Current Volume 9
SyntaxSensei: An Intelligent Programming Learning Platform Using Fine Tuned Language Models, Adaptive Quizzes, And Gamified Leaderboards
Abstract
The domain of software development education faces persistent challenges in effectively translating theoretical, conceptual knowledge into practical problem-solving skills, particularly for novice programmers. While the advent of large language models (LLMs) has revolutionized code generation, their application in educational settings often inadvertently bypasses the learning process, providing direct answers rather than scaffolding understanding. This paper details the architectural design, pedagogical framework, and implementation strategy for SyntaxSensei, an intelligent educational platform engineered to provide structured, self-paced programming instruction. We propose a departure from generic conversational agents in favor of a highly specialized, domain-specific tutoring framework. Our core innovation centers on the instruction fine-tuning of a high-performing, resource-efficient foundation model—specifically, the StarCoder2-3B variant—utilizing Quantized Low-rank Adaptation (QLoRA). By intrinsically linking beginner-friendly code explanations with dynamically generated quizzes and a robust gamified leaderboard module, SyntaxSensei forces active recall and validates student comprehension while sustaining high engagement levels. The system operates on a full-stack architecture prioritizing low-latency inference through FastAPI and a responsive Next.js frontend, culminating in a production-ready pedagogical tool tailored for high school learners, undergraduates, and self-taught developers.
Keywords
Large Language Models, Code Education, Parameter Efficient Fine-Tuning (PEFT), Adaptive Quizzes, Gamification, QLoRA Fine-Tuning, Full-Stack Architecture, FastAPI, Next.js, Cognitive Load Theory.
Citations
IRE Journals:
Kunal Nath, Samridh Chauhan, Tanmay Jadhav, Pranit Virkar "SyntaxSensei: An Intelligent Programming Learning Platform Using Fine Tuned Language Models, Adaptive Quizzes, And Gamified Leaderboards" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 3768-3774 https://doi.org/10.64388/IREV9I10-1717030
IEEE:
Kunal Nath, Samridh Chauhan, Tanmay Jadhav, Pranit Virkar
"SyntaxSensei: An Intelligent Programming Learning Platform Using Fine Tuned Language Models, Adaptive Quizzes, And Gamified Leaderboards" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1717030
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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