<![CDATA[Background: Amid global digital disruption and ecological crises, economic education faces significant challenges regarding the relevance of graduates to the labor market. Objective: This study aims to construct a transformative model of economic education that is adaptive to technology-based market signals to enhance external efficiency within the green industry sector. Methods: Employing a mixed-methods approach with an exploratory sequential design, this research integrates Research and Development (R&D) procedures to design a dynamic curriculum framework. Data were collected through big data analytics from digital labor platforms and validated by a panel of experts and circular industry practitioners. Results: The findings reveal a significant information asymmetry between conventional curricula and the actual green competency requirements in the field. The primary results confirm that the “Adaptive-Green Economic Education” (A-GEE) transformation model, integrated with real-time market-monitoring technology, significantly improves external efficiency. Statistical tests demonstrate a positive correlation between the precision of technological signal capture and the reduction of graduate competency mismatches. These findings indicate that economic education can no longer exist as a static theoretical discipline but must instead function as an adaptive ecosystem responsive to ecological shifts. Conclusion: This study provides theoretical implications for the expansion of Signaling Theory within the context of the circular economy and offers practical recommendations for higher education institutions to reform their curricular structures so they become more agile and data-driven. This transformation is essential for producing competitive human capital in the era of the global green economy transition.]]>
