AI-Driven Systems for Education and Recruitment: Technologies, Applications, and Emerging Trends
An IEEE conference survey examining artificial intelligence across programming education, résumé analysis, mock interviews, and placement prediction, with emphasis on comparative machine-learning methods, explainability, and responsible deployment.
Auteurs et Affiliation Académique
Lieu de Publication
2026 2nd International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE)
Location: Ballari, India
Publisher / Host: IEEE
Full paper published in 2026 IEEE AIKIIE and indexed in IEEE Xplore. Under publisher licensing agreements, redistribution of the final publisher PDF is restricted. Readers are directed to the official IEEE Xplore document record.
Résumé Publié
Artificial Intelligence (AI) has rapidly transformed both educational environments and modern recruitment practices, enabling data-driven decision making across the education-to-employment pipeline. This survey reviews 19 representative studies published between 2020 and 2025 spanning four major domains: AI-based mock interview systems, Natural Language Processing (NLP) for resume parsing, machine learning-based placement prediction, and intelligent programming education platforms. Comparative analysis indicates that ensemble learning approaches, particularly Random Forest, Decision Trees, and XGBoost, consistently demonstrate strong predictive performance in placement analytics, while transformer-based NLP and Named Entity Recognition (NER) substantially improve resume information extraction. Recent developments further highlight the emergence of Large Language Models (LLMs) as intelligent programming assistants capable of providing personalized tutoring, adaptive feedback, and interactive learning support. Despite these advances, challenges related to fairness, explainability, scalability, and ethical deployment remain significant. By critically comparing existing methodologies, identifying research gaps, and discussing emerging trends, this survey provides a roadmap for the development of integrated AI-driven ecosystems supporting education, skill development, and intelligent recruitment.
- Machine Learning
- Natural Language Processing
- Resume Parsing
- Placement Prediction
- Mock Interview Systems
- Programming Education
- Named Entity Recognition
- Random Forest
- Educational Technology
- Explainable AI
Contributions et Résultats Clés
- ◈Comparative evaluation of 19 peer-reviewed studies across 4 critical junctures in the education-to-employment lifecycle.
- ◈Algorithm performance synthesis demonstrating Random Forest and XGBoost dominance in placement prediction accuracy.
- ◈Analysis of transformer-based Named Entity Recognition (NER) models for structured resume information extraction.
- ◈Examination of multimodal feedback pipelines (synchronized voice, speech confidence, facial cues) in automated interview preparation.
- ◈Roadmap for addressing algorithmic fairness, demographic bias, explainability, and student privacy in educational AI.
Architecture Système et Taxonomie de Décision
Domain 1: AI-Based Mock Interview Systems
- Multimodal feedback: Evaluates synchronized video, speech acoustics, and sentiment metrics.
- NLP response evaluation: Transformer-based semantic coherence and keyword relevance scoring.
- Latency constraints: Trade-offs between real-time edge processing and high-accuracy server-side evaluation.
Domain 2: Resume Parsing & Information Extraction
- Information categories: 6 structured zones extracted (Contact, Education, Experience, Skills, Projects, Languages).
- NER models: Transition from regex heuristics to fine-tuned transformer token classification.
- Fairness risks: Identifying and mitigating latent demographic proxies in uncurated training corpora.
Domain 3: Placement Prediction Analytics
- Ensemble methods: Random Forest consistently outperformed standalone Decision Trees and SVMs.
- Feature sets: Academic transcripts, technical certifications, coding project portfolios, and internship tenure.
- Early intervention: Enabling institutions to provide proactive career counseling prior to placement seasons.
Domain 4: Intelligent Programming Education
- Adaptive tutoring: Integration of LLMs as real-time interactive debugging and syntax assistants.
- Augmented modalities: Cloud-based virtual IDEs and OCR-assisted handwritten code digitization.
- Cognitive load: Balancing automated guidance with independent student problem-solving skills.
Citation Académique
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A. Kapoor, A. Muqeet, B. S. Rajput, P. Mohan, and D. Masoodi, "AI-Driven Systems for Education and Recruitment: Technologies, Applications, and Emerging Trends," in 2026 2nd International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), Ballari, India, Aug. 2026. doi: 10.1109/AIKIIE69997.2026.11708130.