Adapting Fair Teaching and Assessment Methods for Fairness in the Age of AI Chabot’s and LLMs
Basheer Riskhan*, Abdullah Hakeem Mohamad and Maria Iqbal
ABSTRACT
This research explores how educators are adapting their teaching and assessment strategies in response to the rise of Large Language Models (LLMs) like ChatGPT. While institutions are increasingly aware of these tools, there is limited understanding of how fairness and academic integrity are maintained when students integrate LLMs into their learning. This study uses a qualitative approach, analyzing responses from educators across different institutions from various countries to understand the practical challenges, strategies, and perceptions surrounding AI in education. Key themes for this paper include LLM detection, redesigning assessments, fairness concerns, institutional support, and the dual role of AI in both enhancing and undermining student learning. The findings suggest that while many educators support responsible use of LLMs, many gaps in policy and support systems persist. Our study highlights certain portions of the ongoing debate on fair educational assessments in the age of AI, and calls for stronger institutional guidance, faculty training, and pedagogical adaptation.
