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Research

The part you can check for yourself

What we ship starts as published work. These are the papers — with DOIs and arXiv links, so you can read them rather than take our word for it.

  1. Artificial intelligence based personalized student feedback system 'Sisu Athwala' to enhance exam performance of medical undergraduates

    Thilanka Seneviratne, Supun Manathunga, Wathsala Idirisingha, Kosala Somaratne, Kosala Marambe, Udaya Dangahadeniya

    PLOS One20(12), e03361544 December 2025University of Peradeniya

    A feedback system for medical undergraduates, built on retrieval augmented generation over a custom knowledge base to reduce inaccurate answers from the underlying model. It gives each student feedback on their MCQ and SAQ performance, along with guidance on stress management and study strategy. It was evaluated both by expert student mentors and by the students who used it.

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  2. Artificial intelligence assisted automated short answer question scoring tool shows high correlation with human examiner markings

    HMTW Seneviratne, SS Manathunga

    BMC Medical Education25(1), 11465 August 2025University of Peradeniya

    A scoring tool that uses a large language model to mark short answer questions. It extracts the key components of a student's answer, applies the scoring rubric supplied by the instructor, and writes individual feedback. It was tested on a systematic pharmacology course, where its marks correlated closely with those of human examiners.

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  3. Aligning large language models for clinical tasks

    Supun Manathunga, Isuru Hettigoda

    arXivarXiv:2309.02884September 2023

    An alignment method for medical question answering, called expand-guess-refine, which combines instruction tuning with in-prompt techniques. It reached 70.63% accuracy on a subset of the USMLE dataset without additional parameters or training data.

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  4. Retrieval augmented generation and representative vector summarization for large unstructured textual data in medical education

    Supun S Manathunga, YA Illangasekara

    arXivarXiv:2308.00479August 2023

    Sets out retrieval augmented generation as a way to attach a customisable knowledge base to a language model, so that answers in a medical context can be grounded in a known source rather than in the model's own memory. It also presents a combined extractive and abstractive summarisation method for large unstructured text, using representative vectors.

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Authors are listed in publication order. Entries marked with a university affiliation were carried out with collaborators at that institution.

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