From Fluent to Useful: Generative AI That Models Purpose, Audience, and Presenter for Scientific Communication
Ishani Mondal
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
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表