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ARTIFICIAL INTELLIGENCE SUPPORTED CAREER PLATFORM MODEL: A PROPOSAL FOR ADAPTIVE DEVELOPMENT IN COMPANIES AND TALENTS

Yıl 2025, Cilt: 7 Sayı: 2, 209 - 230, 31.07.2025

Fatih Bildirici , Tunç Durmuş Medeni , İhsan Tolga Medeni , Demet Soylu , İbrahim Edib Kökdemir

Öz

This study addresses the development of an Artificial Intelligence-based human resources platform that effectively bridges the gap between job seekers and employers. Through analysing the skills and abilities of job seekers with modern artificial intelligence algorithms such as deep learning and natural language processing, the platform recommends the most suitable candidates for the specific requirements of employers. It also provides personalised learning and development opportunities in line with the competencies required by employers to support the career development of users. This system, which evaluates job adverts and applications in a multidimensional way, uses a scoring mechanism and aims to increase the process satisfaction of both job seekers and employers by providing the most appropriate matches. The architecture, functioning mechanisms, algorithmic models and contributions to the user experience of the developed platform are analysed in detail in this study; at the same time, the advantages that this technology brings to human resources processes such as operational efficiency, impartiality and speed in decision-making processes are emphasised. Through these findings, the study reveals the transformative impact of AI-supported HR platforms on the sector and serves as a strategic guide for future applications. The implementation is also expected to meet the needs of both the private sector and public administration.

Anahtar Kelimeler

Artificial Intelligence Human Resource Career Platform Learning Module Machine Learning

Kaynakça

  • Abdeldayem, M. M., & Aldulaimi, S. H. (2020). Trends and opportunities of artificial intelligence in human resource management: Aspirations for public sector in Bahrain. International journal of scientific and technology research, 9(1), 3867-3871.
  • Allal-Chérif, O., Aránega, A. Y., & Sánchez, R. C. (2021). Intelligent recruitment: How to identify, select, and retain talents from around the world using artificial intelligence. Technological Forecasting and Social Change, 169, 120822.
  • Atiku, S. O., & Obagbuwa, I. C. (2021). Machine learning classification techniques for detecting the impact of human resources outcomes on commercial banks performance. Applied Computational Intelligence and Soft Computing, 2021(1), 7747907.
  • Bignami, F. (2022). Artificial intelligence accountability of public administration. The American Journal of Comparative Law, 70(Supplement_1), i312-i346.
  • Cerny, T., Abdelfattah, A. S., Bushong, V., Al Maruf, A., & Taibi, D. (2022, August).
  • Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264-75278.
  • Dogan, M. E., Goru Dogan, T., & Bozkurt, A. (2023). The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies. Applied Sciences, 13(5), 3056.
  • Fotache, M., & Cogean, D. (2013). NoSQL and SQL Databases for Mobile Applications. Case Study: MongoDB versus PostgreSQL. Informatica Economica, 17(2). Gackenheimer, C. (2015). Introduction to React. Apress. Gligorea, I., Cioca, M., Oancea, R., Gorski, A. T., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences, 13(12), 1216. Guo, S., Alamudun, F., & Hammond, T. (2016). RésuMatcher: A personalized résumé-job matching system. Expert Systems with Applications, 60, 169-182.
  • Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Huang, S., Brooks, M., ... & Asadi, H. (2019). Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. American Journal of Roentgenology, 212(1), 38-43.
  • Jadhav, S. D., & Channe, H. P. (2016). Comparative study of K-NN, naive Bayes and decision tree classification techniques. International Journal of Science and Research (IJSR), 5(1), 1842-1845.
  • Jain, D. S. (2018). Human resource management and artificial intelligence. International Journal of Management and Social Sciences Research, 7(3), 56-59.
  • Jia, Q., Guo, Y., Li, R., Li, Y., & Chen, Y. (2018). A conceptual artificial intelligence application framework in human resource management.
  • Johnson, B. A., Coggburn, J. D., & Llorens, J. J. (2022). Artificial intelligence and public human resource management: Questions for research and practice. Public Personnel Management, 51(4), 538-562.
  • Kang, M., & Tian, J. (2018). Machine Learning: Data Pre‐processing. Prognostics and health management of electronics: fundamentals, machine learning, and the internet of things, 111-130.
  • Keppeler, F. (2024). No thanks, dear AI! Understanding the effects of disclosure and deployment of artificial intelligence in public sector recruitment. Journal of Public Administration Research and Theory, 34(1), 39-52.
  • Khatri, S., Pandey, D. K., Penkar, D., & Ramani, J. (2020). Impact of artificial intelligence on human resources. In Data Management, Analytics and Innovation: Proceedings of ICDMAI 2019, Volume 2 (pp. 365-376). Springer Singapore.
  • Llorens, J. J. (2021). Rapid advances in HRM technologies and public employment systems: A research agenda for acquiring and managing talent. Handbook of public administration, 272-281.
  • Makris, A., Tserpes, K., Spiliopoulos, G., & Anagnostopoulos, D. (2019, March). Performance Evaluation of MongoDB and PostgreSQL for Spatio-temporal Data. In EDBT/ICDT Workshops. Microservice architecture reconstruction and visualization techniques: A review. In 2022 IEEE International Conference on Service-Oriented System Engineering (SOSE) (pp. 39-48). IEEE.
  • Nagpal, A., & Gabrani, G. (2019, February). Python for data analytics, scientific and technical applications. In 2019 Amity international conference on artificial intelligence (AICAI) (pp. 140-145). IEEE.
  • Pavitra, K. H., & Agnihotri, A. (2023, August). Artificial Intelligence in Corporate Learning and Development: Current Trends and Future Possibilities. In 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon) (pp. 688-693). IEEE.
  • Plantinga, P. (2024). Digital discretion and public administration in Africa: Implications for the se of artificial intelligence. Information Development, 40(2), 332-352.
  • Pouliakas, K. (2021). Artificial intelligence and job automation: an EU analysis using online job vacancy data. Publications Office of the European Union.
  • Rodney, H., Valaskova, K., & Durana, P. (2019). The artificial intelligence recruitment process: How technological advancements have reshaped job application and selection practices. Psychosociological Issues in Human Resource Management, 7(1), 42-47.
  • Saad, M. F. M., Nugro, A. W. L., Thinakaran, R., & Baijed, M. (2021, December). A review of artificial intelligence based platform in human resource recruitment process. In 2021 6th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) (Vol. 6, pp. 1-5). IEEE.
  • Silaparasetty, N. (2020). Machine learning concepts with python and the jupyter notebook environment: Using tensorflow 2.0. Berkeley, CA: Apress.
  • Syed, B. (2014). Beginning Node. js. Apress.
  • Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15-42.
  • TTemelkovska, I. (2024). STRATEGY FOR THE USE OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT IN THE PUBLIC SECTOR. KNOWLEDGE-International Journal, 67(1), 99-103.
  • TÜBİTAK. (2023). İnsan Kaynakları İçin İnsan Merkezli Yapay Zeka Projesi Çalıştayı Gerçekleştirildi. TÜBİTAK TÜSSİDE. [Erişim Tarihi: 26.02.2025] https://tusside.tubitak.gov.tr/insan-kaynaklari-icin-insan-merkezli-yapay-zeka-projesi-calistayi-gerceklestirildi/
  • Vishwakarma, L. P., & Singh, R. K. (2023). An analysis of the challenges to human resource in implementing artificial intelligence. In The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part B (pp. 81-109). Emerald Publishing Limited
  • Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. The international journal of human resource management, 33(6), 1237-1266.
  • Wexler, J. (2019). Get Programming with Node. js. Simon and Schuster.
  • Wicaksono, F., Aziz, Y. M. A., Ariesmansyah, A., & Arifin, R. K. (2025). Artificial Intelligence in Public Administration: Practice and Ethics for Talent Management in Public Sector. KnE Social Sciences, 10(4), 216-225
  • Yalcin, A., Kaw, A., & Clark, R. (2023). On learning platform metrics as markers for student success in a course. Computer Applications in Engineering Education, 31(5), 1412-1432.
  • Yawalkar, M. V. V. (2019). A study of artificial intelligence and its role in human resource management. International Journal of Research and Analytical Reviews (IJRAR), 6(1), 20-24.
  • Zhu, W. (2020, April). Reconstruction of human resource management under big data and artificial intelligence. In Journal of Physics: Conference Series (Vol. 1533, No. 4, p. 042016). IOP Publishing.

Yapay Zeka Destekli Kariyer Platformu Modeli: Şirketler ve Yeteneklerde Eşleşme ve Gelişim İçin Bir Sistem Önerisi

Yıl 2025, Cilt: 7 Sayı: 2, 209 - 230, 31.07.2025

Fatih Bildirici , Tunç Durmuş Medeni , İhsan Tolga Medeni , Demet Soylu , İbrahim Edib Kökdemir

Öz

Bu çalışma, iş arayanlar ve işverenler arasında etkin bir köprü oluşturan, yapay zeka tabanlı bir insan kaynakları platformunun geliştirilmesini ele almaktadır. Platform, iş arayanların yeteneklerini ve becerilerini derin öğrenme ve doğal dil işleme gibi modern yapay zeka algoritmalarıyla analiz ederek, işverenlerin belirlediği spesifik gereksinimlere en uygun adayları önermektedir. Ayrıca, kullanıcıların kariyer gelişimini desteklemek amacıyla, işverenlerin ihtiyaç duyduğu yetkinlikler doğrultusunda kişiselleştirilmiş öğrenme ve gelişim fırsatları sunmaktadır. Sistem, iş ilanları ve başvuruları çok boyutlu bir şekilde değerlendirerek bir puanlama mekanizması kullanmakta ve en uygun eşleşmeleri sağlayarak hem iş arayanların hem de işverenlerin süreç memnuniyetini artırmayı hedeflemektedir. Çalışmada, geliştirilen platformun mimarisi, işleyiş mekanizmaları, algoritmik modelleri ve kullanıcı deneyimine katkıları detaylı bir şekilde incelenmiş; aynı zamanda bu teknolojinin insan kaynakları süreçlerine kazandırdığı operasyonel verimlilik, tarafsızlık ve karar verme süreçlerindeki hız gibi avantajlar vurgulanmıştır. Elde edilen bulgular, yapay zeka destekli İK platformlarının sektör üzerindeki dönüştürücü etkisini göstermekte ve gelecekteki uygulamalara yönelik stratejik bir rehber niteliği taşımaktadır. Ayrıca uygulamanın hem özel sektör hem de kamu yönetimi alanında ihtiyaçlara cevap verebileceği öngörülmektedir.

Anahtar Kelimeler

Yapay Zeka İnsan Kaynakları Kariyer Platformu Eğitim Modülü Makine Öğrenimi

Kaynakça

  • Abdeldayem, M. M., & Aldulaimi, S. H. (2020). Trends and opportunities of artificial intelligence in human resource management: Aspirations for public sector in Bahrain. International journal of scientific and technology research, 9(1), 3867-3871.
  • Allal-Chérif, O., Aránega, A. Y., & Sánchez, R. C. (2021). Intelligent recruitment: How to identify, select, and retain talents from around the world using artificial intelligence. Technological Forecasting and Social Change, 169, 120822.
  • Atiku, S. O., & Obagbuwa, I. C. (2021). Machine learning classification techniques for detecting the impact of human resources outcomes on commercial banks performance. Applied Computational Intelligence and Soft Computing, 2021(1), 7747907.
  • Bignami, F. (2022). Artificial intelligence accountability of public administration. The American Journal of Comparative Law, 70(Supplement_1), i312-i346.
  • Cerny, T., Abdelfattah, A. S., Bushong, V., Al Maruf, A., & Taibi, D. (2022, August).
  • Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264-75278.
  • Dogan, M. E., Goru Dogan, T., & Bozkurt, A. (2023). The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies. Applied Sciences, 13(5), 3056.
  • Fotache, M., & Cogean, D. (2013). NoSQL and SQL Databases for Mobile Applications. Case Study: MongoDB versus PostgreSQL. Informatica Economica, 17(2). Gackenheimer, C. (2015). Introduction to React. Apress. Gligorea, I., Cioca, M., Oancea, R., Gorski, A. T., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences, 13(12), 1216. Guo, S., Alamudun, F., & Hammond, T. (2016). RésuMatcher: A personalized résumé-job matching system. Expert Systems with Applications, 60, 169-182.
  • Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Huang, S., Brooks, M., ... & Asadi, H. (2019). Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. American Journal of Roentgenology, 212(1), 38-43.
  • Jadhav, S. D., & Channe, H. P. (2016). Comparative study of K-NN, naive Bayes and decision tree classification techniques. International Journal of Science and Research (IJSR), 5(1), 1842-1845.
  • Jain, D. S. (2018). Human resource management and artificial intelligence. International Journal of Management and Social Sciences Research, 7(3), 56-59.
  • Jia, Q., Guo, Y., Li, R., Li, Y., & Chen, Y. (2018). A conceptual artificial intelligence application framework in human resource management.
  • Johnson, B. A., Coggburn, J. D., & Llorens, J. J. (2022). Artificial intelligence and public human resource management: Questions for research and practice. Public Personnel Management, 51(4), 538-562.
  • Kang, M., & Tian, J. (2018). Machine Learning: Data Pre‐processing. Prognostics and health management of electronics: fundamentals, machine learning, and the internet of things, 111-130.
  • Keppeler, F. (2024). No thanks, dear AI! Understanding the effects of disclosure and deployment of artificial intelligence in public sector recruitment. Journal of Public Administration Research and Theory, 34(1), 39-52.
  • Khatri, S., Pandey, D. K., Penkar, D., & Ramani, J. (2020). Impact of artificial intelligence on human resources. In Data Management, Analytics and Innovation: Proceedings of ICDMAI 2019, Volume 2 (pp. 365-376). Springer Singapore.
  • Llorens, J. J. (2021). Rapid advances in HRM technologies and public employment systems: A research agenda for acquiring and managing talent. Handbook of public administration, 272-281.
  • Makris, A., Tserpes, K., Spiliopoulos, G., & Anagnostopoulos, D. (2019, March). Performance Evaluation of MongoDB and PostgreSQL for Spatio-temporal Data. In EDBT/ICDT Workshops. Microservice architecture reconstruction and visualization techniques: A review. In 2022 IEEE International Conference on Service-Oriented System Engineering (SOSE) (pp. 39-48). IEEE.
  • Nagpal, A., & Gabrani, G. (2019, February). Python for data analytics, scientific and technical applications. In 2019 Amity international conference on artificial intelligence (AICAI) (pp. 140-145). IEEE.
  • Pavitra, K. H., & Agnihotri, A. (2023, August). Artificial Intelligence in Corporate Learning and Development: Current Trends and Future Possibilities. In 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon) (pp. 688-693). IEEE.
  • Plantinga, P. (2024). Digital discretion and public administration in Africa: Implications for the se of artificial intelligence. Information Development, 40(2), 332-352.
  • Pouliakas, K. (2021). Artificial intelligence and job automation: an EU analysis using online job vacancy data. Publications Office of the European Union.
  • Rodney, H., Valaskova, K., & Durana, P. (2019). The artificial intelligence recruitment process: How technological advancements have reshaped job application and selection practices. Psychosociological Issues in Human Resource Management, 7(1), 42-47.
  • Saad, M. F. M., Nugro, A. W. L., Thinakaran, R., & Baijed, M. (2021, December). A review of artificial intelligence based platform in human resource recruitment process. In 2021 6th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) (Vol. 6, pp. 1-5). IEEE.
  • Silaparasetty, N. (2020). Machine learning concepts with python and the jupyter notebook environment: Using tensorflow 2.0. Berkeley, CA: Apress.
  • Syed, B. (2014). Beginning Node. js. Apress.
  • Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15-42.
  • TTemelkovska, I. (2024). STRATEGY FOR THE USE OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT IN THE PUBLIC SECTOR. KNOWLEDGE-International Journal, 67(1), 99-103.
  • TÜBİTAK. (2023). İnsan Kaynakları İçin İnsan Merkezli Yapay Zeka Projesi Çalıştayı Gerçekleştirildi. TÜBİTAK TÜSSİDE. [Erişim Tarihi: 26.02.2025] https://tusside.tubitak.gov.tr/insan-kaynaklari-icin-insan-merkezli-yapay-zeka-projesi-calistayi-gerceklestirildi/
  • Vishwakarma, L. P., & Singh, R. K. (2023). An analysis of the challenges to human resource in implementing artificial intelligence. In The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part B (pp. 81-109). Emerald Publishing Limited
  • Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. The international journal of human resource management, 33(6), 1237-1266.
  • Wexler, J. (2019). Get Programming with Node. js. Simon and Schuster.
  • Wicaksono, F., Aziz, Y. M. A., Ariesmansyah, A., & Arifin, R. K. (2025). Artificial Intelligence in Public Administration: Practice and Ethics for Talent Management in Public Sector. KnE Social Sciences, 10(4), 216-225
  • Yalcin, A., Kaw, A., & Clark, R. (2023). On learning platform metrics as markers for student success in a course. Computer Applications in Engineering Education, 31(5), 1412-1432.
  • Yawalkar, M. V. V. (2019). A study of artificial intelligence and its role in human resource management. International Journal of Research and Analytical Reviews (IJRAR), 6(1), 20-24.
  • Zhu, W. (2020, April). Reconstruction of human resource management under big data and artificial intelligence. In Journal of Physics: Conference Series (Vol. 1533, No. 4, p. 042016). IOP Publishing.
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgi Sistemleri (Diğer), Makine Öğrenme (Diğer), Yapay Zeka (Diğer)
Bölüm Makaleler
Yazarlar

Fatih Bildirici 0000-0002-1730-4268

Tunç Durmuş Medeni 0000-0002-2964-3320

İhsan Tolga Medeni 0000-0002-0642-7908

Demet Soylu 0000-0002-2005-6875

İbrahim Edib Kökdemir

Yayımlanma Tarihi 31 Temmuz 2025
Gönderilme Tarihi 3 Ocak 2025
Kabul Tarihi 3 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 7 Sayı: 2

Kaynak Göster

APA Bildirici, F., Medeni, T. D., Medeni, İ. T., Soylu, D., vd. (2025). ARTIFICIAL INTELLIGENCE SUPPORTED CAREER PLATFORM MODEL: A PROPOSAL FOR ADAPTIVE DEVELOPMENT IN COMPANIES AND TALENTS. Kamu Yönetimi Ve Teknoloji Dergisi, 7(2), 209-230.
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