Social interaction design conversational AI generative AI genAI methods user experience interaction design
For an industry that once championed disruption, the disruption wrought by AI and Large Language Models has proven a challenge. Design, be it user experience design, interaction design, user interface design, information or service design, is now forced to adapt to and adopt AI. Where once AI was considered a threat do design jobs, it's now an augmentation, a tool, and accelerant. The performance, speed, and time gains created by AI are simply too great to ignore.
When it comes to design contributions to AI, however, the picture is less clear. Not only is conversation a dominant mode of interaction with LLMs, AI is increasingly automating tasks and workflows such that we provide oversight and supervision. Even this is at risk, as the human-in-the-loop paradigm threatens to become the human-as-bottleneck paradigm.
My primary interests are in conversational AI, and the nuances that apply to the experience design of interactions with AI by chat, text, and voice. Having practiced social interaction design (SxD) previously and for social networking, social tools, and social media, social dimensions of AI are a nascent interest as well.
I maintain a large vault of AI white papers and excerpts, mostly from Arxiv.org, in an Obsidian vault that has been mapped to create topic notes. I use Claude against this vault to research concepts and insights, and to write. A growing number of examples of these projects can be found here, along with prior SxD articles, research, and blog.
Large language models "read" and generate text and documents, generating writing and documents in response. Language models also converse, using styles that are chat conversation or voice-based conversation. From an interaction desigbn persepctive, these are different modes of communication. The conventions common to speech and talk are distinct from those that shape discourse, even while both use language (and language is the foundation of LLMs).
Browse this knowledge graph for a look at the topic notes in my Obsidian vault. The legend at bottom left represents categories with the highest density of connections. Use it to select just a single topic category. Hover over nodes for a mini summary of the topic note. Click to see related connections and associated white papers. White paper titles are active links.
Topic notes are concepts and insights shared across categories in the vault of white papers. They are not directly tied to individual white papers. When writing, Claude traverses topic notes to pull research ideas and insights from across the vault. A similar search with AI or on the web would surface just a fraction of these insights and papers, and each paper alone is a heavy lift and drain on the context window. These excerpts have been embedded already and can be used with vector and deep search.
An essay for ML and experience designers on conversation as shared context. I work with Claude through research on LLMs to shape an argument that the goal of AI-human interaction is interestingness in the experience.
Copy and paste a blog post, LinkedIn post, tweet, or other article into the window. Click to generate markdown. Copy that into Claude, ChatGPT, or another LLM. Copy what you get back into the next panel and view a linguistic analysis of the reasoning, rhetoric, and targeted audiences used in the post.
One research question, three reasoning approaches, three prompts, and three posts. A look at how prompting with different reasoning modules produces posts that make different arguments. Another experiment using Claude on an Obsidian vault of white paper research.
What impact will AI have on knowledge work, and on the role and status of the expert? I examine what makes expertise and how AI will reshape how it is produced and what qualifies it.
More posts written with Claude: Posts written from inside my Obsidian vault of AI white papers
I have a list of the whitepapers I have read here.