Writer-Defined AI Personas for On-Demand Feedback Generation
Open accessAbstract
Compelling writing is tailored to its audience. This is challenging, as writers may struggle to empathize with readers, get feedback in time, or gain access to the target group. We propose a concept that generates on-demand feedback, based on writer-defined AI personas of any target audience. We explore this concept with a prototype (using GPT-3.5) in two user studies (N=5 and N=11): Writers appreciated the concept and strategically used personas for getting different perspectives. The feedback was seen as helpful and inspired revisions of text and personas, although it was often verbose and unspecific. We discuss the impact of on-demand feedback, the limited representativity of contemporary AI systems, and further ideas for defining AI personas. This work contributes to the vision of supporting writers with AI by expanding the socio-technical perspective in AI tool design: To empower creators, we also need to keep in mind their relationship to an audience.
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In the Formal context, translating support is focused on writing efficiency, enabling the AI to write in the same interaction location as the user, or to make suggestions that are integrated directly in the text.
“Translating support is focused on writing efficiency, enabling the AI to write in the same interaction location as the user, or to make suggestions that are integrated directly in the text [8,36].”
From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [8]
The Conclusion (p. 13) identifies direct editor integration as a benefit of AI feedback, and Section 3.2 (p. 4) describes requesting feedback on selected text within the editor. But the feedback appears in a sidebar comment card; the paper does not describe the AI writing alongside the user or inserting suggestions directly into the text, and Section 6.5.1 (p. 12) frames its focus as the writer-reader relationship rather than writing efficiency. (LLM, conf=0.82)
Experimental — accuracy not yet measuredResearchers have investigated visual differentiation of suggestions, enabling users to distinguish their own writing from AI-generated content.
“Researchers have investigated visual differentiation of suggestions [8,92,121], enabling users to clearly distinguish between their own writing and AI-generated content.”
From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [8]
Section 3.2 (p. 4) describes a side-by-side editor and sidebar, with AI-generated feedback displayed in separate cards. This visually separates feedback from the writer’s draft, but the paper does not report investigating visual differentiation as a design question or testing whether users could clearly distinguish AI-generated content from their own writing. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSome systems present suggestions directly in the user's workspace, creating a more integrated experience but potentially blurring boundaries between user and AI contributions.
“Some systems present suggestions directly in the user's workspace [8,14,18], creating a more integrated experience but potentially blurring boundaries between user and AI contributions.”
From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [8]
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Dang, H., Swoopes, C., Buschek, D., & Glassman, E. L. (2025) · International Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713974 · Cited by 13 · see citing works
Self-citationWhere Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems
Carrera, D., Thomas-Mitchell, J., & Wigdor, D. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791952 · Cited by 3 · see citing works
“…at creative writing, there has been research on new techniques to support story writing [33, 80], playwriting [46], character development [56], prewriting [75], non-linear story writing (also known as ‘multiverse’) [27], worldbuilding [14], cartoon-captioning [35], and feedback generation [4].”
Workshop: Designing with AI-based tools
Okopnyi, P., Guribye, F., Bachinski, M., Fjeld, M., Buschek, D., Zindulka, T., Lehmann, F., & Palma, P. B. (2024) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3677045.3685449 · Cited by 3 · see citing works
Self-citationEvaluating the Clinical Safety of LLMs in Response to High-Risk Mental Health DisclosuresPreprint
Santos, J. M., Shah, S., Gupta, A., Mann, A., Vaz, A., Caldwell, B. E., Scholz, R., Awad, P., Allemandi, R., Faust, D., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-7364128/v1 · Cited by 4 · see citing works
Khadar, M., Runningen, D., Tang, J., Chancellor, S., & Kaur, H. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757707 · Cited by 2 · see citing works
Using AI for User Representation: An Analysis of 83 Persona Prompts
Salminen, J. O., Amin, D., & Jansen, B. J. J. (2025) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa66935.2025.11315350 · Cited by 2 · see citing works
DuoDrama: Supporting Screenplay Refinement Through LLM-Assisted Human Reflection
Tang, Y., Chen, X., Li, H., Xie, X., Ma, X., & Qu, H. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790568 · Cited by 2 · see citing works
From Crafting Text to Crafting Thought: Grounding AI Writing Support to Writing Center Pedagogy
Liu, Y., Gallagher, J., Sterman, S., & August, T. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790292 · Cited by 2 · see citing works
GenieWizard: Multimodal App Feature Discovery with Large Language Models
Yang, J., Shi, Y., Gu, C., Zheng, Z., Jain, A., Li, T., Lam, M. S., & Landay, J. A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714327 · Cited by 4 · see citing works
Wan, Q., Li, J., Wang, H., & Lu, Z. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757497 · Cited by 2 · see citing works
Corbita, D. P., Mendoza, M., Cordova, M. A., Balmas, R., Vijayan, P. M., & Madronero, J. (2024) · Contributions to finance and accounting · doi:10.1007/978-3-031-67547-8_43 · Cited by 1 · see citing works
Constella: Supporting Storywriters’ Interconnected Character Creation through LLM-Based Multi-Agents
Park, S., Park, S., & Lim, Y.-K. (2026) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3796234 · Cited by 1 · see citing works
Benharrak, K., Peng, P., & Pavel, A. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747795 · Cited by 1 · see citing works
Self-citationKim, Y., Yin, Y., Karumbaiah, S., & Saxena, D. (2026) · Annual Symposium on Human-Computer Interaction for Work (CHIWORK) · doi:10.1145/3808045.3808094 · Cited by 1 · see citing works
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Lehmann, F., & Buschek, D. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3733053
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Almoqbel, M. Y. (2024) · Journal of Posthumanism · doi:10.63332/joph.v4i3.3243 · Cited by 1 · see citing works
Revolutionizing User Testing: AI-Powered Feedback with Personalized Personas
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Asadi, A. R., Kropczynski, J., Johnson, J., O'Connor, C., Reed, W. C., & Said, H. (2026) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3786304.3788840
CoLyricist: Enhancing Lyric Writing with AI through Workflow-Aligned Support
Yoshida, M., Li, B. Q., Zhao, S., Zhou, Q., Hu, S., Chen, X. '., & Peng, N. (2026) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3742413.3789099
Truss, M. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798543
Lost in Translation: The Value of Verbalizations in Interpreting 3D Computer-Aided Design Workflows
Cheng, K., Vermeulen, J., Fitzmaurice, G. W., & Matejka, J. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791084
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Corvite, S., Matz, R. L., Mills, M., & Hui, J. S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790915
Moodialogue: Transforming Emotions into Personified, Conversational Agents
Jang, S., Kim, S., Bae, G., & Hong, H. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790807
Hu, Y., Zhang, J., Wang, Z., & Yu, C. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790926
From Expectation to Evaluation: Expectation Cues Systematically Bias LLM and Human Judgment
Sun, Y., Dillion, D., Gray, K. J., Lyu, M., Zhang, Z., & Li, F. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790492
AnnotateGPT: Designing Human–AI Collaboration in Pen-Based Document Annotation
Leung, B., Shimabukuro, M., & Collins, C. M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790867
Sci-Fi Spark: A Human-AI Co-Creation System for Science Fiction Ideation
Jiang, Z., Cheang, W., Xu, X., Zuo, H., Zheng, C., & Chen, L. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791950
EvaluAId: Human-AI Collaborative Evaluation of Open-Ended Student Essays
Zhang, C., Ju, K. P., Lu, X., Yen, Y.-C. G., & Rzeszotarski, J. M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790814
Preference-driven prompt refinement for software development: A cross-model analysis of GPT-5
Elnashar, A., White, J., & Schmidt, D. C. (2026) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2026.103783
Supporting Reviewing Reviews: How HCI Authors Handle Peer Reviews of Manuscripts
Yeung, C. A., Stark, J., Li, J., Chevalier, F., Park, J., Kim, Y., & Tang, A. (2025) · International Conference on Human-Engaged Computing (ICHEC) · doi:10.1145/3786995.3786998
Yao, Y., Zhou, K., & Liu, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30089-8_14
Zhang, Z. S., Choi, Y., Xiao, Z., Liao, Q. V., & Zhao, J. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816942
PersonaBase: Computational Resources for Advancing the Research and Practice of Data-Driven Personas
Salminen, J. O., Amin, D., Kaate, I., & Jansen, B. J. J. (2026) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3829807.3829819
Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems
Carrera, D., Thomas-Mitchell, J., & Wigdor, D. (2025) · arXiv · doi:10.48550/arxiv.2509.15440
- Authors
- Benharrak, K., Zindulka, T., Lehmann, F., Heuer, H., & Buschek, D.
- Affiliations
- University of Bayreuth (DE)
- University of Bremen (DE)
- Venue
- Proceedings of the CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2024-05-11
- Pages
- 1-18
- OpenAlex
- W4396833223
- Cited by
- 63
- References
- 46
- Citations by year
- 2026: 21 · 2025: 38 · 2024: 4
- FWCI
- 20.30
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Bavarian State Ministry of Science and the Arts
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Persona Design and Applications
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- AI personas, author-reader relationship, audience empathy, GPT-3.5, AI-assisted writing, textual revision, socio-technical perspective, AI tool design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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