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<p>This study presents a novel AI-based framework that leverages Instagram image and metadata analysis to infer Big Five personality traits and deliver personalized career recommendations for high school students in the UAE. Addressing the limitations of traditional recommender systems that rely on self-reported questionnaires or text, the proposed approach uses multimodal visual features—including profile metrics, HSV color patterns, semantic image labels, and texture analysis—to enable a non-intrusive, scalable personalization method. A pilot study involving data from 30 student accounts served as a proof of concept. Correlation analysis identified profile and HSV features as the most predictive, and four machine learning models were trained, with Logistic Regression achieving 97% accuracy (AUC 0.97) in personality prediction. The inferred traits were mapped to academic majors using a stereotype-based recommender system, achieving 90% alignment with student preferences as measured by the Electronic Emirati Scale for Professional Inclinations (EESPI). Findings demonstrate the feasibility and promise of integrating AI-driven image analysis with personality-aware recommendation. This work contributes to emerging trends in non-verbal, visual data-based personalization, particularly in educational and career guidance domains.</p>

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Image and metadata-driven personality inference for career recommendation: a social media-based AI framework for adolescents

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月20日

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  • Heba Ismail,
  • Maryam Alhefeiti,
  • Ashraf Khalil

摘要

This study presents a novel AI-based framework that leverages Instagram image and metadata analysis to infer Big Five personality traits and deliver personalized career recommendations for high school students in the UAE. Addressing the limitations of traditional recommender systems that rely on self-reported questionnaires or text, the proposed approach uses multimodal visual features—including profile metrics, HSV color patterns, semantic image labels, and texture analysis—to enable a non-intrusive, scalable personalization method. A pilot study involving data from 30 student accounts served as a proof of concept. Correlation analysis identified profile and HSV features as the most predictive, and four machine learning models were trained, with Logistic Regression achieving 97% accuracy (AUC 0.97) in personality prediction. The inferred traits were mapped to academic majors using a stereotype-based recommender system, achieving 90% alignment with student preferences as measured by the Electronic Emirati Scale for Professional Inclinations (EESPI). Findings demonstrate the feasibility and promise of integrating AI-driven image analysis with personality-aware recommendation. This work contributes to emerging trends in non-verbal, visual data-based personalization, particularly in educational and career guidance domains.