Expanding Human-in-the-Loop: Critical Sensemaking for Technical and Professional Communication With Generative AI

Abstract

This article proposes a sensemaking methodology to enhance human-in-the-loop technical and professional communication (TPC) practices when working with generative artificial intelligence (GenAI) output, which is often ambiguous and not always accurate. Sensemaking describes actions and cognitive strategies humans use to make sense of new/ambiguous information. We argue that sensemaking can help TPC students navigate making sense of GenAI output for better judgment in evaluating AI output. Particularly, we leverage sensemaking's Situation-Gap-Bridge-Outcome framework as a heuristic to identify situational contexts outside of GenAI, gaps in knowledge, create bridges for those gaps, and evaluate outcomes and connect this to extant TPC literature and discuss its implications.

Keywords

critical sensemaking generative AI human-in-the-loop methodology technical and professional communication

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References

Agboka

G. Y.

(2013). Participatory localization: A social justice approach to navigating unenfranchised/disenfranchised cultural sites. Technical Communication Quarterly, 22(1), 28–49. https://doi.org/10.1080/10572252.2013.730966

Aguilar

G. L.

(2024). Rhetorically training students to generate with AI: Social justice applications for AI as audience. Computers and Composition, 71, 102828. https://doi.org/10.1016/j.compcom.2024.102828

Alexander

Lunsford

Whithaus

(2020). Toward wayfinding: A metaphor for understanding writing experiences. Written Communication, 37(1), 104–131. https://doi.org/10.1177/0741088319882325

Ali

Sapiezynski

Bogen

Korolova

Mislove

Rieke

(2019). Discrimination through optimization: How Facebook’s ad delivery can lead to skewed outcomes. Retrieved from https://arxiv.org/pdf/1904.02095.pdf.

Ancona

(2012). Framing and acting in the unknown. In Snook

Nohria

Khurana

(Eds.), The handbook for teaching leadership (pp. 3–19). Sage.

BasicAI. (N.D). Exploring Human-in-the-loop in Machine Learning. Retrieved from: https://www.basic.ai/blog-post/human-in-the-loop-in-machine-learning.

Bedington

Halcomb

E. F.

McKee

H. A.

Sargent

Smith

(2024). Writing with generative AI and human-machine teaming: Insights and recommendations from faculty and students. Computers and Composition, 71, 102833. https://doi.org/10.1016/j.compcom.2024.102833

Bender

E. M.

Gebru

McMillan-Major

Shmitchell

(2021, March). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623).

Benjamin

(2019). Race after technology: Abolitionist tools for the new Jim Code. Wiley.

10.

Buolamwini

Gebru

(2018, January). Gender shades: Intersectional accuracy disparities in commercial gender classification. Conference on fairness, accountability and transparency (pp. 77–91). PMLR.

11.

Carradini

(2024). On the current moment in AI: Introduction to special issue on effects of artificial intelligence tools in technical communication pedagogy, practice, and research, part 1. Journal of Business and Technical Communication, 38(3), 187–198. https://doi.org/10.1177/10506519241239638

12.

Costanza-Chock

(2020). Design justice: Community-led practices to build the worlds we need. The MIT Press.

13.

Crowder

(2024). AI chatbots. Springer.

14.

Cummings

R. E.

Monroe

S. M.

Watkins

(2024). Generative AI in first-year writing: An early analysis of affordances, limitations, and a framework for the future. Computers and Composition, 71, 1–11. https://doi.org/10.1016/j.compcom.2024.102827

15.

Deptula

Hunter

P. T.

Johnson-Sheehan

(2024). Rhetorics of authenticity: Ethics, ethos, and artificial intelligence. Journal of Business and Technical Communication. https://doi.org/10.1177/10506519241280639

16.

Dervin

(1999). Chaos, order and sense-making: A proposed theory for information design. Information Design, 35–57.

17.

D’Ignazio

Klein

L. F.

(2020). Data feminism. MIT Press.

18.

Duin

A. H.

Pedersen

(2021). Writing futures: Collaborative, algorithmic, autonomous. Springer.

19.

Duin

A. H.

Pedersen

(2023). Augmentation technologies and artificial intelligence in technical communication: Designing ethical futures. Routledge.

20.

Eloundou

Manning

Mishkin

Rock

(2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv preprint arXiv:2303.10130.

21.

Floridi

(2023). AI As agency without intelligence: On ChatGPT, large language models, and other generative models. Philosophy & Technology, 36(1), 1–15. https://doi.org/10.1007/s13347-022-00600-9

22.

Foreman-Wernet

(2003). Rethinking communication: Introducing the sense-making methodology. In B. Dervin, L. Foreman-Wernet, & E. Lauterbach (Eds.), Sense-making methodology reader: Selected writings of Brenda Dervin (pp. 3–16). Hampton Press.

23.

Garib

Coffelt

T. A.

(2024). DETECTing the anomalies: Exploring implications of qualitative research in identifying AI-generated text for ai-assisted composition instruction. Computers and Composition, 73, 102869. https://doi.org/10.1016/j.compcom.2024.102869

24.

Gaube

Suresh

Raue

Merritt

Berkowitz

S. J.

Lermer

Coughlin

J. F.

Guttag

J. V.

Colak

Ghassemi

(2021). Do as AI say: Susceptibility in deployment of clinical decision-aids. NPJ Digital Medicine, 4(1), 31.

25.

Getto

Kelley

Vance

(2025). How to write with GenAI: A framework for using generative AI to automate writing tasks in technical communication. Journal of Technical Writing and Communication, 00472816251332208. https://doi.org/10.1177/00472816251332208

26.

Graham

S. S.

(2023). Post-process but not post-writing: Large language models and a future for composition pedagogy. Composition Studies, 51(1), 162–168.

27.

Graham

S. S.

Hopkins

H. R.

(2022). AI for social justice: New methodological horizons in technical communication. Technical Communication Quarterly, 31(1), 89–102. https://doi.org/10.1080/10572252.2021.1955151

28.

Gray

M. L.

Suri

(2019). Ghost work: How to stop Silicon Valley from building a new global underclass. Harper Business.

29.

Gupta

Shivers-McNair

(2024). Wayfinding” through the AI wilderness: Mapping rhetorics of ChatGPT prompt writing on X (formerly Twitter) to promote critical AI literacies. Computers and Composition, 74, 102882. https://doi.org/10.1016/j.compcom.2024.102882

30.

Haas

A. M.

(2012). Race, rhetoric, and technology: A case study of decolonial technical communication theory, methodology, and pedagogy. Journal of Business and Technical Communication, 26(3), 277–310. https://doi.org/10.1177/1050651912439539

31.

Hao

(2025). Empire of AI: Dreams and nightmares in Sam Altman's OpenAI. Penguin Random House.

32.

Qiu

Chen

Zhang

Lou

Wang

, …, & Gao

(2023). AI alignment: A comprehensive survey. arXiv preprint arXiv:2310.19852.

33.

Jiang

(2024). When generative artificial intelligence meets multimodal composition: Rethinking the composition process through an AI-assisted design project. Computers and Composition, 74, 102883. https://doi.org/10.1016/j.compcom.2024.102883

34.

Jiang

Verhulsdonck

(2025). Augmenting user experience design with multimodal generative artificial intelligence: A study of technical communication students. Technical Communication Quarterly, 1–20. https://doi.org/10.1080/10572252.2025.2473503

35.

Johnson

R. R.

(1998). User-centered technology: A rhetorical theory for computers and other mundane artifacts. SUNY press.

36.

Johnson-Eilola

Selber

S. A.

York

E. J.

(2024). Can Artificial Intelligence robots write effective instructions? Journal of Business and Technical Communication, 38(3), 199–212. https://doi.org/10.1177/10506519241239641

37.

Knowles

A. M.

(2024). Machine-in-the-loop writing: Optimizing the rhetorical load. Computers and Composition, 71(1). https://doi.org/10.1016/j.Compcom.2024.102826

38.

Kosmyna

Hauptmann

Yuan

Y. T.

Situ

Liao

X. H.

Beresnitzky

A. V.

Braunstein

Maes

(2025). Your brain on chatgpt: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv preprint arXiv:2506.08872.

39.

Kostopolus

(2024). Student use of generative AI as a composing process supplement: Concerns for intellectual property and academic honesty. Computers and Composition, 75, 102894. https://doi.org/10.1016/j.compcom.2024.102894

40.

Lee

H. P. H.

Sarkar

Tankelevitch

Drosos

Rintel

Banks

Wilson

(2025). The impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a. Survey of Knowledge Workers. CHI Conference on Human Factors in Computing Systems (CHI ‘25). New York, NY: ACM. https://doi.org/10.1145/3706598.3713778

41.

Luccioni

Jernite

Strubell

(2024, June). Power hungry processing: Watts driving the cost of AI deployment? Proceedings of the 2024 ACM conference on fairness, accountability, and transparency (pp. 85–99).

42.

Lunsford

Alexander

Whithaus

(2024). Wayfinding: The development of an approach to lifespan writing. Improvisations: Methods and methodologies in lifespan writing research, 271-286.

43.

Mallette

J. C.

(2024). Preparing future technical editors for an Artificial Intelligence-enabled workplace. Journal of Business and Technical Communication, 38(3), 289–302. https://doi.org/10.1177/10506519241239950

44.

Masters-Wheeler

Bay

(2024). Methodologies for Studying Artificial Intelligence in Technical and Professional Communication. Journal of Business and Technical Communication, preprint. https://doi.org/10.1177/10506519241280647

45.

Masters-Wheeler

Bay

Sullivan

(2023). Artificial intelligence, large language models, and design thinking in TPC classrooms. Programmatic Perspectives, 14(2). https://programmaticperspectives.cptsc.org/index.php/jpp/article/view/58 .

46.

McKee

H. A.

Porter

J. E.

(2020). Ethics for AI writing. AIES. https://doi.org/10.1145/3375627.3375811

47.

Meyrick

Kennedy

(2024). Exploring complex storytelling through wayfinding design. Image & Text, 38, 1–24.

48.

Naumer

Fisher

Dervin

(2008, April). Sense-making: a methodological perspective. In Sensemaking Workshop, CHI (Vol. 8, pp. 506–513).

49.

Nielsen

(2023). ChatGPT lifts business professionals’ productivity and improves work quality. Nielsen Norman Group. https://www.nngroup.com/articles/chatgpt- productivity.

50.

Noble

S. U.

(2018). Algorithms of oppression. New York University Press.

51.

Noble

S. U.

(2019). Algorithms of oppression: How search engines reinforceracism. In Algorithms of oppression. New York University Press.

52.

Noy

Zhang

(2023). Experimental evidence on the productivity effects of generative artificial intelligence. http://dx.https://doi.org/10.2139/ ssrn.4375283.

53.

Owusu-Ansah

A. L.

(2023.). Defining moments, definitive programs, and the continued erasure of missing people. Composition Studies, 51(1), 143–148.

54.

Passi

Vorvoreanu

(2022). Overreliance on AI: Literature review. Microsoft Research, https://www.microsoft.com/en-us/research/publication/overreliance-on-ai-literature-review/ .

55.

Pearson

(2022). Making sense. Retrieved from: https://polgovpro.blog/2022/07/06/making-sense/.

56.

Perrigo

(2023). Exclusive: OpenAI used Kenyan workers on less than $2 per hour to make ChatGPT less toxic. Time Magazine, 18, 2023.

57.

Piller

(2024). Inhuman rhetoric: Generative AI and crisis communication. Journal of Business and Technical Communication. https://doi.org/10.1177/10506519241280594

58.

Pirolli

Card

(2005). The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. Proceedings of International Conference on Intelligence Analysis, 5, 2–4.

59.

Pirolli

Russell

D. M.

(2011). Introduction to this special issue on sensemaking. Human-Computer Interaction, 26(1–2), 1–8. https://doi.org/10.1080/07370024.2011.556557

60.

Ponce

T. M.

(2024). AI resume writing: How prompt confidence shapes output and AI literacies. Business and Professional Communication Quarterly. https://doi.org/10.1177/23294906241273317

61.

Reeves

Sylvia

(2024). Generative AI in technical communication: A review of research from 2023 to 2024. Journal of Technical Writing and Communication, 54(4), 439–462. https://doi.org/10.1177/00472816241260043

62.

Rose

E. J.

Walton

(2015, July). Factors to actors: Implications of posthumanism for social justice work. Proceedings of the 33rd annual international conference on the design of communication (pp. 1–10). New York: ACM.

63.

Selber

S. A.

(2024). Artificial intelligence and the intellectual legacy of Johndan Johnson-Eilola. Programmatic Perspectives, 15(1).

64.

Selfe

C. L.

(1999). Technology and literacy in the 21st century: The importance of paying attention. SIU Press.

65.

Shalamova

Rice-Bailey

(2024). Leveraging design thinking and generative AI to transform TPC pedagogy. Programmatic Perspectives, 15(1).

66.

Shehabi

Hubbard

Newkirk

Lei

Siddik

M. A. B.

Holecek

, …, & Sartor

(2024). 2024 United States data center energy usage report. Lawrence Berkeley National Laboratory. Retrieved from: https://eta-publications.lbl.gov/sites/default/files/2024-12/lbnl-2024-united-states-data-center-energy-usage-report.pdf.

67.

Springer

Hollis

Whittaker

(2017, March 20). Dice in the black box: User experiences with an inscrutable algorithm. 2017 AAAI Spring Symposium Series. 2017 AAAI Spring Symposium Series. https://cdn.aaai.org/ocs/15372/15372-68262-1-PB.pdf.

68.

Strubell

Ganesh

McCallum

(2020, April). Energy and policy considerations for modern deep learning research. Proceedings of the AAAI Conference on Artificial Intelligence, 34(9), 13693–13696. https://doi.org/10.1609/aaai.v34i09.7123

69.

Tham

Howard

Verhulsdonck

(2022). Extending design thinking, content strategy, and artificial intelligence into technical communication and user experience design programs: Further pedagogical implications. Journal of Technical Writing and Communication, 52(4), 428–459. https://doi.org/10.1177/00472816211072533

70.

Tham

Howard

Verhulsdonck

(2023). UX writing: Designing user-centered content. Routledge.

71.

Urquhart

Cheuk

Lam

Snowden

(2024). Sense-making, sensemaking and sense making—a systematic review and meta-synthesis of literature in information science and education: An Annual Review of Information Science and Technology (ARIST) paper. Journal of the Association for Information Science and Technology.

72.

Veeramoothoo

(2022). Social justice and the portrayal of migrants in international organization for migration’s world migration reports. Journal of Technical Writing and Communication, 52(1), 57–93.

73.

Verhulsdonck

Shalamova

(2020). Creating content that influences people: Considering user experience and behavioral design in technical communication. Journal of Technical Writing and Communication, 50(4), 376–400. https://doi.org/10.1177/0047281619880286

74.

Verhulsdonck

Weible

Stambler

D. M.

Howard

Tham

(2024). Incorporating human judgment in AI-assisted content development: The HEAT heuristic. Technical Communication, 71(3), 60–72. https://doi.org/10.55177/tc286621

75.

Vetter

M. A.

Lucia

Jiang

Othman

(2024). Towards a framework for local interrogation of AI ethics: A case study on text generators, academic integrity, and composing with ChatGPT. Computers and Composition, 71, 1–12. https://doi.org/10.1016/j.compcom.2024.102831

76.

Wadinambiarachchi

Kelly

R. M.

Pareek

Zhou

Velloso

(2024, May). The effects of generative AI on design fixation and divergent thinking. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1–18).

77.

Walton

Moore

Jones

(2019). Technical communication after the social justice turn: Building coalitions for action. Routledge.

78.

Weick

K. E.

(1993). The collapse of sensemaking in organizations: The Mann Gulch disaster. Administrative Science Quarterly, 38, 628–652. https://doi.org/10.2307/2393339

79.

Weick

K. E.

(1995). Sensemaking in organizations. Sage.

80.

West

S. M.

Whittaker

Crawford

(2019). Discriminating systems: Gender, race and power in AI. AI Now Institute. Retrieved from https://ainowinstitute.org/discriminatingsystems.html.

81.

White

(2025). The AI labor playbook: How to build, lead, and scale generative AI and AI agents in your organization. Retrieved from: https://www.gaiin.org/content/files/2025/04/The-AI-Labor-Playbook_4-17-25.pdf.

82.

Zaga

Lupetti

M. L.

(2022). Diversity equity and inclusion in embodied AI: reflecting on and re-imagining our future with embodied AI. 4TU. Federation. Retrieved from: https://ris.utwente.nl/ws/portalfiles/portal/285680270/DEI4EAIBOOKLET_WEB_SINGLEPAGES.pdf.

83.

Ziegler

D. M.

Stiennon

Brown

T. B.

Radford

Amodei

, …, & Irving

(2019). Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593.

84.

Zuboff

(2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.