Enhancing Calculus Learning Outcomes for Engineering Students through the ADAB Framework and AI Tools

Abstract

Mastering calculus is vital for engineering students, yet many struggle due to inadequate basic mathematical knowledge. Weaknesses in mastering the theory and basic concepts of mathematics significantly hinder calculus learning outcomes, as evidenced by a clear positive relationship between basic mathematics proficiency and calculus achievement. Common failure factors include an inability to understand question requirements or translate them into appropriate calculations, alongside fundamental conceptual gaps that negatively impact topics like differentiation. To address these challenges, this study explores an innovative pedagogical intervention combining the ADAB framework with AI-assisted learning technologies like NotebookLM. The primary objective is to evaluate how integrating structured frameworks and AI tools can minimize conceptual errors, bridge foundational gaps, and improve overall performance in calculus courses. The methodology implements a blended learning design utilizing the ADAB framework to structure student engagement, supported by NotebookLM to provide personalized, interactive guidance and targeted practice on complex calculus topics. Preliminary findings indicate that integrating these AI-driven tools enhances students' comprehension of mathematical rules and significantly reduces procedural errors. Ultimately, these results demonstrate that combining structured pedagogical frameworks with modern AI technologies can transform calculus education, offering educators scalable strategies to cultivate deeper analytical skills and support academic success across engineering disciplines.