{"id":1260,"date":"2026-07-20T10:00:16","date_gmt":"2026-07-20T10:00:16","guid":{"rendered":"https:\/\/facultyhub.elsevier.com\/en\/?p=1260"},"modified":"2026-07-20T10:00:17","modified_gmt":"2026-07-20T10:00:17","slug":"from-launch-to-learning-elsevier-at-asme-asm-2026","status":"publish","type":"post","link":"https:\/\/facultyhub.elsevier.com\/en\/from-launch-to-learning-elsevier-at-asme-asm-2026","title":{"rendered":"From Launch to Learning: Elsevier at ASME ASM 2026"},"content":{"rendered":"\n

Elsevier was delighted to attend and sponsor the ASME Annual Scholarship Meeting (ASM) 2026<\/a>, held in Birmingham, UK from 30 June to 2 July. <\/p>\n\n\n\n

One of the highlights of Elsevier’s presence at the conference was a talk delivered by Gaia Ferrarin, Academic Success Manager at Elsevier, <\/strong>titled ‘From Launch to Learning: Lessons and Insights from Embedding AI-Powered Study Tools<\/em>‘. Gaia shared real-world data and reflections from the first eight weeks following the launch of Elsevier\u2019s StudyFinder AI<\/a> \u2013 the AI-powered study tool within ClinicalKey Student<\/a> – and offered a first look at what’s coming next with Assessment Creator.<\/p>\n\n\n\n

This article brings together Gaia’s slides and the key points she discussed on the day. The following is based on Gaia’s own words from the talk, edited lightly for clarity.<\/em><\/p>\n\n\n\n

From Launch to Learning: Lessons and Insights from Embedding AI-Powered Study Tools<\/em> – Gaia Ferrarin, Academic Success Manager at Elsevier<\/strong><\/h2>\n\n\n\n

The problem we set out to solve<\/h2>\n\n\n\n

At last year’s ASME ASM, Elsevier presented two AI capabilities we were developing for ClinicalKey Student. One was Assessment Creator, a tool for faculty to generate high-quality assessments. The other was StudyFinder AI. The question we were asking ourselves was: can we use AI to genuinely help how medical students learn? Not just give them more content, but actually help them study better, and save them time. The answer to that question was StudyFinder AI, which launched in March this year.<\/p>\n\n\n\n

But to understand why we built it, you have to start with the problem. Students have access to a lot of content. The problem was that they were overwhelmed by it. They didn’t know where to start, they were jumping between resources, losing time, and increasingly turning to generic AI tools which are useful but unreliable, and completely disconnected from the trusted sources that medical education depends on.<\/p>\n\n\n\n

This tool was built with <\/em>students, not just for them. We ran research sessions, student forums, usability tests, faculty interviews \u2013 and we co-designed the experience around how students actually think about learning at different moments in their journey.<\/p>\n\n\n\n

That’s where the depth settings come from – Introduce, Understand, Master. Students told us exactly what those should feel like.<\/p>\n\n\n\n

    \n
  • Introduce <\/strong>is orientation \u2013 buzzwords, definitions, getting your bearings on a new topic.<\/li>\n\n\n\n
  • Understand <\/strong>is \"I’ve just had a lecture, and I need to go deeper\" \u2013 diagrams, mechanisms, the fuller picture.<\/li>\n\n\n\n
  • Master <\/strong>is everything \u2013 the level you go to when you really need to own it.<\/li>\n<\/ul>\n\n\n\n

    And then the five modes \u2013 Learn<\/strong>, Revise<\/strong>, Test<\/strong>, Practice<\/strong>, and Upload<\/strong>. Those came from the same process: students telling us how their study needs shift depending on where they are in the week, the term, or the exam cycle.<\/p>\n\n\n\n

    How Students Learn with AI – Query Patterns<\/h2>\n\n\n\n

    Gaia presented the following visual to demonstrate the range of query patterns in StudyFinder AI, from least to most common:<\/em><\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    It’s worth noting that we\u2019re still only eight weeks in. These are early patterns, not conclusions. But the signals are consistent enough to be worth talking about, and the hope is that by next year’s conference, we will bring a fuller picture.<\/p>\n\n\n\n

    The top bar \u2013 topic-based queries \u2013 is by far the biggest. What students are mostly typing into StudyFinder AI is just a topic. \"Chest pain.\" \"Antiphospholipid syndrome.\" \"The renal system.\" Not a question. Just a concept they want to get into.<\/p>\n\n\n\n

    At the other end, direct questions account for fewer than one in ten interactions. Students aren’t typing \"what’s the treatment for X?\" They’re not trying to get to the answer quickly. They’re using StudyFinder AI to start somewhere, not to finish somewhere.<\/p>\n\n\n\n

    What we’re seeing is exploration first. Students begin broad and then they narrow themselves down \u2013 nobody’s telling them to. That iterative pattern \u2013 broad topic, then something more specific, then a clinical angle \u2013 isn\u2019t shortcut behavior. It\u2019s actually what learning looks like when it’s working.<\/p>\n\n\n\n

    Depth of Engagement: Students Don’t Just Dip In<\/h2>\n\n\n\n

    We know what students are querying \u2013 but what happens after that first query is just as compelling. A typical student StudyFinder AI session follows a recognizable pattern.  <\/p>\n\n\n\n

      \n
    • Initial query<\/strong>\n
        \n
      • A student opens StudyFinder AI and types \"Chest pain.\"<\/li>\n\n\n\n
      • That’s broad and they’re not sure where they want to go yet. They’re just starting. And then \u2014 more often than not \u2014 they don’t stop there.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
      • First follow-up<\/strong>\n
          \n
        • They follow up: \"Focus on the cardiac causes.\" <\/li>\n\n\n\n
        • They’re narrowing.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
        • Second follow-up<\/strong>\n
            \n
          • They follow-up again: \"What would I see on the ECG?\" <\/li>\n\n\n\n
          • Now we’re in clinical application territory.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
          • Third follow-up <\/strong>\n
              \n
            • And again: \"Walk me through STEMI versus NSTEMI.\"<\/li>\n\n\n\n
            • That’s synthesis. That’s assessment-level thinking.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n

              All within one session. All self-directed. No one told that student to go deeper \u2013 StudyFinder AI just made it easy to keep going.<\/p>\n\n\n\n

              That’s what makes this finding significant for a medical education audience. Because that session doesn\u2019t look like someone using AI to avoid thinking. It looks like someone actually learning. Starting broad, narrowing, applying, synthesizing \u2014 that’s the pattern we’d want to see in a tutorial or a case-based session. And it’s happening here, at scale, driven entirely by the students themselves.<\/p>\n\n\n\n

              \"\"<\/figure>\n\n\n\n

              Key Lessons for Medical Education\u200b<\/h2>\n\n\n\n

              Lesson 1: Students are explorers, not questioners<\/strong><\/h5>\n\n\n\n

              The first lesson is perhaps the most counterintuitive \u2013 and the one I’d most want anyone designing AI tools for medical education to hear. Students are explorers, not questioners. They don’t come to StudyFinder AI with a fully formed question looking for a shortcut to the answer. They come with a topic, a concept, a thing they want to understand. If you design an AI tool around answer retrieval, you’ll miss what students actually need: something that supports curiosity-led, open-ended inquiry.<\/p>\n\n\n\n

              Lesson 2: Iterative learning is the natural mode<\/strong><\/h5>\n\n\n\n

              The second follows naturally from the first. Iterative learning isn’t something we’ve had to engineer in \u2014 it’s just what happens when you remove the friction. The follow-up behavior we’re seeing is students doing what students do when the environment supports it. AI, at its best, is just making it easier for them to keep going.<\/p>\n\n\n\n

              Lesson 3: Students self-regulate depth<\/strong><\/h5>\n\n\n\n

              When students choose to override the default and go deeper, the data shows they consistently go to the highest level. They want to master it, not just be introduced to it. That metacognitive awareness is already there.<\/p>\n\n\n\n

              Assessment Creator AI – Coming Soon<\/h2>\n\n\n\n

              Everything I’ve talked about so far has been from the student side \u2013 what students are searching for, how they’re learning, how deep they’re going. But there’s an obvious question sitting underneath all of that: what does this mean for faculty? What does this mean for assessment?<\/p>\n\n\n\n

              That\u2019s where Assessment Creator comes in. Launching in September, it lets faculty generate assessments \u2013 MCQs, problem-based learning cases \u2013 directly from Elsevier source material. The same trusted content students are learning from in StudyFinder AI. You pick your book, your chapter, your difficulty level, your question style. It generates, you review and edit, and assign to your students.<\/p>\n\n\n\n

              The thing that excites me about this, in the context of everything we’ve talked about today, is that it closes the loop. Students exploring a topic in StudyFinder AI, faculty assessing that same topic in Assessment Creator \u2013 built from the same content, on the same platform.<\/p>\n\n\n\n

              More to come on that in September!<\/p>\n\n\n\n

              Key Takeaway<\/h2>\n\n\n\n

              When we launched StudyFinder AI, the question we were asking was whether AI could help students find better learning materials, faster. Eight weeks in, the data is telling us yes \u2013 but it’s also something more than that.<\/p>\n\n\n\n

              Students who engage with this tool aren’t just finding things more quickly. They’re learning differently. They’re starting broad and going deep. They’re following up, refining, building. In a digital environment, they\u2019re doing the kinds of things we’d hope to see in a seminar room or at the bedside. AI study tools are not replacing learning. They’re extending it.<\/p>\n\n\n\n

              That\u2019s the thing worth taking away from today \u2013 not the numbers, not the conversion rates, but the behavioral pattern underneath all of it. Students, when given the right environment, will seek depth. They will self-direct. They will keep going.<\/p>\n\n\n\n

              Our job \u2013 as educators, as product designers, as people who care about medical education \u2013 is to make sure that environment exists for as many students as possible.<\/p>\n","protected":false},"excerpt":{"rendered":"

              How are students actually using AI study tools? Eight weeks after the launch of StudyFinder \u2013 Elsevier’s AI-powered study tool within ClinicalKey Student \u2013 Gaia Ferrarin took to the stage at ASME ASM 2026 to share the early data and what it reveals about how medical students are really learning. In this article, we’ve captured the key points from her talk, including a first look at what’s coming next for faculty with Assessment Creator.<\/p>\n","protected":false},"author":289,"featured_media":1437,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"event_date":"","event_end_date":"","event_location":"","event_cta_text":"","event_form_url":"","event_bigmarker_id":"","testimonial_name":"","testimonial_role":"","testimonial_institution":"","testimonial_country":"","_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[2,3],"tags":[],"class_list":["post-1260","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","category-medical"],"yoast_head":"\nFrom Launch to Learning: Elsevier at ASME ASM 2026 - Faculty Hub EN<\/title>\n<meta name=\"description\" content=\"At ASME ASM 2026, Gaia Ferrarin shared early data and insights eight weeks after the launch of StudyFinder AI \u2013 Elsevier\u2019s AI-powered study tool within ClinicalKey Student. 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