A Proposed AI-Driven EdTech Model to Improve Credit Behaviour and Enterprise Growth

Abstract

Learning tools powered by artificial intelligence are proposed to improve credit behavior and enterprise growth among microfinance borrowers. Existing reviews do not provide a pilot-ready blueprint that links educational content to measurable intermediate outcomes and governance for microfinance pilots. This manuscript defines a reproducible design-science blueprint and a pilot protocol that implements tailored microlearning, personalized nudges, advisory guided by predictions, and human review. The primary outcome is the on-time repayment rate measured over six months (180 days) post-index. Secondary outcomes are default and days delinquent. Deliverables include a logic model, an outcome catalogue, a pilot protocol with leakage controls, governance materials, and a synthetic data generator to support partner pilots. These artifacts enable implementers to run a governed, entity-level randomized pilot in partnership with microfinance lenders.