Abstract
How should instructors adapt technical editing courses to account for generative artificial intelligence (AI)? This article addresses what generative AI means for technical editing pedagogy. While AI tools may be able to address rote editing tasks, expert editors are still needed to provide accessible, ethical, and justice-oriented edits. After reviewing impacts of generative AI on editing praxis, the author focuses on the microcredentials that she built into an editing course in order to address these impacts pedagogically. The goal was to enable students to understand AI, argue for their expertise, and edit from ethical and social justice perspectives.
Thanks to the emergence of generative artificial intelligence (AI), creating content without the use of a human writer seems more and more possible. While formulaic kinds of writing might be more frequently produced using generative AI—as Tang (2021) showed in a study of AI-produced news stories—human writers and editors are still integral to creating tailored, rhetorically aware, user-centered communication. And these rhetorical skills and language expertise will continue to be central to technical communication courses, even as our field adapts to the use of new technologies. Although I believe that concerns about the use of AI in higher education are overblown, I realized I needed to address generative AI in teaching a technical editing course for undergraduate students.
Technical editing courses are a mainstay of technical communication education (Eaton, 2023; Melonçon, 2019). At Boise State University, WRITE 403 Editing for Clear Communication is a required upper-division course for the technical communication certificate and an option for the Bachelor of Arts in Writing, Rhetoric, and Technical Communication. Thus, the course should not focus solely on technical editing best practices; it should also equip students to adapt to changing communication landscapes, many of which may involve generative AI.
In my course design, I also wanted to address the arguments raised by Clem and Cheek (2022), Clem (2023), and Benjamin and Schreiber (2021) that editing courses should focus more centrally on social justice, accessibility, and advocacy issues. This focus would allow us to understand the human impacts of AI, as well as changes to technical communication content and practices. One approach is to center rhetorical orientations on editing and human-centered editing practices while specifically attending to the use of AI tools. I knew that students should know how to use generative AI critically to support editorial praxis with an understanding of ethics and justice. Moreover, they should be able to demonstrate how their editing expertise provides contributions that cannot be readily replaced by AI.
In this article, I begin by exploring how AI tools have affected and will continue to affect editorial work in technical communication. Then, I share how I have addressed the use of AI tools in my fall 2023 Editing for Clear Communication course through microcredential modules. Finally, I suggest revisions for future iterations of the course.
How is AI Changing Technical Editing?
While generative AI cannot replace human writers and editors, it has and will continue to change how technical writers and editors work. Through conversations with practitioners, I have learned how AI has been used for creating content, including for outlining, generating ideas, and drafting. In addition to generative AI tools such as ChatGPT and Google Bard, writers and editors use AI text-assistant tools such as Grammarly or Microsoft Editor, as well as grammar and spellcheckers, to identify and address text issues. These tools can help writers and editors save time when creating and reviewing documents and have been embraced by technical communicators. In this section, I examine how these tools affect editing praxis and what that may mean for educating students.
Grammarly and Other AI Writing Assistants
AI writing assistants such as Grammarly purport to offer the kinds of editorial guidance expected to be driven by human editors, such as genre-specific suggestions, style guide applications, and tone detection (How Grammarly Works, n.d.). Discussions about Grammarly note that it is useful for rote editing tasks, such as catching spelling, mechanical, and formatting errors, but that it requires a premium subscription for more advanced forms of support (Moore, 2020; Wallen, 2023) and still does not replace an experienced, expert technical editor. In my editing class, the topic of Grammarly's use as an editing tool came up at the start of the semester, and I noted how it could catch the kinds of errors we might miss. For instance, when we did an editing activity on paper, most of us (including me) overlooked some basic misspellings, which we would see when electronically editing using a spellchecker (e.g. the one built into Microsoft Word). I recommended to the students, then, that they use Grammarly as they would use a spellchecker—not as a replacement for their own reading and review but as a supplemental tool to address editorial concerns that may not require a sensitive or rhetorically informed approach. In fact, a practitioner shared with me that she tends to use Grammarly as a tool to catch major typos and other issues before sending content off to human editors (K. Watson, personal communication, September 8, 2023). This use of Grammarly is similar to the use of generative AI that McCarthy (2023) has confirmed is typical for writers in creating and editing content.
Generative AI
While Grammarly is a tool specifically built to address writing and editing concerns that emerge in the composition process, how does generative AI affect the work of technical writers and editors? Current conversations indicate that generative AI will change not only how writers create content but also how they edit content. For instance, in a blog post with the tagline “Give your AI tinman a beating heart,” Carter (2023) recommended that writers “edit extensively and skeptically” because AI often ignores word count and neglects to follow style guidelines. Critically, editors need to fact-check any content it produces, given the ways that generative AI can more rapidly spread disinformation, conspiracy theories, and blatant biases (Hsu & Thompson, 2023). This potential for problematic content makes human editors even more crucial for robustly managing the content produced by generative AI. While accuracy and ethics should always be considerations in editing praxis, how a text is produced (e.g. not solely human authored) will change the editorial concerns and processes in reviewing that information, as well as collaborations with the humans involved in the content creation. If a text is produced with AI, the main concerns that editors may need to address include ensuring that the content is accurate and ethical and that the writing is rhetorically appropriate and stylistically engaging. While these are elements that skilled editors typically address, AI-produced text tends to be more general rather than specific and ill-suited to the rhetorical situation, requiring additional attention for these concerns.
As an example, while writing this article, I decided to ask Google Gemini (formerly Bard, which faculty, staff, and students have access to at my university) to “write an article about using AI as a technical editor.” But the output focused more on using generative AI for writing rather than for editing, claiming that “while AI is not yet capable of replacing human technical editors, it is a valuable tool that can help them to improve the quality of their work.” The output also pointed out that AI is probably best used for rote editorial tasks that do not require the background knowledge and rhetorical awareness of a trained, experienced technical editor (for the full AI-produced text, see Write an Article About Using AI as a Technical Editor, 2023). Had I planned to use this output for anything other than an exploration, however, I would have had to edit it to add more specificity, pull in legitimate citations, and adjust the style and organization to work for a specific rhetorical situation.
Generative AI also raises questions for editors about intellectual property. One practitioner explained to me that her company created guidelines for the use of generative AI because of the proprietary and sensitive information that the programs store (K. Watson, personal communication, September 8, 2023). This storage of user-generated information is key to how the large language models work. As Google Bard indicates, “When you interact with Bard, Google collects your conversations, your location, your feedback, and usage information. That data helps us provide, improve and develop Google products, services, and machine-learning technologies” (Bard FAQ, n.d.). In addition, with large language models, the input affects the output, so if the model pulls from inherently biased or problematic information, it will produce the same. Thus, technical editors will need to understand how AI functions and stores data and their organization's policies concerning AI use. Those working in fields with sensitive or proprietary data and information will not want that information to become part of generative AI systems and responses. And they will need to critically evaluate any information that AI produces not only for accuracy but also for implicit or explicit bias.
Thus, students must be prepared to evaluate whether they can use AI tools in their editing work and how such tools will affect their approaches to editing tasks. This preparation should occur throughout a technical editing course, with a strong emphasis on using these tools critically. In addition, students should be prepared to check in with their employer's or client's policies and approaches regarding the use of generative AI tools and understand the ways these tools store data. Technical editing students might go on to work for companies with proprietary information that will not want this content shared on AI—or perhaps already do. Or employers might take an ethics-driven stance toward AI use, wanting to ensure that the content produced is created and reviewed by humans, particularly if these individuals are being paid for their knowledge and expertise. Finally, future technical editors must address the ethical and social justice concerns inherent in uncritical uses of generative AI, requiring technical editing pedagogy to shift away from a solely instrumentalist approach and toward the inclusive editing paradigm that Clem and Cheek (2022) have advocated.
AI Impact on Editing Expertise
Finally, AI will affect the ways that technical editors need to advocate for the skills they bring to a project, particularly as employers compare the costs between employing a technical editor and merely relying on generative AI or AI writing assistants like Grammarly. Prior to the emergence of generative AI, scholars noted the challenges in explaining what editors do because the terminology we may use in the classroom does not always make sense to employers or subject-matter experts in industry (Eaton, 2023). As I have argued (Mallette & Gehrke, 2018), students need to practice communicating their expertise in the classroom because they will be required to do so in the workplace. In defining expertise, Schriver (2012) helpfully distinguished between experience and expertise, with the latter being characterized by experts’ willingness to engage in further learning and add emerging technologies to their toolkit. For technical editors, arguing for their expertise requires them to demonstrate what they bring to a project and the ways that their skills are worth their cost. These skills include their ability to advocate for user needs, their rhetorical sensitivity, and their ability to bridge the gap between specialist knowledge and nonspecialist audiences, as well as their contributions to project management and adherence to the writing standards aligned with an organization's communication goals. These skills cannot be replaced by generative AI, even if AI can support these processes. With the further development of AI tools and their use in the workplace, technical editors will need to be able to articulate the skills and expertise they bring to an organization that cannot be replicated by generative AI alone.
How Can We Incorporate AI into Editing Courses?
With these two elements—knowledge and expertise—in mind, I wanted my undergraduate technical editing course to move beyond a focus on levels of edit, approaches to edit, and grammar skills, as advocated in recent scholarship on technical editing courses (Benjamin & Schrieber, 2021; Clem, 2023; Clem & Cheek, 2022; Melonçon, 2019), so that students could develop skills relevant to their goals and interests. To that end, I incorporated eight microcredentials into my fall 2023 Editing for Clear Communication course.
According to an article in Inside Higher Ed, microcredentials do not yet have a consistent use or definition (D’Agostino, 2023). In general, microcredentials are an opportunity to engage with professional development around specific skills through short learning modules. Johansen (n.d.) defined microcredentials as “a series of courses that culminate in a digital badge from an accredited university. They let employers and your professional network know that you have proficiency in a subject area.” The National Education Association (Micro-Credentials, n.d.) uses microcredentials to provide flexible, personalized, and skill-based content for educators whereas educational institutions and professional organizations such as the State University of New York (Gain New Skills, Knowledge and Experience with Microcredentials at SUNY, n.d.) and the American Health Information Management Association (Accelerate Your Healthcare Career With Skill-Based Microcredentials, n.d.) have created microcredentials as shorter, stackable, and timely educational modules to support a range of learner needs. Essentially, microcredentials are shorter and more specific than traditional certificate, certification, or degree programs.
In my course, I used microcredentials to give students a chance to delve deeply into a topic in editing and cultivate a skill specific to their goals and interests. Depending on what final grade they wanted to earn, students were required to complete two or three microcredential modules. When building each microcredential, I focused on content, activities, and a summative assessment that would allow students to practice the skill with an authentic assessment. The module concluded with students reflecting on what they learned and how they will engage with further professional development on the topic. Each microcredential module was designed to take 4–6 hours to complete. After students completed the module, they were awarded a badge—and as a fun twist, a colleague and I made physical buttons for students. From the first day of class, students were interested in and excited about the microcredentials, in part because of the topics and in part because they could select topics that were most relevant to their own goals. In fact, partway into the semester, a student requested a new option (the Editing Proposals microcredential), so I was able to use the microcredentials to respond quickly to student needs and create relevant content. These modules and short descriptions of what each contains are listed in Table 1. I will now showcase two of these microcredentials—(a) Technology and Editing and (b) Social Justice, Ethics, and Editing—that are relevant for this discussion about AI tools and using these tools critically to support social justice–oriented and ethical editing.
Summary of Microcredential Modules, Content, and Assessments.
Note. SME = subject-matter expert; AI = artificial intelligence.
Technology and Editing Microcredential
The goals for the technology and editing microcredential include both learning about tools to enhance editing praxis and building expertise. Schriver's (2012) framing of expertise notes that professional development is key—communicators must be able to learn new tools and adapt their practices. To encourage this approach, I divided the module into two content areas: using technology to edit and exploring emerging technology. For the first area, students examine a range of technology used in editing, including the tools available in their word processing programs and other authoring technologies. They then complete an activity in which they learn about a specific tool (e.g. Markdown, LaTeX, XML, or another tool they are interested in) and reflect on what they learned and how it will influence their approaches to editing.
For the second area, students explore emerging technologies, namely generative AI. They are given an overview of how to use these tools for editing, how to use these tools critically (with a connection to the social justice, ethics, and editing microcredential), and how to assert their own editing expertise while using generative AI. The activity for this section asks them to explore an AI tool, including generative AI options as well as AI assistants, such as Microsoft Editor or Grammarly, or AI image generators, such as DALL-E. They can also select another emerging technology they identify. After they explore the tool by asking it to generate and edit text, they are asked to reflect on their experience, answering questions about what the tool could or could not do, how it might be useful in editing situations, what the ethical and legal implications might be, and how they might use the tool while still asserting their editorial expertise.
This microcredential content allows students to learn more about the tools and approaches we have touched on in class. For instance, in class we covered editing for accuracy and completeness, and I asked students to consider how AI will affect editing for those concerns. The students already had some experience with AI—and they shared how they have used AI and the issues it creates if used uncritically, such as the tendency for these generators to fabricate citations when asked to provide references. While we did not have time in class to delve into using these tools, we discussed how AI will affect how we approach editing for accuracy and other objectives, and then I pointed students to this module if they wanted to learn more.
Social Justice, Ethics, and Editing Microcredential
To take up issues of ethics and justice around generative AI, the Social Justice, Ethics, and Editing microcredential provides content on the ways that AI tools perpetuate biases and how students should address those elements in their editing. In other words, in this module, editing students will consider ways that AI can spread misinformation and biases and how to edit for those concerns. This module was based on content from Haas's workshop at the Association for Teachers of Technical Writing (ATTW) 2023 Conference and was informed by Clem and Cheek's (2022) and Clem's (2023) work on revising technical editing courses to better account for social justice. In the ATTW workshop, Haas shared social justice exercises that she uses to teach technical editing, which she frames with anticolonial approaches, to help students understand the power of editing to either indoctrinate or liberate learners. In this workshop, she shared samples of textbook materials that whitewash history or elide ongoing instances of racism and oppression. Students are then asked to revise these texts to address the content as well as the writing of these samples. This exercise reveals that if editors only address grammatical concerns, content that perpetuates bias and injustice will be left unchallenged. Given the instrumentalist orientation that editing tends to take (Clem & Cheek, 2022), Haas's (2023) goal—and the goal of the workshop—was to share the power that editors have to intervene in and challenge oppression and bias. Haas generously shared this exercise for technical editing instructors to use, and it became one of the activities I built this module around.
Thus, this module includes content on social justice as well as ethical frameworks in editing. In the first half of the module, students read Jones and Walton's (2023) short introduction to social justice and Clem and Cheek's (2022) article. The activity they complete is built on Haas's (2023) workshop materials, with a reflection on the ideological orientations, choices, and elisions in the content they review. Students must reflect on who is harmed by various choices as well as how they can incorporate the inclusive editing paradigm that Clem and Cheek (2022) described in order to support social justice through editing. In the second half of the module, students review content on ethics in technical communication. Students then examine a variety of short scenarios and answer questions about what the ethical issue is, who the stakeholders are, how to intervene, and how to address the issue with an author. The final assessment for this module focuses on choosing a scenario to research and respond to. Students have two choices: (a) to make recommendations to an author about using AI as a writer or editor in an organization or (b) to research and recommend an ideal option for creating images (stock photography, in-house/freelance photographer/graphic designer, or AI-generated images). The goal is for students to conduct research and offer recommendations that do not just go for the cheapest or most expedient option but that build from decision-making processes related to editing, particularly around the use of generative AI, that are value driven.
Future Revisions
In researching this article and building the microcredentials, I found ways that I might revise for future iterations of the course. For instance, I would like to more fully integrate the current conversations about AI—and its implications for social justice and ethics—into the main content of the course, using the inclusive editing paradigm that Clem and Cheek (2022) described and approaches to defining editing that Clem (2023) outlined. While we discussed generative AI when we began discussing editing for accuracy and at other moments in class, the content is less visible on the syllabus, which still heavily emphasizes the skills typically taught in editing courses (Melonçon, 2019). Currently, the bulk of this content is relegated to the microcredential modules on Technology and Editing and on Social Justice, Ethics, and Editing, which students could opt to not complete in favor of other modules. For the next iteration of this course, I will develop an outcome focused on these tools to ensure that we more fully cover emerging technologies and trends. Also, as I continue to teach the class, I will need to stay up to date on conversations around AI, editing, and tools and then incorporate that knowledge into the class content. Further, I need to continue to discuss these tools and practices with technical communication practitioners. This professional development will allow me not only to stay updated on tools that will affect editorial work but also to model how to build and advocate for my own expertise in editing.
Conclusion
Since editing praxis itself may not change significantly, it would be easy to dismiss the impact of AI on teaching technical editing. But ignoring the ways that generative AI will change the nature of technical communication work would be a disservice to students. Ultimately, my goal is to equip future editors to edit effectively, potentially in an AI-enabled workplace. By the end of my editing course, I want students to be able to articulate what the tools can and cannot do for them as professional editors and to assert their expertise as technical communicators and editors. Furthermore, if our goal is to produce editors who can address justice through editing and adhere to ethical standards (Clem, 2023; Clem & Cheek, 2022), we need to engage students in this conversation. By integrating knowledge of generative AI and providing space to experiment and learn more about these tools, we will equip these technical editing students to edit critically, ethically, and effectively.
Footnotes
Acknowledgments
Thank you to Kate Watson for providing a practitioner perspective that shaped my ideas in this article. Thanks to Debra Purdy and Sean Scheibe for assisting in the planning, creation, and editing of several of the microcredentials in Editing for Clear Communication in fall 2023. And thank you to the fantastic students of WRITE 403 for your excitement, engagement, and focus on learning.
Declaration of Conflicting Interests
The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author received no financial support for the research, authorship, and/or publication of this article.