Generative AI and the Future of Writing, Authorship, and Judgment
Speaker: Duncan P Brumby – London, United KingdomTopic(s): Human Computer Interaction , Society and the Computing Profession
Abstract
As generative AI becomes routine in writing, a simple question becomes harder to answer: if a text was written with help from AI, who is the author? Students use AI to brainstorm, plan, draft, revise, and polish assessed work. Professionals use it to summarize information, prepare documents, generate ideas, and improve communication. Researchers and reviewers are beginning to ask whether AI should support scholarly evaluation itself. These tools are becoming part of how people write, think, learn, and judge.
But this shift raises a deeper question. If generative AI lowers the cost of producing text and ideas, what happens to the human work of authorship, evaluation, and responsibility? Universities are systems for assessment, credentialing, and judgment. Workplaces are communities where effort, competence, originality, and trust are continually evaluated. Academic publishing is a system for deciding what counts as credible knowledge.
In this lecture, Professor Duncan Brumby examines how generative AI is reshaping writing, authorship, and judgment in education, work, and research. Drawing on Human-Computer Interaction research, studies of AI-supported academic writing, workplace AI literacy, and AI in peer review, the talk explores how people actually use these tools, why they often conceal or downplay their use, and what this means for learning, expertise, accountability, and trust.
The lecture is structured around three themes:
- Writing as workflow
AI-supported writing is not a single act of automation. Students and knowledge workers use AI selectively across ideation, sourcing, planning, drafting, reviewing, editing, and sense-making. This section examines how people assemble workflows around competing priorities: learning, quality, productivity, voice, and authorship. - Authorship as responsibility
Authorship is no longer simply about who typed each sentence. It is about who frames the problem, directs the tool, evaluates the output, and stands behind the claims. This section explores why disclosure can feel risky, how AI use shapes judgments about effort, competence, originality, integrity, and trust, and why responsible practice depends on shared norms for talking openly about AI use. - Judgment as the new bottleneck
As AI makes it easier to produce fluent text and plausible ideas, the limiting factor shifts toward evaluation. In peer review, education, and professional work, the central challenge is not whether content can be generated, but whether people have the time, expertise, and attention to assess its quality, credibility, meaning, and consequences.
The central message is that generative AI is not simply a new productivity tool. It changes the conditions under which knowledge work is produced, assessed, and trusted. When writing becomes abundant, human attention becomes newly scarce. The bottleneck is no longer writing alone. It is judgment — and the attention needed to exercise it well.
Watch a sample of the lecture online: The LLM Paradox: We Use Them Constantly, But Are They Helping?
https://www.youtube.com/watch?v=3y4ehueNSpY
About this Lecture
Number of Slides: 50Duration: 45 minutes
Languages Available: English
Last Updated: 14/05/2026
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