The Level of Comprehension of Implicit Meanings in English and Turkish Proverbs by AI-Based Text-to-Image Generation Tools

Öz

This study examines the level of comprehension of underlying meanings in Turkish and English proverbs by AI-based text-to-image generation tools (ChatGPT 4o and Fooocus.ai). The research employed descriptive analysis and document analysis methods, selecting a total of four proverbs from both languages (two Turkish, two English). These proverbs were first illustrated using ChatGPT 4o and then with Fooocus.ai, producing eight images in total. The proverbs were chosen based on criteria including frequency of use, level of metaphorical meaning, cultural specificity, and visualization potential. The images generated by the AI tools were evaluated in terms of their semantic and contextual features. Findings indicate that AI systems visualize English proverbs more successfully compared to Turkish ones, yet they struggle to fully capture metaphorical and cultural contexts. The study offers recommendations regarding the potential use of AI-supported visualization tools in language teaching, cultural transmission, and cognitive processes.

Anahtar Kelimeler

• Artificial intelligence
• Proverb
• Cultural context
• Language teaching

Metinden Görsel Oluşturan Yapay Zekâ Araçlarının İngilizce ve Türkçe Atasözlerindeki Alt Anlamları Algılama Düzeyi

Öz

Bu çalışma, yapay zekâ tabanlı metinden görsel üretim araçlarının (ChatGPT 4o ve Fooocus.ai) Türkçe ve İngilizce atasözlerindeki alt anlamları algılama düzeyini incelemektedir. Araştırmada, betimsel analiz ve doküman analizi yöntemleri kullanılmış, her iki dilden toplam dört atasözü (iki Türkçe, iki İngilizce) seçilmiştir. Bu dört atasözü önce ChatGPT 4o aracında daha sonra ise Fooocus.ai aracında resmedilerek toplamda sekiz adet görsel oluşturulmuştur. Atasözleri, kullanım sıklığı, metaforik anlam düzeyi, kültürel özgünlük ve görselleştirme potansiyeli kriterlerine göre belirlenmiş ve yapay zekâ araçları tarafından üretilen görseller, anlambilimsel ve bağlamsal özellikleri açısından değerlendirilmiştir. Bulgular, yapay zekâ sistemlerinin İngilizce atasözlerini Türkçeyle karşılaştırıldığında daha başarılı bir şekilde görselleştirdiğini, ancak metaforik ve kültürel bağlamları tam olarak yansıtmakta güçlük çektiğini göstermektedir. Çalışma, yapay zekâ destekli görselleştirme araçlarının dil öğretimi, kültürel aktarım ve bilişsel süreçlerdeki potansiyel kullanımına yönelik öneriler sunmaktadır.

Anahtar Kelimeler

• Yapay zekâ
• Atasözü
• Kültürel bağlam
• Dil öğretimi

Kaynakça

1. Abushaev, A. (2024). English proverbs and their usage. In Conference on The Role and Importance of Science in the Modern World, 1(5), 197-204.
2. Aksan, D. (1990). Her yönüyle dil-Ana çizgileriyle dilbilim. TDK.
3. Aksoy, Ö. A. (1981). Atasözleri sözlüğü. TDK.
4. Aksoy, Ö. A. (1998). Atasözleri ve deyimler sözlüğü. İnkılâp.
5. Aktaş, Ş. T. (2004). Seçme atasözleri ve eleştirmeli açıklamaları. Akçağ.
6. Al-Shoubaki, A., & Eid, A. G. (2024). The role of artificial intelligence in representing the mental image of popular proverbs. International Journal of Contemporary Humanities and Educational Science, 3(3), 257-290.
7. Atagül, Y. (2015). Yabancı dil olarak Türkçe öğretiminde atasözleri ve deyimlerin sıklık analizi. Journal of Turkish Studies, 10(7), 1021-1036. http://dx.doi.org/10.7827/TurkishStudies.8213
8. Berg, C., Omsén, L., Hansson, H., & Mozelius, P. (2024). Students' AI-generated Images: Impact on Motivation, Learning and, Satisfaction. In ICAIR 2024, 4, 500-506. ACI Academic Conferences International. https://doi.org/10.34190/icair.4.1.3243

9. Celik, I. (2023). Towards intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computer in Human Behavior, 138. https://doi.org/10.1016/j.chb.2022.107468
10. Chakrabarty, T., Saakyan, A., Ghosh, D., & Muresan, S. (2022). FLUTE: Figurative language understanding through Textual Explanations. ArXiv: 2205.12404. https://doi.org/10.4550/arXiv.2205.12404
11. Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational psychology review, 3, 149-210.
12. Collier, J., & Collier, M. (1986). Visual anthropology: Photography as a research method. University of New Mexico Press.
13. Fountoulakis, M. S. (2024). Evaluating the Impact of AI Tools on Language Proficiency and Intercultural Communication in Second Language Education. International Journal of Second and Foreign Language Education, 3(1), 12–26. https://doi.org/10.33422/ijsfle.v3i1.768
14. Gibbs, R. W., Jr. (1994). Figurative thought and figurative language. In M. A. Gernsbacher (Ed.), Handbook of psycholinguistics (pp. 411–446). Academic Press.
15. Godwin-Jones, R. (2024). Distributed agency in language learning and teaching through generative AI. Language Learning & Technology, 28(2), 5–31. https://hdl.handle.net/10125/73570
16. Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning, MIT.
17. Kato, Y., Bolstad, F., & Watari, H. (2015). Cooperative and collaborative learning in the language classroom. The Language Teacher, 39(2), 22-26.
18. Kemp, S. E., Ng, M., Hollowood, T. & Hort, J. (2018). Introduction to descriptive analysis. In S.E. Kemp, J. Hort & T. Hollowood (Eds.), Descriptive analysis in sensory evaluation. Wiley.
19. Koller S, Müller N and Kauschke C (2022) The elephant in the room: A systematic review of stimulus control in neuro-measurement studies on figurative language processing. Frontiers in Human Neuroscience, 15(791374).https://doi.org/10.3389/fnhum.2021.791374
20. Kramsch, C., & Zhu, H. (2020). Translating culture in global times: Dialogues. Applied Linguistics, 41(1), 148-160.
21. Kress, G., & Van Leeuwen, T. (2006). Reading images: The grammar of visual design (2nd ed.). Routledge.
22. Lakoff, G., & Johnson, M. (2008). Metaphors we live by. University of Chicago press.
23. Li, S., & Zhao, Y. (2025). Text leads, images and videos follow: the impact of processing paths and input modalities on metaphorical competence in Chinese foreign language learners. Front. Lang. Sci. 4:1497066. https://doi.org/10.3389/flang.2025.1497066
24. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43-52.
25. Miles, B. M., & Huberman M. A. (2016). Nitel veri analizi (S. Akbaba Altun & A. Ersoy, Çev. Ed.). Pegem.
26. Mishra, P., Warr, M. & Islam, R. (2023). TPACK in the age of ChatGPT and generative AI. Journal of Digital Learning in Teacher Education, 39(4), 235-251. https://doi.org/10.1080/21532974.2023.22474 80
27. Ning, Y., Zhang, C., Xu, B., Zhou, Y. & Wijaya T. T. (2024). Teachers’ AI-TPACK: exploring the relationship between knowledge elements. Sustainability, 16, 978. https://doi.org/10.3390/su16030978
28. Paivio, A. (1986). Mental representations: A dual coding approach. Oxford University Press.
29. Rose, G. (2016). Visual methodologies: An introduction to researching with visual materials (4th ed.). Sage Publications.
30. Sapkota, R. (2023). Harnessing the power of ai based image generation model DALLE2 in agricultural settings. Preprint at http://arxiv.org/abs/2307.08789.
31. Simpson, J. (2000). The concise Oxford dictionary of proverbs (3rd ed.). Oxford University Press.
32. Steinel, P. M., Hulstijn, H. J., & Steinel, W. (2007). Second language ıdiom learning in a paired-associate paradigm: effects of direction of learning, direction of testing, ıdiom ımageability, and ıdiom transparency. Studies in Second Language Acquisition, 29(3), 449-484.
33. Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68(1), 1-16.
34. Tang, Y. Y. (2005). Document analysis and understanding. In C. H. Chen & P. S. P. Wang (Eds.), Handbook Of pattern recognition and computer vision (pp. 219-240). World Scientific.
35. Wach, E., & Ward, R. (2013). Learning about Qualitative Document Analysis (Version 1). The Institute of Development Studies and Partner Organisations. https://hdl.handle.net/20.500.12413/2989
36. Xu, W., & Tan, X. (2024). Beyond words: L2 writing teachers’ visual conceptualizations of ChatGPT in teaching and learning. Journal of Second Language Writing, 64, 101110.
37. Yoncacı, A. (2019). Ortaokul 7. sınıf öğrencilerinin atasözü dağarcıklarının belirlenmesi (Konya ili Selçuklu ilçesi örneği) [Yayımlanmamış yüksek lisans tezi]. Muğla Sıtkı Koçman Üniversitesi.