Leveraging VR and Fine-Tuned Language Models for UAE Dialect Interview Training: The TQDR Case Study

Document Type

Conference Proceeding

Source of Publication

Lecture Notes in Networks and Systems

Publication Date

1-2-2026

Abstract

The increasing number of job seekers in the UAE faces major challenges during interviews, demanding effective preparation methods. This study introduces TQDAR, an AI and VR-based game designed to enhance interview skills and reduce stress among UAE graduates. Using a case study approach, we evaluated participants’ experiences through pre- and post-training surveys. Results demonstrated an important reduction in interview-related stress levels, with a mean score decrease from 2.1 to 1.3 (p-value = 0.00004, t-value = − 5.7275), indicating the effectiveness of TQDAR in enhancing user confidence. These findings suggest that TQDAR system produced consistent improvements, emphasizing its role in preparing users for real-life interview scenarios.

ISBN

[9789819503773]

ISSN

2367-3370

Publisher

Springer Nature Singapore

Volume

1566 LNNS

First Page

429

Last Page

440

Disciplines

Computer Sciences | Linguistics

Keywords

Fine-tuning, Interview training, Large language models, UAE dialect, Virtual reality

Scopus ID

105027934038

Indexed in Scopus

yes

Open Access

no

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