Areas of use and generational differences in artificial intelligence

Öz

Artificial intelligence (AI) is playing an increasingly important role in social and economic systems, especially in education, research, information processing and digital interactions. The aim of this paper is to investigate the use of AI-based solutions along generational dimensions, with a particular focus on the different usage frequencies and preferences of different age groups. Ten different application domains were analysed, including language learning, research and data collection, health and fitness advice, travel management, information summarisation, homework writing, literature search, translation tasks, role-playing and essay writing. The study used a five-point Likert scale to measure the intensity of AI use, allowing statistical analysis of differences between generations. The results indicate that language learning, research and data collection, and searching for literary sources are the areas where the use of AI solutions is most frequent. In contrast, role-playing and essay writing were found to be less commonly used. Generational differences are clearly visible: while Generation Z is the most intensive user of AI in all the areas studied, Generations X and Y use these technologies mainly for professional and research purposes. Our results show that the prevalence and functional diversity of AI use is closely related to generational characteristics. The study contributes to a deeper understanding of the embeddedness of AI in society and provides support for education policy makers and technology developers to design targeted applications and strategic developments

Anahtar Kelimeler

• artificial intelligence
• areas of AI use
• generational differences

Yapay zekanın kullanım alanları ve nesiller arası farklar

Öz

Yapay zeka (AI), sosyal ve ekonomik sistemlerde, özellikle eğitim, araştırma, bilgi işleme ve dijital etkileşimlerde giderek daha önemli bir rol oynamaktadır. Bu makalenin amacı, farklı yaş gruplarının farklı kullanım sıklıkları ve tercihlerine odaklanarak, nesil boyutları boyunca AI tabanlı çözümlerin kullanımını araştırmaktır. Dil öğrenimi, araştırma ve veri toplama, sağlık ve fitness danışmanlığı, seyahat yönetimi, bilgi özetleme, ödev yazma, literatür araştırması, çeviri görevleri, rol oynama ve kompozisyon yazma dahil olmak üzere on farklı uygulama alanı analiz edilmiştir. Çalışmada, AI kullanımının yoğunluğunu ölçmek için beş puanlık Likert ölçeği kullanılmış ve nesiller arasındaki farkların istatistiksel analizi yapılmıştır. Sonuçlar, dil öğrenimi, araştırma ve veri toplama ile edebi kaynak arama alanlarının AI çözümlerinin en sık kullanıldığı alanlar olduğunu göstermektedir. Buna karşılık, rol yapma ve kompozisyon yazma daha az kullanıldığı tespit edilmiştir. Kuşak farklılıkları açıkça görülmektedir: Z kuşağı, incelenen tüm alanlarda AI'yı en yoğun şekilde kullanırken, X ve Y kuşakları bu teknolojileri ağırlıklı olarak mesleki ve araştırma amaçlı kullanmaktadır. Sonuçlarımız, AI kullanımının yaygınlığı ve işlevsel çeşitliliğinin nesil özellikleriyle yakından ilişkili olduğunu göstermektedir. Çalışma, AI'nın topluma yerleşikliğinin daha iyi anlaşılmasına katkıda bulunmakta ve eğitim politika yapıcıları ile teknoloji geliştiricilerin hedefli uygulamalar ve stratejik gelişmeler tasarlamasına destek sağlamaktadır.

Anahtar Kelimeler

• yapay zeka
• yapay zekanın kullanım alanları
• nesil farkları

Kaynakça

1. Balogh, A., & Varga, J. (2024). Superapps: At the crossroads of enhanced customer experience and innovation management theories. Edelweiss Applied Science and Technology, 8(4), 848–860.
2. Bessen, J. (2018).AI and jobs: the role of demand (No. w24235).National Bureau of Economic Research.
3. Brynjolfsson, E., & McAfee, A. (2014) The second machine age: work, progress, and prosperity in a time of brilliant technologies. WW Norton & company.
4. Brynjolfsson, E., & McAfee, A. (2017) The Business of Artificial Intelligence. Harvard Business Review. https://hbr.org/cover-story/2017/07/the-business-of-artificial-intelligence
5. Charness, N., & Boot, W. R. (2009). aging and information technology use: potential and barriers. current directions in psychological science, 18(5), 253-258.
6. Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443-3463.
7. Czaja, S. J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S. N., Rogers, W. A., & Sharit, J. (2006). Factors predicting the use of technology: findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). Psychology and aging, 21(2), 333.
8. Davenport, T. H., & Ronanki, R. (2018). artificial intelligence for the real world. harvard Business Review, 96(1), 108-116.

9. Dignum, V. (2019). Responsible artificial intelligence: how to develop and use AI in a responsible way (Vol. 2156). Cham: Springer. https://doi.org/10.1007/978-3-030-30371-6
10. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People-an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds and machines, 28, 689-707.
11. Garai-Fodor, M. (2022) The Impact of the Coronavirus on Competence, from a Generation-Specific Perspective, Acta Polytechnica Hungarica. 19(8) pp. 111-125.
12. Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). consumer acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157-169.
13. Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review, 61(4), 5-14.
14. Haleem, A., Javaid, M., & Khan, I. H. (2019). Current status and applications of Artificial Intelligence (AI) in medical field: an overview. Current Medicine Research and Practice, 9(6), 231-237.
15. Jordan, M. I., & Mitchell, T. M. (2015). machine learning: trends, perspectives, and prospects. science, 349(6245), 255-260.
16. Luckin, R., & Holmes, W. (2016) Intelligence unleashed: an argument for AI in education.
17. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine, 27(4), 12-14. https://doi.org/10.1609/aimag.v27i4.1904
18. Menon, R., Tiwari, A., Chhabra, A., & Singh, D. (2014) Study on the higher education in India and the need for a paradigm shift. Procedia Economics and Finance, II, 1, 886-871. https://doi.org/10.1016/S2212- 5671(14)00250-0
19. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: mapping the debate. big data & society, 3(2), 2053951716679679.
20. Mitzner, T. L., Boron, J. B., Fausset, C. B., Adams, A. E., Charness, N., Czaja, S. J., ... & Sharit, J. (2010). older adults talk technology: Technology usage and attitudes. Computers in human behavior, 26(6), 1710-1721.
21. Morris, M. G., & Venkatesh, V. (2000) Age differences in technology adoption decisions: Implications for a changing work force Personnel psychology, 53(2), 375-403.
22. Pannu, A. (2015). artificial intelligence and its application in different areas. artificial intelligence, 4(10), 79-84.
23. Prensky, M (2001): 'Digital Natives, Digital Immigrants', in On the Horizon, Vol. 9, No. 5.
24. Rai, A. (2020) Explainable AI: From black box to glass box. Journal of the academy of marketing science, 48, 137-141.
25. Russell, S. J., & Norvig, P. (2016). artificial intelligence: a modern approach. pearson.
26. Varga, J. (2021). Defining the economic role and benefits of micro, small and medium-sized enterprises in the 21st century with a systematic review of the literature. Acta Polytechnica Hungarica, 18(11), 209-228.
27. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
28. Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., ... Artificial intelligence: a powerful paradigm for scientific research, The Innovation, 2(4).
29. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education-where are the educators?. International journal of educational technology in higher education, 16(1), 1-27.
30. Zhang, B., & Dafoe, A. (2019) Artificial intelligence: American attitudes and trends Available at SSRN 3312874