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	<title>Siddharth Vij &#8211; Loop11</title>
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		<title>8 Key UX Research Trends Shaping 2025 (and What to Watch in 2026)</title>
		<link>https://www.loop11.com/8-key-ux-research-trends-shaping-2025-and-what-to-watch-in-2026/</link>
		
		<dc:creator><![CDATA[Siddharth Vij]]></dc:creator>
		<pubDate>Mon, 10 Nov 2025 22:40:39 +0000</pubDate>
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					<description><![CDATA[It’s no secret that UX research is having a moment. In 2025, it’s not just about testing usability or validating ideas after launch — it’s about shaping products from day one. As teams move faster and users expect more, understanding real behavior, needs, and context has become non-negotiable.  This article dives deeper into the biggest [&#8230;]]]></description>
		
		
		
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		<title>9 Ways to Use AI for UX Design &#038; Stay Ahead of the Curve</title>
		<link>https://www.loop11.com/9-ways-to-use-ai-for-ux-design-stay-ahead-of-the-curve/</link>
		
		<dc:creator><![CDATA[Siddharth Vij]]></dc:creator>
		<pubDate>Mon, 20 Oct 2025 23:26:45 +0000</pubDate>
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		<guid isPermaLink="false">https://www.loop11.com/?p=23581</guid>

					<description><![CDATA[The UX conversation in the last year or so has shifted from “should we use AI?” to “how do we use it effectively?” And with good reason. Research shows over half of design and research teams now use AI, primarily for speed and scale. Meanwhile, SaaS companies that adopt AI in product workflows are reporting [&#8230;]]]></description>
		
		
		
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		<title>The Iceberg UX Model: Designing for AI-First Interfaces</title>
		<link>https://www.loop11.com/the-iceberg-ux-model-designing-for-ai-first-interfaces/</link>
		
		<dc:creator><![CDATA[Siddharth Vij]]></dc:creator>
		<pubDate>Tue, 09 Sep 2025 02:07:17 +0000</pubDate>
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		<category><![CDATA[UX Resources]]></category>
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					<description><![CDATA[You build a polished, user-friendly AI product—just what stakeholders wanted.But post-launch, cracks appear. Users get confused, frustrated, and stop trusting the system. Sound familiar? That’s because most teams focus only on the UI, while the real issues lie deeper. In AI products, success depends on what’s beneath the surface—how your system handles uncertainty, errors, and [&#8230;]]]></description>
		
		
		
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