Fostering critical thinking skills in students through prompt engineering training

The development of generative artificial intelligence (GenAI) and its integration into the industrial sector is influencing the range of skills in demand in the labor market. To prepare competitive professionals, higher education systems need to develop students’ skills in interacting with large language models (LLMs) as well as soft skills, such as critical thinking, which enable them to adapt to changing requirements. Prompt engineering, as the process of creating and modifying queries to LLMs, is becoming an important tool in education. However, existing research on the potential of prompt engineering for critical thinking development is primarily theoretical. The aim of this empirical study was to identify opportunities for developing specific aspects of critical thinking through prompt engineering training. The target sample included 93 engineering students who collaboratively completed tasks in the discipline “Legal regulation in the field of communications” using generative AI tools. Thirty-eight student dialogues with LLMs were analyzed using qualitative content analysis. Eight interaction patterns were identified, some of which were associated with the use of prompt engineering techniques. The most common pattern was the addition of information from lectures or other sources, indicating students’ efforts to refine and improve LLM responses. The data analysis revealed the manifestation of critical thinking aspects, such as analysis, comparison, and synthesis of information, in student prompts. A comparison of the results from two coding stages demonstrated a connection between three of the eight patterns and the aforementioned critical thinking aspects. The study suggests that prompt engineering training can become a promising tool for developing critical thinking in students, provided certain educational conditions are met. Additionally, the results indicate the potential of using LLM dialogue analysis to assess student skills.

1. Theophilou E., Koyutürk C., Yavari M., et al. Learning to prompt in the classroom to understand AI limits: A pilot study. AIxIA 2023 — Advances in Artificial Intelligence. Proc. 22nd Int. Conf. of the Italian Association for Artificial Intelligence. Cham, Springer; 2023:481–496. DOI: 10.1007/978-3-031-47546-7_33.

2. Wei J., Wang X., Schuurmans D., Bosma M., Ichter B., Xia F., Chi E. H., Le Q. V., Zhou D. Chain-of-thought prompting elicits reasoning in large language models. arXiv preprint. arXiv: 2201.11903. 2022:1–13. DOI: 10.48550/arXiv.2201.11903.

3. Konstantinova L. V., Vorozhikhin V. V., Petrov A. M., Titova E. S., Shtykhno D. A. Generative artificial intelligence in education: Discussions and forecasts. Open Education. 2023;27(2):36–48. (In Russian.) DOI: 10.21686/1818-4243-2023-2-36-48. EDN: VPMIZK.

4. Broecke S. Artificial intelligence and the labour market: Introduction. OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market. Paris, OECD Publishing; 2023;(1):93–101. DOI: 10.1787/08785bba-en.

5. Zarifhonarvar A. Economics of ChatGPT: A labor market view on the occupational impact of artificial intelligence. Journal of Electronic Business & Digital Economics. 2024;3(2):100–116. DOI: 10.1108/JEBDE-10-2023-0021. EDN: FQQRJP.

6. Alekseeva L., Azar J., Giné M., Samila S., Taska B. The demand for AI skills in the labor market. Labour Economics. 2021;71:102002. DOI: 10.1016/j.labeco.2021.102002. EDN: XDITGT.

7. Noy Sh., Zhang W. Experimental evidence on the productivity effects of generative artificial intelligence. Science. 2023;381(6654):187–192. DOI: 10.1126/science.adh2586. EDN: FLOVBY.

8. Brynjolfsson E., Li D., Raymond L. Generative AI at work. The Quarterly Journal of Economics. 2025;140(2):889-942. DOI: 10.1093/qje/qjae044.

9. Peng S., Kalliamvakou E., Cihon P., Demirer M. The impact of AI on developer productivity: Evidence from github copilot. arXiv preprint. arXiv:2302.06590. 2023:1–19. DOI: 10.48550/arXiv.2302.06590.

10. Vuorikari R., Kluzer S., Punie Y. DigComp 2.2: The Digital Competence framework for citizens. With new examples of knowledge, skills and attitudes. Luxembourg, Publications Office of the European Union; 2022. 126 p. DOI: 10.2760/115376.

11. Annapureddy R., Fornaroli A., Gatica-Perez D. Generative AI literacy: Twelve defining competencies. Digital Government: Research and Practice. 2025;6(1):1–21. DOI: 10.1145/3685680. EDN: MPNNXE.

12. Green A. Artificial intelligence and the changing demand for skills in the labour market. OECD Artificial Intelligence Papers. Paris, OECD Publishing. 2024;14:1–55. DOI: 10.1787/88684e36-en.

13. Hui X., Reshef O., Zhou L. The short-term effects of generative artificial intelligence on employment: Evidence from an online labor market. Organization Science. 2024;35(6):1957–2332. DOI: 10.1287/orsc.2023.18441. EDN: SUGLHV.

14. Joshi S. Retraining US workforce in the age of agentic GenAI: Role of prompt engineering and up-skilling initiatives. International Journal of Advanced Research in Science, Communication and Technology. 2025;5(1):1–15. DOI: 10.48175/IJARSCT-23272.

15. Wach K., Duong C. D., Ejdys J., Kazlauskaitė R., Korzynski P., Mazurek G., Paliszkiewicz J., Ziemba E. The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial

16. Business and Economics Review. 2023;11(2):7–30. DOI: 10.15678/eber.2023.110201. EDN: MKFWFG.

17. Lee H. P., Sarkar A., Tankelevitch L., Drosos I., Rintel S., Banks R., Wilson N. The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. Proc. 2025 CHI Conf. on Human Factors in Computing Systems. NYC, ACM; 2025;(1121):1–22. DOI: 10.1145/3706598.3713778.

18. Ivakhnenko E. N., Nikolskiy V. S. ChatGPT in higher education and science: A threat or a valuable resource? Higher Education in Russia. 2023;32(4):9–22. (In Russian.) DOI: 10.31992/0869-3617-2023-32-4-9-22. EDN: TZHIHU.

19. Fui-Hoon Nah F., Zheng R., Cai J., Siau K., Chen L. Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research. 2023;25(3):277–304. DOI: 10.1080/15228053.2023.2233814. EDN: UKLUAZ.

20. Larson B. Z., Moser C., Caza A., Muehlfeld K., Colombo L. Critical thinking in the age of generative AI. Academy of Management Learning & Education. 2024;23(3):373–378. DOI: 10.5465/amle.2024.0338. EDN: BHTYQO.

21. Liu J., Liu A., Lu X., Welleck S., West P., Le Bras R., Choi Y., Hajishirzi H. Generated knowledge prompting for commonsense reasoning. arXiv preprint. arXiv:2110.08387. 2021:1–16. DOI: 10.48550/arXiv.2110.08387.

22. Marvin G., Hellen N., Jjingo D., Nakatumba-Nabende J. Prompt engineering in large language models. Proc. Int. Conf. on Data Intelligence and Cognitive Informatics. Singapore, Springer; 2023:387–402. DOI: 10.1007/978-981-99-7962-2_30.

23. Van den Berg G., du Plessis E. ChatGPT and generative AI: Possibilities for its contribution to lesson planning, critical thinking and openness in teacher education. Education Sciences. 2023;13(10):998. DOI: 10.3390/educsci13100998. EDN: GPLFSW.

24. Cain W. Prompting change: Exploring prompt engineering in large language model AI and its potential to transform education. TechTrends. 2024;68(1):47–57. DOI: 10.1007/s11528-023-00896-0. EDN: DECGPI.

25. Lo L. S. The art and science of prompt engineering: A new literacy in the information age. Internet Reference Services Quarterly. 2023;27(4):203–210. DOI: 10.1080/10875301.2023.2227621. EDN: XIODQG.

26. Shanto S. S., Ahmed Z., Jony A. I. Enriching the learning process with generative AI: A proposed framework to cultivate critical thinking in higher education using ChatGPT. Tuijin Jishu / Journal of Propulsion Technology. 2024;45(1):3019–3029. DOI: 10.52783/tjjpt.v45.i01.4680.

27. Leung Ch. H. Promoting optimal learning with ChatGPT: A comprehensive exploration of prompt engineering in education. Asian Journal of Contemporary Education. 2024;8(2):104–114. DOI: 10.55493/5052.v8i2.5101. EDN: XASBRA.

28. Walter Y. Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education. 2024;21(1):15. DOI: 10.1186/s41239-024-00448-3. EDN: BTLYPD.

29. Lee D., Palmer E. Prompt engineering in higher education: A systematic review to help inform curricula. International Journal of Educational Technology in Higher Education. 2025;22(7):1–22. DOI: 10.1186/s41239-025-00503-7.

30. Angeli C., Valanides N. Instructional effects on critical thinking: Performance on ill-defined issues. Learning and Instruction. 2009;19(4):322–334. DOI: 10.1016/j.learninstruc.2008.06.010.

31. Behar-Horenstein L., Niu L. Teaching critical thinking skills in higher education: A review of the literature. Journal of College Teaching & Learning. 2011;8(2):25–42. DOI: 10.19030/tlc.v8i2.3554.

32. Bezanilla M. J., Fernandez-Nogueira D., Poblete M., Galindo-Domínguez H. Methodologies for teaching-learning critical thinking in higher education: The teacher’s view. Thinking Skills and Creativity. 2019;33(1):100584. DOI: 10.1016/j.tsc.2019.100584.

33. Abrami P. C., Bernard R. M., Borokhovski E., Wade A., Surkes M. A., Tamim R., Znang D. Instructional interventions affecting critical thinking skills and dispositions: Critical thinking and science education. Review of Educational Research. 2008;78(4):1102–1134. DOI: 10.3102/0034654308326084.

34. Abrami P. C., Bernard R. M., Borokhovski E., Waddington D. I., Wade C. A., Persson T. Strategies for teaching students to think critically: A meta-analysis. Review of Educational Research. 2015;85(2):275–314. DOI: 10.3102/0034654314551063.

35. Cui R., Teo P. Thinking through talk: Using dialogue to develop students’ critical thinking. Teaching and Teacher Education. 2023;125:104068. DOI: 10.1016/j.tate.2023.104068. EDN: BWJXNO.

36. Shor I., Freire P. What is the “dialogical method” of teaching? Journal of Education. 1987;169(3):11–31. DOI: 10.1177/002205748716900303.

37. O’Riordan T., Millard D. E., Schulz J. Is critical thinking happening? Testing content analysis schemes applied to MOOC discussion forums. Computer Applications in Engineering Education. 2020;29(4):690–709. DOI: 10.1002/cae.22314.

38. Uvarov A. Yu., Geybl E., Dvoretskaya I. V., Zaslavskiy I. M., Karlov I. A., Mertsalova T. A., Sergomanov P. A., Frumin I. D. Challenges and prospects of digital transformation of education. Moscow, HSE; 2019. 344 p. (In Russian.) DOI: 10.17323/978-5-7598-1990-5. EDN: ANYGHO.

39. Goda Y., Yamada M., Matsukawa H., Hata K., Yasunami S. Conversation with a chatbot before an online EFL group discussion and the effects on critical thinking. The Journal of Information and Systems in Education. 2014;13(1):1–7. DOI: 10.12937/ejsise.13.1.

40. Essel H. B., Vlachopoulos D., Essuman A. B., Amankwa J. O. ChatGPT effects on cognitive skills of undergraduate students: Receiving instant responses from AI-based conversational large language models (LLMs). Computers and Education: Artificial Intelligence. 2024;6:100198. DOI: 10.1016/j.caeai.2023.100198. EDN: LKYNRR.

41. Ruiz-Rojas L. I., Salvador-Ullauri L., Acosta-Vargas P. Collaborative working and critical thinking: Adoption of generative artificial intelligence tools in higher education. Sustainability. 2024;16(13):5367. DOI: 10.3390/su16135367. EDN: MOBTDE.

42. Robertson J., Ferreira C., Botha E., Oosthuizen K. Game changers: A generative AI prompt protocol to enhance human-AI knowledge co-construction. Business Horizons. 2024;67(5):499-510. DOI: 10.1016/j.bushor.2024.04.008. EDN: BWXXQN.

43. Sahoo P., Singh A. K., Saha S., Jain V., Mondal S., Chadha A. A systematic survey of prompt engineering in large language models: Techniques and applications. arXiv preprint. arXiv:2402.07927. 2024:1–12. DOI: 10.48550/arXiv.2402.07927.

44. Chen B., Zhang Z., Langrené N., Zhu S. Unleashing the potential of prompt engineering in large language models: A comprehensive review. arXiv preprint. arXiv:2310.14735. 2023;(1):1–58. DOI: 10.48550/arXiv.2310.14735.

45. Brown T., Mann B., Ryder N., Subbiah M., Kaplan J., Dhariwal P., Neelakantan A., Shyam P., Sastry G., Askell A., Agarwal S., Herbert-Voss A., Krueger G., Henighan T., Child R., Ramesh A., Ziegler D.M., Wu J., Winter C., Hesse C., Chen M., Sigler E., Litwin M., Gray S., Chess B., Clark J., Berner C., McCandlish S., Radford A., Sutskever I., Amodei D. Language models are few-shot learners. arXiv preprint. arXiv:2005.14165. 2020:1–75. DOI: 10.48550/arXiv.2005.14165.

46. Webb N. M. Student interaction and learning in small groups. Review of Educational Research. 1982;52(3):421-445. DOI: 10.3102/00346543052003421. EDN: JTSPUD.

47. Saldaña J. The coding manual for qualitative researchers. London, SAGE; 2013. 329 p.

48. Fereday J., Muir-Cochrane E. Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods. 2006;5(1):80–92. DOI: 10.1177/160940690600500107.

49. Schmidt D. C., Spencer-Smith J., Fu Q., White Ju. Towards a catalog of prompt patterns to enhance the discipline of prompt engineering. ACM SIGAda Ada Letters. NYC, ACM; 2024;43(2):43–51. DOI: 10.1145/3672359.3672364. EDN: ZXSFNW.

50. O’Riordan T., Millard D. E., Schulz J. Is critical thinking happening? Testing content analysis schemes applied to MOOC discussion forums. Computer Applications in Engineering Education. 2021;29(4):690–709. DOI: 10.1002/cae.22314. EDN: DEVBAL.

51. Bloom B. S., Engelhart M. D., Furst E. J., Hill W. H., Krathwohl D. R. Taxonomy of educational objectives: The classification of educational goals. Handbook 1: Cognitive Domain. NYC, Longman; 1956:403 p.

52. Adams N. E. Bloom’s taxonomy of cognitive learning objectives. Journal of the Medical Library Association. 2015;103(3):152–153. DOI: 10.3163/1536-5050.103.3.010.

53. Miri B., David B. C., Uri Z. Purposely teaching for the promotion of higher-order thinking skills: A case of critical thinking. Research in Science Education. 2007;37(4):353-369. DOI: 10.1007/s11165-006-9029-2. EDN: FXJUWK.

54. Duron R., Limbach B., Waugh W. Critical thinking framework for any discipline. International Journal of Teaching and Learning in Higher Education. 2006;17(2):160–166. Available at: https://scispace.com/pdf/critical-thinking-framework-for-any-discipline-45qgwsa0dn.pdf.

55. Dovguchits S. I. Semantic features of complex technosocial systems: On the taxonomy of artificial intelligence technological packages. Russian Technological Journal. 2023;11(6):89–98. (In Russian.) DOI: 10.32362/2500-316X-2023-11-6-89-98. EDN: ORVURY.

56. Alkaissi H., McFarlane S. I. Artificial hallucinations in ChatGPT: Implications in scientific writing. Cureus. 2023;15(2):e35179. DOI: 10.7759/cureus.35179. EDN: TDERIU.