A pilot pre–post study of a scaffolded AI chatbot for financial literacy education among university students

This pilot study examines pre- to post-test changes in university students’ financial literacy knowledge following a one-week student-directed learning period using a pedagogically scaffolded GPT‑4o Application Programming Interface (API) chatbot. Guided by constructivist learning theory [29], the immediate feedback model [10], and self-determination theory [24], the study employed a single-group pre-test–post-test quantitative design with 32 undergraduate students at a Turkish public university. The primary outcome was financial literacy knowledge, measured with 32 factual statements rated on a six-point agreement scale and keyed against correct responses, across six subscales (Basic Economics and Finance, Individual Banking, Retirement and Insurance, Financial Statements, Investment, and Tax and Legislation). Five objective multiple-choice items served as a limited, complementary performance check. Paired-samples t-tests were used for normally distributed difference scores, and Wilcoxon signed-rank tests for non-normal difference scores; six subscale comparisons were interpreted using Holm-adjusted p values. The overall score increased from pre-test to post-test, with a large standardized mean change (d = 1.09, 95% CI [0.64, 1.52]), and all six exploratory subscale comparisons remained statistically significant after Holm adjustment. Objective item performance also increased on several items, including the compound-interest item (3.1% to 81.2%). However, because the design included no control or comparison group, identical objective items were repeated, and chatbot interaction logs were not systematically recorded, the observed changes cannot be causally attributed to the chatbot. The findings therefore provide preliminary, uncontrolled evidence that warrants testing in controlled studies rather than evidence of intervention effectiveness.