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Published Paper Details:
HireIQ - AI Integrated Web Platform for End-to-End Tech Interview Preparation
Authors
Prof. Shivaji Vasekar,Mr. Devashya Patil,Mr. Gaurav Pawar,Mr. Pavan Dekate,Mr. Shravan Bobade
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Keywords
AI, Interview preparation, Interview Assistant, Chatbot, Natural Language Processing, Web development, Sentiment Analysis, Voice-based interaction, Job Interview Simulation, Personalized Feedback System, Recruitment, Company-specific roadmap, Career Development
Abstract
Technical interviews for software engineering roles demand both deep technical competence and the ability to communicate solutions clearly and confidently. Candidates typically prepare using a patchwork of resources -- coding platforms, textual guides on system design, recorded mock interviews -- none of which provide an integrated, adaptive, and evidence-backed training pipeline. HireIQ is an AI-integrated web platform designed to replace that fragmentation with a single, end-to-end preparation ecosystem. The platform first ingests a candidate's resume and profile and produces a tailored, prioritized skill inventory using layout-aware parsing and transformer-based embeddings. Using that profile, HireIQ generates company- and role-specific roadmaps, micro-projects, and question sequences that combine coding problems, system-design cases, and behavioral prompts. Mock interviews are delivered in both synchronous and asynchronous modes; responses are captured multimodally (audio + transcript + optional video + code / whiteboard sketches) and evaluated by a suite of specialized graders: a sandboxed code judge for correctness and complexity, transformer-based semantic scorers for explanatory answers, and prosody/voice models for delivery and confidence. Session scoring uses temporally aware, window-consistency fusion to stabilize judgments and produce session-level reports with confidence intervals. All automated judgments are supplemented with human-readable evidence (transcript highlights, failing test cases, design checklist gaps) to make feedback actionable. HireIQ also embeds fairness-aware model training and operational privacy controls (consented video capture, encryption, minimal retention) to reduce bias and protect candidates. This document describes HireIQ's architecture, algorithms, evaluation strategy (including data collection, human rater alignment, ablation and fairness studies), sample report templates, and a staged deployment plan. The platform's goals are to (1) accelerate measurable improvement in candidate readiness, (2) provide interpretable, reproducible feedback comparable to expert evaluation, and (3) scale to institutions and training programs while protecting candidate privacy and fairness.
IJCRT's Publication Details
Unique Identification Number - IJCRTBH02020
Paper ID - 295194
Author type - Indian Author
Page Number(s) - 91-96
Pubished in - Volume 13 | Issue 10 | October 2025
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No Of Downloads - 41
Author Country - India, -, -, -, -, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Prof. Shivaji Vasekar,Mr. Devashya Patil,Mr. Gaurav Pawar,Mr. Pavan Dekate,Mr. Shravan Bobade, "HireIQ - AI Integrated Web Platform for End-to-End Tech Interview Preparation", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 10, pp.91-96, October 2025, Available at :http://www.ijcrt.org/papers/IJCRTBH02020.pdf
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The International Journal of Creative Research Thoughts (IJCRT) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies.
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