@inproceedings{mondal-2026-fluent,
    title = "From Fluent to Useful: Generative {AI} That Models Purpose, Audience, and Presenter for Scientific Communication",
    author = "Mondal, Ishani",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-form-platform/2026.acl-srw.87/",
    pages = "995--1006",
    ISBN = "979-8-89176-393-7",
    abstract = "Modern generative AI produces fluent text,polished slides, and clean diagrams {---} yetstill fails when an artifact must serve a specificpurpose for a specific reader, used by aspecific presenter. The missing piece is notfluency but a model of why content is beingproduced, for whom (presenter and audiencealike), and how it should adapt as goalsshift. My completed and published work developsfive systems across the scientific communicationpipeline: ADAPTIVE IE for intentdrivenextraction; Persona-Aware Slide Generationfor audience reframing rather than blanketsimplification; GPA for reconciling divergentgroup preferences; SciDoc2Diagrammer-MAF,whose multi-aspect critics distinguish purposefulabstraction from genuine omission or hallucination;and SMART-Editor, which modelscascading edits across multimodal layouts. Togetherthey show that aligning with intent, audience,and structure is necessary{---}but cannotanswer whether the resulting artifacts actuallycommunicate. I therefore propose three directionsin priority order: (RQ1) a goal-drivenframework that measures the educational utilityof document-to-video generation throughIRT-calibrated diagnostic questions, validatedagainst measured learning outcomes and accompaniedby inter-annotator agreement studieson human effectiveness judgments; (RQ2)presenter-side personalization that treats thepresenter{---}not just the audience{---}as a firstclassuser; and (RQ3) a unified SuperPersonalizationbenchmark for transferable user preferences.RQ3 is scoped to be deferrable topost-dissertation work if RQ1 expands. Thethesis shifts the target from generative AI thatproduces content that looks correct to systemswhose outputs demonstrably communicate"
}