Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering

Juan David Velásquez-Henao; Carlos Jaime Franco-Cardona; Lorena Cadavid-Higuita

doi:10.15446/dyna.v90n230.111700

Publicado

2023-11-03

Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering

Ingeniería de instrucciones: una metodología para optimizar interacciones con Modelos de Lenguaje de IA en el campo de ingeniería

DOI:

https://doi.org/10.15446/dyna.v90n230.111700

Palabras clave:

ChatGPT; prompt engineering; large language models; prompt design (en)
ChatGPT; ingeniería de instrucciones; grandes modelos de lenguaje; diseño de instrucciones (es)

Descargas

Autores/as

Juan David Velásquez-Henao Universidad Nacional de Colombia, sede Medellín, Facultad de Minas, Medellín, Colombia https://orcid.org/0000-0003-3043-3037
Carlos Jaime Franco-Cardona Universidad Nacional de Colombia, sede Medellín, Facultad de Minas, Medellín, Colombia https://orcid.org/0000-0002-7750-857X
Lorena Cadavid-Higuita Universidad Nacional de Colombia, sede Medellín, Facultad de Minas, Medellín, Colombia https://orcid.org/0000-0002-6025-5940

Resumen (en)
Resumen (es)

ChatGPT is a versatile conversational Artificial Intelligence model that responds to user input prompts, with applications in academia and various sectors. However, crafting effective prompts can be challenging, leading to potentially inaccurate or contextually inappropriate responses, emphasizing the importance of prompt engineering in achieving accurate outcomes across different domains. This study aims to address this void by introducing a methodology for optimizing interactions with Artificial Intelligence language models, like ChatGPT, through prompts in the field of engineering. The approach is called GPEI and relies on the latest advancements in this area; and consists of four steps: define the objective, design the prompt, evaluate the response, and iterate. Our proposal involves two key aspects: data inclusion in prompt design for engineering applications and the integration of Explainable Artificial Intelligence principles to assess responses, enhancing transparency. It combines insights from various methodologies to address issues like hallucinations, emphasizing iterative prompt refinement techniques like posing opposing questions and using specific patterns for improvement. This methodology could improve prompt precision and utility in engineering.

ChatGPT es un modelo de Inteligencia Artificial conversacional versátil que responde a las indicaciones de entrada del usuario, con aplicaciones en el mundo académico y diversos sectores. Sin embargo, elaborar indicaciones efectivas puede ser un desafío, lo que lleva a respuestas potencialmente inexactas o contextualmente inapropiadas, lo que enfatiza la importancia de la ingeniería de instrucciones para lograr resultados precisos en diferentes dominios. Este estudio pretende abordar este vacío introduciendo una metodología para optimizar las interacciones con modelos de lenguaje de Inteligencia Artificial, como ChatGPT, a través de instrucciones en el campo de la ingeniería. El enfoque es llamado GPEI, y se basa en los últimos avances en esta área, el cual consta de cuatro pasos: definir el objetivo, diseñar el mensaje, evaluar la respuesta e iterar. Nuestra propuesta involucra dos aspectos clave: la inclusión de datos en el diseño rápido para aplicaciones de ingeniería y la integración de principios de Inteligencia Artificial Explicable para evaluar las respuestas, mejorando la transparencia. Combina conocimientos de varias metodologías para abordar problemas como las alucinaciones, enfatizando técnicas iterativas de refinamiento rápido, como plantear preguntas opuestas y usar patrones específicos para mejorar. Esta metodología podría mejorar la precisión y la utilidad rápidas en ingeniería.

Referencias

Lund, B.D., Wang, T., Mannuru, N.R., Nie, B., Shimray, S., and Wang, Z., ChatGPT and a new academic reality: Artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing, J. Assoc. Inf. Sci. Technol., 74(5), pp. 570-581, 2023. DOI: https://doi.org/10.1002/asi.24750.

Macdonald, C., Adeloye, D., Sheikh, A., and Rudan, I., Can ChatGPT draft a research article? An example of population-level vaccine effectiveness analysis, J. Glob. Health, 13, 2023. DOI: https://doi.org/10.7189/JOGH.13.01003.

Rudolph, J., Tan, S., and Tan, S., ChatGPT: bullshit spewer or the end of traditional assessments in higher education? J. Appl. Learn. Teach., 6(1), pp. 342-363, 2023. DOI: https://doi.org/10.37074/jalt.2023.6.1.9.

Pavlik, J.V., Collaborating with ChatGPT: considering the implications of generative artificial intelligence for journalism and media education, Journal. Mass Commun. Educ., 78(1), pp. 84-93, 2023. DOI: https://doi.org/10.1177/10776958221149577.

Jha, S., Jha, S.K., Lincoln, P., Bastian, N.D., Velasquez, A., and Neema, S., Dehallucinating large language models using formal methods guided iterative prompting, in: 2023 IEEE International Conference on Assured Autonomy (ICAA), IEEE, 2023. pp. 149-152. DOI: https://doi.org/10.1109/ICAA58325.2023.00029

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G., Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing, ACM Comput. Surv., 55(9), pp. 1-35, 2023. DOI: https://doi.org/10.1145/3560815

Lo, L.S., The art and science of prompt engineering: a new literacy in the information age, Internet Ref. Serv. Q., 2023. DOI: https://doi.org/10.1080/10875301.2023.2227621.

Giray, L., Prompt engineering with ChatGPT: a guide for academic writers, Ann. Biomed. Eng., 2023. DOI: https://doi.org/10.1007/s10439-023-03272-4.

White, J. et al., A prompt pattern catalog to enhance prompt engineering with chatgpt, ArXiv Prepr. ArXiv230211382, 2023.

Busch, K., Rochlitzer, A., Sola, D., and Leopold, H., Just tell me: prompt engineering in business process management, in: International Conference on Business Process Modeling, Development and Support, Springer, 2023, pp. 3-11. DOI: https://doi.org/10.1007/978-3-031-34241-7_1

Lecler, A., Duron, L., and Soander, P., Revolutionizing radiologand with GPT-based models: current applications, future possibilities and limitations of ChatGPT, Diagn. Interv. Imaging, 104(6), pp. 269-274, 2023. DOI: https://doi.org/10.1016/j.diii.2023.02.003.

Eandsenbach, G., The role of ChatGPT, generative language models, and artificial intelligence in medical education: a conversation with ChatGPT and a call for papers, JMIR Med. Educ., 9, 2023. DOI: https://doi.org/10.2196/46885.

Wu, T., Terrand, M., and Cai, C.J., AI chains: transparent and controllable Human-AI interaction band chaining large language model prompts, in: Conference on Human Factors in Computing Sandstems - Proceedings, 2022. DOI: https://doi.org/10.1145/3491102.3517582.

Raand P.P., and Majumder, P., Assessing the Accuracand of responses band the language model ChatGPT to questions regarding bariatric surgerand: a critical appraisal, Obes. Surg., 33(8), pp. 2588-2589, 2023. DOI: https://doi.org/10.1007/s11695-023-06664-6.

Gupta, R., Herzog, I., Weisberger, J., Chao, J., Chaiandasate, K., and Lee, E.S., Utilization of ChatGPT for plastic surgerand research: friend or foe?, J. Plast. Reconstr. Aesthet. Surg., 80, pp. 145-147, 2023. DOI: https://doi.org/10.1016/j.bjps.2023.03.004.

Deiana, G., Dettori, M., Arghittu, A., Azara, A., Gabutti, G., and Castiglia, P., Artificial intelligence and public health: evaluating ChatGPT responses to vaccination mandths and misconceptions, Vaccines, 11(7), art. 11071217, 2023. DOI: https://doi.org/10.3390/vaccines11071217.

Corsello, A. and Santangelo, A., Maand artificial intelligence influence future pediatric research?—The case of ChatGPT, children, 10(4), 2023. DOI: https://doi.org/10.3390/children10040757.

Eager, B., and Brunton, R., Prompting higher education towards AI-Augmented teaching and learning practice, J. Univ. Teach. Learn. Pract., 20(5), 2023. DOI: https://doi.org/10.53761/1.20.5.02.

Lo, L.S., The CLEAR path: a framework for enhancing information literacand through prompt engineering, J. Acad. Librariansh., 49(4), 2023. DOI: https://doi.org/10.1016/j.acalib.2023.102720.

DK. Dwivedi, et al., So what if ChatGPT wrote it? Multidisciplinarand perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policand, Int. J. Inf. Manag., 71, 2023. DOI: https://doi.org/10.1016/j.ijinfomgt.2023.102642.

Harrer, S., Attention is not all andou need: the complicated case of ethicalland using large language models in healthcare and medicine, eBioMedicine, 90, 2023. DOI: https://doi.org/10.1016/j.ebiom.2023.104512.

Crawford, J., Cowling, M., and Allen, K.-A., Leadership is needed for ethical ChatGPT: character, assessment, and learning using artificial

Velásquez-Henao et al / Revista DYNA, 90 (230), Especial Conmemoración 90 años, pp. 9-17, Noviembre, 2023. DOI: https://doi.org/10.15446/dyna.v90n230.111700

intelligence (AI), J. Univ. Teach. Learn. Pract., 20(3), 2023. DOI: https://doi.org/10.53761/1.20.3.02.

Alexander, K., Savvidou, C., and Alexander, C., Who wrote this essaand? Detecting ai-generated writing in second language education in higher education, Teach. Engl. Technol., 23(2), pp. 25-43, 2023. DOI: https://doi.org/10.56297/BUKA4060/XHLD5365.

Lim, W.M., Gunasekara, A., Pallant, J.L., Pallant, J.I., and Pechenkina, E., Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators, Int. J. Manag. Educ., 21(2), 2023, DOI: https://doi.org/10.1016/j.ijme.2023.100790.

Bilal, M., Jamil, Y., Rana, D., and Shah, H.H., Enhancing awareness and Self-diagnosis of obstructive sleep apnea using AI-Powered chatbots: the role of ChatGPT in revolutionizing healthcare, Ann. Biomed. Eng., 2023. DOI: https://doi.org/10.1007/s10439-023-03298-8.

Epstein, R.H., and Dexter, F., Variabilitand in large language Models' responses to medical licensing and certification examinations. comment on "How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment", JMIR Med. Educ., 9, 2023. DOI: https://doi.org/10.2196/48305.

Marchandot, B., Matsushita, K., Carmona, A., Trimaille, A., and Morel, O., ChatGPT: the next frontier in academic writing for cardiologists or a pandora's box of ethical dilemmas, Eur. Heart J. Open, 3(2), 2023. DOI: https://doi.org/10.1093/ehjopen/oead007.

Graf, A. and Bernardi, R.E., ChatGPT in research: balancing ethics, transparencand and advancement, Neuroscience, 515, pp. 71-73, 2023. DOI: https://doi.org/10.1016/j.neuroscience.2023.02.008.

Yan, D., Impact of ChatGPT on learners in a L2 writing practicum: an exploratorand investigation, Educ. Inf. Technol., 2023. DOI: https://doi.org/10.1007/s10639-023-11742-4.

Ruksakulpiwat, S., Kumar, A., and Ajibade, A., Using ChatGPT in Medical research: current status and future directions, J. Multidiscip. Healthc., 16, pp. 1513-1520, 2023. DOI: https://doi.org/10.2147/JMDH.S413470.

Májovský, M., Černý, M., Kasal, M., Komarc, M., and Netuka, D., Artificial intelligence can generate fraudulent but authentic-looking scientific medical articles: pandora's box has been opened, J. Med. Internet Res., 25, 2023. DOI: https://doi.org/10.2196/46924.

Spirling, A., Whand open-source generative AI models are an ethical waand forward for science, nature, 616(7957), art. 413, 2023. DOI: https://doi.org/10.1038/d41586-023-01295-4.

Wang, S.H., OpenAI — explain whand some countries are excluded from ChatGPT, nature, 615(7950), art. 34, 2023. DOI: https://doi.org/10.1038/d41586-023-00553-9.

Cooper, G., Examining science education in ChatGPT: An exploratorand studand of generative artificial intelligence, J. Sci. Educ. Technol., 32(3), pp. 444-452, 2023. DOI: https://doi.org/10.1007/s10956-023-10039-and.

Chang, E.Y., Prompting large language models with the socratic method, in: 2023 IEEE 13th Annual Computing and Communication Workshop and Conference, CCWC 2023, 2023. pp. 351-360. DOI: https://doi.org/10.1109/CCWC57344.2023.10099179.

Heston, T.F., and Khun, C., Prompt engineering in medical education, Int. Med. Educ., 2(3), pp. 198-205, 2023. DOI: https://doi.org/10.3390/ime2030019

Yong, G., Jeon, K., Gil, D., and Lee, G., Prompt engineering for zero-shot and few-shot defect detection and classification using a visual-language pretrained model, Comput.-Aided Civ. Infrastruct. Eng., 38(11), pp. 1536-1554, 2023. DOI: https://doi.org/10.1111/mice.12954.

Bozkurt, A., and Sharma, R.C., Generative AI and prompt engineering: the art of whispering to let the genie out of the algorithmic world, Asian J. Distance Educ., 2023.

Elsevier, Whand choose Scopus - Scopus benefits. [online]. Accessed: July 31th of 2020] Available at: https://www.elsevier.com/solutions/scopus/whand-choose-scopus

Shieh, J., Best practices for prompt engineering with OpenAI API, OpenIA, [online]. Sept. 2023. Accessed: October 3rd of 2023. Available at: https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api

Yao, S. et al., Tree of thoughts: deliberate problem solving with large language models, ArXiv Prepr. ArXiv230510601, 2023.

Henrickson, L., and Meroño-Peñuela, A., Prompting meaning: a hermeneutic approach to optimising prompt engineering with ChatGPT, AI Soc., 2023, DOI: https://doi.org/10.1007/s00146-023-01752-8.

Google, Bard - Chat based AI tool from Google, powered band PaLM 2. [online]. Accessed: October 4th of 2023. Available at: https://bard.google.com

Microsoft, Your AI-Powered Copilot for the Web | Microsoft Bing. [online]. Accessed: October 4th of 2023. Available at: https://www.microsoft.com/en-us/bing

Spasic, A.J., and Jankovic, D.S., Using ChatGPT standard prompt engineering techniques in lesson preparation: role, instructions and seed-word prompts, in: 2023 58th International Scientific Conference on Information, Communication and Energand Sandstems and Technologies, ICEST 2023 - Proceedings, 2023, pp. 47-50. DOI: https://doi.org/10.1109/ICEST58410.2023.10187269.

Rudin, C., and Radin, J., Whand are we using black box models in AI when we don't need to? A lesson from an explainable AI competition, Harv. Data Sci. Rev., 1.2, p. 9, 2019. DOI: https://doi.org/10.1162/99608f92.5a8a3a3d.

Salkin, H.M., and De Kluandver, C.A., The knapsack problem: a surveand, Nav. Res. Logist. Q., 22(1), pp. 127-144, 1975. DOI: https://doi.org/10.1002/nav.3800220110.

Bisong, E., Google Colaboratorand, en Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, E. Bisong, Ed., Berkeleand, CA: Apress, 2019, pp. 59-64. DOI: https://doi.org/10.1007/978-1-4842-4470-8_7.

Cómo citar

IEEE

[1]

J. D. Velásquez-Henao, C. J. Franco-Cardona, y L. Cadavid-Higuita, «Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering», DYNA, vol. 90, n.º 230, pp. 9–17, nov. 2023.

ACM

[1]

Velásquez-Henao, J.D., Franco-Cardona, C.J. y Cadavid-Higuita, L. 2023. Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering. DYNA. 90, 230 (nov. 2023), 9–17. DOI:https://doi.org/10.15446/dyna.v90n230.111700.

ACS

(1)

Velásquez-Henao, J. D.; Franco-Cardona, C. J.; Cadavid-Higuita, L. Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering. DYNA 2023, 90, 9-17.

APA

Velásquez-Henao, J. D., Franco-Cardona, C. J. & Cadavid-Higuita, L. (2023). Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering. DYNA, 90(230), 9–17. https://doi.org/10.15446/dyna.v90n230.111700

ABNT

VELÁSQUEZ-HENAO, J. D.; FRANCO-CARDONA, C. J.; CADAVID-HIGUITA, L. Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering. DYNA, [S. l.], v. 90, n. 230, p. 9–17, 2023. DOI: 10.15446/dyna.v90n230.111700. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/111700. Acesso em: 6 mar. 2026.

Chicago

Velásquez-Henao, Juan David, Carlos Jaime Franco-Cardona, y Lorena Cadavid-Higuita. 2023. «Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering». DYNA 90 (230):9-17. https://doi.org/10.15446/dyna.v90n230.111700.

Harvard

Velásquez-Henao, J. D., Franco-Cardona, C. J. y Cadavid-Higuita, L. (2023) «Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering», DYNA, 90(230), pp. 9–17. doi: 10.15446/dyna.v90n230.111700.

MLA

Velásquez-Henao, J. D., C. J. Franco-Cardona, y L. Cadavid-Higuita. «Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering». DYNA, vol. 90, n.º 230, noviembre de 2023, pp. 9-17, doi:10.15446/dyna.v90n230.111700.

Turabian

Velásquez-Henao, Juan David, Carlos Jaime Franco-Cardona, y Lorena Cadavid-Higuita. «Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering». DYNA 90, no. 230 (noviembre 3, 2023): 9–17. Accedido marzo 6, 2026. https://revistas.unal.edu.co/index.php/dyna/article/view/111700.

Vancouver

1.

Velásquez-Henao JD, Franco-Cardona CJ, Cadavid-Higuita L. Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering. DYNA [Internet]. 3 de noviembre de 2023 [citado 6 de marzo de 2026];90(230):9-17. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/111700

Descargar cita

CrossRef Cited-by

99

1. Michael Rodriguez, Kevin J. Trainor. (2025). INTEGRATING GENERATIVE ARTIFICIAL INTELLIGENCE (GEnAI) IN A PROFESSIONAL SELLING COURSE: APPLICATION OF PROMPT ENGINEERING. Marketing Education Review, , p.1. https://doi.org/10.1080/10528008.2025.2507682.

2. Diana C. Burbano G., José F. Ibarra C.. (2024). Telematics and Computing. Communications in Computer and Information Science. 2250, p.283. https://doi.org/10.1007/978-3-031-77293-1_20.

3. Camilo Vieira, Jose L. De la Hoz, Alejandra J. Magana, David Restrepo. (2025). Engineering Students' Experiences With ChatGPT to Generate Code for Disciplinary Programming. Computer Applications in Engineering Education, 33(6) https://doi.org/10.1002/cae.70090.

4. Chae-Byeol Lee, Kwon-Taeg Choi. (2024). Hierarchical Representation and Visualization of Customer Query Response Data Through Discriminative Prompt Learning. Journal of Digital Contents Society, 25(12), p.3837. https://doi.org/10.9728/dcs.2024.25.12.3837.

5. Sri Rama Chandra Charan Teja Tadi. (2025). Developer and LLM Pair Programming: An Empirical Study of Role Dynamics and Prompt-Based Collaboration. International Journal of Advanced Research in Science, Communication and Technology, , p.436. https://doi.org/10.48175/IJARSCT-26358.

6. Yookyoung Lee, Ju Hyun Oh, Dongik Lee, Mijeong Kang, Seunghun Lee. (2025). Prompt engineering in ChatGPT for literature review: practical guide exemplified with studies on white phosphors. Scientific Reports, 15(1) https://doi.org/10.1038/s41598-025-99423-9.

7. Javier Munguia. (2026). Leveraging AI for 2D technical drawing analysis, feedback and assessment in higher education. International Journal of Mechanical Engineering Education, https://doi.org/10.1177/03064190251414394.

8. Yeeun Park, DongHun Kim, Sihyeon Yoo, Yunjin Bae, Keungoui Kim. (2024). Reseach on Prompmt Engineering for Generative AI to Create Business Data Analysis Reports. The Journal of Korean Institute of Information Technology, 22(11), p.167. https://doi.org/10.14801/jkiit.2024.22.11.167.

9. Aditya Narvekar, Trisha Mukherjee. (2025). Boosting LLM Performance on Boolean Questions with Cluster-Based Embeddings. 2025 5th Asian Conference on Innovation in Technology (ASIANCON). , p.1. https://doi.org/10.1109/ASIANCON66527.2025.11280651.

10. Christian Lopez, Miles Morrison, Matthew Deacon. (2024). Modelos de lenguaje para la generación de preguntas de programación con diferentes niveles de dificultad. European Public & Social Innovation Review, 9, p.1. https://doi.org/10.31637/epsir-2024-760.

11. Yuwei Wan, Zheyuan Chen, Ying Liu, Chong Chen, Michael Packianather. (2025). Empowering LLMs by hybrid retrieval-augmented generation for domain-centric Q&A in smart manufacturing. Advanced Engineering Informatics, 65, p.103212. https://doi.org/10.1016/j.aei.2025.103212.

12. Albert Nagy, Andrea Tick. (2025). From Innovation to Implementation: Leveraging AI-Driven Automation in Smart Urban Railway Operations. 2025 IEEE 12th International Conference on Computational Cybernetics and Cyber-Medical Systems (ICCC). , p.000123. https://doi.org/10.1109/ICCC64928.2025.10999126.

13. Pascal Adomako, Talha Ali Khan, Raja Hashim Ali, Rand Koutaly. (2025). Comparison of Leading AI Models an Analytical Study of ChatGPT Google Bard and Microsoft Bing. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 14, p.e31857. https://doi.org/10.14201/adcaij.31857.

14. Laurie Faith, Tiffanie Zaugg, Nicole Stolys, Madeline Szabo, Fatemeh Haghi, Charles Badlis, Simon Lefever Olmedo. (2025). Persona, Break Glass, Name Plan, Jam (PBNJ): A New AI Workflow for Planning and Problem Solving. AI, 6(12), p.310. https://doi.org/10.3390/ai6120310.

15. Matei A. Golesteanu, Garrett B. Vowinkel, Ryan E. Dougherty. (2024). Can ChatGPT pass a Theory of Computing Course?. Proceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 1. , p.33. https://doi.org/10.1145/3649165.3690116.

16. Luis Josue Lugo Sanchez. (2025). Innovación social académica en tiempos de capitalismo cognitivo: El caso de la Biblioteca de Prompts Colaborativos. Teknokultura. Revista de Cultura Digital y Movimientos Sociales, Avance en línea, p.1. https://doi.org/10.5209/tekn.97189.

17. Pavan Subhash Chandrabose Nara, Reshmi Mitra, Indranil Roy, T. Robin Cole III. (2025). Computer Applications in Industry and Engineering. Communications in Computer and Information Science. 2242, p.115. https://doi.org/10.1007/978-3-031-76273-4_9.

18. Jialin Liu, Fang Liu, Changyu Wang, Siru Liu. (2025). Prompt Engineering in Clinical Practice: Tutorial for Clinicians. Journal of Medical Internet Research, 27, p.e72644. https://doi.org/10.2196/72644.

19. Ziyang Liu, Yi Zhu, Jianhao Li, Guosheng Hao. (2025). CAFEM: Context-Aware Feature Enhancement with Multi-Stage Sampling for Statement-Level Defect Prediction. 2025 25th International Conference on Software Quality, Reliability and Security (QRS). , p.497. https://doi.org/10.1109/QRS65678.2025.00056.

20. Shih-Hsuan Tai, Chi-Chuan Yeh, Jann-Yuan Wang, Rey-Heng Hu, Po-Huang Lee, Cheng-Maw Ho. (2025). Integration of ChatGPT in medical learning: An analysis of interaction and contradictions. Medical Teacher, , p.1. https://doi.org/10.1080/0142159X.2025.2604245.

21. Soohwan Jeong, Mingyu Choi, Joon-Young Kim, Susik Yoon, Sungsu Lim. (2026). Metadata Meets LLMs: Constructing Knowledge-Rich Citation Networks with CoT-Enhanced Representations. Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining. , p.1155. https://doi.org/10.1145/3773966.3779395.

22. Jessie S Barrot, Hung Phu Bui. (2026). Generative Artificial Intelligence for Automated Qualitative Feedback: A Cross-Comparison of Prompting Strategies. RELC Journal, https://doi.org/10.1177/00336882251412561.

23. Zhenhua Wang, Wei Xie, Shuoyoucheng Ma, Enze Wang, Baosheng Wang. (2025). Evading LLMs’ Safety Boundary with Adaptive Role-Play Jailbreaking. Electronics, 14(24), p.4808. https://doi.org/10.3390/electronics14244808.

24. Juan David Velásquez-Henao, Carlos Jaime Franco-Cardona, Lorena Cadavid-Higuita. (2023). Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering. DYNA, 90(230), p.9. https://doi.org/10.15446/dyna.v90n230.111700.

25. Niklas Meißner, Sandro Speth, Steffen Becker. (2024). Automated Programming Exercise Generation in the Era of Large Language Models. 2024 36th International Conference on Software Engineering Education and Training (CSEE&T). , p.1. https://doi.org/10.1109/CSEET62301.2024.10662984.

26. Badr Machkour, Ahmed Abriane. (2026). Artificial Intelligence and Cognitive Sciences for Emerging Technologies. Lecture Notes in Networks and Systems. 1781, p.550. https://doi.org/10.1007/978-3-032-14430-0_47.

27. Medamanoori Dhanush, Briso Becky Bell. (2025). PromptFixer: An AI-based System for Improving and Structuring User Prompts for Language Model. 2025 9th International Conference on Electronics, Communication and Aerospace Technology (ICECA). , p.796. https://doi.org/10.1109/ICECA66444.2025.11382812.

28. Elena Sblendorio, Vincenzo Dentamaro, Alessio Lo Cascio, Francesco Germini, Michela Piredda, Giancarlo Cicolini. (2024). Integrating human expertise & automated methods for a dynamic and multi-parametric evaluation of large language models’ feasibility in clinical decision-making. International Journal of Medical Informatics, 188, p.105501. https://doi.org/10.1016/j.ijmedinf.2024.105501.

29. Kaicheng Huang, Fanyu Wang, Yutan Huang, Chetan Arora. (2025). Prompt Engineering for Requirements Engineering: A Literature Review and Roadmap. 2025 IEEE 33rd International Requirements Engineering Conference Workshops (REW). , p.548. https://doi.org/10.1109/REW66121.2025.00081.

30. Anastasios Nikolakopoulos, Antonios Litke, Alexandros Psychas, Eleni Veroni, Theodora Varvarigou. (2025). Exploring the Potential of Offline LLMs in Data Science: A Study on Code Generation for Data Analysis. IEEE Access, 13, p.64087. https://doi.org/10.1109/ACCESS.2025.3556973.

31. Maik Philipp. (2025). DeutschGPT – Deutschunterricht im Dialog mit Künstlicher Intelligenz. Literatur – Medien – Didaktik. 16, p.39. https://doi.org/10.57088/978-3-7329-8796-2_2.

32. Ismail Ait Talghalit, Hamza Alami, Said Ouatik El Alaoui. (2025). Textual backdoor attacks and a novel defense method for context-aware Arabic biomedical questions classifiers. Engineering Applications of Artificial Intelligence, 162, p.112583. https://doi.org/10.1016/j.engappai.2025.112583.

33. Zhaojun Steven Li, Kalyan Bhagavan Tadaka, Rocco Cassandro, William R. Tonti. (2025). How Much Confidence Do We Have for GenAI-Based Reasoning for Reliability Estimation?. 2025 IEEE 4th International Conference on Intelligent Reality (ICIR). , p.1. https://doi.org/10.1109/ICIR68135.2025.11361598.

34. Salvatore F. Pileggi, Gnana Bharathy. (2025). Computational Science – ICCS 2025 Workshops. Lecture Notes in Computer Science. 15912, p.125. https://doi.org/10.1007/978-3-031-97573-8_9.

35. Abderrahmane Kefali, Ismail Bouacha, Ryene Benatti, Nor Elhouda Chaib Rassou. (2026). Semi-automatic framework for layout annotation of arabic document images. Signal, Image and Video Processing, 20(2) https://doi.org/10.1007/s11760-026-05154-3.

36. Fei Wang, Yuewen Zheng, Jingyi Wu, Qing Li, Pengfei Li, Luxia Zhang. (2025). ChatSchema: Development and validation of a pipeline for extracting structured nursing information with large multimodal models. Interdisciplinary Nursing Research, 4(1), p.14. https://doi.org/10.1097/NR9.0000000000000081.

37. Aytuǧ Onan, Hesham Alhumyani. (2025). Computer Applications in Industry and Engineering. Communications in Computer and Information Science. 2242, p.41. https://doi.org/10.1007/978-3-031-76273-4_4.

38. Taiwo Raphael Feyijimi, John Ogbeleakhu Aliu, Ayodeji Emmanuel Oke, Douglas Omoregie Aghimien. (2025). ChatGPT’s Expanding Horizons and Transformative Impact Across Domains: A Critical Review of Capabilities, Challenges, and Future Directions. Computers, 14(9), p.366. https://doi.org/10.3390/computers14090366.

39. Nurullah Şimşek. (2025). Integration of ChatGPT in mathematical story-focused 5E lesson planning: Teachers and pre-service teachers' interactions with ChatGPT. Education and Information Technologies, 30(8), p.11391. https://doi.org/10.1007/s10639-024-13258-x.

40. Rawan AlMakinah, Andrea Norcini-Pala, Lindsey Disney, M. Abdullah Canbaz. (2025). Enhancing Mental Health Support Through Human-AI Collaboration: Toward Secure and Empathetic AI-Enabled Chatbots. 2025 IEEE Conference on Artificial Intelligence (CAI). , p.196. https://doi.org/10.1109/CAI64502.2025.00038.

41. Ibomoiye Domor Mienye, Nobert Jere, George Obaido, Oyindamola Omolara Ogunruku, Ebenezer Esenogho, Cameron Modisane. (2025). Large language models: an overview of foundational architectures, recent trends, and a new taxonomy. Discover Applied Sciences, 7(9) https://doi.org/10.1007/s42452-025-07668-w.

42. Rushali Deshmukh, Rutuj Raut, Mayur Bhavsar, Sanika Gurav, Yash Patil. (2025). Optimizing Human-AI Interaction: Innovations in Prompt Engineering. 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). , p.1240. https://doi.org/10.1109/IDCIOT64235.2025.10914815.

43. Jingwei Zhang. (2024). RoBERTa-based Video-Associated Text Feature Explainable Performance Prediction. Proceedings of the 2024 7th International Conference on Computer Information Science and Artificial Intelligence. , p.587. https://doi.org/10.1145/3703187.3703286.

44. Tyler Alfonzetti, Junyi Xia. (2025). Transforming the Landscape of Clinical Information Retrieval Using Generative Artificial Intelligence: An Application in Machine Fault Analysis. Practical Radiation Oncology, 15(5), p.e492. https://doi.org/10.1016/j.prro.2025.02.006.

45. Hyerin Kwon, Laila Abbas, Jiwon Kang, Linqi Lu, Douglas M. McLeod. (2026). Leveraging the power of ChatGPT to analyze policy framing: policy agendas and issue positions of U.S. governors during the COVID-19 crisis. Data & Policy, 8 https://doi.org/10.1017/dap.2025.10052.

46. Denis Donadel, Francesco Marchiori, Luca Pajola, Mauro Conti. (2024). Can LLMs Understand Computer Networks? Towards a Virtual System Administrator. 2024 IEEE 49th Conference on Local Computer Networks (LCN). , p.1. https://doi.org/10.1109/LCN60385.2024.10639641.

47. Joanna Alcruz, Mubina Khan Schroeder. (2025). Integrating Generative AI into writing instruction: A cognitive apprenticeship approach to navigating technology and pedagogy. Theory Into Practice, 64(4), p.390. https://doi.org/10.1080/00405841.2025.2528542.

48. Anna Alexandrovna Arinushkina, Vladimir I. Abramov, Eteri V. Mindzaeva. (2024). Integration Strategies of Generative AI in Higher Education. Advances in Educational Technologies and Instructional Design. , p.283. https://doi.org/10.4018/979-8-3693-5518-3.ch014.

49. Aras Bozkurt. (2024). Tell Me Your Prompts and I Will Make Them True: The Alchemy of Prompt Engineering and Generative AI. Open Praxis, 16(2), p.111. https://doi.org/10.55982/openpraxis.16.2.661.

50. Magdalena Pando, Maricela León. (2025). Mathematics disciplinary literacy: a case study of a bilingual teacher’s interaction with ChatGPT. Language and Education, , p.1. https://doi.org/10.1080/09500782.2025.2601055.

51. Giuliana Barba, Mariangela Lazoi, Marianna Lezzi. (2025). Human-Centred Technology Management for a Sustainable Future. Springer Proceedings in Business and Economics. , p.109. https://doi.org/10.1007/978-3-031-72494-7_12.

52. Jakob Güttinger, Jan-Niclas Fenger, Thomas Kirchmeier, Christoph Legat. (2025). Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. Studies in Computational Intelligence. 1197, p.17. https://doi.org/10.1007/978-3-031-85316-6_2.

53. James F. Leathrum, Yuzhong Shen, Masha Sosonkina. (2024). Investigating the Use of Generative ai in M&S Education. 2024 Winter Simulation Conference (WSC). , p.3142. https://doi.org/10.1109/WSC63780.2024.10838805.

54. Samuel Ojuri, The Anh Han, Raymond Chiong, Alessandro Di Stefano. (2025). Optimizing text-to-SQL conversion techniques through the integration of intelligent agents and large language models. Information Processing & Management, 62(5), p.104136. https://doi.org/10.1016/j.ipm.2025.104136.

55. Vishwa Bhatt, Zhixin Yu, Yunfei Hou, Bilal Khan, Khalil Dajani, Jennifer Jin. (2025). From Prompts to Performance: Leveraging LLMs for Enhanced Educational AI Interactions. 2025 IEEE Conference on Artificial Intelligence (CAI). , p.188. https://doi.org/10.1109/CAI64502.2025.00037.

56. Jiyeon Park, Sam Choo. (2025). Generative AI Prompt Engineering for Educators: Practical Strategies. Journal of Special Education Technology, 40(3), p.411. https://doi.org/10.1177/01626434241298954.

57. Yan Ma, Jingjing Xie, Liang He, Kailong Zhang, Xiongmei Zeng, Quan Ouyang, Danwei Wang. (2025). A personalized human–machine cooperative approach with transformer-based recognition for longitudinal and lateral control of intelligent vehicles. Engineering Applications of Artificial Intelligence, 160, p.111816. https://doi.org/10.1016/j.engappai.2025.111816.

58. Mohamadreza Azar Nasrabadi, Yvan Beauregard, Amir Ekhlassi. (2026). A Consumer-Centric Framework for Measuring Product Obsolescence Using User-Generated Content and Large Language Models: Evidence From IoT Devices. IEEE Transactions on Engineering Management, 73, p.448. https://doi.org/10.1109/TEM.2025.3640012.

59. Mehedi Tajrian, Azizur Rahman, Muhammad Ashad Kabir, Rafiqul Islam. (2026). Leveraging Bayesian models to classify myths and facts in child development. Discover Artificial Intelligence, https://doi.org/10.1007/s44163-025-00771-2.

60. Simon Wilbers, Leonardo Espinosa-Leal, Ron van de Sand, Jörg Reiff-Stephan. (2023). Overall Prompting Effectiveness for Optimising Human-Machine Interaction in Cyber-Physical Systems. Journal of Integrated Design and Process Science: Transactions of the SDPS, Official Journal of the Society for Design and Process Science, 27(3-4), p.211. https://doi.org/10.1177/10920617241295859.

61. Giuliana Barba, Angelo Corallo, Mariangela Lazoi, Marianna Lezzi. (2025). Large language models for competence-based HRM: A case study in the aerospace industry. Journal of Innovation & Knowledge, 10(5), p.100780. https://doi.org/10.1016/j.jik.2025.100780.

62. Benjamin Kasierski, Emma Fagnano. (2024). Optimizing the Grant Writing Process: A Framework for Creating a Grant Writing Assistant Using ChatGPT 4. Proceedings of the 42nd ACM International Conference on Design of Communication. , p.286. https://doi.org/10.1145/3641237.3691693.

63. Peter Crosthwaite, Simone Smala, Franciele Spinelli. (2025). Prompting for pedagogy? Australian F-10 teachers’ generative AI prompting use cases. The Australian Educational Researcher, 52(3), p.1795. https://doi.org/10.1007/s13384-024-00787-0.

64. Ali Akhavan, Mohammad S. Jalali. (2023). Generative AI and Simulation Modeling: How Should You (Not) Use Large Language Models Like ChatGPT. SSRN Electronic Journal, https://doi.org/10.2139/ssrn.4675409.

65. Gwo-Jen Hwang, Pei-Yu Cheng, Ching-Yi Chang. (2025). Facilitating students’ critical thinking, metacognition and problem-solving tendencies in geriatric nursing class: A mixed-method study. Nurse Education in Practice, 83, p.104266. https://doi.org/10.1016/j.nepr.2025.104266.

66. Ioannis Dourvas, George Kokkonis, Sotirios Kontogiannis. (2026). Reconceptualizing Prompt Engineering as Reflective Professional Practice: A Framework for Teacher Development. Electronics, 15(5), p.930. https://doi.org/10.3390/electronics15050930.

67. Ke Shen, Mayank Kejriwal. (2025). Defining and evaluating decision and composite risk in language models applied to natural language inference. Engineering Applications of Artificial Intelligence, 161, p.112253. https://doi.org/10.1016/j.engappai.2025.112253.

68. Xiao Tang, Zhuying Li, Xin Sun, Xuhai Xu, Min-Ling Zhang. (2025). ZzzMate: Designing an Empathetic Chatbot for Addressing Self-Conscious Emotions for Sleep Adherence. International Journal of Human–Computer Interaction, , p.1. https://doi.org/10.1080/10447318.2025.2589503.

69. Ali Akhavan, Mohammad S. Jalali. (2024). Generative AI and simulation modeling: how should you (not) use large language models like ChatGPT. System Dynamics Review, 40(3) https://doi.org/10.1002/sdr.1773.

70. Βασιλική Ροδοπούλου, Αγγελική Χρυσάνθη. (2026). Μπορεί η Τεχνητή Νοημοσύνη να σχεδιάσει πολιτιστικές διαδραστικές αφηγήσεις; Μια συγκριτική αξιολόγηση σχεδίασης διαδραστικών αφηγήσεων μέσω ΠΤΝ. Ετήσιο Ελληνόφωνο Επιστημονικό Συνέδριο Εργαστηρίων Επικοινωνίας, 4(1), p.347. https://doi.org/10.12681/cclabs.9693.

71. Yiziting Zhu, Xin Shu, Xiang Liu, Yujie Li, Bin Yi, Daqing Ma. (2025). Applications and challenges of large language models in anesthesiology: narrative review and future perspectives. Anesthesiology and Perioperative Science, 3(4) https://doi.org/10.1007/s44254-025-00156-9.

72. Eyman Alyahyan, Mireilla Bikanga Ada, Jake Lever. (2025). WIP: A Pedagogical Prompt Engineering Framework for LLM-Based Feedback in Higher Education (PPE-LLM). 2025 IEEE Frontiers in Education Conference (FIE). , p.1. https://doi.org/10.1109/FIE63693.2025.11328552.

73. Xinyu Zhang, Siddharth Muralee, Sourag Cherupattamoolayil, Aravind Machiry. (2024). On the Effectiveness of Large Language Models for GitHub Workflows. Proceedings of the 19th International Conference on Availability, Reliability and Security. , p.1. https://doi.org/10.1145/3664476.3664497.

74. Aisvarya Adeseye, Jouni Isoaho, Mohammad Tahir. (2025). Systematic Prompt Framework for Qualitative Data Analysis: Designing System and User Prompts. 2025 IEEE 5th International Conference on Human-Machine Systems (ICHMS). , p.229. https://doi.org/10.1109/ICHMS65439.2025.11154183.

75. Aishwarya Padmanabhan, Simon Stankoski, Filip Panchevski, Rhiannon M. Armitage, Sophia Cox, Claire Baert, Borjan Sazdov, Mia Darkovska, Ivana Kiprijanovska, Mohsen Fatoorechi, Bojan Sofronievski, Elena Indovska, Angela Nikodinovska, Stefani Kulebanova, Andrew Cleal, Martin Gjoreski, Tatum Sevenoaks, Charles Nduka, Hristijan Gjoreski, Martin R. Yeomans. (2026). Validating OCOsense smart glasses in a three-week home-based study: Assessing detection of eating, food identification and the use of haptic feedback to aid behaviour modification. Appetite, 218, p.108382. https://doi.org/10.1016/j.appet.2025.108382.

76. Anjani Kumar, Deepanshu Mishra, Harsh Raj, Yash Tomar. (2024). Proceedings of 4th International Conference on ICT for Digital, Smart, and Sustainable Development. Innovations in Sustainable Technologies and Computing. , p.593. https://doi.org/10.1007/978-981-97-7831-7_38.

77. Xiaorui Yang, Honghui Li, Yi Xu, Yunhao Deng, Yanhui Bai, Shufang Liu. (2025). Hybrid semantic-structural graph neural network for fault knowledge graph completion in railway operational equipment. Engineering Applications of Artificial Intelligence, 162, p.112490. https://doi.org/10.1016/j.engappai.2025.112490.

78. Phillip Olla, Lauren Elliott, Mustafa Abumeeiz, Karen Mihelich, Joshua Olson. (2024). Promptology: Enhancing Human–AI Interaction in Large Language Models. Information, 15(10), p.634. https://doi.org/10.3390/info15100634.

79. Ana Teixeira de Melo, Letícia Renault, Leo S. D. Caves, Philip Garnett, Paula Duarte Lopes, Raquel Ribeiro, Filipe Santos. (2025). An AI tool for scaffolding complex thinking: challenges and solutions in developing an LLM prompt protocol suite. Cognition, Technology & Work, 27(3), p.651. https://doi.org/10.1007/s10111-025-00817-6.

80. David Badajoz-Dávila, Silvia Andrea Cristian Ladaga. (2025). Redefiniendo el diseño gráfico: La disrupción de la inteligencia artificial en la profesión. Temes de Disseny, https://doi.org/10.46467/TdD.2025.431638.

81. Zhuoxuan Jiang, Tianyang Zhang, Shengguang Bai, Lin Lin, Haotian Zhang, Yinong Xun, Jiawei Ren, Wen Si, Shaohua Zhang. (2026). Towards enterprise-specific question-answering for IT operations and maintenance based on retrieval-augmented generation mechanism. Expert Systems with Applications, 307, p.130961. https://doi.org/10.1016/j.eswa.2025.130961.

82. Anton Bashev, Stanislav Asenov. (2025). AI in Computer-Aid Design of Components and Assemblies in Mechanical Engineering Education. 2025 33rd National Conference with International Participation (TELECOM). , p.1. https://doi.org/10.1109/TELECOM66943.2025.11304041.

83. Sung Hyun Lee, John A. Velez, Dae-won Noh. (2025). Beyond Prompt Engineering: Exploring Collaborative Dialogue with Generative AI for Problem-Solving. Cyberpsychology, Behavior, and Social Networking, 28(8), p.574. https://doi.org/10.1089/cyber.2024.0614.

84. Jinghong Li, Huy Phan, Wen Gu, Koichi Ota, Shinobu Hasegawa. (2024). Fish-Bone Diagram of Research Issue: Gain a Bird's-Eye View on a Specific Research Topic. 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). , p.4936. https://doi.org/10.1109/SMC54092.2024.10830995.

85. Abrar Khan Alvi, Rafi Ibne Azad, Md Elhum Uddin, Saha Reno. (2025). Preserving Ownership and Trading of AI Prompts in Decentralized Manner via Hyperledger Besu-Based Blockchain and IPFS-Backed NFTs. 2025 2nd International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM). , p.1. https://doi.org/10.1109/NCIM65934.2025.11160076.

86. Sandro Speth, Niklas Meißner, Steffen Becker. (2024). ChatGPT's Aptitude in Utilizing UML Diagrams for Software Engineering Exercise Generation. 2024 36th International Conference on Software Engineering Education and Training (CSEE&T). , p.1. https://doi.org/10.1109/CSEET62301.2024.10663027.

87. Christopher J. Lynch, Erik Jensen, Ross Gore, Virginia Zamponi, Kevin O’Brien, Brandon Feldhaus, Katherine Smith, Joseph Martínez, Madison H. Munro, Timur E. Ozkose, Tugce B. Gundogdu, Ann Marie Reinhold, Hamdi Kavak, Barry Ezell. (2025). AI-Generated Messaging for Life Events Using Structured Prompts: A Comparative Study of GPT With Human Experts and Machine Learning. IEEE Access, 13, p.147002. https://doi.org/10.1109/ACCESS.2025.3600146.

88. Alireza Mortezapour. (2026). Large language models in the service of ergonomics education: a theoretical discussion on extending the ergonomics curriculum through prompt engineering. Theoretical Issues in Ergonomics Science, 27(1), p.78. https://doi.org/10.1080/1463922X.2025.2568125.

89. Daniel Camacho Leal, Azael Capetillo, David Güemes Castorena. (2025). Prompting Literature Review: The Impact of Transformer Models and ChatGPT on Prompt Engineering and AI Research Publications. 2025 Institute for the Future of Education Conference (IFE). , p.1. https://doi.org/10.1109/IFE63672.2025.11024600.

90. Philip Slobodsky, Mariana Durcheva. (2024). Comprehensive E-learning of Mathematics using the Halomda Platform enhanced with AI tools. Journal of Engineering Research and Sciences, 3(4), p.10. https://doi.org/10.55708/js0304002.

91. Ana Rita Correia, Jorge Brandão Pereira. (2025). Marketing and Smart Technologies. Smart Innovation, Systems and Technologies. 438, p.249. https://doi.org/10.1007/978-981-96-3077-6_15.

92. Jan Ferrer i Picó, Michelle Catta-Preta, Alex Trejo Omeñaca, Marc Vidal, Josep Maria Monguet i Fierro. (2025). The Time Machine: Future Scenario Generation Through Generative AI Tools. Future Internet, 17(1), p.48. https://doi.org/10.3390/fi17010048.

93. Vasiliki Kollia, Dimosthenis Manias, Irene Mavrommati, Tharrenos Bratitsis, Iro Laskari, Ioannis Chatzigiannakis. (2026). ArtsIT, Interactivity and Game Creation. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 650, p.269. https://doi.org/10.1007/978-3-031-97254-6_19.

94. Jianhua Ma, Yongzhang Zhou, Luhao He, Qianlong Zhang, Muhammad Atif Bilal, Yuqing Zhang. (2025). Enhancing Geological Knowledge Engineering with Retrieval-Augmented Generation: A Case Study of the Qin–Hang Metallogenic Belt. Minerals, 15(10), p.1023. https://doi.org/10.3390/min15101023.

95. Ana-Paula Correia, Sean Hickey, Fan Xu. (2025). Realizing the possibilities of the large language models: Strategies for prompt engineering in educational inquiries. Theory Into Practice, 64(4), p.434. https://doi.org/10.1080/00405841.2025.2528545.

96. Yoonkyoung Choi, Minjeong Lee, Sooyoung Han, Jinyoung Han. (2025). Effects of Prompt Elements on Problem-Solving Performance and User Experience: Insights from ChatGPT Interactions. Sage Open, 15(4) https://doi.org/10.1177/21582440251381680.

97. Amrita Singh, Aditya Joshi, Jiaojiao Jiang, Hye-young Paik. (2025). A survey of classification tasks and approaches for legal contracts. Artificial Intelligence Review, 58(12) https://doi.org/10.1007/s10462-025-11359-8.

98. Dina Nawara, Rasha Kashef. (2025). A Comprehensive Survey on LLM-Powered Recommender Systems: From Discriminative, Generative to Multi-Modal Paradigms. IEEE Access, 13, p.145772. https://doi.org/10.1109/ACCESS.2025.3599832.

99. Konstantinos Pechlivanis, Dimitrios Ziakkas. (2026). Prompt engineering in generative AI applications for commercial single pilot operations (SiPO): An emerging competency. Journal of Air Transport Management, 131, p.102916. https://doi.org/10.1016/j.jairtraman.2025.102916.

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