{"id":142343,"date":"2026-01-22T11:00:46","date_gmt":"2026-01-22T16:00:46","guid":{"rendered":"https:\/\/trainingindustry.com\/?post_type=magazine&p=142343"},"modified":"2026-01-22T12:57:15","modified_gmt":"2026-01-22T17:57:15","slug":"designing-learning-for-the-distracted-brain","status":"publish","type":"magazine","link":"https:\/\/trainingindustry.com\/magazine\/winter-2026\/designing-learning-for-the-distracted-brain\/","title":{"rendered":"Designing Learning for the Distracted Brain"},"author":52,"featured_media":142411,"template":"","tags":[2875,36529,3091],"class_list":["post-142343","magazine","type-magazine","status-publish","has-post-thumbnail","hentry","tag-digital-literacy","tag-instructional-design-strategy","tag-learning-design","global_topic_tax-artificial-intelligence","global_topic_tax-content-development","global_topic_tax-strategy-alignment-and-planning","magazine_issue_tax-winter-2026","magazine_article_type_tax-feature"],"acf":{"sponsored":false,"gated":false,"gated_content_type":"","file_attachment":null,"gated_content":"","form_instruction_header":"To access the full article, please fill out the form below:","pardot_html_embed":"","author_override":true,"author_name":"Dr. Grace Chang and Dr. Karina Freitag","author_image":"","author_bio":"Dr. Grace Chang, associate director of behavioral science and insights at Ernst & Young LLP, is a cognitive neuroscientist specializing in learning, behavior change, and leadership development. She leverages her research and industry expertise to design strategies that drive meaningful change and explores how AI-human interactions will shape the future of learning and work.\r\n\r\nDr. Karina Freitag, assistant director and senior researcher of behavioral science and insights at Ernst & Young LLP, is an organizational psychologist who applies behavioral science to design evidence-based learning and leadership solutions. Her work also explores AI-human interaction to advance workplace effectiveness and innovation.","excerpt":"A well-designed beginner AI course builds confidence by teaching how models predict, how humans prompt and why people must remain editors.","main_content":"Learners today face an overwhelming influx of information. Their attention is fragmented by a steady stream of notifications, emails, meetings and shifting priorities. The result is a growing cognitive load that strains the brain\u2019s natural capacity to process and retain information.\r\n\r\nAs artificial intelligence (AI)<\/a> accelerates the pace of change, knowledge workers are expected to learn faster and adapt more quickly or risk being outpaced by AI-savvy peers or even replaced by AI itself. The pressure to keep up has never been greater. Yet, the human brain wasn\u2019t built for this level of ongoing mental strain.\r\n\r\nSo, how can we design learning that truly works?\r\n
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\r\nWhile technology and AI are often seen as contributors to cognitive burden, they can also be powerful tools for reducing it \u2014 if used strategically. This article explores how AI can support learning designers in creating experiences that are not only more effective but also more sustainable for today\u2019s workforce.<\/div>\r\n

What Is Cognitive Load, and Why Does it Matter?<\/h2>\r\nTo acquire long-lasting knowledge, the brain must successfully go through two key steps: encoding and consolidation.\r\n
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  1. Encoding<\/strong> is the process of transforming information into a format that the brain can store. This process is facilitated by working memory, which provides a mental workspace for holding and manipulating information we are currently focusing on. When we actively engage with this information by repeating it to ourselves or connecting it to things we already know, we increase the likelihood of successful encoding.<\/li>\r\n \t
  2. Consolidation<\/strong> is the process of stabilizing and integrating that information with existing memories stored in long-term memory. It transforms something temporary into durable memories that can be later retrieved and used. Even when encoding is successful, most information is quickly forgotten unless it undergoes consolidation.<\/li>\r\n<\/ol>\r\nAs learning designers, we can shape experiences that support both encoding and consolidation<\/a>, making it more likely that new information is truly learned. One powerful way to do this is by designing with cognitive load<\/a> in mind.\r\n\r\nCognitive load is the mental effort it takes to process information in our working memory. Think of working memory not as a spacious warehouse, but as a small bag that can only hold a few items at a time. For example, when we try to absorb a new concept during a training session while also replying to emails, tracking upcoming meetings and mentally preparing for a presentation later in the day, our mental bag \u2014 our capacity to process and retain information \u2014 quickly becomes overloaded. When the cognitive load is too high, meaning a task or learning experience demands more than our mental bag can carry, things start to spill out.\r\n\r\nHigh cognitive load is the norm today, resulting in mental fatigue, fragmented attention and learning that doesn\u2019t stick. By designing learning experiences that manage cognitive load, we can help learners focus on what matters, retain information and apply it when it counts.\r\n

    The AI Paradox: Burden and Benefit<\/h2>\r\nAI presents a paradox for today\u2019s learners. On one hand, it contributes to cognitive burden, accelerating the pace of change and requiring constant upskilling on new tools, content and processes. This adds to an already overloaded mental workspace.\r\n\r\nOn the other hand, when used intentionally, AI can help alleviate that very burden. Learning designers can leverage AI to personalize learning, reduce unnecessary complexity and surface what matters most, making it easier for the brain to encode and consolidate new information.\r\n\r\nRather than overwhelming learners with more AI-generated content, we can position AI as a strategic partner<\/a>: one that filters out noise, directs attention to what matters and helps build learning experiences that align with how the brain learns best. Let\u2019s explore how.","full_width":false,"content_band":[{"acf_fc_layout":"social_callout","blockquote":"As learning designers, we can shape experiences that support both encoding and consolidation, making it more likely that new information is truly learned. One powerful way to do this is by designing with cognitive load in mind. "},{"acf_fc_layout":"content_area","wysiwyg":"

    The EY Cognitive Load Learning Design Framework<\/h2>\r\n

    1. Prioritize the essential.<\/h3>\r\nFocus on what matters: <\/strong>Learning objectives act like a packing list for a trip. Just as you wouldn\u2019t pack snow boots for a beach vacation, you shouldn\u2019t include content that doesn\u2019t directly support the learning goal. When objectives are specific and concise, such as \u201cdescribe what makes feedback effective,\u201d they help sort through content and eliminate what\u2019s unnecessary. This reduces extraneous cognitive load by narrowing the focus to what truly matters.\r\n\r\nAI tools can help you prioritize the most relevant content by reviewing your objectives and flagging content that may be redundant or off-topic.\r\n\r\nUse this AI prompt:<\/em><\/strong> \u201cAnalyze this document and highlight only the sections that directly support the learning objectives: [specify the learning objectives]. Remove any content that doesn\u2019t align with these objectives.\u201d<\/em>\r\n

    2. Highlight relevance.<\/h3>\r\nMake it personally relevant: <\/strong>Content that feels personally relevant to the learner is more likely to be processed deeply. When learners recognize how content connects to their own goals, experiences or challenges, it becomes more meaningful. That relevance activates their intrinsic motivation, which enhances focus and supports deeper encoding.\r\n\r\nUse this AI<\/em><\/strong> prompt:<\/strong> \u201cGenerate a \u2018what\u2019s in it for me\u2019 introduction for managers and individual contributors learning these skills.\u201d<\/em>\r\n\r\nMake practice realistic: <\/strong>Practice helps consolidate learning, but not all practice is equal. Learners are more likely to stay engaged and remember what they\u2019ve learned when they can apply new knowledge in realistic, varied situations that feel relevant to them. Practicing in authentic scenarios not only reinforces understanding but also strengthens memory consolidation. This kind of practice supports long-term learning and improves the transfer of knowledge to real-world situations.\r\n\r\nUse this AI prompt:<\/em><\/strong> \u201cGenerate three realistic workplace scenarios where learners can practice using the feedback model from this module. Each scenario should describe the context and include a specific situation that requires giving feedback.\u201d<\/em>\r\n

    3. Structure for clarity.<\/h3>\r\nOrganize information: <\/strong>Our brains crave patterns and predictability. When learning flows logically and is chunked into digestible segments, cognitive load is reduced and it\u2019s easier for our brains to digest and integrate with existing stored knowledge. Ways to organize information include chunking and scaffolding.\r\n
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    • Chunking<\/strong> is the practice of grouping information. For example, if you\u2019ve changed a lightbulb many times before, you don\u2019t need to remember each step individually; your brain has grouped them into a single schema: change lightbulb.<\/li>\r\n \t
    • Scaffolding<\/strong> is a way to prioritize and sequence information so that foundational knowledge comes first, followed by new or progressively more complex concepts. This structure can happen within the same learning session or across multiple sessions. For example, think of scaffolding like constructing a building: you start with a solid foundation before adding subsequent floors.<\/li>\r\n<\/ul>\r\nUse this AI prompt:<\/em><\/strong> \u201cGroup this information into 3-5 modules, each covering a single main idea. Suggest clear headings and subheadings. Then, sequence the modules so that the learning starts with basic or foundational concepts, and then builds toward more complex or advanced ideas.\u201d<\/em>\r\n\r\nReduce friction: <\/strong>Processing new information already requires effort. If the presentation is cluttered, confusing or overly complex, it adds unnecessary load. Reducing friction means making content easy to scan, visually clean and intuitively structured, freeing up cognitive resources for actual learning. For example, using clear fonts, highlighting key words and limiting the number of visuals per slide can reduce extraneous cognitive load.\r\n\r\nUse this AI prompt:<\/em><\/strong> \u201cRewrite this section for a nonexpert audience, simplifying technical terms and clarifying instructions.\u201d<\/em>\r\n\r\nConnect to familiar language and concepts: <\/strong>When learners can connect new information to something they already know, it becomes easier to understand and remember. This is because the brain links new knowledge to existing networks of prior knowledge and experience, known as schemas \u2014 cognitive frameworks that help organize and interpret information. Linking learning to existing schemas reduces unnecessary mental effort and allows learners to focus on what truly matters.\r\n\r\nUse this AI prompt:<\/em><\/strong> \u201cReview the following content: [insert content or describe topic]. Suggest ways to connect this content to what learners likely already know based on their persona: [describe audience persona]. Focus on using familiar language, relevant scenarios and examples from daily experiences. Your goal is to make the content feel relatable and easy to understand by linking it to their existing knowledge and experiences.\u201d<\/em>\r\n

      4. Design for attention.<\/h3>\r\nIncorporate restorative breaks: <\/strong>Sustained attention is limited. Our brains\u2019 prefrontal cortex (PFC) \u2014 an area especially evolved in humans that is responsible for attention, critical thinking and decision-making \u2014 easily fatigues. Without breaks, learners lose focus and learning suffers.\r\n\r\nRestorative breaks are essential for maintaining attention, but there\u2019s no universal formula for how long or how often they should be. The ideal break depends on many factors, such as prior knowledge, expertise, individual differences, learning modality and how much mental effort the learning requires.\r\n\r\nActivities that evoke fascination, comfort, joy or physical movement can help restore cognitive function and reduce mental fatigue. Know your audience and adjust breaks based on their needs.\r\n\r\nUse this AI prompt:<\/em><\/strong> \u201cReview this two-hour workshop agenda and recommend where to insert breaks to maximize attention based on the audience\u2019s needs [insert description].\u201d<\/em>\r\n

      Designing for What Matters<\/h2>\r\nDesigning with cognitive load in mind isn\u2019t about making learning effortless \u2014 it\u2019s about making it purposeful. The goal should be to help ensure that learners\u2019 limited cognitive resources are spent engaging with meaningful content, not wasted on navigating poorly designed learning experiences.\r\n\r\nWhile AI can contribute to an overwhelming cognitive load, it also offers powerful tools to reduce it \u2014 if used strategically. By combining behavioral science with the strategic use of AI, we can create experiences that are not only brain friendly but also relevant, efficient and impactful.\r\n\r\nThe views reflected in this article are the views of the authors and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.<\/em>"},{"acf_fc_layout":"social_callout","blockquote":"Rather than overwhelming learners with more AI-generated content, we can position AI as a strategic partner: one that filters out noise, directs attention to what matters and helps build learning experiences that align with how the brain learns best."},{"acf_fc_layout":"content_area","wysiwyg":"