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Artificial Intelligence Awareness Scale Development Study

Yıl 2025, Cilt: 22 Sayı: 4, 657 - 672, 04.08.2025
https://doi.org/10.26466/opusjsr.1680531

Öz

The aim of the study is to develop a valid and reliable measurement tool to measure employees' perceptions of artificial intelligence awareness. In the first stage of the three-stage scale development study, in-depth interviews were conducted. As a result of the content analysis of the data obtained from the interviews, a 41-item proposition pool was created. In the second stage, a draft of the items was created and the scale was structured by consulting expert opinions in order to ensure meaning, face and scope validity. In the last stage, the scale was evaluated and a 14-item draft scale was created. As a result of the pilot application conducted on 132 employees working in the healthcare sector using the draft scale, it was decided to remove one item from the scale. Then, the final scale consisting of 13 items was reached: 139 employees in the automotive sector and 152 employees in the logistics sector. As a result of the analysis, a 13-item one-dimensional scale emerged. The CFA server determined that the scale provided an acceptable level of fit. Cronbach's Alpha values were 0.834 in the healthcare sector; It was calculated as 0.810 in the automotive sector and 0.867 in the logistics sector, and the scale was found to be valid and reliable.
The scale is an important measurement tool to be used to analyze individuals' awareness of AI in measuring the level of individual awareness, determining educational needs, conducting attitude analysis, developing policies and strategies, and academic research.

Kaynakça

  • Adamopoulou, E., & Moussiades, L. (2020). Chatbot systems: A survey. Computer Science Review, 34, 100204. https://doi.org/10.1016/-j.cosrev.2019.100204
  • Asan, O., & Choudhury, A. (2021). Artificial intelligence and machine learning approaches to improving risk prediction and management in healthcare: a systematic review. Journal of the American Medical Informatics Association, 28(7), 1421-1429. https://doi.org/10.1093/jamia/ocab070
  • Asatiani, A., Apte, U. M., & Penttinen, E. (2023). Generative artificial intelligence in finance: risk considerations. International Monetary Fund, 2023/006, 1-25. https://doi.org/10.5089/979-8400236622.066
  • Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J. F., & Rahwan, I. (2020). The moral machine experiment. Nature, 563(7729), 59-64.
  • Bakir, S., Dogru, T., Bilgihan, A., & Ayoun, B. (2025). AI awareness and employee-related outcomes: A systematic review of the hospitality literature and a framework for future research. International Journal of Hospitality Management, 124, 103973. https://doi.org/10.-1016/j.ijhm.2024.103973
  • Bickmore, T. W., & Marsella, S. (2021). Virtual human technologies for health care applications. Health Informatics Journal, 27(3), 211-223. https://doi.org/10.1177/1460458220937882
  • Bogue, R. (2020). Industrial robots: A review of recent developments. Industrial Robot: An International Journal, 47(4), 463-469. https://doi.org/10.1108/IR-12-2019-0213
  • Borana J., (2016), Applications of Artificial Intelligence & Associated Technologies,Proceeding of International Conference on Emerging Technologies in Engineering, Biomedical, Management and Science
  • Bostrom, N. (2021). The vulnerable world hypothesis. Global Policy, 12(1), 45-60.
  • Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239-257. https://doi.org/10.1017/jmo.2016.55
  • Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial intelligence and the 'Good Society': The US, EU, and UK Approach. Science and Engineering Ethics, 24(2), 505-528.
  • Chen, H., Zhang, Y., & Zhang, C. (2021). A review of deep learning techniques for drug discovery. Journal of Biomedical Informatics, 113, 103638. https://doi.org/10.1016/.jbi.-2021.103638
  • Chen, X., Chen, R. R., Wei, S., & Davison, R. M. (2025). Herd behavior in social commerce: understanding the interplay between self-awareness and environment-awareness. Internet Research, 35(3), 947-980. https://doi.org/10.1108/INTR-05-2022-0359
  • Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). "Two decades of Artificial Intelligence in Education: Contributors, collaborations, Research Topics, challenges, and future directions." Educational Technology and Society, 25(1), 28–47.
  • Dolgikh, S. (2024). Self-awareness in natural and artificial intelligent systems: a unified information-based approach. Evolutionary Intelligence, 17(5), 4095-4114. https://doi.org/10.1007/s12065-024-00974-z
  • Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2023). Art, Creativity, and the Potential of Artificial Intelligence. Computational Creativity, Artistic Behavior, and Tools for Creatives. Springer. https://doi.org/10.1007/978-3-031-15020-6
  • Endsley, M. R., & Jones, D. G. (2024). Situation awareness-oriented design: review and future directions. International Journal of Human–Computer Interaction, 40(7),1487-1504. https://doi.org/10.1080/10447318.2024.2318884
  • Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056
  • Ferikoğlu, D. (2021). Öğretmenler için yapay zekâ farkındalık düzeyi ölçeği: güvenilirlik ve geçerlilik çalışması (Yayımlanmamış Yüksek Lisans Tezi). Bahçeşehir Üniversitesi.
  • Floridi, L., & Cowls, J. (2020). AI Ethics: A Systematic Review of the Literature. Science and Engineering Ethics, 26(5), 2655-2683.
  • Garikapati, D., & Shetiya, S. (2024). Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape. Big Data and Cognitive Computing, 8(4), 42. https://doi.org/10.3390/bdcc8040042.
  • Gatt, A., & Krahmer, E. (2018). Survey of the state of the art in natural language generation: Core tasks, applications and evaluation. Journal of Artificial Intelligence Research, 61, 65-170. https://doi.org/10.1613/jair.1.11153
  • Ghahramani, Z. (2020). Probabilistic Machine Learning and Artificial Intelligence. Nature, 521(7553), 452-459.
  • Ghali, M. A., et al. "An Intelligent Tutoring System for Teaching English Grammar." International Journal of Academic Engineering Research, vol. 2, no. 2, 2018, pp. 1–6.
  • Haenlein, M. & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California ManagementRreview, 61(4), 5-14.
  • Hassabis, D., & Maguire, E. A. (2020). The Neuroscience-Inspired Artificial Intelligence. Neuron, 107(1), 15-19.
  • Hsu, C.-H., & Chen, Y.-S. (2021). Simulation-based optimization for manufacturing systems using artificial intelligence techniques. Computers & Industrial Engineering, 156, 107261. https://doi.org/10.1016/j.cie.2021.107261
  • Jannach, D., & Adomavicius, G. (2016). Recommender Systems: Challenges and Research Opportunities. Springer. https://doi.org/10.1007/978-3-319-29659-3
  • Jayaraman, V., & Keoleian, G. A. (2020). Predictive maintenance using artificial intelligence: A review. Journal of Quality in Maintenance Engineering, 26(3), 338-353. https://doi.org/10.1108/JQME-09-2019-0063
  • Jobin, A., Ienca, M., & Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. Nature Machine Intelligence, 1(9), 389-399.
  • Kartal E.,(2021). Machine Learning and Artificial Intelligence,Tıp Bilişimi,18.Bölüm,İstanbul Üniversitesi, Enformatik Bölümü, İstanbul, Türkiye,DOI: 10.26650/B/ET07.2021.003.18
  • Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017, 2021 Updated Edition). Building Machines That Learn and Think Like People. Behavioral and Brain Sciences, 40, e253.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2021). Deep Learning. Nature, 521(7553), 436-444.
  • Marcus, G. (2020). The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence. AI Magazine, 41(3), 35-49.
  • Mishra, A., & Li, L. (2022). AI-driven patient monitoring systems: A review. Health Information Science and Systems, 10(1), 15. https://doi.org/10.1186/s13755-022-00733-3
  • Nabiyev. V.V., (2016), Yapay Zeka:İnsan-Bilgisayar Etkileşimi, Seçkin Yayıncılık San. ve Tic.A.Ş., ISBN:978-975-02-3727-0
  • Owsley, C. S., & Greenwood, K. (2024). Awareness and perception of artificial intelligence operationalized integration in news media industry and society. Ai & Society, 39(1), 417-431. https://doi.org/10.1007/s00146-022-01386-2
  • Razzak, M. I., Naz, S., & Zaheer, R. (2018). Deep learning for medical image processing: Overview, challenges and the future. Classification in BioApps, 61-80. https://doi.org/10.1016/B978-0-12-814759-1.00004-8
  • Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson Education.
  • Saleh Z.M.(2019), Artificial Intelligence Definition, Ethics and Standards,The British Universty in Egypt, Electronics and Communications: Law, Standards and Practice | 18ELEC07I
  • Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., ... & Hassabis, D. (2020). Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. Science, 362(6419), 1140-1144.
  • Wamba, S. F., & Akter, S. (2020). How artificial intelligence improves supply chain management: A review. Journal of Supply Chain Management, 56(2), 25-40. https://doi.org/10.1111/jscm.12181
  • Wang, J., & Xiang, Z. (2020). The role of artificial intelligence in tourism: A review and research agenda. Tourism Management Perspectives, 34, 100665. https://doi.org/10.1016/j.tmp.2020.100665
  • West, S. M., Whittaker, M., & Crawford, K. (2019). Discriminating Systems: Gender, Race, and Power in AI. AI Now Institute.
  • Xu, C., Xu, X., & Wang, L. (2018). Smart manufacturing systems for Industry 4.0: A review. Journal of Manufacturing Science and Engineering, 140(8), 081007. https://doi.org/10.1115/1.4039686
  • Yang, Y., & Wei, X. (2021). Artificial intelligence applications in finance: A survey. Financial Innovation, 7(1), 31. https://doi.org/10.1186/s40854-021-00234-1
  • Yang, Y., & Zheng, X. (2020). Robotic surgery: Current status and future perspectives. Journal of Robotic Surgery, 14, 445-458. https://doi.org/10.1007/s11701-020-01123-2
  • Yudong Zhang, Saeed Balochian, Praveen Agarwal, Vishal Bhatnagar, Orwa Jaber Housheya, "Artificial Intelligence and Its Applications", Mathematical Problems in Engineering, vol. 2014, Article ID 840491, 10 pages, 2014. https://doi.org/10.1155/2014/840491
  • Zador, A. M. (2020). A Critique of Pure Learning and What Artificial Neural Networks Can Learn from Animal Brains. Nature Communications, 11(1), 1-7.
  • Zhang, L., & Liu, J. (2020). Personalized medicine in the era of artificial intelligence. Frontiers in Medicine, 7, 45. https://doi.org/10.3389/fmed.2020.00045
  • Zhang, Y., & Yang, X. (2021). Quality control in manufacturing using artificial intelligence: A review. IEEE Transactions on Industrial Informatics, 17(5), 3316-3326. https://doi.org/10.1109/TII.2020.3025282
  • Zhang, Y., & Zhao, X. (2022). Leveraging artificial intelligence for production optimization: A review. *Journal of Manufacturing Processes, 64*, 387-403. https://doi.org/10.1016/j.jmapro.2021.11.023
  • Zheng, L., & Li, S. (2021). Artificial intelligence in education: A review of recent advancements. Education and Information Technologies, 26(3), 3435-3464. https://doi.org/10.1007/s10639-020-10448-6

Yapay Zekâ Farkındalık Ölçeği Geliştirme Çalışması

Yıl 2025, Cilt: 22 Sayı: 4, 657 - 672, 04.08.2025
https://doi.org/10.26466/opusjsr.1680531

Öz

Bu çalışmada, çalışanların yapay zekâ farkındalığına ilişkin algılarını ölçmek üzere geçerli ve güvenilir bir ölçüm aracı geliştirmek amaçlanmıştır. Üç aşamalı ölçek geliştirme çalışmasının ilk aşamasında derinlemesine görüşmeler yapılmıştır. Bu görüşmelerden elde edilen verilerin içerik analizi sonucunda 41 maddelik önerme havuzu oluşturulmuştur. İkinci aşamada maddelerin taslağı oluşturulmuş ve anlam, görünüş ve kapsam geçerliliğini sağlamak amacıyla uzman görüşlerine başvurularak ölçek yapılandırılmıştır. Son aşamada ölçek değerlendirilmiş ve 14 maddelik taslak ölçek oluşturulmuştur. Taslak ölçek kullanılarak sağlık sektöründe çalışan 132 çalışan üzerinde yapılan pilot uygulama sonucunda ölçekten bir maddenin çıkarılmasına karar verilmiştir. Daha sonra 13 maddeden oluşan nihai ölçeğe ulaşılmıştır: Otomotiv sektöründe 139 çalışan ve lojistik sektöründe 152 çalışan. Yapılan analizler sonucunda 13 maddelik tek boyutlu bir ölçek ortaya çıkmıştır. CFA sunucusu ölçeğin kabul edilebilir düzeyde uyum sağladığını belirlemiştir. Cronbach's Alpha değerleri sağlık sektöründe 0,834; otomotiv sektöründe 0,810 ve lojistik sektöründe 0,867 olarak hesaplanmış olup ölçeğin geçerli ve güvenilir olduğu bulunmuştur. Ölçek, bireysel farkındalık düzeyinin ölçülmesinde, eğitim ihtiyaçlarının belirlenmesinde, tutum analizlerinin yapılmasında, politika ve stratejiler geliştirilmesinde, akademik araştırmalarda bireylerin yapay zekaya ilişkin farkındalıklarının analiz edilmesinde kullanılabilecek önemli bir ölçüm aracıdır.

Kaynakça

  • Adamopoulou, E., & Moussiades, L. (2020). Chatbot systems: A survey. Computer Science Review, 34, 100204. https://doi.org/10.1016/-j.cosrev.2019.100204
  • Asan, O., & Choudhury, A. (2021). Artificial intelligence and machine learning approaches to improving risk prediction and management in healthcare: a systematic review. Journal of the American Medical Informatics Association, 28(7), 1421-1429. https://doi.org/10.1093/jamia/ocab070
  • Asatiani, A., Apte, U. M., & Penttinen, E. (2023). Generative artificial intelligence in finance: risk considerations. International Monetary Fund, 2023/006, 1-25. https://doi.org/10.5089/979-8400236622.066
  • Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J. F., & Rahwan, I. (2020). The moral machine experiment. Nature, 563(7729), 59-64.
  • Bakir, S., Dogru, T., Bilgihan, A., & Ayoun, B. (2025). AI awareness and employee-related outcomes: A systematic review of the hospitality literature and a framework for future research. International Journal of Hospitality Management, 124, 103973. https://doi.org/10.-1016/j.ijhm.2024.103973
  • Bickmore, T. W., & Marsella, S. (2021). Virtual human technologies for health care applications. Health Informatics Journal, 27(3), 211-223. https://doi.org/10.1177/1460458220937882
  • Bogue, R. (2020). Industrial robots: A review of recent developments. Industrial Robot: An International Journal, 47(4), 463-469. https://doi.org/10.1108/IR-12-2019-0213
  • Borana J., (2016), Applications of Artificial Intelligence & Associated Technologies,Proceeding of International Conference on Emerging Technologies in Engineering, Biomedical, Management and Science
  • Bostrom, N. (2021). The vulnerable world hypothesis. Global Policy, 12(1), 45-60.
  • Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239-257. https://doi.org/10.1017/jmo.2016.55
  • Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial intelligence and the 'Good Society': The US, EU, and UK Approach. Science and Engineering Ethics, 24(2), 505-528.
  • Chen, H., Zhang, Y., & Zhang, C. (2021). A review of deep learning techniques for drug discovery. Journal of Biomedical Informatics, 113, 103638. https://doi.org/10.1016/.jbi.-2021.103638
  • Chen, X., Chen, R. R., Wei, S., & Davison, R. M. (2025). Herd behavior in social commerce: understanding the interplay between self-awareness and environment-awareness. Internet Research, 35(3), 947-980. https://doi.org/10.1108/INTR-05-2022-0359
  • Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). "Two decades of Artificial Intelligence in Education: Contributors, collaborations, Research Topics, challenges, and future directions." Educational Technology and Society, 25(1), 28–47.
  • Dolgikh, S. (2024). Self-awareness in natural and artificial intelligent systems: a unified information-based approach. Evolutionary Intelligence, 17(5), 4095-4114. https://doi.org/10.1007/s12065-024-00974-z
  • Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2023). Art, Creativity, and the Potential of Artificial Intelligence. Computational Creativity, Artistic Behavior, and Tools for Creatives. Springer. https://doi.org/10.1007/978-3-031-15020-6
  • Endsley, M. R., & Jones, D. G. (2024). Situation awareness-oriented design: review and future directions. International Journal of Human–Computer Interaction, 40(7),1487-1504. https://doi.org/10.1080/10447318.2024.2318884
  • Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056
  • Ferikoğlu, D. (2021). Öğretmenler için yapay zekâ farkındalık düzeyi ölçeği: güvenilirlik ve geçerlilik çalışması (Yayımlanmamış Yüksek Lisans Tezi). Bahçeşehir Üniversitesi.
  • Floridi, L., & Cowls, J. (2020). AI Ethics: A Systematic Review of the Literature. Science and Engineering Ethics, 26(5), 2655-2683.
  • Garikapati, D., & Shetiya, S. (2024). Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape. Big Data and Cognitive Computing, 8(4), 42. https://doi.org/10.3390/bdcc8040042.
  • Gatt, A., & Krahmer, E. (2018). Survey of the state of the art in natural language generation: Core tasks, applications and evaluation. Journal of Artificial Intelligence Research, 61, 65-170. https://doi.org/10.1613/jair.1.11153
  • Ghahramani, Z. (2020). Probabilistic Machine Learning and Artificial Intelligence. Nature, 521(7553), 452-459.
  • Ghali, M. A., et al. "An Intelligent Tutoring System for Teaching English Grammar." International Journal of Academic Engineering Research, vol. 2, no. 2, 2018, pp. 1–6.
  • Haenlein, M. & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California ManagementRreview, 61(4), 5-14.
  • Hassabis, D., & Maguire, E. A. (2020). The Neuroscience-Inspired Artificial Intelligence. Neuron, 107(1), 15-19.
  • Hsu, C.-H., & Chen, Y.-S. (2021). Simulation-based optimization for manufacturing systems using artificial intelligence techniques. Computers & Industrial Engineering, 156, 107261. https://doi.org/10.1016/j.cie.2021.107261
  • Jannach, D., & Adomavicius, G. (2016). Recommender Systems: Challenges and Research Opportunities. Springer. https://doi.org/10.1007/978-3-319-29659-3
  • Jayaraman, V., & Keoleian, G. A. (2020). Predictive maintenance using artificial intelligence: A review. Journal of Quality in Maintenance Engineering, 26(3), 338-353. https://doi.org/10.1108/JQME-09-2019-0063
  • Jobin, A., Ienca, M., & Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. Nature Machine Intelligence, 1(9), 389-399.
  • Kartal E.,(2021). Machine Learning and Artificial Intelligence,Tıp Bilişimi,18.Bölüm,İstanbul Üniversitesi, Enformatik Bölümü, İstanbul, Türkiye,DOI: 10.26650/B/ET07.2021.003.18
  • Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017, 2021 Updated Edition). Building Machines That Learn and Think Like People. Behavioral and Brain Sciences, 40, e253.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2021). Deep Learning. Nature, 521(7553), 436-444.
  • Marcus, G. (2020). The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence. AI Magazine, 41(3), 35-49.
  • Mishra, A., & Li, L. (2022). AI-driven patient monitoring systems: A review. Health Information Science and Systems, 10(1), 15. https://doi.org/10.1186/s13755-022-00733-3
  • Nabiyev. V.V., (2016), Yapay Zeka:İnsan-Bilgisayar Etkileşimi, Seçkin Yayıncılık San. ve Tic.A.Ş., ISBN:978-975-02-3727-0
  • Owsley, C. S., & Greenwood, K. (2024). Awareness and perception of artificial intelligence operationalized integration in news media industry and society. Ai & Society, 39(1), 417-431. https://doi.org/10.1007/s00146-022-01386-2
  • Razzak, M. I., Naz, S., & Zaheer, R. (2018). Deep learning for medical image processing: Overview, challenges and the future. Classification in BioApps, 61-80. https://doi.org/10.1016/B978-0-12-814759-1.00004-8
  • Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson Education.
  • Saleh Z.M.(2019), Artificial Intelligence Definition, Ethics and Standards,The British Universty in Egypt, Electronics and Communications: Law, Standards and Practice | 18ELEC07I
  • Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., ... & Hassabis, D. (2020). Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. Science, 362(6419), 1140-1144.
  • Wamba, S. F., & Akter, S. (2020). How artificial intelligence improves supply chain management: A review. Journal of Supply Chain Management, 56(2), 25-40. https://doi.org/10.1111/jscm.12181
  • Wang, J., & Xiang, Z. (2020). The role of artificial intelligence in tourism: A review and research agenda. Tourism Management Perspectives, 34, 100665. https://doi.org/10.1016/j.tmp.2020.100665
  • West, S. M., Whittaker, M., & Crawford, K. (2019). Discriminating Systems: Gender, Race, and Power in AI. AI Now Institute.
  • Xu, C., Xu, X., & Wang, L. (2018). Smart manufacturing systems for Industry 4.0: A review. Journal of Manufacturing Science and Engineering, 140(8), 081007. https://doi.org/10.1115/1.4039686
  • Yang, Y., & Wei, X. (2021). Artificial intelligence applications in finance: A survey. Financial Innovation, 7(1), 31. https://doi.org/10.1186/s40854-021-00234-1
  • Yang, Y., & Zheng, X. (2020). Robotic surgery: Current status and future perspectives. Journal of Robotic Surgery, 14, 445-458. https://doi.org/10.1007/s11701-020-01123-2
  • Yudong Zhang, Saeed Balochian, Praveen Agarwal, Vishal Bhatnagar, Orwa Jaber Housheya, "Artificial Intelligence and Its Applications", Mathematical Problems in Engineering, vol. 2014, Article ID 840491, 10 pages, 2014. https://doi.org/10.1155/2014/840491
  • Zador, A. M. (2020). A Critique of Pure Learning and What Artificial Neural Networks Can Learn from Animal Brains. Nature Communications, 11(1), 1-7.
  • Zhang, L., & Liu, J. (2020). Personalized medicine in the era of artificial intelligence. Frontiers in Medicine, 7, 45. https://doi.org/10.3389/fmed.2020.00045
  • Zhang, Y., & Yang, X. (2021). Quality control in manufacturing using artificial intelligence: A review. IEEE Transactions on Industrial Informatics, 17(5), 3316-3326. https://doi.org/10.1109/TII.2020.3025282
  • Zhang, Y., & Zhao, X. (2022). Leveraging artificial intelligence for production optimization: A review. *Journal of Manufacturing Processes, 64*, 387-403. https://doi.org/10.1016/j.jmapro.2021.11.023
  • Zheng, L., & Li, S. (2021). Artificial intelligence in education: A review of recent advancements. Education and Information Technologies, 26(3), 3435-3464. https://doi.org/10.1007/s10639-020-10448-6
Toplam 53 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ampirik Yazılım Mühendisliği
Bölüm Research Articles
Yazarlar

Şükrü Mustafa Kaya 0000-0003-2710-0063

Vildan Bayram 0000-0001-7526-6485

Erken Görünüm Tarihi 27 Temmuz 2025
Yayımlanma Tarihi 4 Ağustos 2025
Gönderilme Tarihi 20 Nisan 2025
Kabul Tarihi 23 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 22 Sayı: 4

Kaynak Göster

APA Kaya, Ş. M., & Bayram, V. (2025). Artificial Intelligence Awareness Scale Development Study. OPUS Journal of Society Research, 22(4), 657-672. https://doi.org/10.26466/opusjsr.1680531