Araştırma Makalesi
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Üniversite Öğrencilerinin Kariyer Planlamasında Yapay Zeka Okuryazarlığının Etkisi: Ampirik Bir Araştırma

Yıl 2026, Cilt: 28 Sayı: 1 , 203 - 224 , 20.04.2026
https://doi.org/10.26745/ahbvuibfd.1792241
https://izlik.org/JA46EN94WX

Öz

Hayatın neredeyse her alanında karşımıza çıkan yapay zeka konusu iş hayatı başta olmak üzere tartışılan konuların başında yer almaktadır. İçerisinde bulunduğumuz yapay zeka çağında insanlar bu çağın gerektirdiği yetkinlikleri bilmek durumundadırlar. Bu kapsamda, çalışmada, yapay zeka okuryazarlığının kariyer planlaması üzerindeki etkisinin incelenmesi amaçlanmaktadır. Bolu Abant İzzet Baysal Üniversitesindeki 361 öğrenciden anket yöntemiyle elde edilen veriler SPSS 25 ve AMOS programlarıyla Yapısal Eşitlik Modeli-Yol Diyagramı ile analiz edilmiştir. Bulgulara göre, yapay zeka okuryazarlığı boyutu olan farkındalık ve kullanım, kariyer planlaması boyutları olan kariyer farkındalığı, mesleki farkındalık, kariyere yönelik inanç, seçimin doğruluğu ve eğitimin yeterliliğini etkilememektedir. Yapay zeka okuryazarlığının değerlendirme boyutu, mesleki farkındalığı, kariyere yönelik inancı, seçimin doğruluğunu negatif şekilde etkilerken, kariyer farkındalığı ve eğitimin yeterliliğini ise etkilememektedir. Yapay zeka okuryazarlığının etik boyutu mesleki farkındalığı, kariyere yönelik inancı, seçimin doğruluğunu ve eğitimin yeterliliğini pozitif ve anlamlı şekilde etkilerken, kariyer farkındalığını etkilememektedir. Kariyer planlamalarında, yapay zeka okuryazarlığının üniversite öğrencilerinin bilinçli hareket etmelerine önemli düzeyde etki edeceği düşünülmektedir.

Kaynakça

  • Arslan, K. (2020). Eğitimde yapay zekâ ve uygulamaları. Batı Anadolu Eğitim Bilimleri Dergisi, 11(1), 71–88. https://izlik.org/JA76ZX87TN.
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
  • Belahouaoui, R., & Attak, E. H. (2024). Digital taxation, artificial intelligence and tax administration 3.0: Improving tax compliance behavior – A systematic literature review using textometry (2016–2023). Accounting Research Journal, 37(2), 172–191. https://doi.org/10.1108/ARJ-12-2023-0372.
  • Belshaw, D. (2012). The essential elements of digital literacies. Self-Published.
  • Brown, T. A. (2015). Confirmatory factor analysis for applied research (2th Edition). New York: Guilford Press.
  • Chen, X., Chiu, T. K. F., et al. (2023). GenAI and chatbots in education: Impact on student engagement and performance. Frontiers in Education, 8, 1578451. https://doi.org/10.3389/feduc.2023.1578451.
  • Eroglu, S. Y., & Eroğlu, E. (2020). Career Planning Scale of Students Studied in Sports Sciences (CPS): Validity and Reliability Study. International Journal of Progressive Education, 16 (3), 123–131. https://doi.org/10.29329/ijpe.2020.248.9.
  • George, D., & Mallery, P. (2010). SPSS for Windows step by step: A simple guide and reference (10th ed.). Pearson.
  • Gürbüz, S., & Şahin, F. (2018). Sosyal bilimlerde araştırma yöntemleri: Felsefe–yöntem–analiz (5. baskı). Seçkin Yayıncılık. ISBN 9789750251276.
  • Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2014). Multivariate data analysis. (7th Edition). Harlow: Pearson New International Edition.
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
  • Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: guidelines for determining model fit. The Electronic Journal of Business Research Methods, (6), 53-60. https://doi.org/10.21427/D7CF7R.
  • Kizilcec, R. F. (2024). Educators’ acceptance of AI-driven technologies: Factors and framework. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-023-00351-4.
  • Kline, R. B. (2010). Principles and practice of structural equation modeling (2th Edition). New York: The Guilford Press.
  • Köken, R. F. (2025). Antrenörlerin yapay zekâ okuryazarlığının kariyer kararlılığına etkisine yönelik bir araştırma [Yüksek lisans tezi, Pamukkale Üniversitesi].
  • Krumboltz, J. D. (2009). The happenstance learning theory. Journal of Career Assessment, 17(2), 135–154. https://doi.org/10.1177/1069072708328861.
  • Lei, H., Le, P. B. & Nguyen, H. T. H. (2017). How collaborative culture supports for competitive advantage: the mediating role of organizational learning. International Journal of Business Administration, 8(2), 73. https://doi.org/10.5430/ijba.v8n2p73.
  • Liang, J., Stephens, R., & Brown, M. (2025). Artificial intelligence in higher education: A systematic review. Frontiers in Education, 10, 1522841. https://doi.org/10.3389/feduc.2025.1522841.
  • Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL IOE Press.
  • Marsh, H. W., Hau, K. T., Artelt, C., Baumert, J. & Peschar, J. L. (2006). OECD’s brief self-report measure of educational psychology’s most useful affective constructs: cross- cultural, psychometric comparisons across 25 countries. International Journal of Testing, 6(4), 311–360. https://doi.org/10.1207/s15327574ijt0604_1.
  • Mustafa, A., Rahman, M., & Chen, J. (2024). Artificial intelligence for education: A meta-synthesis. Smart Learning Environments, 11(1), 10. https://doi.org/10.1186/s40561-024-00350-5.
  • Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078. https://doi.org/10.1016/j.compedu.2012.04.016.
  • Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric theory (3rd Edition). New York: McGraw-Hill Inc.
  • Ocen, E., Namagembe, S., & Kintu, M. J. (2025). Bibliometric and content analysis of AI research in education. Discover Education, 3, Article 21. https://doi.org/10.1007/s43621-025-01086-z.
  • OECD. (2021). AI and the future of skills: Learning for life. OECD Publishing. https://www.oecd.org/publications/ai-and-the-future-of-skills-5f4b4c38-en.htm
  • Özer, T., & Eker, H. S. (2025). Yapay Zekâ Okuryazarlığının Kişisel Başarı ve Kariyer Kararlılığına Etkisinin Araştırılması: Lise Öğrencileri Örneği. Bilgisayar Bilimleri ve Mühendisliği Dergisi, (Advanced Online Publication), 133-144. https://doi.org/10.54525/bbmd.1763200.
  • Polatgil, M., & Güler, A. (2023). Yapay zekâ okuryazarlığı ölçeğinin geliştirilmesi ve geçerlik–güvenirlik çalışması. Sosyal Bilimlerde Nicel Araştırmalar Dergisi, 3(2), 99–114. https://sobinarder.com/index.php/sbd/article/view/65.
  • Rezaei, H., Saeed, A. F. M., Abdi, K., Ebadi, A., Gheshlagh, R. G. & Kurdi, A. (2020). Translation and validation of the farsi version of the pain management self-efficacy questionnaire. Journal of Pain Research, 13, 719. https://doi.org/10.2147/JPR.S246077.
  • Savickas, M. L. (2005). The theory and practice of career construction. In S. D. Brown & R. W. Lent (Eds.), Career development and counseling: Putting theory and research to work (pp. 42–70). John Wiley & Sons.
  • Tabachnick, B. G. & Fidell, L. S. (2007). Using multivariate statistics (5th Edition). MA, Boston: Pearson Education.
  • UNESCO. (2023). Guidance for generative AI in education and research. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000386283.
  • Wang, B., Rau, P.-L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324-1337. https://doi.org/10.1080/0144929X.2022.2072768.
  • Wei L., Zhou S., Hu S., Zhou Z. & Chen J, (2021). “Influences of nursing students’ career planning, ınternship experience, and other factors on professional identity”, Nurse Educ. Today, 99, 2-7. https://doi.org/10.1016/j.nedt.2021.104781.
  • Yetişensoy, O., & Rapoport, A. (2023). Artificial ıntelligence literacy teaching in social studies education. Journal of Pedagogical Research, 7(3), 100-110. https://doi.org/10.33902/JPR.202320866.
  • Zhai, X., Wibowo, R., & Li, M. (2024). Cognitive effects of AI dependency in students: A longitudinal study. International Journal of Educational Technology in Higher Education, 21(1), 10. https://doi.org/10.1186/s41239-023-00436-z.

Understanding the Impact of Artificial Intelligence Literacy on University Students Career Planning: An Empirical Study

Yıl 2026, Cilt: 28 Sayı: 1 , 203 - 224 , 20.04.2026
https://doi.org/10.26745/ahbvuibfd.1792241
https://izlik.org/JA46EN94WX

Öz

The subject of artificial intelligence, which we encounter in almost every aspect of life, is one of the most discussed topics, especially in business life. In the age of artificial intelligence we are in, people have to know the competencies required by this age. In this status, the study aims to examine the impact of artificial intelligence literacy on career planning. Data obtained from 361 students at Bolu Abant İzzet Baysal University aim survey method were analyzed with Structural Equation Model-Path Diagram using SPSS 25- AMOS programs. According to the findings, awareness and usage, which are the size of artificial intelligence literacy, don’t affect the career planning dimensions of career awareness, vocational awareness, belief in career, accuracy of selection and adequacy of education. While the evaluation dimension of artificial intelligence literacy negatively affects professional awareness, career belief, and the accuracy of selection, it doesn’t affect career awareness and adequacy of education. While the ethical dimension of artificial intelligence literacy positively and significantly affects professional awareness, belief in career, accuracy of selection and adequacy of education, it doesn’t affect career awareness. It is thought that artificial intelligence literacy will have significant impact on university students' conscious actions in their career planning.

Kaynakça

  • Arslan, K. (2020). Eğitimde yapay zekâ ve uygulamaları. Batı Anadolu Eğitim Bilimleri Dergisi, 11(1), 71–88. https://izlik.org/JA76ZX87TN.
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
  • Belahouaoui, R., & Attak, E. H. (2024). Digital taxation, artificial intelligence and tax administration 3.0: Improving tax compliance behavior – A systematic literature review using textometry (2016–2023). Accounting Research Journal, 37(2), 172–191. https://doi.org/10.1108/ARJ-12-2023-0372.
  • Belshaw, D. (2012). The essential elements of digital literacies. Self-Published.
  • Brown, T. A. (2015). Confirmatory factor analysis for applied research (2th Edition). New York: Guilford Press.
  • Chen, X., Chiu, T. K. F., et al. (2023). GenAI and chatbots in education: Impact on student engagement and performance. Frontiers in Education, 8, 1578451. https://doi.org/10.3389/feduc.2023.1578451.
  • Eroglu, S. Y., & Eroğlu, E. (2020). Career Planning Scale of Students Studied in Sports Sciences (CPS): Validity and Reliability Study. International Journal of Progressive Education, 16 (3), 123–131. https://doi.org/10.29329/ijpe.2020.248.9.
  • George, D., & Mallery, P. (2010). SPSS for Windows step by step: A simple guide and reference (10th ed.). Pearson.
  • Gürbüz, S., & Şahin, F. (2018). Sosyal bilimlerde araştırma yöntemleri: Felsefe–yöntem–analiz (5. baskı). Seçkin Yayıncılık. ISBN 9789750251276.
  • Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2014). Multivariate data analysis. (7th Edition). Harlow: Pearson New International Edition.
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
  • Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: guidelines for determining model fit. The Electronic Journal of Business Research Methods, (6), 53-60. https://doi.org/10.21427/D7CF7R.
  • Kizilcec, R. F. (2024). Educators’ acceptance of AI-driven technologies: Factors and framework. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-023-00351-4.
  • Kline, R. B. (2010). Principles and practice of structural equation modeling (2th Edition). New York: The Guilford Press.
  • Köken, R. F. (2025). Antrenörlerin yapay zekâ okuryazarlığının kariyer kararlılığına etkisine yönelik bir araştırma [Yüksek lisans tezi, Pamukkale Üniversitesi].
  • Krumboltz, J. D. (2009). The happenstance learning theory. Journal of Career Assessment, 17(2), 135–154. https://doi.org/10.1177/1069072708328861.
  • Lei, H., Le, P. B. & Nguyen, H. T. H. (2017). How collaborative culture supports for competitive advantage: the mediating role of organizational learning. International Journal of Business Administration, 8(2), 73. https://doi.org/10.5430/ijba.v8n2p73.
  • Liang, J., Stephens, R., & Brown, M. (2025). Artificial intelligence in higher education: A systematic review. Frontiers in Education, 10, 1522841. https://doi.org/10.3389/feduc.2025.1522841.
  • Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL IOE Press.
  • Marsh, H. W., Hau, K. T., Artelt, C., Baumert, J. & Peschar, J. L. (2006). OECD’s brief self-report measure of educational psychology’s most useful affective constructs: cross- cultural, psychometric comparisons across 25 countries. International Journal of Testing, 6(4), 311–360. https://doi.org/10.1207/s15327574ijt0604_1.
  • Mustafa, A., Rahman, M., & Chen, J. (2024). Artificial intelligence for education: A meta-synthesis. Smart Learning Environments, 11(1), 10. https://doi.org/10.1186/s40561-024-00350-5.
  • Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078. https://doi.org/10.1016/j.compedu.2012.04.016.
  • Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric theory (3rd Edition). New York: McGraw-Hill Inc.
  • Ocen, E., Namagembe, S., & Kintu, M. J. (2025). Bibliometric and content analysis of AI research in education. Discover Education, 3, Article 21. https://doi.org/10.1007/s43621-025-01086-z.
  • OECD. (2021). AI and the future of skills: Learning for life. OECD Publishing. https://www.oecd.org/publications/ai-and-the-future-of-skills-5f4b4c38-en.htm
  • Özer, T., & Eker, H. S. (2025). Yapay Zekâ Okuryazarlığının Kişisel Başarı ve Kariyer Kararlılığına Etkisinin Araştırılması: Lise Öğrencileri Örneği. Bilgisayar Bilimleri ve Mühendisliği Dergisi, (Advanced Online Publication), 133-144. https://doi.org/10.54525/bbmd.1763200.
  • Polatgil, M., & Güler, A. (2023). Yapay zekâ okuryazarlığı ölçeğinin geliştirilmesi ve geçerlik–güvenirlik çalışması. Sosyal Bilimlerde Nicel Araştırmalar Dergisi, 3(2), 99–114. https://sobinarder.com/index.php/sbd/article/view/65.
  • Rezaei, H., Saeed, A. F. M., Abdi, K., Ebadi, A., Gheshlagh, R. G. & Kurdi, A. (2020). Translation and validation of the farsi version of the pain management self-efficacy questionnaire. Journal of Pain Research, 13, 719. https://doi.org/10.2147/JPR.S246077.
  • Savickas, M. L. (2005). The theory and practice of career construction. In S. D. Brown & R. W. Lent (Eds.), Career development and counseling: Putting theory and research to work (pp. 42–70). John Wiley & Sons.
  • Tabachnick, B. G. & Fidell, L. S. (2007). Using multivariate statistics (5th Edition). MA, Boston: Pearson Education.
  • UNESCO. (2023). Guidance for generative AI in education and research. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000386283.
  • Wang, B., Rau, P.-L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324-1337. https://doi.org/10.1080/0144929X.2022.2072768.
  • Wei L., Zhou S., Hu S., Zhou Z. & Chen J, (2021). “Influences of nursing students’ career planning, ınternship experience, and other factors on professional identity”, Nurse Educ. Today, 99, 2-7. https://doi.org/10.1016/j.nedt.2021.104781.
  • Yetişensoy, O., & Rapoport, A. (2023). Artificial ıntelligence literacy teaching in social studies education. Journal of Pedagogical Research, 7(3), 100-110. https://doi.org/10.33902/JPR.202320866.
  • Zhai, X., Wibowo, R., & Li, M. (2024). Cognitive effects of AI dependency in students: A longitudinal study. International Journal of Educational Technology in Higher Education, 21(1), 10. https://doi.org/10.1186/s41239-023-00436-z.
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yönetim Bilişim Sistemleri, Çalışma Ekonomisi ve Endüstri İlişkileri, İşletme
Bölüm Araştırma Makalesi
Yazarlar

Esra Yılmaz 0000-0001-9028-7145

Elif Çetin 0000-0002-8051-0152

Gönderilme Tarihi 27 Eylül 2025
Kabul Tarihi 10 Nisan 2026
Yayımlanma Tarihi 20 Nisan 2026
DOI https://doi.org/10.26745/ahbvuibfd.1792241
IZ https://izlik.org/JA46EN94WX
Yayımlandığı Sayı Yıl 2026 Cilt: 28 Sayı: 1

Kaynak Göster

APA Yılmaz, E., & Çetin, E. (2026). Understanding the Impact of Artificial Intelligence Literacy on University Students Career Planning: An Empirical Study. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 28(1), 203-224. https://doi.org/10.26745/ahbvuibfd.1792241