Reciprocal Prompt Co-Adaptation with GPT-4 for entrepreneurship education

Abstract

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.

Keywords

Entrepreneurship education / Entrepreneurial mindset / Human–artificial intelligence interaction / Large language models / Prompt engineering / Creative self-efficacy

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