Employability assessment using multimodal deep learning framework
Year: 2026
Type: article
Abstract: The integration of artificial intelligence into talent acquisition has accelerated the development of multimodal frameworks for employability assessment, offering greater accuracy, scalability, and objectivity. In this paper, we propose a novel Multimodal Deep Learning Framework that unifies textual resumes, video interviews, and audio responses into a single, interpretable decision system. Our approach leverages state-of-the-art neural architectures, including Transformer-based models for textual analysis, wav2vec 2.0 embeddings for speech, and three-dimensional facial expression modeling with EfficientNet and OpenFace. A dynamic attention-driven fusion module adaptively balances contributions from different modalities, while built-in explainability mechanisms support transparent and fair decision-making. We evaluate the framework on a rigorously curated multimodal dataset annotated by HR professionals. Results demonstrate that our method outperforms unimodal and hybrid baselines, achieving a 14% improvement in F1-score and 90% top-1 accuracy in employability prediction. Importantly, integrated bias mitigation techniques reduce gender- and ethnicity-related disparities by more than 25%, underscoring the framework’s potential for fair, responsible, and practical AI deployment in modern talent analytics.
Source: Discover Artificial Intelligence
Author: Fengjin Zhou
Institutions: Nanyang Institute of Technology, East China Normal University
Language: English
Topic: AI and HR Technologies
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7135043067