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

Document Type

Article

Source of Publication

Discover Artificial Intelligence

Publication Date

12-1-2026

Abstract

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.

ISSN

2731-0809

Publisher

Springer Science and Business Media LLC

Volume

6

Issue

1

Disciplines

Computer Sciences

Keywords

Big five, Career recommendations, Image recognition, Machine learning, Personality analysis, Recommender system, Social media mining

Scopus ID

105035055073

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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