From Disruption to Innovation: Integrating Active Learning in AI-Resilient Assessment Design - Natacha Jesus-Silva; Dos-Santos, M.J.P.L.; Maria Duarte Bello (International Conference on AI Research, 2025)
Artificial Intelligence (AI) and generative learning technologies are transforming the landscape of higher education. With tools capable of producing essays/reports, solving complex problems, and simulating critical thought, traditional assessment practices are becoming increasingly vulnerable. The rapid, widespread, and easy accessibility of generative AI raises concerns about academic dishonesty, plagiarism, and the erosion of original thought. This disruption calls for a reimagining of assessment models that are not only robust in the face of AI but also pedagogically sound. Active Learning Strategies (ALS) offer a pathway forward. Rooted in constructivist and experiential learning theories, ALS emphasizes student participation, collaboration, and real-world application. By shifting from passive learning methods to active learning engagement, these strategies promote higher-order thinking and personal investment in learning, qualities that AI cannot easily replicate. This paper aims to analyze how ALS can underpin AI-resilient assessment design, drawing insights from a scoping literature review, an applied case study from the UNESCO-ESCS Chair in Portugal and results from inquiries to students.