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Prompt Engineering Awareness: A Study on Google Trends Data

Yıl 2024, Cilt: 5 Sayı: 2, 248 - 268, 31.12.2024
https://doi.org/10.62001/gsijses.1532474

Öz

Human intelligence learns by identifying events in its surroundings through the five senses. In contrast, artificial intelligence learns by analysing data and knowledge. Today, the rapid advancement in generative artificial intelligence necessitates the collaboration of humans and artificial intelligence. This collaboration has given rise to hybrid intelligence, which combines human and artificial intelligence capabilities. For hybrid intelligence to be effectively developed and to ensure efficient collaboration between humans and artificial intelligence, appropriate inputs must be provided to artificial intelligence. The discipline that addresses this process is known as prompt engineering. In this context, this study aims to evaluate and compare the awareness of the prompt engineering discipline among the Organization of Turkic States and G7 member countries. Awareness was measured using Google Trends data. The study concluded that while the member countries of the Organization of Turkic States and G7 countries exhibit a high level of awareness regarding artificial intelligence, the member countries of the Organization of Turkic States, except Türkiye, have a lower awareness of prompt engineering than G7 countries.

Kaynakça

  • Akbıyık, A. & Coşkun, E. (2013). Eğitsel sosyal yazılımların kabul ve kullanımına yönelik bir model. Online Academic Journal of Information Technology. 4(13), 39-62.
  • Akça, Y. & Özer, G. (2012). Teknoloji kabul model’inin kurumsal kaynak planlaması uygulamalarında kullanılması. Business and Economics Research Journal, 3(2), 79-96.
  • Ali, Mohammed. ‘The Human Intelligence vs. Artificial Intelligence: Issues and Challenges in Computer Assisted Language Learning’. International Journal of English Linguistics, vol. 8, no. 5, 2018, pp. 259–71, doi:10.5539/ijel.v8n5p259.
  • Audrin, Catherine, and Bertrand Audrin. ‘More than Just Emotional Intelligence Online: Introducing “Digital Emotional Intelligence”’. Frontiers in Psychology, vol. 14, 2023, pp. 1–12, doi:10.3389/fpsyg.2023.1154355.
  • Behnert, Jan, et al. ‘Can We Predict Multi-Party Elections with Google Trends Data? Evidence across Elections, Data Windows, and Model Classes’. Journal of Big Data, vol. 11, no. 30, 2024, pp. 1–22, doi:10.1186/s40537-023-00868-4.
  • Cain, W. ‘Prompting Change: Exploring Prompt Engineering in Large Language Model AI and Its Potential to Transform Education’. TECHTRENDS, vol. 68, no. 1, 2024, pp. 47–57, doi:10.1007/s11528-023-00896-0.
  • Cannataro, Mario, et al. ‘Artificial Intelligence’. Artificial Intelligence in Bioinformatics, Jan. 2022, pp. 29–33, doi:10.1016/B978-0-12-822952-1.00012-7.
  • Dai, Yun, et al. ‘Reconceptualising ChatGPT and Generative AI as a Student-Driven Innovation in Higher Education’. Procedia CIRP, vol. 119, Jan. 2023, pp. 84–90, doi:10.1016/J.PROCIR.2023.05.002.
  • Ertürk, Merve. ‘Google Trendlere Göre Uzaktan Çalışmaya Yönelik Halk İlgisi: Covid-19 Pandemisi Öncesi ve Pandemi Dönemi’. Süleyman Demirel University Journal of Human Resources Management, vol. 1, no. 2, 2022, pp. 1–14, https://orcid.org/0000-0002-6622-0204.
  • Feng, Y. C. J., et al. ‘PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation’. IEEE TRANSACTIONS ON VISUALISATION AND COMPUTER GRAPHICS, vol. 30, no. 1, 2024, pp. 295–305, doi:10.1109/TVCG.2023.3327168.
  • Giray, L. ‘Prompt Engineering with ChatGPT: A Guide for Academic Writers’. ANNALS OF BIOMEDICAL ENGINEERING, vol. 51, no. 12, 2023, pp. 2629–33, doi:10.1007/s10439-023-03272-4.
  • Gülbaşı, A., & Karahan, F. (2023). Finansal Sistemde Bilgi Teknolojileri ve Kullanımı. Uluslararası Sosyal Ve Ekonomik Çalışmalar Dergisi, 4(2), 296-319. https://doi.org/10.62001/gsijses.1393072
  • Google Trends. Basics of Google Trends - Google News Initiative. 2023, https://newsinitiative.withgoogle.com/resources/trainings/basics- of-google-trends/.
  • Havelsan. ‘Main GPT’. Linkedin, https://www.linkedin.com/posts/havelsan_maingpt-t%C3%BCrkiyeyapayzekas%C4%B1-verig%C3%BCvenli%C4%9Fi- activity-7153066408334049280-mXrv/?originalSubdomain=tr. (Accessed 07.02.2024)
  • Henrickson, L., and A. Merono-Penuela. ‘Prompting Meaning: A Hermeneutic Approach to Optimising Prompt Engineering with ChatGPT’. AI & SOCIETY, 2023, doi:10.1007/s00146-023-01752-8.
  • Johnson, T. L., et al. ‘How and Why We Need to Capture Tacit Knowledge in Manufacturing: Case Studies of Visual Inspection’. Applied Ergonomics, vol. 74, Jan. 2019, pp. 1–9, doi:10.1016/j.apergo.2018.07.016.
  • Korzynski, P., et al. ‘Artificial Intelligence Prompt Engineering as a New Digital Competence: Analysis of Generative AI Technologies Such as ChatGPT’. ENTREPRENEURIAL BUSINESS AND ECONOMICS REVIEW, vol. 11, no. 3, 2023, pp. 25–37, doi:10.15678/EBER.2023.110302.
  • Külcü, Recep. ‘Ortaçağ Anadolu’sunun Büyük Mühendisi El-Cezeri The Great Engineer of the Medieval Anatolia: Al-Jazari’. Academia Journal of Engineering and Applied Sciences, vol. 1, 2015, pp. 1–9.
  • Liu, Zhenghao, and Xi Zeng. ‘Hybrid Intelligence in Big Data Environment: Concepts, Architectures, and Applications of Intelligent Service’. Data and Information Management, vol. 5, no. 2, 2021, doi:10.2478/dim-2020-0051.
  • Maurseth, Per Botolf, and Roger Svensson. ‘The Importance of Tacit Knowledge: Dynamic Inventor Activity in the Commercialization Phase’. Research Policy, vol. 49, no. 7, Sept. 2020, pp. 1–12, doi:10.1016/j.respol.2020.104012.
  • Meskó, B. ‘Prompt Engineering as an Important Emerging Skill for Medical Professionals: Tutorial’. JOURNAL OF MEDICAL INTERNET RESEARCH, vol. 25, 2023, pp. 1–6, doi:10.2196/50638.
  • Park, D., et al. ‘A Study on Performance Improvement of Prompt Engineering for Generative AI with a Large Language Model’. JOURNAL OF WEB ENGINEERING, vol. 22, no. 8, 2023, pp. 1187–206, doi:10.13052/jwe1540-9589.2285.
  • Pescetelli, Niccolo. ‘A Brief Taxonomy of Hybrid Intelligence’. Forecasting, vol. 3, no. 3, 2021, pp. 633–43, doi:10.3390/forecast3030039.
  • Prentice, Catherine, et al. ‘Emotional Intelligence or Artificial Intelligence– an Employee Perspective’. Journal of Hospitality Marketing and Management, vol. 29, no. 4, 2020, pp. 377–403, doi:10.1080/19368623.2019.1647124.
  • Salovey, Peter, and John D. Mayer. ‘Emotional Intelligence’. Imagination, Cognition and Personality, vol. 9, no. 3, Mar. 1990, pp. 185–211, doi:10.2190/DUGG-P24E-52WK-6CDG.
  • Shin, Sunny Y., et al. ‘Cultural Affinity and International Trade in Motion Pictures: Empirical Evidence Using Categorised Internet Search Activity’. Economic Modelling, vol. 136, July 2024, pp. 1–8, doi:10.1016/J.ECONMOD.2024.106732.
  • Trad, F., and A. Chehab. ‘Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models’. MACHINE LEARNING AND KNOWLEDGE EXTRACTION, vol. 6, no. 1, 2024, pp. 367–84, doi:10.3390/make6010018.
  • Türkiye Gazetesi Ansiklopedi Grubu. Müslüman Bilim Adamları 1. İhlas Gazetecilik A.Ş. İstanbul, 2005.
  • Venerito, V., et al. ‘Prompt Engineering: The next Big Skill in Rheumatology Research’. INTERNATIONAL JOURNAL OF RHEUMATIC DISEASES, vol. 27, no. 5, 2024, pp. 1–6, doi:10.1111/1756-185X.15157.
  • Walter, Yoshija. ‘Embracing the Future of Artificial Intelligence in the Classroom: The Relevance of AI Literacy, Prompt Engineering, and Critical Thinking in Modern Education’. Walter Int J Educ Technol High Educ, vol. 21, no. 15, 2024, pp. 1–29, doi:10.1186/s41239-024-00448-3.
  • Yeke, Selcuk. ‘Digital Intelligence as a Partner of Emotional Intelligence in Business Administration’. Asia Pacific Management Review, vol. 28, no. 4, Dec. 2023, pp. 390–400, doi:10.1016/J.APMRV.2023.01.001.
  • Zhang, Chao, et al. ‘A Deep Learning-Enabled Human-Cyber-Physical Fusion Method towards Human-Robot Collaborative Assembly’. Robotics and Computer-Integrated Manufacturing, vol. 83, Oct. 2023, pp. 1–13, doi:10.1016/j.rcim.2023.102571.

Sufle Mühendisliği Farkındalığı: Google Trends Verileri Üzerine Bir Çalışma

Yıl 2024, Cilt: 5 Sayı: 2, 248 - 268, 31.12.2024
https://doi.org/10.62001/gsijses.1532474

Öz

İnsan zekâsı, beş duyu aracılığıyla çevresindeki olayları tanımlayarak öğrenir. Buna karşılık, yapay zekâ veri ve bilgiyi analiz ederek öğrenir. Günümüzde, üretken yapay zekâ alanındaki hızlı ilerleme, insan ve yapay zekanın birlikte çalışmasını gerektirmektedir. Bu iş birliği, insan ve yapay zekâ yeteneklerini birleştiren hibrit zeka kavramını ortaya çıkarmıştır. Hibrit zekânın etkin bir şekilde geliştirilebilmesi ve insan ile yapay zekâ arasında verimli bir iş birliğinin sağlanabilmesi için yapay zekâya uygun girdilerin sağlanması gerekmektedir. Bu süreci ele alan disiplin ise prompt mühendisliği olarak bilinmektedir. Bu bağlamda, bu çalışma Türk Devletleri Örgütü ve G7 üyesi ülkeler arasında prompt mühendisliği disiplininin farkındalığını değerlendirmeyi ve karşılaştırmayı amaçlamaktadır. Farkındalık, Google Trends verileri kullanılarak ölçülmüştür. Çalışma sonucunda, Türk Devletleri Teşkilatı üye ülkeleri ve G7 ülkeleri yapay zekâ konusunda yüksek düzeyde farkındalık sergilerken, Türk Devletleri Teşkilatı üye ülkelerinin, Türkiye hariç, G7 ülkelerine kıyasla prompt mühendisliği konusunda daha düşük bir farkındalığa sahiptir.

Kaynakça

  • Akbıyık, A. & Coşkun, E. (2013). Eğitsel sosyal yazılımların kabul ve kullanımına yönelik bir model. Online Academic Journal of Information Technology. 4(13), 39-62.
  • Akça, Y. & Özer, G. (2012). Teknoloji kabul model’inin kurumsal kaynak planlaması uygulamalarında kullanılması. Business and Economics Research Journal, 3(2), 79-96.
  • Ali, Mohammed. ‘The Human Intelligence vs. Artificial Intelligence: Issues and Challenges in Computer Assisted Language Learning’. International Journal of English Linguistics, vol. 8, no. 5, 2018, pp. 259–71, doi:10.5539/ijel.v8n5p259.
  • Audrin, Catherine, and Bertrand Audrin. ‘More than Just Emotional Intelligence Online: Introducing “Digital Emotional Intelligence”’. Frontiers in Psychology, vol. 14, 2023, pp. 1–12, doi:10.3389/fpsyg.2023.1154355.
  • Behnert, Jan, et al. ‘Can We Predict Multi-Party Elections with Google Trends Data? Evidence across Elections, Data Windows, and Model Classes’. Journal of Big Data, vol. 11, no. 30, 2024, pp. 1–22, doi:10.1186/s40537-023-00868-4.
  • Cain, W. ‘Prompting Change: Exploring Prompt Engineering in Large Language Model AI and Its Potential to Transform Education’. TECHTRENDS, vol. 68, no. 1, 2024, pp. 47–57, doi:10.1007/s11528-023-00896-0.
  • Cannataro, Mario, et al. ‘Artificial Intelligence’. Artificial Intelligence in Bioinformatics, Jan. 2022, pp. 29–33, doi:10.1016/B978-0-12-822952-1.00012-7.
  • Dai, Yun, et al. ‘Reconceptualising ChatGPT and Generative AI as a Student-Driven Innovation in Higher Education’. Procedia CIRP, vol. 119, Jan. 2023, pp. 84–90, doi:10.1016/J.PROCIR.2023.05.002.
  • Ertürk, Merve. ‘Google Trendlere Göre Uzaktan Çalışmaya Yönelik Halk İlgisi: Covid-19 Pandemisi Öncesi ve Pandemi Dönemi’. Süleyman Demirel University Journal of Human Resources Management, vol. 1, no. 2, 2022, pp. 1–14, https://orcid.org/0000-0002-6622-0204.
  • Feng, Y. C. J., et al. ‘PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation’. IEEE TRANSACTIONS ON VISUALISATION AND COMPUTER GRAPHICS, vol. 30, no. 1, 2024, pp. 295–305, doi:10.1109/TVCG.2023.3327168.
  • Giray, L. ‘Prompt Engineering with ChatGPT: A Guide for Academic Writers’. ANNALS OF BIOMEDICAL ENGINEERING, vol. 51, no. 12, 2023, pp. 2629–33, doi:10.1007/s10439-023-03272-4.
  • Gülbaşı, A., & Karahan, F. (2023). Finansal Sistemde Bilgi Teknolojileri ve Kullanımı. Uluslararası Sosyal Ve Ekonomik Çalışmalar Dergisi, 4(2), 296-319. https://doi.org/10.62001/gsijses.1393072
  • Google Trends. Basics of Google Trends - Google News Initiative. 2023, https://newsinitiative.withgoogle.com/resources/trainings/basics- of-google-trends/.
  • Havelsan. ‘Main GPT’. Linkedin, https://www.linkedin.com/posts/havelsan_maingpt-t%C3%BCrkiyeyapayzekas%C4%B1-verig%C3%BCvenli%C4%9Fi- activity-7153066408334049280-mXrv/?originalSubdomain=tr. (Accessed 07.02.2024)
  • Henrickson, L., and A. Merono-Penuela. ‘Prompting Meaning: A Hermeneutic Approach to Optimising Prompt Engineering with ChatGPT’. AI & SOCIETY, 2023, doi:10.1007/s00146-023-01752-8.
  • Johnson, T. L., et al. ‘How and Why We Need to Capture Tacit Knowledge in Manufacturing: Case Studies of Visual Inspection’. Applied Ergonomics, vol. 74, Jan. 2019, pp. 1–9, doi:10.1016/j.apergo.2018.07.016.
  • Korzynski, P., et al. ‘Artificial Intelligence Prompt Engineering as a New Digital Competence: Analysis of Generative AI Technologies Such as ChatGPT’. ENTREPRENEURIAL BUSINESS AND ECONOMICS REVIEW, vol. 11, no. 3, 2023, pp. 25–37, doi:10.15678/EBER.2023.110302.
  • Külcü, Recep. ‘Ortaçağ Anadolu’sunun Büyük Mühendisi El-Cezeri The Great Engineer of the Medieval Anatolia: Al-Jazari’. Academia Journal of Engineering and Applied Sciences, vol. 1, 2015, pp. 1–9.
  • Liu, Zhenghao, and Xi Zeng. ‘Hybrid Intelligence in Big Data Environment: Concepts, Architectures, and Applications of Intelligent Service’. Data and Information Management, vol. 5, no. 2, 2021, doi:10.2478/dim-2020-0051.
  • Maurseth, Per Botolf, and Roger Svensson. ‘The Importance of Tacit Knowledge: Dynamic Inventor Activity in the Commercialization Phase’. Research Policy, vol. 49, no. 7, Sept. 2020, pp. 1–12, doi:10.1016/j.respol.2020.104012.
  • Meskó, B. ‘Prompt Engineering as an Important Emerging Skill for Medical Professionals: Tutorial’. JOURNAL OF MEDICAL INTERNET RESEARCH, vol. 25, 2023, pp. 1–6, doi:10.2196/50638.
  • Park, D., et al. ‘A Study on Performance Improvement of Prompt Engineering for Generative AI with a Large Language Model’. JOURNAL OF WEB ENGINEERING, vol. 22, no. 8, 2023, pp. 1187–206, doi:10.13052/jwe1540-9589.2285.
  • Pescetelli, Niccolo. ‘A Brief Taxonomy of Hybrid Intelligence’. Forecasting, vol. 3, no. 3, 2021, pp. 633–43, doi:10.3390/forecast3030039.
  • Prentice, Catherine, et al. ‘Emotional Intelligence or Artificial Intelligence– an Employee Perspective’. Journal of Hospitality Marketing and Management, vol. 29, no. 4, 2020, pp. 377–403, doi:10.1080/19368623.2019.1647124.
  • Salovey, Peter, and John D. Mayer. ‘Emotional Intelligence’. Imagination, Cognition and Personality, vol. 9, no. 3, Mar. 1990, pp. 185–211, doi:10.2190/DUGG-P24E-52WK-6CDG.
  • Shin, Sunny Y., et al. ‘Cultural Affinity and International Trade in Motion Pictures: Empirical Evidence Using Categorised Internet Search Activity’. Economic Modelling, vol. 136, July 2024, pp. 1–8, doi:10.1016/J.ECONMOD.2024.106732.
  • Trad, F., and A. Chehab. ‘Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models’. MACHINE LEARNING AND KNOWLEDGE EXTRACTION, vol. 6, no. 1, 2024, pp. 367–84, doi:10.3390/make6010018.
  • Türkiye Gazetesi Ansiklopedi Grubu. Müslüman Bilim Adamları 1. İhlas Gazetecilik A.Ş. İstanbul, 2005.
  • Venerito, V., et al. ‘Prompt Engineering: The next Big Skill in Rheumatology Research’. INTERNATIONAL JOURNAL OF RHEUMATIC DISEASES, vol. 27, no. 5, 2024, pp. 1–6, doi:10.1111/1756-185X.15157.
  • Walter, Yoshija. ‘Embracing the Future of Artificial Intelligence in the Classroom: The Relevance of AI Literacy, Prompt Engineering, and Critical Thinking in Modern Education’. Walter Int J Educ Technol High Educ, vol. 21, no. 15, 2024, pp. 1–29, doi:10.1186/s41239-024-00448-3.
  • Yeke, Selcuk. ‘Digital Intelligence as a Partner of Emotional Intelligence in Business Administration’. Asia Pacific Management Review, vol. 28, no. 4, Dec. 2023, pp. 390–400, doi:10.1016/J.APMRV.2023.01.001.
  • Zhang, Chao, et al. ‘A Deep Learning-Enabled Human-Cyber-Physical Fusion Method towards Human-Robot Collaborative Assembly’. Robotics and Computer-Integrated Manufacturing, vol. 83, Oct. 2023, pp. 1–13, doi:10.1016/j.rcim.2023.102571.
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Araştırma Makaleleri
Yazarlar

İsmail Yoşumaz 0000-0002-2287-4994

Yayımlanma Tarihi 31 Aralık 2024
Gönderilme Tarihi 13 Ağustos 2024
Kabul Tarihi 12 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 5 Sayı: 2

Kaynak Göster

APA Yoşumaz, İ. (2024). Prompt Engineering Awareness: A Study on Google Trends Data. Uluslararası Sosyal Ve Ekonomik Çalışmalar Dergisi, 5(2), 248-268. https://doi.org/10.62001/gsijses.1532474