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From Fluent to Useful: Generative AI That Models Purpose, Audience, and Presenter for Scientific Communication

Paper · 2026 · Ishani Mondal

Summary

This doctoral proposal argues that current generative AI writes fluently but often produces scientific communication artifacts (slides, diagrams, posters, videos) that do not serve their actual goal. The core issue is that systems do not represent why the content exists, who will read it, and who will present it. The author describes five completed, published systems along the communication pipeline:

  • ADAPTIVE IE extracts information following the user's evolving intent.
  • Persona-Aware Slide Generation reframes a paper for a given audience instead of simply simplifying it.
  • GPA reconciles conflicting group feedback.
  • SciDoc2Diagrammer-MAF uses multi-aspect critics to separate deliberate abstraction from omission or hallucination in diagrams.
  • SMART-Editor handles edits that cascade through multimodal layouts.

These systems show that alignment with intent, audience and structure matters. They still cannot verify whether an artifact truly communicates. The proposal therefore sets out three research questions:

  1. Evaluate document-to-video generation by measured learning outcomes.
  2. Personalize for the presenter, not only the audience.
  3. Build a benchmark for preferences that transfer across tasks.

The work matters because it reframes the evaluation target from outputs that look correct to outputs that measurably help people understand.

Key points

  • ADAPTIVE IE is schema-free and human-in-the-loop. It combines QA-guided clustering with merge, split, rearrange and delete operations. Tested on GENEVA, Biomedical Slot Filling and CrisisNLP, ten users raised F1 by up to +0.17 within 30 minutes, at lower cost and latency than full-corpus GPT-4 prompting.
  • Persona-Aware Slide Generation introduces a corpus of 75 *ACL papers. Each paper has four deck variants, crossing expert vs. non-expert audiences with short vs. long talks. The outline generator is preference-tuned against reward models for comprehensibility and length-fit.
  • RQ1 (top priority) proposes IRT-calibrated diagnostic questions to measure the educational utility of document-to-video output. It will be validated against learning outcomes, with inter-annotator agreement studies on human judgments.
  • RQ2 models presenters' stable revision habits across sessions. It pairs this with audience tests of recall, reasoning and perceived clarity.
  • RQ3, a unified SuperPersonalization benchmark with user-held-out and dataset-held-out splits, may be deferred until after the dissertation if RQ1 grows in scope.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of Maryland, College ParkCollege Park · United States141 works · 29,174 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksHistory and Philosophy of Science · 62 works

Cite

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

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-srw.87

@inproceedings{mondal2026fluent,
  title = {{From Fluent to Useful: Generative AI That Models Purpose, Audience, and Presenter for Scientific Communication}},
  author = {Ishani Mondal},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-srw.87},
  url = {https://doi.org/10.18653/v1/2026.acl-srw.87}
}

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