LibGuides: Technical Communication: Generative Artificial Intelligence

For more information on using generative AI in content creation, check out Technical Writing Essentials by by Robin L. Potter and Tricia Nicola Hylton.

The library's Generative Artificial Intelligence (GAI) guide provides an overview of GAI and includes such as using GAI tools, AI and academic integrity, and copyright considerations.

Gen AI and Technical Communication
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How can GAI be used in everyday technical communication?

Generative AI can be a useful tool in everyday technical communication to automate tasks, and enhance productivity, and efficiency. Some examples where generative AI can be used in technical communication include:

Content Generation: Generative AI can help with automate repetitive tasks including generating templates for documents such as user manuals and SOPs.
Data Analysis: Generative AI can help analyze large and complex datasets, generate insights, summaries, reports, and visualizations.
Ideation: Generative AI can help with brainstorming ideas for content and projects.
Topic overviews: Generative AI can provide some background information to learn more about a concept or topic.
Emails and other correspondence: Generative AI can help employees write clearer emails and messages, or create templates.

However, it is essential to be aware of the limitations and potential risks of using generative AI tools for technical communication. This includes:

Biased and inaccurate information: Generative AI tools can generate content that is biased and/or includes inaccurate information. It's always a good idea to review generative AI output to check for accuracy and bias. For tips on evaluating generative AI content, check out the ROBOT test or SIFT method.
Privacy & Security Concerns: Individuals should be cautious about the information they share with generative AI models to reduce the risk of data misuse or breaches, particularly when it comes to sensitive or confidential information.
Copyright Concerns: The two main areas of copyright concern revolve around generative AI input and output. Generative AI relies on large amounts of training data. Some tools may have trained their systems by copying content without permission from the creators or copyright holders. This has raised copyright infringement concerns and has been the focus of several lawsuits.

Note: Content is adapted from response generated by Microsoft Copilot

Seneca Polytechnic Generative Artificial Intelligence (GenAI) Policy

Articles

AI plagiarism hits the workplace, creating new hazards for employers: KPMG survey found about a quarter of Canadian generative AI users have inputted company information into tools such as ChatGPT (The Globe and Mail)
Applications of GenAI for Business Communication (Carnegie Mellon Tepper School of Business)
The challenges and opportunities of AI-Assisted writing (Business and Professional Communication Quarterly)
ChatGPT Replicates Gender Bias in Recommendation Letters (Scientific American)
Competencies needed by business professionals in the AI age: Character and communication lead the way (Business and Professional Communication Quarterly)
A Double-Edged Sword: The Risks of Using Generative AI in Employee Communication (Sociabble)
How generative AI can boost highly skilled workers’ productivity (MIT Sloan School of Management)
Machines of mind: The case for an AI-powered productivity boom (Brookings Institution)
Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality (Harvard Business School Technology & Operations Mgt. Unit Working Paper)