Engaging HBCU Students in Active Learning for Blockchain, FinTech, and Generative AI: A Human-Centered Pedagogical Framework

Abstract

The convergence of blockchain, financial technology, digital assets, and generative artificial intelligence creates important opportunities and challenges for undergraduate education, especially at Historically Black Colleges and Universities. Students preparing for careers in business, computing, analytics, cybersecurity, and financial services need more than exposure to emerging technologies; they also need confidence, collaboration skills, ethical awareness, computational fluency, and experience applying tools to realistic problems. This paper proposes a human-centered pedagogical framework to engage HBCU students in active learning about blockchain, FinTech, and generative AI. The framework synthesizes classroom-tested strategies from computer information systems, business analytics, programming, mathematics, and interdisciplinary student research. It emphasizes structured collaboration, low-stakes practice, role rotation, peer evaluation, reflective learning, responsible use of generative AI, reproducible computational notebooks, and applied problem contexts such as credit risk, fraud detection, investment analysis, digital trust, and smart financial systems. The paper presents an evidence-informed model that instructors can adapt across introductory and advanced undergraduate courses. The framework suggests that active, collaborative, and AI-supported pedagogy can increase student participation, reduce anxiety around technical topics, support inclusive learning, and strengthen workforce readiness for emerging blockchain and FinTech ecosystems.