This research evaluates generative AI role-play simulations for developing professional competencies in Law and Forensic Science. Using a mixed-methods approach, it validates scalable, AI-mediated practice and assessment frameworks.

This ongoing research (2025–Present) utilizes Coursemology data to analyze AI help-seeking in programming. By applying AST-based frameworks, it distinguishes between genuine scaffolded learning and simple task offloading to improve educational outcomes.

This longitudinal NUS study (2024–Ongoing) investigates interdisciplinary LLM adoption by comparing self-reported attitudes with actual usage logs. It identifies behavioral gaps to understand how students critically engage with AI over time.

This 2024–2025 research addresses LLM-as-a-judge “agreeableness bias” (TNR < 25%). We propose a minority-veto strategy and regression-based framework using human-annotated data to ensure reliable, scalable, and unbiased evaluation of LLM-generated content.

This 2024–2025 research at IIT Kanpur explores a hybrid model where Codaveri AI augments Teaching Assistants. This human-in-the-loop approach provides scalable, scaffolded programming feedback, ensuring high-quality instruction and enhanced lab efficiency.