Academic Text Writing with Artificial Intelligence/Smart Learning Technologies: The ChatGPT Example

Öz

Artificial intelligence and smart learning are considered to be one of the most important technological developments of recent years. This technology focuses on computers and robots gaining human-like intelligence and learning abilities. Artificial intelligence is used in many fields and has a great impact especially in sectors such as industry, health, internet applications, information technologies, finance and education. Artificial intelligence and smart learning technology make people's lives easier and more productive by enabling them to make faster, more accurate and more efficient decisions. It is seen that artificial intelligence and smart learning technologies bring many negative effects as well as positive effects. While some of the researchers, who are divided into two on this subject, welcome the developments optimistically, some criticize them harshly. The positive or negative effects of artificial intelligence and smart learning technologies on human life in the future are a matter of great curiosity and concern. This study was conducted to understand the potential of ChatGPT, which is a popular example of artificial intelligence and smart learning technology in recent days. Added as a co-author because ChatGPT was used directly in its creation.

Anahtar Kelimeler

• Artificial Intelligence
• Smart Learning
• Internet
• Information Technologies
• Academic Text

YAPAY ZEKÂ/AKILLI ÖĞRENME TEKNOLOJİLERİYLE AKADEMİK METİN YAZMA: CHATGPT ÖRNEĞİ

Öz

Yapay zekâ ve akıllı öğrenme, son yılların en önemli teknolojik gelişmelerinden biri olarak kabul edilmektedir. Bu teknoloji, bilgisayar ve robotların insan benzeri zekâ ve öğrenme yetenekleri kazanması üzerine odaklanmaktadır. Yapay zekâ, birçok alanda kullanılmakta olup, özellikle sanayi, sağlık, internet uygulamaları, bilişim teknolojileri, finans ve eğitim gibi sektörlerde büyük bir etkiye sahiptir. Yapay zekâ ve akıllı öğrenme teknolojisi daha hızlı, daha doğru ve daha verimli kararlar verme imkânı sağlayarak insanların hayatını kolaylaştırmakta ve daha üretken bir hâle getirmektedir. Yapay zekâ ve akıllı öğrenme teknolojilerinin olumlu etkilerinin yanında birçok olumsuz etkiyi de beraberinde getirdiği görülmektedir. Bu konuda ikiye ayrılan araştırmacıların bir kısmı gelişmeleri iyimser karşılarken, bir kısmı ise katı şekilde eleştirmektedir. Yapay zekâ ve akıllı öğrenme teknolojilerinin gelecekte insan hayatına yapacağı olumlu ya da olumsuz etkileri büyük bir merak ve endişe konusudur. Bu çalışma son günlerin popüler bir yapay zekâ ve akıllı öğrenme teknolojisi örneği olan ChatGPT’nin potansiyelini anlamak amacıyla yapılmıştır. Hazırlanmasında doğrudan ChatGPT kullanıldığı için ortak yazar olarak eklenmiştir.

Anahtar Kelimeler

• Yapay Zekâ
• Akıllı Öğrenme
• İnternet
• Bilişim Teknolojileri
• ChatGPT
• Akademik Metin.
• Akademik Metin.
• Artificial Intelligence
• Smart Learning
• Internet
• Information Technologies
• Academic Text.

Kaynakça

1. Acemoglu, D. ve Restrepo, P (2020). Robots and jobs: Evidence from US labor markets. NBER Working Paper, 24285. https://doi.org/10.3386/w24285
2. Adalı, E. (2012). Doğal Dil İşleme. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 5 (2).
3. Aggarwal, C. C (2015). Data mining: the textbook. Springer.
4. Ali, M., Naseem, A., & Khan, F. A (2019). Artificial intelligence in finance. In M. A. Wani, & M. A. Ahangar (Eds.).,
5. Artificial Intelligence: Theory and Applications (s. 83-93). Springer.
6. Alpaydin, E (2010). Introduction to machine learning (2nd ed.). MIT Press
7. Arbib, M. A., Bonaiuto, J. J., Ranganath, R., & Alexander, A. M. (2015). Neural networks for control. MIT Press.
8. Baddeley, A. D (1999). Essentials of human memory. Psychology Press.

9. Bishop, C. M (2006). Pattern recognition and machine learning (Vol. 4). New York: Springer.
10. Bishop, C.M., & Nasrabadi, N.M. (2006). Pattern Recognition and Machine Learning. J. Electronic Imaging, 16, 049901. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., ... & Zhang, X (2016). End to end learning for self-driving cars.
11. Bostrom, N (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
12. Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
13. Bransford, J. D., Brown, A. L., & Cocking, R. R (2000). How people learn: Brain, mind, experience, and school. National Academy Press.
14. Brockman, J. (Ed.). (2019). Possible Minds: Twenty-Five Ways of Looking at AI. New York, NY: Penguin Press.
15. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165.
16. Buolamwini, J., & Gebru, T (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Conference on Fairness, Accountability and Transparency, Proceedings of Machine Learning Research, 81, 77-91.
17. Burrell, J (2016). How the machine 'thinks': Understanding opacity in machine learning algorithms. Big Data & Society, 3(1)., 1-12.
18. Chan, L., Hogaboam, L., Cao, R. (2022). Artificial Intelligence in Transportation. In: Applied Artificial Intelligence in Business. Applied Innovation and Technology Management. Springer, Cham.
19. Chen, M., Mao, S., & Liu, Y (2014). Big data: a survey. Mobile Networks and Applications, 19(2)., 171-209.
20. Crawford, K., Dobbe, R., Dryer, T., Fried, G., Green, B., Kaziunas, E., Kak, A., Mathur, V., Polli, A., and York, C (2019). AI Now Report 2018. AI Now Institute.
21. Çakır, M., & Zhao, O. I (2022). A bıblıometrıc analysıs on machıne learnıng applıcatıons ın the fınance sector, 2012-2025.
22. Dai, Z., Yang, Z., Yang, Y., Carbonell, J. G., Le, Q. V., & Salakhutdinov, R (2021). Sparse Sinkhorn Attention. arXiv preprint arXiv:2102.11582.
23. Dartmouth Artificial Intelligence Conference (1956). The Dartmouth conference: A summer research project on artificial intelligence.
24. Domjan, M (2018). The Principles of Learning and Behavior. Cengage Learning.
25. Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern Classification (2nd ed.). Wiley.
26. Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., & Plunkett, K (1996). Rethinking Innateness: A Connectionist Perspective on Development. MIT Press.
27. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3)., 37-54.
28. Ferguson, R., & Shum, S. B (2012). Towards a social learning space for open educational resources. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK '12). (pp. 40-47). Floridi, L (2019). The Logic of Information: A Theory of Philosophy as Conceptual Design. Oxford University Press.
29. Ford, M (2015). Rise of the robots: Technology and the threat of a jobless future. Basic Books.
30. Garcia, M. (2019). The Impact of Technology on Education. Educational Research Quarterly, 42(2), 89-98. doi: 10.3102/0013189X12457158
31. Geitgey, A (2016). Machine Learning is Fun Part 5: Language Translation with Deep Learning and the Magic of Sequences. Medium.
32. Goldberg, D. E (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley.
33. Goodfellow, I., Bengio, Y., & Courville, A (2016). Deep learning. MIT press.
34. Gross, R (2014). Psychology: The science of mind and behaviour. Hodder Education.
35. Gwenn Schurgin O'Keeffe, Kathleen Clarke-Pearson, Council on Communications and Media; The Impact of Social Media on Children, Adolescents, and Families. Pediatrics April 2011; 127 (4): 800–804. 10.1542/peds.2011-0054
36. Han, J., & Kamber, M (2006). Data mining: concepts and techniques. Morgan Kaufmann.
37. Hattie, J., & Donoghue, G. M (2016). Learning strategies: A synthesis and conceptual model. npj Science of Learning, 1(1)., 1-13.
38. Haykin, S (1999). Neural networks: a comprehensive foundation. Pearson.
39. Hinton, G., Deng, L., Yu, D., Dahl, G. E., rahman Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P.,
40. Sainath, T. N., , and Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine, 82.
41. Holzinger, A., Kieseberg, P., Weippl, E., & Tjoa, A. (2017) Machine Learning and Knowledge Extraction. https://openai.com/ Huang, Y., Chen, F., Lv, S., & Wang, X. (2019). Facial Expression Recognition: A Survey. Symmetry, 11, 1189.Pyle, D (1999). Data preparation for data mining. Morgan Kaufmann.
42. Jordan, M. I., & Mitchell, T. M (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245)., 255-260.
43. Jurafsky, D., & Martin, J. H (2019). Speech and language processing. Pearson.
44. Kandel, E. R., Schwartz, J. H., & Jessell, T. M (2000). Principles of neural science (4th ed.). McGraw-Hill, 834-883.
45. Khan, S. I. (2022). Impact of artificial intelligence on consumer buying behaviors: Study about the online retail purchase. International Journal of Health Sciences, 6(S2), 8121–8129.
46. Kirschner, P. A., & van Merriënboer, J. J. G (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3)., 169-183.
47. Kizilcec, R. F., Piech, C., & Schneider, E (2013). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK '13). (pp. 170-179).
48. Krizhevsky, A., Sutskever, I., & Hinton, G. E (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (s. 1097-1105).
49. LeCun, Y., Bengio, Y., & Hinton, G (2015). Deep learning. Nature, 521(7553)., 436-444.
50. Lohr, S (2016, March 24). Microsoft silences its new A.I. bot Tay, after Twitter users teach it racism. The New York Times. https://www.nytimes.com/2016/03/25/technology/microsoft-silences-its-new-a-i-bot-tay-after-twitter-users-teach-it-racism.html
51. Luck, M. & Aylett, R (2000) Applying artificial intelligence to virtual reality: Intelligent virtual environments, Applied Artificial Intelligence, 14:1, 3-32, MacKay, D.J. (2004). Information Theory, Inference, and Learning Algorithms. IEEE Transactions on Information Theory, 50, 2544-2545.
52. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, 1(4)., 1-26.
53. Matielo, R., & Farias, P.F. (2014). Language Learning with Technology – Ideas for Integrating Technology in the Classroom. Ilha do Desterro: A Journal of English Language, Literatures in English and Cultural Studies, 301-307.
54. Maulud, D. H., Ameen, S. Y., Omar, N., Kak, S. F., Rashid, Z. N., Yasin, H. M., Ibrahim, I. M., Salih, A. A., Salim, N. O. M., & Ahmed, D. M. (2021). Review on Natural Language Processing Based on Different Techniques. Asian Journal of Research in Computer Science, 10(1), 1–17.
55. Mayer, R. E (2014). Cognitive theory of multimedia learning. The Cambridge Handbook of Multimedia Learning, 43-71.
56. McCorduck, P (2004). Machines Who Think (2. baskı). AK Peters, Ltd.
57. Miller, J (2021). The Relationship Between Success and Self-Confidence. Journal of Positive Psychology, 16(3)., 67-76.
58. Mitchell, T (1997). Machine Learning. McGraw-Hill.
59. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., and Floridi, L (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2)., 1-21.
60. Newell, A., & Simon, H. A. (1972). Human problem solving (Vol. 104). Prentice-Hall. (s. 59-60)
61. Nilsson, N. J (2014). Principles of artificial intelligence. Morgan Kaufmann.
62. OpenAI. (2021). What is artificial intelligence (AI)? OpenAI. https://openai.com/learn/what-is-ai
63. Ormrod, J. E (2014). Human learning (7th ed.). Pearson.
64. Park, H (2020). Practical Applications of Deep Learning for Finance. Journal of Open Innovation: Technology, Market, and Complexity, 6(1)., 1-28.
65. Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education : challenges and opportunities for sustainable development.
66. Pinnington, A.H. (2011), "Competence development and career advancement in professional service firms", Personnel Review, Vol. 40 No. 4, pp. 443-465.
67. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I (2021). Language models are few-shot learners. arXiv preprint arXiv:2005.14165.
68. Refaat F. M, Gouda M. M, Omar M. Detection and Classification of Brain Tumor Using Machine Learning Algorithms. Biomed Pharmacol J 2022;15(4).
69. Roediger, H. L., III, & Butler, A. C. (2010). The critical role of retrieval practice in long-term retention. Trends in Cognitive Sciences, 15(1), 20-27.
70. Russell, S. J., & Norvig, P (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall.
71. Schunk, D. H (2012). Learning theories: An educational perspective (6th ed.). Pearson.
72. Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J (2019). Fairness and Abstraction in Sociotechnical Systems. Proceedings of the Conference on Fairness, Accountability, and Transparency, 59- 68.
73. Siemens, G (2013). Learning Analytics: Envisioning a Research Discipline and a Domain of Practice (1st ed.). Society for Learning Analytics Research (SoLAR).
74. Simon, H. A. (1995). Explaining the ineffable: AI on the topics of intuition, insight, and inspiration. In S. Shapiro (Ed.), Encyclopedia of Artificial Intelligence (2nd ed., pp. 457-465). Wiley.
75. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). The MIT Press.
76. Szeliski, R (2010). Computer vision: algorithms and applications. Springer.
77. Tegmark, M (2017). Life 3.0: Being human in the age of artificial intelligence. Knopf Doubleday Publishing Group.
78. Topol, E. J (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1)., 44-56.
79. Tulving, E., & Craik, F. I. (2000). The Oxford Handbook of Memory. Oxford University Press.
80. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460.
81. Wang, L. ., Sarker, P., Alam, K., & Sumon, S. . (2021). Artificial Intelligence and Economic Growth: A Theoretical Framework. Scientific Annals of Economics and Business, 68(4), 421–443.
82. Witten, I. H., Frank, E., & Hall, M. A (2016). Data mining: practical machine learning tools and techniques. Morgan Kaufmann.
83. Yampolskiy, R. V (2018). Artificial intelligence safety and security. CRC Press.