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Author: search.filters.author.Limcharoen, Chananyu

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    A Persona-based Automated Evaluation Framework for Intelligent AI Teaching Assistants
    (2026-06-16)
    Limcharoen, Chananyu
    ;
    Sripinta, Keetaphat
    ;
    Pasupa, Kitsuchart
    To address global teacher shortages and the need for personalized learning, we develop an intelligent teaching assistant leveraging multimodal large language models, agentic retrieval-augmented generation, and function calling. Unlike standard models, our system provides verifiable pedagogical guidance by reasoning across heterogeneous resources, including lecture slides and instructional videos. To overcome the scarcity of real-world datasets and high human evaluation costs, we propose a scalable, human-annotation-free framework utilizing simulated learner agents grounded in the Big Five personality theory. This allows for systematic assessment across 18 distinct student personas. We introduce a novel process-level metric, the dialogue recovery rate, and a dynamic adaptive policy to mitigate conversational deadlocks. Experimental results across 1,800 simulated dialogues using the Qwen3 family (8B, 14B, 32B) and its Thai-variant (Typhoon 2.5) reveal that Qwen3-14B attains the highest robustness and aggregate tutor-performance score within the proposed framework (72.6%). Analysis demonstrates significant correlations between learner traits and performance: Conscientiousness positively correlates with success (r = 0.49), while Extraversion is negatively associated with structural adherence (r = -0.54). This work establishes a reproducible benchmarking protocol for persona-aware, adaptive intelligent tutoring systems.
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    ThaiBKD: Effective of Continual Pre-Training LLM in Thai Language Based on Knowledge Dataset
    (2024-01-01)
    Phasook, Pakawat
    ;
    Pranee, Jessada
    ;
    Limcharoen, Chananyu
    ;
    Sukhantharat, Kittisak
    ;
    Saeoueng, Anon
    Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing (NLP) tasks. Existing works emphasize the importance of continual pretraining with high-quality knowledge data to enhance LLM performance. This paper investigates the comparative effectiveness of using meticulously curated, high-quality knowledge datasets - comprising scholarly articles, journalism, medical expertise, financial data, legal documents, and verified library resources - against general data sourced from social media and diverse domain sources, particularly from the popular Thai blog platform Pantip, as pre-training data for LLMs.The evaluation results show that ThaiBKD-based LLM, trained on clean and domain-specific data, consistently outperforms Pantip-based LLM in most aspects of Thai language evaluation tasks, demonstrating more effective reasoning capabilities and achieving higher scores across various benchmarks. Notably, the Thai Language Based on Knowledge Dataset (ThaiBKD) LLM outperforms or higher than the performance of GPT-3.5 Turbo in a versatile evaluation set, particularly in tasks requiring specialized knowledge.However, the Pantip-based LLM exhibits substantial strengths in culturally nuanced tasks, such as XCOPA, XNLI, and Belebele, where its understanding of informal and diverse language structures from social media provides a competitive edge. These findings highlight the nuanced trade-offs between data quality, domain specificity, and cultural relevance, underscoring the need for strategic data selection in the pre-training of advanced Large language Models and Continual-pretraining Large Language Models.