@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 ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/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"
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<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</abstract>
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%0 Conference Proceedings
%T From Fluent to Useful: Generative AI That Models Purpose, Audience, and Presenter for Scientific Communication
%A Mondal, Ishani
%Y T.Y.S.S., Santosh
%Y Rodriguez, Juan Diego
%Y de Gibert, Ona
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-393-7
%F mondal-2026-fluent
%X 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
%U https://aclanthology.org/2026.acl-srw.87/
%P 995-1006
Markdown (Informal)
[From Fluent to Useful: Generative AI That Models Purpose, Audience, and Presenter for Scientific Communication](https://aclanthology.org/2026.acl-srw.87/) (Mondal, ACL 2026)
ACL