TY  - EJOU
AU  - Tonde, Jeevan Pralhad 
AU  - Sankaye, Satish 

TI  - Fine Tuned QA Models for Java Programming
T2  - Journal on Artificial Intelligence

PY  - 2026
VL  - 8
IS  - 1
SN  - 2579-003X

AB  - As education continues to evolve alongside artificial intelligence, there is growing interest in how large language models (LLMs) can support more personalized and intelligent learning experiences. This study focuses on building a domain-specific question answering (QA) system tailored to computer science education, with a particular emphasis on Java programming. While transformer-based models such as BERT, RoBERTa, and DistilBERT have demonstrated strong performance on general-purpose datasets like SQuAD, they often struggle with technical educational content where annotated data is scarce. To address this challenge, we developed a custom dataset, JavaFactoidQA, consisting of 1000 fact-based question–answer pairs derived from Java course materials and textbooks. A two-step fine-tuning strategy was adopted, in which models were first fine-tuned on the SQuAD dataset to capture general language understanding and subsequently fine-tuned on the Java-specific dataset to adapt to programming terminology and structure. Experimental results show that RoBERTa-Base achieved the best performance, with an F1 score of 88.7% and an Exact Match (EM) score of 82.4%, followed closely by BERT-Base and DistilBERT. The results were further compared with domain-specific QA models from healthcare and finance, demonstrating that the proposed approach performs competitively despite using a relatively small dataset. Overall, this study shows that careful dataset design combined with sequential fine-tuning enables effective adaptation of transformer-based QA models for educational applications, including automated assessment, intelligent tutoring, and interactive learning environments. Future work will explore extending the approach to additional subjects, incorporating cognitive-level tagging, and evaluating performance on broader educational QA benchmarks.
KW  - Question answering; transfer learning; factoid question finetuning; large language model; transformers; BERT

DO  - 10.32604/jai.2026.075857