Our Research
These are the research projects AICET leads directly, spanning studies into how AI is reshaping teaching, learning, and assessment across NUS and beyond.
AI Role-Play for Competency-Based Education
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.
Explore Details →
Student Help-Seeking Behaviour with AI Tutors
This ongoing research utilises 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.
Explore Details →
Understanding How University Students Really Use LLMs
This ongoing longitudinal NUS study 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.
Explore Details →
LLM-As-A-Judge Bias
This 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.
Explore Details →
Codaveri AI-Assisted Feedback
This 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.
Explore Details →Research We Support
Beyond the projects we lead directly, AICET also contributes to and supports research led by partners and collaborators across NUS and beyond. Here’s a selection of recent work our team has been part of.
Re-imagining Discharge Summary Training through Artificial Intelligence
Proposes a training model that folds AI-generated feedback into discharge-summary writing practice for junior doctors, aiming to give trainees more individualised feedback without relying solely on faculty-intensive small-group teaching.
Evaluates INSPIRE-AI, a virtual teaching assistant used by final-year nursing honors students, finding notable gains in students’ research self-efficacy alongside qualitative insight into how they experienced the tool.