Preplyte: An Integrated AI-Powered Placement Preparation and Simulation Platform for Student and Institutions
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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.
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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.
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.
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.
Pagar, Khairnar, and Talekar presented an AI-driven interview preparation system that combines resume 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.
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
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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