An AI-Driven Decision Framework for Promoting Sustainable Entrepreneurship in Vocational Colleges

Chao Rong

doi:10.31181/dmame7220251481

Authors

Chao Rong Wuxi Vocational Institute of Arts & Technology, personnel department, Yixing,214200, China https://orcid.org/0009-0003-0936-6033

DOI:

https://doi.org/10.31181/dmame7220251481

Keywords:

Sustainable Entrepreneurship, Vocational Education, Artificial Intelligence, Green Innovation, Decision-Making Framework

Abstract

This research investigates the application of artificial intelligence (AI)-enabled tools within vocational education to advance green innovation start-ups and promote sustainable entrepreneurship, with particular emphasis on the role of educational institutions in contributing to Sustainable Development Goal 4 (SDG 4). It addresses the pressing need to explore the interconnections between AI utilisation, digital literacy, and sustainability achievements in vocational learning environments across global contexts. The study employed a quantitative and descriptive research design, making use of secondary data sourced from the UNESCO Institute for Statistics, World Bank Open Data, OECD education statistics, and the Global Entrepreneurship Monitor. A consolidated dataset was compiled for 200 vocational institutions, encompassing seven primary attributes: regional coding, type of institution, national income classification, participation rates, digital literacy levels, AI adoption levels, and per capita funding. A refined AI-based decision-making framework was developed, integrating data pre-processing procedures, AI model construction, multi-criteria decision-making techniques, and mechanisms for continuous performance evaluation. The results demonstrate a robust positive relationship between digital literacy and AI adoption (correlation coefficient: 0.805). Employment outcomes exhibited the strongest association with institutional success (correlation coefficient: 0.965). Random Forest classification models achieved an accuracy rate of 93.3% in forecasting sustainability adoption, with AI adoption emerging as the most influential factor, contributing 89.6% to employment-related outcomes. Regional comparisons highlight pronounced inequalities, as developed regions record AI adoption levels approximately three to four times higher than those in developing areas. Bayesian analysis further indicated that institutions combining substantial funding with high AI adoption display a 92.9% likelihood of achieving effective sustainability integration.

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