Integrating the FILA Framework with Generative AI to Support Independent Learning in Engineering Dynamics

This paper presents a pedagogical case study that integrates the FILA framework with generative artificial intelligence (AI) to support independent learning in Engineering Dynamics. The learning outcome (LO) requires students to analyze particle motion in planar systems by combining the Principle of Work and Energy, or the Conservation of Energy, with equations of curvilinear motion and projectile kinematics. The model was designed within Outcome-Based Education (OBE), constructive alignment, problem-based learning and self-regulated learning. In the learning task, students completed a handwritten quiz submission by applying FILA-Facts, Ideas, Learning Issues and Actions- to three selected Dynamics problems from the textbook. Generative AI is positioned as a bounded digital tutor that supports prompting, questioning, scaffolding, error checking and self-explanation, not as a source for copying final solutions. The paper describes the learning mechanism and assessment alignment. Qualitative findings were generated from an anonymized review of 51 student submissions assessed using readability, FILA accuracy and solution correctness. The class achieved a mean score of 79.9%, a median of 82.0%, and 31 of 51 submissions scored 80% or higher. The evidence suggests that FILA made problem representation visible, helped students separate facts from governing theories, and provided a pathway from conceptual planning to mathematical action. Weaker submissions revealed persistent difficulty in articulating specific learning issues and checking assumptions. The paper concludes that FILA supported by carefully guided generative AI can strengthen independent problem solving and provide visible evidence of LO attainment.

Outcome-Based Education; FILA framework; generative AI; Engineering Dynamics; self-regulated learning.

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