Reciprocal Prompt Co-Adaptation with GPT-4 for entrepreneurship education
Generative artificial intelligence (AI) is increasingly used in education, but its role in the development of an entrepreneurial mindset remains underexplored. We propose Reciprocal Prompt Co-Adaptation (RPCA), a closed-loop human–AI scaffolding framework that supports the development of an entrepreneurial mindset through iterative prompt refinement between students and GPT-4. In this framework, “reciprocal” refers to interaction-level adaptation: students provide ratings and reflections, and the system revises prompts and coaching strategies accordingly. It does not imply that GPT-4 learns in the human sense or updates its model parameters. Unlike static or one-sided adaptive systems, RPCA integrates three components: (i) a prompt alignment module that incorporates student ratings and reflections through meta-prompting, (ii) an entrepreneurship-specific reward-shaping engine inspired by reinforcement learning from human feedback, and (iii) a confidence tracking layer that adjusts scaffolding and challenge based on textual, behavioral, and brief self-report signals. In an eight-week randomized experiment with 120 Thai undergraduates across four conditions, namely RPCA, static templates, adaptive prompting, and human–AI hybrid, RPCA produced significantly larger pre-to-post gains in opportunity recognition, risk propensity, and creative self-efficacy compared with all baseline conditions. It also achieved higher session-level prompt relevance and faster convergence to effective prompts. An embedded ablation study further suggested that removing reward shaping reduced advantages in opportunity recognition and creative self-efficacy, whereas removing confidence tracking reduced creative self-efficacy and perceived prompt quality. To support transparency and replicability, the interaction protocol, rubric-guided scoring criteria, and illustrative examples of prompt revision are specified in the appendices. These findings provide initial evidence that off-the-shelf large language models, when embedded in structured, feedback-driven tutoring protocols, can serve as adaptive, dialogic scaffolds rather than static content providers, offering a scalable approach to supporting the development of a nonlinear mindset in entrepreneurship education.
Abulela, M. A. (2023). Development and initial validation of a creative self-efficacy scale for undergraduates: Categorical confirmatory factor analysis and multidimensional item response theory. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1306532
Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431–440. https://doi.org/10.1007/s43681-021-00096-7
Alvero, A. J., Lee, J., Regla-Vargas, A., Kizilcec, R. F., Joachims, T., & Antonio, A. L. (2024). Large language models, social demography, and hegemony: Comparing authorship in human and synthetic text. Journal of Big Data, 11(1), 138. https://doi.org/10.1186/s40537-024-00986-7
Chaudhari, S., Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., Deshpande, A., & Castro da Silva, B. (2025). RLHF deciphered: A critical analysis of reinforcement learning from human feedback for LLMs. ACM Computing Surveys, 58(2), 1–37. https://doi.org/10.1145/3743127
Chen, L., Ifenthaler, D., Yau, J. Y.-K., & Sun, W. (2024). Artificial intelligence in entrepreneurship education: A scoping review. Education + Training, 66(6), 589–608. https://doi.org/10.1108/ET-05-2023-0169
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2024.105224
Diyab, A., Frost, R. M., Fedoruk, B. D., & Diyab, A. (2025). Engineered prompts in ChatGPT for educational assessment in software engineering and computer science. Education Sciences, 15(2), 156. https://doi.org/10.3390/educsci15020156
Dokic, K., Pisker, B., & Radisic, B. (2025). Mirroring cultural dominance: Disclosing large language models’ social values, attitudes and stereotypes. Societies, 15(5), 142. https://doi.org/10.3390/soc15050142
Federiakin, D., Molerov, D., & Zlatkin-Troitschanskaia, O. (2024). Prompt engineering as a new 21st-century skill: A systematic review of emerging research. Frontiers in Education, 9, 1366434. https://doi.org/10.3389/feduc.2024.1366434
Gedrimiene, E., Celik, I., Kaasila, A., Mäkitalo, K., & Muukkonen, H. (2023). Learning analytics in the context of lifelong guidance: User expectations. In Proceedings of the 17th International Conference of the Learning Sciences (pp. 1795– 1796). https://doi.org/10.22318/icls2023.936720
Glass, C. (2025). A systems approach to creative flourishing: Conceptual foundations and implications for development. Frontiers in Psychology, 16, 1518993. https://doi.org/10.3389/fpsyg.2025.1518993
González Barman, K., Lohse, S., & de Regt, H. W. (2025). Reinforcement learning from human feedback in LLMs: Whose culture, whose values, whose perspectives? Philosophy & Technology, 38(2), 35. https://doi.org/10.1007/s13347-025-00861-0
Guerrero-Sosa, J. D., Romero, F. P., Menéndez-Domínguez, V. H., Serrano-Guerrero, J., Montoro-Montarroso, A., & Olivas, J. A. (2025). A comprehensive review of multimodal analysis in education. Applied Sciences, 15(11), 5896. https://doi.org/10.3390/app15115896
He, W.-J., & Chiang, T.-W. (2024). From growth and fixed creative mindsets to creative thinking: An investigation of the mediating role of creativity motivation. Frontiers in Psychology, 15, 1353271. https://doi.org/10.3389/fpsyg.2024.1353271
Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542–570. https://doi.org/10.1111/ejed.12533
Hou, F., et al. (2022). A multilevel model of entrepreneurship education and entrepreneurial intention: Opportunity recognition as a mediator and entrepreneurial learning as a moderator. Frontiers in Psychology, 13, 837388. https://doi.org/10.3389/fpsyg.2022.837388
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2022). Lora: Low-rank adaptation of large language models. arXiv. https://doi.org/10.48550/arXiv.2106.09685
Karwowski, M., & Beghetto, R. A. (2019). Creative behavior as agentic action. Psychology of Aesthetics, Creativity, and the Arts, 13(4), 402–415. https://doi.org/10.1037/aca0000190
Kim, J., Lee, H., & Cho, Y. H. (2022). Learning design to support student–AI collaboration: Perspectives of leading teachers for AI in education. Education and Information Technologies, 27(5), 6069–6104. https://doi.org/10.1007/s10639-021-10831-6
Kuckertz, A., Kollmann, T., Krell, P., & Stöckmann, C. (2017). Understanding, differentiating, and measuring opportunity recognition and opportunity exploitation. International Journal of Entrepreneurial Behavior & Research, 23(1), 78–97. https://doi.org/10.1108/IJEBR-12-2015-0290
Lee, A., & Jung, E. (2021). The mediating role of entrepreneurial mindset between intolerance of uncertainty and career adaptability. Sustainability, 13(13), 7099. https://doi.org/10.3390/su13137099
Lee, D., & Palmer, E. (2025). Prompt engineering in higher education: A systematic review to help inform curricula. International Journal of Educational Technology in Higher Education, 22(1), 7. https://doi.org/10.1186/s41239-025-00503-7
Létourneau, A., Deslandes Martineau, M., Charland, P., Boasen, J., & Léger, P. M. (2025). A systematic review of AI-driven intelligent tutoring systems in K-12 education. npj Science of Learning, 10(1), 29. https://doi.org/10.1038/s41539-025-00320-7
Liu, Q., & Khalil, M. (2023). Understanding privacy and data protection issues in learning analytics using a systematic review. British Journal of Educational Technology, 54(6), 1715–1747. https://doi.org/10.1111/bjet.13388
López-Muñoz, J. F., Mira-Solves, I., Novejarque-Civera, J., & Pisá-Bó, M. (2023). Entrepreneurial education and opportunity entrepreneurship: The mediation of self-efficacy belief. Economic Research-Ekonomska Istraživanja, 36(3). https://doi.org/10.1080/1331677X.2022.2159472
Molenaar, I. (2022). Towards hybrid human–AI learning technologies. European Journal of Education, 57(4), 632– 645. https://doi.org/10.1111/ejed.12527
Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & Fernández-Leal, Á. (2023). Human-in-the-loop machine learning: A state of the art. Artificial Intelligence Review, 56(4), 3005–3054. https://doi.org/10.1007/s10462-022-10246-w
Novelli, C., Casolari, F., Hacker, P., Spedicato, G., & Floridi, L. (2024). Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity. Computer Law & Security Review, 55, 106066. https://doi.org/10.1016/j.clsr.2024.106066
Qian, Y. (2025). Prompt engineering in education: A systematic review of approaches and educational applications. Journal of Educational Computing Research, 63(7–8), 1782–1818. https://doi.org/10.1177/07356331251365189
Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT networks. arXiv. https://doi.org/10.18653/v1/D19-1410
Richard, V., Holder, D., & Cairney, J. (2021). Creativity in motion: Examining the creative potential system and enriched movement activities as a way to ignite it. Frontiers in Psychology, 12, 690710. https://doi.org/10.3389/fpsyg.2021.690710
Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3
Sandhu, K., Sarkar, P., & Subburaj, K. (2025). Entrepreneurial thinking in engineering design education: A comparative study of cognitive paradigms with insights from industry and academia. Entrepreneurship Education, 1–44. https://doi.org/10.1007/s41959-025-00158-5
Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: A tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19(1), 12.https://doi.org/10.1186/s41239-021-00313-7
Tao, Y., Viberg, O., Baker, R. S., & Kizilcec, R. F. (2024). Cultural bias and cultural alignment of large language models. PNAS Nexus, 3(9), 346. https://doi.org/10.1093/pnasnexus/pgae346
Tierney, P., & Farmer, S. M. (2002). Creative self-efficacy: Its potential antecedents and relationship to creative performance. Academy of Management Journal, 45(6), 1137–1148. https://doi.org/10.5465/3069429
Valcea, S., Hamdani, M. R., & Wang, S. (2024). Exploring the impact of ChatGPT on business school education: Prospects, boundaries, and paradoxes. Journal of Management Education, 48(5), 915–947. https://doi.org/10.1177/10525629241261313
Yan, Y., Liu, H., & Chau, T. (2025). A systematic review of AI ethics in education: Challenges, policy gaps, and future directions. Journal of Global Information Management, 33(1), 1–50. https://doi.org/10.4018/JGIM.386381
Yan, Z., Wang, Y., & Zhang, L. (2022). Enhancing students’ self-efficacy in creativity and creative thinking: A self-assessment mind-map intervention. Frontiers in Psychology, 13, 871781. https://doi.org/10.3389/fpsyg.2022.871781
Zhang, D. C., Highhouse, S., & Nye, C. D. (2019). Development and validation of the general risk propensity scale (GRiPS). Journal of Behavioral Decision Making, 32(2), 152– 167. https://doi.org/10.1002/bdm.2102