<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ai-Design on AI For Human Expertise</title><link>https://aiforhumanexpertise.com/tags/ai-design/</link><description>Recent content in Ai-Design on AI For Human Expertise</description><generator>Hugo</generator><language>en-gb</language><lastBuildDate>Sat, 30 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://aiforhumanexpertise.com/tags/ai-design/index.xml" rel="self" type="application/rss+xml"/><item><title>Designing AI That Protects Expertise</title><link>https://aiforhumanexpertise.com/blog/designing-ai-to-protect-expertise/</link><pubDate>Sat, 30 May 2026 00:00:00 +0000</pubDate><guid>https://aiforhumanexpertise.com/blog/designing-ai-to-protect-expertise/</guid><description>&lt;h2 id="erosion-is-a-design-choice-not-a-destiny"&gt;Erosion Is a Design Choice, Not a Destiny&lt;/h2&gt;
&lt;p&gt;The first two posts in this series made an uncomfortable case: AI can quietly erode the very expertise it depends on, and across domains &lt;a href="https://aiforhumanexpertise.com/blog/deskilling-across-domains/"&gt;that erosion is already happening&lt;/a&gt;. It would be easy to read that as an argument against AI. It is not.&lt;/p&gt;
&lt;p&gt;AI systems do not have to deskill the people who use them. That outcome is the result of design decisions, usually unexamined ones. Make different decisions and the same technology can preserve expertise, and in some cases actively strengthen it. This post is about those decisions.&lt;/p&gt;</description></item></channel></rss>