real-world campus placement processes by modelling company-specific hiring workflows  
within the platform.  
4. To provide analytical dashboards and monitoring tools that enable institutions to track student progress,  
performance, and training effectiveness.  
5. To evaluate the effectiveness of the proposed system in improving student skills, confidence, and overall  
placement readiness.  
Problem Statement  
Campus placement preparation involves a lot of skills, and students have to learn aptitude, coding, interviews,  
and creating resumes. However, at present, different tools and platforms are being used by different students  
for different purposes, leaving  
campus placement preparation unorganized and disperse on a scattered basis for students. Additionally, these  
tools do not render or give a realistic impression of what actually exists in campus placement drives that take  
place for actual recruitment by companies or organizations. Educational institutions also have a problem tracking  
or preparing for campus placement activities for their students. There does not exist an overall analytical pattern  
for campus placement preparation and readiness on a combined or integrated platform.  
LITERATURE SURVEY  
The following literature survey provides an overview of the key research contributions that inform the design of  
AI-driven interview preparation systems. These contributions include conversational AI frameworks,  
multimodal feedback methods, and adaptive learning platforms.  
Daryanto et al. [1] introduced Conversate, a web-based interview preparation system that uses large language  
models to conduct interactive mock interviews. The system emphasizes dialogic and reflective feedback,  
allowing learners to improve their communication skills through conversational learning. This work shows how  
LLM-based conversational agents can simulate realistic interview scenarios and guide users in self-reflection.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026  
An AI-powered mock interview system published in IJNRD [2] looked at combining conversational AI with  
real-time speech recognition and emotion analysis. By using voice processing and emotion detection APIs, the  
system increases interview realism and provides detailed performance feedback. While this approach enhances  
user immersion, its dependence on multiple external APIs raises challenges related to scalability, cost, and long-  
term use.  
A study published in IJIRT [3] proposed a real-time web platform designed to support interview preparation  
through video-based mock interviews. The platform adjusts question difficulty based on user responses and  
offers personalized learning pathways, verbal feedback, and motivational content. This adaptive feature boosts  
user engagement, though the system mainly focuses on interview practice rather than thorough skill evaluation.  
Pagar, Khairnar, and Talekar [4] presented an AI-driven interview preparation framework that combines resume  
analysis with mock interviews. Their system uses natural language processing for resume parsing, convolutional  
neural networks for emotion detection, and speech recognition modules, along with integration of an applicant  
tracking system (ATS). This work illustrates the potential of full automation in interview preparation, while also  
introducing greater computational complexity due to multimodal processing.  
Gomez et al. [5] carried out a qualitative study on AI-driven mock technical interviews that involved multimodal  
interactions, such as whiteboarding tasks and real-time feedback. The results showed significant improvements  
in student confidence and readiness for interviews. However, the study also pointed out limitations in  
conversational adaptability, stressing the need for more context-aware and flexible AI interview agents.  
Existing research largely focuses on AI-driven interview preparation as a standalone activity, with limited  
integration of aptitude assessment, coding evaluation, resume optimization, and institutional monitoring. In  
contrast, the proposed Preplyte platform offers a unified placement preparation environment that combines  
multiple assessment modules, AI-based feedback, and institutional dashboards to better align student preparation  
with real campus recruitment processes.  
Proposed System  
The proposed system is a complete Placement Preparation and Simulation Platform that combines all the key  
elements of placement training—aptitude, coding, interviews, resume building, and mock drives—into one easy-  
to-use digital environment.  
It aims to help students prepare confidently for campus placements while providing educational institutions and  
administrators effective tools to monitor progress and performance. The system supports three main user groups:  
1. Students (individual college users) can practice aptitude questions, solve coding problems, attend mock  
interviews, build resumes, enroll in courses, and participate in full placement drives.  
2. Institutes can manage their students, organize mock drives, track performance through reports, and view  
rankings via leaderboards.  
3. Super Admins oversee institute registrations, manage student access, and maintain overall platform  
analytics.  
This platform offers a realistic, end-to-end placement simulation, from aptitude tests to AI-powered interviews,  
with detailed feedback reports, competitive leaderboards, and resumes evaluated through ATS scoring. It bridges  
the gap between traditional preparation methods and real-world placement processes by integrating AI, data  
analytics, and engaging user experiences. The system follows a three-tier architecture model, which improves  
organization, scalability, and performance.  
1. Presentation Layer (Frontend)  
ï‚·
This layer is responsible for all user interactions with the system. It is developed using web technologies  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026  
such as HTML, CSS, and JavaScript, along with frameworks like React or Angular.  
ï‚·
ï‚·
It provides user-friendly interfaces such as dashboards, online tests, resume builders, and AI-based  
interview modules.  
It ensures smooth navigation and a responsive user experience for students, institutes, and  
administrators.  
2. Application Layer (Backend)  
ï‚·
ï‚·
ï‚·
Handles core system logic, user authentication, and communication between the frontend and database.  
Developed using backend technologies such as Node.js or Python frameworks (Flask/Django).  
Resume analysis and ATS scoring are implemented using NLP techniques with libraries such as spaCy  
or Scikit-learn.  
ï‚·
Coding assessments are managed through an online judge mechanism that executes code against  
predefined test cases.  
3. Data Layer (Database)  
ï‚·
ï‚·
ï‚·
ï‚·
This layer stores all the data required by the system.  
It includes user profiles, question banks, test results, resumes, and performance reports.  
It is implemented using databases such as MySQL or MongoDB.  
It ensures secure, reliable, and scalable storage of information.  
Overall System Workflow  
The system starts when a user accesses the platform and goes through authentication. Existing users log in with  
their credentials, while new users register by creating an account. After successful authentication, the user selects  
a role: Student, Institute, or Admin. Based on this role selection, the system redirects the user to a specific  
dashboard. This ensures controlled access and functionality.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026  
Student Workflow and Algorithm Integration  
Once logged in as a student, the user can access the participation module. Here, students take aptitude tests,  
coding tests, AI- driven mock interviews, and resume building with ATS scanning.  
Aptitude and Coding Evaluation Algorithm  
When a student takes an aptitude or basic coding test, the system presents predefined questions pulled from the  
database. Each response is recorded along with the time taken. After submission, the algorithm compares the  
student's answers with the correct answers stored in the database. The score is calculated using the formula:  
Score = (Correct Answers/Total Questions) × 100.  
Accuracy and time metrics are analyzed together to provide meaningful feedback. This algorithm ensures fair,  
objective, and consistent evaluation for all users.  
Coding Test Execution Algorithm  
For advanced coding assessments, the student submits executable code through the platform. The Coding Test  
Execution Algorithm compiles and runs the submitted code against predefined test cases. Each test case checks  
for correctness, edge cases, and logic validity. Execution time, memory usage, and output accuracy are recorded.  
Based on the passed test cases, the system assigns a coding score and performance level, simulating real technical  
interview conditions.  
AI Interview Evaluation Algorithm  
In the AI interview module, the system conducts mock interviews using an AI-driven conversational engine.  
Questions are generated based on the student's profile, skills, and previous performance. The algorithm evaluates  
responses based on content relevance, clarity, confidence, response structure, and emotional cues (if enabled).  
After the interview, detailed feedback is generated, highlighting communication strengths, areas for  
improvement, and suggestions.  
Resume ATS Scoring Algorithm  
When the student uploads a resume, the Resume ATS Scoring Algorithm processes the document using NLP  
techniques. It extracts skills, education, experience, and keywords, and compares them against predefined job  
role templates. The resume is scored based on ATS compatibility, keyword matching, formatting, and relevance.  
The system then provides actionable suggestions to improve resume visibility and selection probability.  
After completing these modules, all results are stored securely. Students can view consolidated performance  
analytics and feedback directly on their dashboard.  
Institute Workflow  
When an institute logs in, the system provides access to analytics and management features. Institutes can  
organize mock drives, track student participation, monitor progress, and view complete performance analytics.  
The results from the evaluation algorithms help institutes assess readiness levels and identify training gaps  
among students.  
Admin Workflow  
Admin users oversee the entire system. They manage users, provide services, maintain the learning management  
system, and ensure the system runs smoothly. Admins also monitor leaderboard integrity, platform performance,  
and data consistency.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026  
Leaderboard and Ranking Algorithm  
The Leaderboard and Ranking Algorithm processes evaluation data from aptitude tests, coding assessments, AI  
interviews, and resume scores. It normalizes scores and ranks students based on overall performance,  
consistency, and accuracy. Rankings are updated dynamically and displayed on dashboards, promoting healthy  
competition and motivation among users.  
Final Feedback and Analytics Loop  
At the end of every assessment cycle, the system performs automated scoring and analysis. Feedback is generated  
and reviewed, then stored on the dashboard for future reference. This feedback loop supports continuous  
improvement, personalized learning, and performance tracking over time.  
Evaluation and Validation  
A preliminary validation of the Preplyte platform was carried out through pilot usage with final-year students  
and faculty involved in placement preparation. The platform was used for aptitude practice, coding assessments,  
AI-driven mock interviews, and resume evaluation with ATS scoring. Feedback indicated that students found.  
the integrated approach helpful for structured preparation, while faculty benefited from improved visibility into  
student progress through centralized dashboards. Although detailed quantitative analysis was not performed, the  
pilot usage demonstrates the practical applicability of the proposed system  
User Feedback and Usability Insights  
Feedback collected from students and faculty during pilot usage indicated that the platform was easy to use and  
reduced the need to switch between multiple tools for placement preparation. Students appreciated the clear  
structure of aptitude tests, coding practice, and interview preparation, while faculty members found the  
monitoring features helpful for tracking progress and identifying areas where students needed additional support.  
Overall, the feedback suggested that the platform improved usability and supported more effective placement  
training.  
IMPLEMENTATION CHALLENGES AND PRACTICAL CONSIDERATIONS  
The proposed system aims to establish a strong foundation for placement preparation for both graduating and  
graduate students by providing a unified and structured platform that integrates aptitude practice, coding  
assessments, resume creation, and AI-driven mock interviews. By consolidating these resources into a single  
platform, institutions gain centralized access to student performance data, which helps reduce administrative  
effort and improves the effectiveness of training while building student confidence and preparedness. Despite  
these advantages, several practical challenges must be addressed. The system processes and stores highly  
sensitive information such as resumes, interview feedback, and performance analytics, making data privacy and  
security a critical concern. In addition, the fairness and accuracy of AI-driven evaluation play a vital role in  
maintaining student trust, as incorrect or biased feedback may negatively affect motivation and learning  
outcomes. To mitigate this risk, the system can incorporate Explainable AI (XAI) techniques and a human-in-  
the-loop mechanism, enabling transparency in scoring and allowing mentors to review or refine AI-generated  
feedback when necessary. To further address AI-related limitations, the platform also considers bias mitigation,  
secure handling of sensitive data, and adaptive learning support. Human review mechanisms help reduce the  
impact of biased or incorrect AI feedback, while controlled access and secure storage protect user data. In future  
versions, adaptive learning features can personalize assessments and feedback based on individual learning  
profiles, making the system more inclusive and responsive to diverse student needs. Furthermore, integrating  
multiple intelligent components increases system complexity in terms of design, deployment, and maintenance.  
From an operational standpoint, scalability and user experience also present challenges, particularly for features  
such as real-time speech and emotion recognition, which require reliable internet connectivity and sufficient  
computational resources. Dependence on third-party APIs may further increase operational costs and impact  
long-term reliability. Therefore, continuous optimization, regular system updates, and a strong focus on usability  
are essential to ensure the platform remains scalable, secure, and beneficial for all stakeholders.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026  
FUTURE SCOPE  
[1] The development of Roadmap based Skill Certification, which will provide students with step-by-step  
guidance through the skills necessary for certification, with a certificate issued upon completion of the course,  
can also be included in the future platform to provide students with clear direction on their next steps throughout  
their learning process. The certification can serve as a great motivator for students as they prepare for a new  
career and provide the student with a way to showcase their abilities on their resume, which will increase their  
chances of obtaining a position at a company.  
[2] A possible enhancement to the future platform would be AI- assisted Placement Prediction. This will allow  
the platform to evaluate a student's abilities, performance and progress, and predict their potential for being ready  
for placement. This will give students a good idea of their current status and areas in need of improvement, as  
well as provide colleges and universities with an overview of current placement trends. [3] The future platform  
could also have additional Mentor and Company Dashboards. Mentors will have access to monitor their student's  
progress as well as provide tailored support. Company Dashboards will allow companies to quickly assess  
prospective candidates for placement. This will help to establish a stronger and more transparent connection  
between students, mentors and recruiters.  
CONCLUSION  
The Placement Preparation and Simulation Platform brings together all essential components of campus  
placement training, including aptitude practice, coding assessments, interview preparation, resume building, and  
learning modules, within a single integrated system. Unlike traditional approaches that rely on multiple  
unconnected tools, the proposed platform offers an end-to-end placement preparation solution with real-time  
assessment and AI-driven feedback to support continuous improvement.  
Through role-based access, students are able to practice, evaluate their performance, and improve their skills in  
a structured manner, while institutions can efficiently organize mock placement drives and monitor student  
progress through centralized analytics. The platform also enables administrators to manage system-wide  
operations and performance insights effectively.  
By combining automation, analytics, and user engagement, the platform provides a structured and personalized  
preparation experience that aligns academic training with industry expectations. Overall, the proposed system  
demonstrates how artificial intelligence and data-driven technologies can enhance placement readiness, support  
institutional training efforts, and create a comprehensive ecosystem for graduate career development..  
REFERENCES  
1. Recent research has focused on using artificial intelligence to improve interview preparation with  
realistic simulations and personalized feedback. Daryanto et al. (2024) introduced Conversate, a web-  
based system that uses large language models to conduct interactive mock interviews and provide  
reflective feedback. Their work emphasizes learning through conversation, helping students think about  
their responses and improve communication skills over time.  
2. An AI-powered mock interview system published in JNRD examined how conversational AI integrates  
with real-time voice and emotion analysis. By using speech recognition and emotion detection APIs, the  
system creates realistic interview environments and delivers detailed performance feedback. While  
effective at improving realism, the system relies heavily on external APIs, which may impact scalability  
and long-term use.  
3. Another study published in UIRT proposed a real-time web platform designed to assist students in  
interview preparation through video-based mock interviews. The platform adjusts question difficulty  
based on user responses and provides personalized learning plans, spoken feedback, and motivational  
content. This method showed better engagement and adaptability but mainly focused on interview  
practice rather than overall skill assessment.  
4. Pagar, Khairnar, and Talekar presented an AI-driven interview preparation system that combines resume  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026  
analysis with mock interviews. Their system uses NLP techniques for resume parsing, CNN-based  
emotion detection, speech recognition, and applicant tracking system (ATS) integration. This work  
highlights the potential for complete automation in interview preparation but adds extra computational  
complexity.  
5. Gomez et al. (2025) conducted a qualitative study on AI- driven mock technical interviews involving  
multimodal interactions, such as whiteboarding tasks and real-time feedback. Their findings showed  
clear improvements in student confidence  
6. and interview readiness. However, the study also pointed out limitations in conversational flexibility,  
indicating a need for more adaptive and context-aware AI systems  
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