This article presents an innovative pedagogical framework that integrates real-world data analysis and artificial intelligence (AI) tools into macroeconomic education within the context of a flipped classroom model. Through a structured four-phase methodology—diagnostic pre-test, empirical data construction and visualization, post-test, and class discussion—, the model promotes visual literacy, analytical reasoning, and evidence-based policy evaluation. The Phillips curve is used as a core case study, allowing students to assess its empirical relevance from the Samuelson-Solow formulation to its recent flattening in the context of credible monetary policy regimes and anchored inflation expectations. The proposed methodology employs the FRED® platform (Federal Reserve Bank of St. Louis) and ChatGPT 4.0 to support autonomous learning, enhance students’ capacity for data mining and visualization, and guide hypothesis formulation. AI tools are integrated to perform K-means cluster analysis, identify structural shifts in inflation-unemployment dynamics, and statistically validate model assumptions. This integration of data-driven learning, AI-based feedback, and theoretical content enables students to bridge the gap between abstract theoretical models and economic reality. Evidence gathered from two academic cohorts reveals substantial improvements in students’ capacity to analyze and interpret economic data, apply theoretical knowledge to real-world scenarios, and formulate well-founded economic diagnoses. The activity reinforces core professional competencies—such as data literacy, digital fluency, and critical thinking—that are essential in contemporary economic practice.