Holland’s Vocational Interest Theory (RIASEC) and Artificial Intelligence Research: A Systematic Review

Abstract

Çalışmanın amacı, Holland'ın Mesleki İlgi Teorisi (RIASEC) ile yapay zekâ (YZ) ve makine öğrenmesi (MÖ) tekniklerinin kesişimindeki akademik araştırmaları sistematik olarak incelemek ve alandaki araştırma boşluğunu ortaya koymaktır. Altmış yılı aşkın süredir kariyer danışmanlığına yön veren bu kuramsal çerçevenin güncel YZ teknolojileriyle nasıl entegre edildiğine dair kapsamlı bir değerlendirme ihtiyacı, çalışmanın temel motivasyonunu oluşturmaktadır. Çalışma, PRISMA 2020 rehberine uygun bir sistematik derleme ve içerik analizi olarak yürütülmüştür. Web of Science Core Collection veritabanında üç aşamalı bir sorgu stratejisi ile gerçekleştirilen taramada herhangi bir yıl veya dil sınırlaması uygulanmamıştır. Belirlenen dahil etme ve dışlama kriterleri çerçevesinde 29 ham kayıt çok aşamalı bir eleme sürecinden geçirilmiş ve nihai olarak 13 çalışma senteze dâhil edilmiştir. Bu 13 çalışmanın tamamı 2018 yılı ve sonrasında yayımlanmıştır. Yayın sayısı 2020'de zirve yapmış, 2021-2022 yıllarında kesintiye uğramış, son üç yılda ise yeniden canlanma eğilimi göstermiştir. Kullanılan YZ/MÖ teknikleri ağırlıklı olarak Doğal Dil İşleme (NLP) ve Ağaç Tabanlı Modeller kategorilerinde yoğunlaşmaktadır. Transformer mimarileri ve büyük dil modelleri (LLM'ler) ise hiçbir çalışmada kullanılmamıştır. Sonuç olarak, Holland'ın RIASEC modeli ile yapay zekâ tekniklerinin entegrasyonu yalnızca 13 çalışmaya dayanmakta olup oldukça sınırlı bir araştırma birikimine sahiptir. Güncel YZ mimarilerinin bu çalışmalarda yer almaması, önemli bir metodolojik araştırma boşluğuna işaret etmektedir. Bu nedenle, gelecek araştırmaların RIASEC modelini büyük dil modelleri ile bütünleştiren yeni nesil kariyer danışmanlığı sistemlerine odaklanması önerilmektedir.

Keywords

• Holland'ın mesleki ilgi teorisi
• RIASEC
• yapay zekâ
• sistematik derleme
• kariyer danışmanlığı

Holland’s Vocational Interest Theory (RIASEC) and Artificial Intelligence Research: A Systematic Review

Abstract

This study aims to systematically examine the academic research at the intersection of Holland's Vocational Interest Theory (RIASEC) and artificial intelligence (AI) and machine learning (ML) techniques, and to identify the research gap in this field. The need for a comprehensive assessment of how this theoretical framework, which has guided career counseling for over six decades, is being integrated with current AI technologies constitutes the primary motivation for this study. The study was conducted as a systematic review and content analysis in accordance with the PRISMA 2020 guidelines. A three-stage query strategy was applied in the Web of Science Core Collection database without imposing any year or language restrictions. Following the application of predefined inclusion and exclusion criteria, 29 raw records were subjected to a multi-stage screening process, resulting in 13 studies being included in the final synthesis. All 13 studies were published in or after 2018. The number of publications peaked in 2020, experienced a decline in 2021-2022, and has shown a resurgence over the last three years. The AI/ML techniques used are predominantly concentrated in the categories of Natural Language Processing (NLP) and Tree-Based Models. Notably, Transformer architectures and Large Language Models (LLMs) were not used in any of the studies. The integration of Holland's RIASEC model with artificial intelligence techniques is based on only 13 studies, indicating a very limited body of research. The absence of current AI architectures in these studies points to a significant methodological research gap. Therefore, it is recommended that future research focus on next-generation career counseling systems that integrate the RIASEC model with large language models.

Keywords

• Holland's vocational interest theory
• RIASEC
• artificial intelligence
• systematic review
• career counseling

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