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    SinXdetect · Final Year Research Project · 2025 – 2026

    Built a Sinhala AI-text detector with explainable predictions

    AIResearchEngineering
    GitHub repository

    Context

    Sinhala-language academic and media content is increasingly AI-generated, but existing detectors are English-first and unusable for Sinhala script.

    Problem

    Educators need a verdict they can trust and defend. A raw probability score is not enough — reviewers need to see which words drove the classification.

    My role

    Product owner and lead engineer for a final-year research project: problem framing, dataset decisions, model evaluation, application build and containerisation.

    Constraints

    • Limited labelled Sinhala corpus
    • Single GPU budget for fine-tuning
    • Explanations had to be fast enough for interactive use

    What I did

    My contribution

    • Framed the product around explainability rather than a single confidence score
    • Fine-tuned a SinBERT transformer for human vs AI binary classification
    • Added LIME-based word-level explanations to every prediction
    • Built the FastAPI backend with batch processing and the React + Tailwind UI
    • Containerised the stack with Docker Compose for reproducible deployment

    Owned by the wider team

    • Supervisor provided research direction and evaluation review

    What I'd do differently

    • I would validate the explanation UI with real educators before tuning the model further — trust was the actual product problem.
    • I would version datasets formally from the first experiment.