Fractional differential equations (FDEs) extend classical calculus to non-integer and even complex orders, providing powerful models for memory-dependent and nonlocal phenomena in science and engineering. At the same time, mathematics education increasingly relies on digital and AI-powered tools to support conceptual understanding, experimentation, and verification. This paper synthesizes three case studies on fractional calculus and the MathHandbook symbolic computation platform into an educational framework for teaching and learning fractional differential equations in secondary and tertiary settings, with a focus on Mathematics Education audience.

We review unified operator notation for integer, fractional, integral, complex, and variable orders; illustrate analytical solutions of ordinary and partial fractional differential equations; and highlight the role of validation tools such as symbolic and numerical testing. Building on the observation that “solution = general solution + particular solution = gsolution() + psolution()” and that particular solutions remain invariant across constant orders, we propose pedagogical strategies that leverage structural patterns, visualization, and AI-driven computation to scaffold students’ understanding of FDEs. The integration of MathHandbook into mathematics education demonstrates how students can explore fractional models, compare orders, and verify solutions interactively, thereby bridging theory and computation and supporting inquiry-based learning in differential equations.

Keywords: fractional differential equations, fractional calculus, mathematics education, symbolic computation, AI in education, MathHandbook.