My research spans work in adaptive AI and distributed systems.

My work sits at the intersection of AI, education, distributed computing, and autonomous systems. I've researched and designed adaptive assessment systems, distributed training pipelines, and LLM-based content generation tools to improve how students learn and how institutions support them. I’m also open to collaborating on broader research in AI and distributed systems. Explore my active research projects and recent outcomes below.

Research Interests

AIED, Distributed Systems, Multiagent Systems, Reinforcement Learning, Bandit Algorithms, Deep Learning

Publications

An Adaptable Client-Server Architecture for Generating Educational Content using Large Language Models

2025 - Bulletin of the TCLT

This paper presents a scalable client-server architecture that integrates large language models (LLMs) with modern web technologies to generate educational content aligned with learning objectives using retrieval-augmented generation (RAG) and structured prompt engineering. We report findings from research studies evaluating this framework in practice, highlighting both its potential and the challenges of deploying LLM-driven tools in real educational settings.

Fast Weakness Identification for Adaptive Feedback

2024 - Generative Intelligence and Intelligent Tutoring Systems - 20th International Conference on ITS

This paper presents a fast, adaptive approach to formative assessment for online learning that identifies a learner’s most critical skill weakness with minimal questioning. Framed as a good arm identification problem in multi-armed bandits, we propose three algorithms that balance assessment efficiency and reliability while enabling targeted feedback and iterative remediation. Simulation results demonstrate the sensitivity and effectiveness of the proposed methods.