Enhancing Assessment Through AI: Practical Strategies and Implications
TL;DR
A dual pedagogical approach is proposed in which AI is integrated into assessment as a tool that supports learning while preserving meaningful evaluation of learning outcomes, demonstrating that AI-supported assessment can be implemented without introducing additional privacy risks in higher education contexts.
Abstract
The rapid adoption of artificial intelligence (AI) in higher education raises a critical challenge: how can student learning be assessed when AI systems are readily available to solve complex problems? Designing AI-proof problems that reliably assess student mastery is often infeasible. To address this challenge, this study investigates a practical assessment approach implemented in an undergraduate engineering mathematics course. Interactive exercises were developed using the STACK computer algebra system and JavaScript to create personalized tasks and structured feedback within a controlled learning environment. A sequential mixed-methods design was used, combining quantitative analysis of student performance with qualitative analysis of student feedback. The results suggest that the proposed assessment method can support students’ cognitive engagement with AI while still allowing teachers to evaluate student understanding. Rather than focusing solely on AI-proof problems, the study proposes a dual pedagogical approach in which AI is integrated into assessment as a tool that supports learning while preserving meaningful evaluation of learning outcomes. The resulting framework combines interactive tasks with a hint matrix that guides students’ interaction with AI tools and supports mastery-oriented learning. Importantly, the procedure does not require the submission of sensitive or personal data to AI systems, demonstrating that AI-supported assessment can be implemented without introducing additional privacy risks in higher education contexts.