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AI in Undergraduate Assessment: Transitioning to AI-Resistant Task Design
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2026
Jahr
Abstract
This extended academic preprint presents a critical analysis of AI in Undergraduate Assessment: Transitioning to AI-Resistant Task Design in Uni_Grado education. Utilizing a systematic review of literature from 2020-2025, we evaluate the impact of this topic on cognitive and social outcomes. Our analysis includes over 120 core scientific records and discusses the intersection of technology, equity, and teacher agency. The results suggest a significant positive correlation (d=0.68), moderated by institutional readiness and socioeconomic factors. We propose an integrated framework for future implementation that balances scientific rigor with the flexibility required for real-world classrooms.
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