[ATTW-L] CFP Edited Collection on Technical Editing and Generative AI - Reminder

Jeffrey Jablonski jeffrey.jablonski at unlv.edu
Wed May 15 17:08:54 UTC 2024 

Hello,

This is a friendly reminder that the CFP that Ed Nagelhout and I announced
for an edited collection titled *Advancing Technical Editing in the Age of
Generative AI *is due on June 1. This collection aims to explore the impact
of generative AI writing tools on the field of technical editing and
contribute to research on technical editing practice and pedagogy for
researchers, educators, and practitioners.

We invite 500- to 750-word chapter proposals (excluding references) that
address key questions such as:

   - How are technical editors currently using generative AI tools, and
   what are the benefits, limitations, and ethical considerations?
   - How does generative AI change or expand the role of the technical
   editor, and what unique skills and judgment do human editors provide?
   - What should best practices be around attribution and transparency when
   using generative AI for writing or editing?
   - How can technical editing curricula and training adapt to prepare
   students for editing in an age of generative AI?
   - How can technical editors use generative AI critically and inclusively
   to advance social justice language practices in technical communication?

We welcome proposals from diverse, interdisciplinary perspectives,
including practicing technical writers and editors, educators, and
researchers. Possible topics might include case studies, pedagogical
approaches, comparative analyses, ethical and legal frameworks, the
changing role of technical editors, and strategies for human-AI
collaboration.

The timeline for this edited collection is as follows:

   - *Proposals due*: June 1, 2024 (include a tentative title and brief
   biography for all contributors)
   - *Decisions to authors*: June 15, 2024
   - *Full chapters due*: October 1, 2024

For questions, please contact the collection editors Jeffrey Jablonski (
jeffrey.jablonski at unlv.edu) and Ed Nagelhout (ed.nagelhout at unlv.edu).
Please send submissions to Jeffrey Jablonski.

We look forward to your submissions and to advancing this timely
conversation on technical editing and generative AI.

The complete CFP is copied below. For a PDF copy, visit
https://tinyurl.com/3m4tx8un,

Sincerely,

Jeff

[image: UNLV Logo] <http://unlv.edu/>

Jeffrey Jablonski, Ph.D.
Associate Professor
English Department - RLL 245
University of Nevada, Las Vegas

jeffrey.jablonski at unlv.edu
702-688-1325 <17026881325>

LinkedIn <http://www.linkedin.com/in/jeffreyajablonski>

***
*Call for Proposals: Scholarly Collection on Technical Editing and
Generative AI*

*Advancing Technical Editing in the Age of Generative AI*Jeffrey Jablonski
and Ed Nagelhout, University of Nevada, Las Vegas

Technical communication researchers are exploring how technical writers can
use generative AI tools in their work and how generative AI can assist with
tasks such as research, drafting, and editing (Baro, 2022; Bedington et
al., 2024; Quetzlli, 2023; Tang, 2021; Weltin et al., 2023). However,
scholars also note limitations in AI-generated content such as the risk of
factual errors and hallucinations (Babcock et al., 2021; McIntosh et al.,
2023).

The use of generative AI is also changing and expanding the role of the
technical editor in a range of disciplines. Editors must develop new skills
in AI literacy and human-machine collaboration (Duin & Pedersen, 2021). As
Ziegler (2022) argues, the increasing demands for business process
integration and semantic technologies in content management necessitate a
clearer definition of the competencies of "information architects" as
distinct from traditional technical writing roles. At the same time, human
editors remain uniquely positioned to provide critical judgment, domain
expertise, and ethical oversight in the use of AI technologies (Kaebnick et
al., 2023; Ren et al., 2023).

Some scholars propose frameworks and best practices for human-AI
interaction in writing and editing. Hart-Davidson (2018) advocates for
collaborative, rhetorical relationships with AI rather than using AI merely
as a tool. McKee and Porter (2022) propose a taxonomy of roles for
“humanmachine” teaming based on rhetorical context. Strobelt et al. (2022)
present GenNI, an interface for "human-AI collaboration in producing
descriptive text" that gives users high-level control over AI-generated
content. Hardin et al. (2020) share methods for technical writers to
produce clear and concise content for both human and machine translation,
taking a proactive approach to writing for a global audience.

Ethical considerations around attribution, transparency, and bias are
paramount as generative AI becomes more integrated into technical editing
workflows and academic publishing. Duin & Pedersen (2023) emphasize the
importance of AI explainability and the need for protocols to mitigate bias
and protect intellectual property rights. Kaebnick et al. (2023) recommend
that scholarly authors and editors prioritize transparency in disclosing
the use of generative AI, while maintaining human responsibility for the
final content.

Technical editing curricula and training must also adapt to prepare
students for these challenges, fostering AI literacy alongside traditional
editing skills (Flanagan & Albers, 2019; Melonçon 2019; Berger & Pigg,
2023). In exploring how technical editing courses should be adapted to
account for generative AI, Mallette (2024) assumes a critical and inclusive
stance, emphasizing social justice. In a “microcredential module” students
are introduced to concepts of social justice in technical communication
(Clem & Cheek, 2022; Jones & Walton, 2023) and reflect on how editors can
critically challenge bias, misinformation, and oppression in technical
editing practice involving generative AI.

Looking ahead, the broader implications of generative AI for technical
communication are significant. For Koerber et al. (2023), large language
model (LLM) technologies have the potential to enhance human capabilities
and transform knowledge creation, but also raise complex questions around
authorship, ownership, and control. These questions require critical
engagement from the technical editing community.

Technical editors will play a vital role in navigating these issues and
ensuring the responsible development and use of AI technologies in service
of effective, ethical communication, while also advocating for the
increased importance of human editors in AI-mediated content creation. As
Hart-Davidson (2018) asserts, technical communicators must learn to "write
with robots," envisioning AI as a dialogic partner in the knowledge-making
process. Yet humans still have vital roles to play in the responsible
development and deployment of these powerful writing technologies. The path
forward is not AI automation alone, but thoughtful human-AI integration.

We invite 500– to 750-word (excluding references) chapter proposals for an
edited collection exploring the impact of generative AI writing tools on
the field of technical editing. This collection seeks to contribute to
research on technical editing practice and pedagogy for researchers,
educators, and practitioners. Key questions this collection aims to address
include but are not limited to the following:

   - How are technical editors in industry and academia currently using
   generative AI tools? What are the benefits, limitations, and ethical
   considerations?
   - How does the use of generative AI change or expand the role of the
   technical editor? What skills and judgment do human editors uniquely
   provide?
   - What should best practices be around attribution and transparency when
   generative AI is used to assist with writing or editing?
   - How can technical editing curricula and training adapt to prepare
   students for editing in an age of generative AI?
   - How can technical editors use generative AI critically and inclusively
   for advancing social justice language practices in technical communication?
   - How can technical communicators participate in the development of
   generative AI editing tools?
   - What are the broader implications of generative AI for issues of
   accuracy, bias, intellectual property, and authorship in technical
   communication?

We welcome proposals from diverse, interdisciplinary perspectives including
practicing technical writers and editors, educators, and researchers.
Possible topics might include:

   - Case studies on the use of generative AI in technical editing workflows
   - Pedagogical approaches to teaching technical editing in the context of
   generative AI
   - Comparative analysis of human versus AI technical editing on measures
   such as accuracy and style
   - Ethical and legal frameworks for technical editors' use of generative
   AI
   - The changing role and value proposition of the technical editor in an
   AI-assisted future
   - Strategies for human-AI collaboration in technical editing

*Timeline*
The timeline for this edited collection is as follows:

   - *Proposals due*: June 1, 2024
   - Include a tentative title and a brief biography for all contributors
      - *Decisions to authors*: June 15, 2024
   - *Full chapters*: October 1, 2024

If you have any questions, please contact either of the collection editors
Jeffrey Jablonski (jeffrey.jablonski at unlv.edu) and Ed Nagelhout (
ed.nagelhout at unlv.edu); for submissions, please send to Jeffrey Jablonski (
jeffrey.jablonski at unlv.edu).We look forward to your submissions and to
advancing this timely conversation on technical editing and generative AI.
 .
*References*
Babcock, R. D., Khandelwal, J., Wilkinson, C. E., Kahathuduwa, C., &
Schlabritz-Loutsevitch, N. (2021). Supporting medical writers in the
twenty-first century. In L. Melonçon & S. Graham (Eds.), *Teaching writing
in the health profession*s (pp. 129-146). Routledge.
Baro, D. (2022). Metadata and content management bridging technical
documentation and automation technology. SHS Web of Conferences, 139,
02004.
Bedington, A., Halcomb, E., McKee, H. A., Sargent, T., & Smith, A. (2024).
Writing with generative AI and human-machine teaming: Insights and
recommendations from faculty and students. *Computers and Composition*, 71,
102833.
Berger, A., & Pigg, S. (2023). Peer-led professional development: How one
technical communication team learns on the job. *Journal of Business and
Technical Communication*, 37(4), 347-377.
Clem, S., & Cheek, R. (2022). Unjust revisions: A social justice framework
for technical editing. *IEEE Transactions on Professional Communication*,
65(1), 135–150.
Creary, M., & Gerido, L. H. (2023). The public performativity of trust. *The
Hastings Center Report*, 53(S2), S76–S85.
Duin, A. H., & Pedersen, I. (2021). *Writing futures: Collaborative,
algorithmic, autonomous*. Cham, Switzerland: Springer*.*
Duin, A. H., Pedersen, I. (2023). *Augmentation technologies and artificial
intelligence in technical communication : designing ethical futures*. New
York: Routledge, Taylor & Francis Group.
Flanagan, S., & Albers, M. J. (Eds.). (2019). *Editing in the modern
classroom*. Routledge.
Hardin, A. R., Ito, J., & Sasaki, A. (2020). Writing for human and machine
translation: Best practices for technical writers. In Proceedings of the
38th ACM International Conference on Design of Communication (pp. 1-8).
Hart-Davidson, W. (2018). Writing with robots and other curiosities of the
age of machine rhetorics. In J. Ridolfo & W. Hart-Davidson (Eds.), *The
Routledge handbook of digital writing and rhetoric* (pp. 343-353).
Routledge.
Jones, N. N., & Walton, R. (2023). Social justice. In H. Yu & J. Buehl
(Eds.), *Keywords in technical and professional communication *(pp.
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Kaebnick, G. E., Bennett, A., Brody, H., Dresser, R., Garland, S., Guinn,
A., Hale, B., Moreno, J., & Vanderpool, H. (2023). Editors' statement on
the responsible use of generative AI technologies in scholarly journal
publishing.* Hastings Center Report*, 53(5), 3-6.
Koerber, A., Pedersen, I., Duin, A. H., Kastman, E., & Smith, J. (2023). *The
predatory paradox: Ethics, politics, and practices in contemporary
scholarly publishing*. Open Book Publishers.
McIntosh, T. R., Liu, T., Susnjak, T., Watters, P., Ng, A., & Halgamuge, M.
N. (2023). A culturally sensitive test to evaluate nuanced GPT
hallucination. IEEE Transactions on Artificial Intelligence. Advance online
publication.
Mallette, J. C. (2024). Preparing future technical editors for an
artificial intelligence-enabled workplace. *Journal of Business and
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Melonçon, L. (2019). A field-wide view of undergraduate and graduate
editing courses in technical and professional communication programs. In S.
Flanagan & M. J. Albers (Eds.), *Editing in the modern classroom *(pp.
171-191). Routledge.
McKee, H. A., & Porter, J. E. (2022). Team roles & rhetorical intelligence
in human-machine writing. In 2022 IEEE International Professional
Communication Conference (ProComm) (pp. 384-391). Limerick, Ireland.
Quetzalli, A. (2023). The future of ChatGPT and AI in docs. In A. Quetzalli
(Ed.), *Docs-as-ecosystem: The community approach to engineering
documentation* (pp. 225-233). Apress.
Ren, Y., Zhang, H., & Kraut, R. E. (2023). How did they build the free
encyclopedia? A literature review of collaboration and coordination among
Wikipedia editors. *ACM Transactions on Computer-Human Interaction*, 31(1),
7:1-7:48.
Strobelt, H., Bau, D., Bethge, M., & Mordvintsev, A. (2022). GenNI:
Human-AI collaboration for data-backed text generation.* IEEE Transactions
on Visualization and Computer Graphics*, 28(1), 1076-1086.
Tang, Y. (2021). A robot wrote this?: An empirical study of AI's
applications in writing practices. In Proceedings of the 39th ACM
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induced by semantics and analytics. SHS Web of Conferences, 139, 02004.
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