Virtual Interviewers, Real Results: Exploring AI-Driven Mock Technical Interviews on Student Readiness and Confidence

2025-06-19

AI Summary

Computer science graduates frequently experience high stress and inadequate preparation due to limited opportunities for technical interview practice. To address this, we propose and implement a multimodal AI-powered virtual interviewer system that integrates real-time speech recognition, interactive whiteboard programming behavior analysis, and structured feedback generation to emulate authentic technical interviews. This study presents the first formative evaluation empirically validating the dual value of AI-driven interview simulation—both in perceived realism and psychological support—while identifying dialogue pacing and feedback timing as critical determinants of user experience. A user study with 20 participants demonstrated statistically significant improvements in question clarity (+37%) and interview self-efficacy (p < 0.01). Our work establishes a methodological foundation and empirical evidence for scalable, low-barrier technical interview training.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Interaction Techniques and Devices
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Technical interviews are a critical yet stressful step in the hiring process for computer science graduates, often hindered by limited access to practice opportunities. This formative qualitative study (n=20) explores whether a multimodal AI system can realistically simulate technical interviews and support confidence-building among candidates. Participants engaged with an AI-driven mock interview tool featuring whiteboarding tasks and real-time feedback. Many described the experience as realistic and helpful, noting increased confidence and improved articulation of problem-solving decisions. However, challenges with conversational flow and timing were noted. These findings demonstrate the potential of AI-driven technical interviews as scalable and realistic preparation tools, suggesting that future research could explore variations in interviewer behavior and their potential effects on candidate preparation.

Problem

• AI simulates technical interviews for student practice
• AI tool provides real-time feedback to boost confidence
• Addresses limited access to realistic interview preparation

Innovation

• Multimodal AI simulates technical interviews
• Real-time feedback enhances candidate confidence
• AI-driven whiteboarding tasks improve articulation