In this webinar, you will: 

• See learning solutions created by your peers.
• Learn about the tools and technologies used to create these projects.
• Hear about the obstacles faced along the way and how they were overcome.
• Leave with inspiration that can be applied in your work.

The post 2025 Best of DevLearn DemoFest Webinar  appeared first on Learning Guild.

While companies pour money into lengthy courses that demand full attention and perfect conditions, employees are multitasking, moving, and simply too busy for bloated content. The solution isn’t more “hyper-engaging videos” or “interactive workflow simulations”; it’s audio-first podcast-style eLearning that fits into real life.

Evidence shows that people want to learn during their commute, 94% prefer audio learning formats, and around 600 million people globally already listen to podcasts. Meanwhile, screen fatigue affects more than half of us, and countless employees in hands-on jobs can’t access computer-based training during work hours.

Podcast-style training solves these problems by delivering focused, practical lessons that people can consume while walking, commuting, or working. It’s accessible, private, convenient, and proven effective. Best of all, you can create professional podcast training in minutes using AI tools like Google’s NotebookLM.

Traditional training doesn’t fit real life

Your employees aren’t sitting in quiet rooms waiting for the next corporate training video. They’re juggling emails while listening to webinars, scrolling phones during presentations, and frankly struggling to find 30 uninterrupted minutes for that mandatory course.

Traditional eLearning assumes people have screens, silence, and undivided attention. But many Gen Z and Millennials want to learn on their commute, and most workers are constantly multitasking. When your training demands perfect conditions, it simply doesn’t happen.

The numbers tell the story. Companies invest millions in learning programs with engagement rates that would embarrass a YouTube channel about paint drying. Meanwhile, on average people consume 9 podcasts per week, proving they’re hungry for audio content that fits their lifestyle.

Screen fatigue is crushing your training ROI

More than 50% of us suffer from screen fatigue, and research shows vision problems are higher among online learners due to increased screen time. After eight hours staring at monitors, the last thing employees want is more screen-based learning.

Podcasts offer a break from digital eye strain while keeping productivity high. Instead of adding screen time, audio learning removes it entirely. Your workforce can rest their eyes while strengthening their skills.

Audio learning reaches more people

Traditional training excludes huge portions of your workforce. Construction workers, nurses, drivers, and warehouse staff don’t have constant computer access. Millions of people live with sight loss, making screen-based content challenging or impossible. Neurodivergent employees often find audio helps reduce over-stimulation and sensory challenges.

Podcast-style training works for so many more people and situations. It requires only a mobile device and headphones—technology that a huge majority of the working population already carries. No special software, no perfect lighting, no quiet conference rooms required.

There’s love for podcast-style eLearning

The global podcast audience is estimated to be 600 million people in 2025, and they’re not just listening for entertainment. Shows like Diary of a CEO, HBR’s Ideacast, and even light-hearted programs like No Such Thing As a Fish prove people actively seek knowledge through audio.

This habit reveals something crucial: people don’t resist learning—they resist inconvenient learning. When education fits seamlessly into daily routines, consumption skyrockets.

People don’t resist learning—they resist inconvenient learning.

Create podcast-style training content in 4 simple steps

Creating professional podcast-style training based on your content used to require expensive script-writing, recording equipment, eLearning voice-over experts, and editing skills. Now, AI tools like NotebookLM generate educational audio content in minutes from your existing training materials.

Step 1: Go to NotebookLM

Navigate to NotebookLM and sign in with your Google account. The platform is free and requires no special setup.

Step 2: Click create new

Look for the “Create New” button, typically in the top right corner of your screen. This opens your workspace for the new podcast project.

Step 3: Add Your sources

Upload your training materials through the pop-up box. NotebookLM accepts multiple formats:

• PDF files and text documents
• Google Workspace content
• Website links
• YouTube videos
• Pasted written content
• Audio files like MP3s

Step 4: Generate audio overview

After adding sources, click “Audio Overview” to create your podcast. The AI will also offer study guides, mind maps, and flashcards if needed. Generation takes just a couple of minutes, then you can download the finished audio file.

Top Tip: If you click the edit pencil on the tile, you can tweak the settings and add some guidance for the AI, e.g., What should the hosts focus on in this episode?

The result sounds like two professional hosts discussing your content in an engaging, conversational style. No recording studios, no script writing, no editing required.

The learning styles myth (& why audio still wins)

Despite popular belief, research shows that learning style preferences don’t translate to effectiveness. You’re not a “visual learner” who needs pictures to understand concepts.

However, this false belief doesn’t eliminate the case for audio. Some people simply enjoy podcasts more than videos. Enjoyment drives engagement, and engagement drives results. If your learners prefer audio, that preference alone makes it more effective than formats they avoid.

Audio + everything else: The blended approach that actually works

The argument for podcast-style learning isn’t about replacing your entire training library with podcasts. It’s about using each format where it excels most. Think of audio as your training foundation: It dominates at delivering the “why”—the context, motivation, and behavioral foundations that make everything else stick.

A seven-minute podcast episode can explain why a new safety protocol matters, how it connects to company values, and what success looks like in practice. That understanding and motivation becomes the scaffold for everything else. Then supplement with focused visual materials where they add genuine value.

This blended approach fixes the core problem with monolithic training modules: They try to be everything at once and end up being effective at nothing. Instead of cramming context, procedures, and motivation into one overwhelming package, separate them strategically. Use audio for high-level concepts and behavioral change, then layer in visual elements for specific, procedural needs.

The result? Employees understand why before they learn how, making retention dramatically higher when they do encounter visual or written materials.

Microlearning meets podcasts: A perfect match

Microlearning—delivering educational content in short, focused bursts—improves knowledge retention and prevents cognitive overload. Most effective podcast episodes for learning run under 10 minutes, hitting the sweet spot for attention spans and busy schedules.

Pilot feedback by Assemble You (a leader in audio-only learning content) from 18 organizations across retail, healthcare, and logistics showed remarkable results: 94% of users wanted more audio learning, and 85% said it changed how they think or act at work. These aren’t just satisfaction scores; they’re behavior change metrics.

Convenience beats perfection every time

Josh Bersin advocates for learning in the flow of work—removing friction and delivering training when and where it’s most convenient. Podcasts take this further by fitting into the flow of life itself.

Audio learning doesn’t demand a 25th hour or 8th day. It dovetails into existing schedules. Employees can upskill while commuting, exercising, doing chores, or even during certain types of work. Studies show that even car drivers and cyclists want to learn during commutes, not just those using public transport, proving the appetite for commute-based learning (CBL) exists.

Privacy problems you haven’t considered

Consider the employee researching harassment policies, or the new manager seeking guidance on sensitive team conflicts. Screen-based training broadcasts these topics to any roaming eyes that pass their desk. Audio learning offers privacy that screens can’t match.

Podcasts create psychological safety by removing visible barriers to accessing sensitive content. Employees feel comfortable exploring difficult topics without judgment or unwanted attention from colleagues.

The ROI of podcast-style eLearning

Smart companies align training methods with strategic objectives like employee development and talent retention. When 85% of audio learners change their workplace behavior, you’re not just checking compliance boxes—you’re driving real performance improvements.

Using the method above, podcast-style educational content costs far less to produce. Audio training reaches more employees and generates higher engagement than traditional methods. Most importantly, people actually complete it. That’s ROI you can measure.

Turn learning into listening

Your employees are already listening to podcasts, learning from audio content, and craving convenient education that fits their lives. The infrastructure exists, the appetite is proven, and the latest technology makes creation simple.

Stop fighting against how people actually consume information. Instead of demanding they adapt to your training methods, adapt your methods to their preferences. Turn learning into listening, and watch completion rates soar while training costs plummet.

If you’re still not convinced, or you’re curious, check out some samples; new examples will be added regularly!

Image credit: Liubomyr Vorona

The post Death by PowerPoint? Create Podcast-Style eLearning in 4 Simple Steps appeared first on Learning Guild.

This session shows how L&D and enablement leaders are using Vyond to turn those challenges into wins. You’ll see how:

• Anyone: anyone can create videos that help Sales cut onboarding time, CS reduce churn, and Compliance pass audits.
• Any Style makes the difference: Matching the right video style to the right problem, avatars for roleplays, motion graphics for compliance, branded templates for CS onboarding, drives impact without adding headcount.
• Anywhere means inside your existing stack: G suite, slack, or your LMS. Vyond delivers enterprise-ready AI, with translations and brand governance built in.

You’ll leave with practical fast facts, short examples you can replicate, and a clear playbook for proving ROI inside your org.

During this session, you will learn:

• How to connect learning content to measurable business outcomes.
• How to make AI usable at scale= enterprise-ready, brand-safe, and integrated.
• How to deliver value with fewer resources by using the right style in the right place.

The post AI: Anyone. Any Style. Anywhere. appeared first on Learning Guild.

During this session, you will learn:

• How to deliver hands-on labs across AI, data, and software stacks without infrastructure setup.
• How to drop learners directly into a coding or data project so they can start learning by doing immediately.
• How Codio’s Sandboxes replicate production-like setups for real-world relevance.
• How to manage access, costs, and scale while giving learners a seamless browser-based experience.
• How to package and reuse labs across cohorts to save time and improve consistency.
• How instructors can integrate both AI and data tools alongside traditional developer stacks.

The post Scalable, Hands-On Labs for Any Tech Stack appeared first on Learning Guild.

During this session, you will learn:

• How to make all of your existing materials or new learning content interactive in minutes with Kahoot! 360
• How to connect your existing slide software, creator tools and LMS platforms with Kahoot
• How to transform learning into impactful experiences that boost knowledge retention and drive performance.

The post Make Your Learning Content Interactive in Minutes appeared first on Learning Guild.

In 1984, educational psychologist Benjamin Bloom proved the power of this idea with his “2 sigma problem,” showing that one-on-one tutoring consistently outperformed the vast majority of students in traditional classrooms.

For decades, L&D leaders have chased that gold standard, but the high cost and complexity made it impossible.

Now, AI and automation are finally making it a reality—and at scale.

In this session, you’ll see how AI-powered tools provide personalized, human-like feedback, safe skills practice, and adaptive learning paths without overloading your team or budget and learn why a unified learning ecosystem outperforms stitched-together tech stacks in sustaining engagement, connecting data, and proving real impact.

If you’ve ever wanted to give every learner the best possible experience, this is where learning science meets the scale and impact your organization needs.

During this session, you will learn:

• Understand the “2 sigma problem” and why it has defined the holy grail of learning design for 40 years
• See how AI and automation replicate one-on-one tutoring at scale with personalized feedback, practice, and guidance
• Learn why integrated platforms outperform multi-vendor solutions in engagement, data insight, and ROI
• Explore real-world examples of AI solutions delivering human-like guidance and skills practice
• Leave with the key criteria to evaluate if vendors can deliver truly personalized, scalable learning experiences

The post Finally Solving the 2-Sigma Problem? AI’s Answer to Personalized Learning appeared first on Learning Guild.

Let’s set the record straight: AI is here to stay, whether we love it or not. Within instructional design circles, there’s a tendency to see artificial intelligence as a threat. But, let’s face it—AI won’t replace you any more than calculators replaced accountants or Adobe Illustrator replaced graphic designers. AI is simply a tool. One that, if put to work wisely, can take the repetitive tasks off your hands so you’re free to focus on building truly impactful learning experiences.

So, what does it look like to really integrate AI into your work? Your real task is learning how to leverage AI so you can work smarter. If you’re an instructional designer aiming to maximize the benefits of AI as your writing partner, you’re in the right place. This article will walk you through how to make AI your competitive edge, not your replacement.

What AI is (& isn’t)

First, let’s get something clear: AI is not magic. It doesn’t understand what you’re trying to do unless you tell it. AI doesn’t know your learners, your educational goals, or the organizational nuances. What AI can do for you is generate ideas, structure and content and support you as you move through the design process.

Think of AI as a writing partner who never gets tired and is always willing to help with brainstorming. But just like any partner, it needs guidance. You still have to bring the subject matter knowledge, design know-how, and the ability to connect with your learners.

Again, AI is a tool, not a replacement. Your creativity, insight, and connection with your learners cannot be replicated by an algorithm.

Getting started with AI for writing

You don’t need to be an AI expert to start using it effectively. Start small. Utilize AI to assist with brainstorming course titles or refining learning objectives in clearer language. Try feeding it a course outline and asking for a first draft of your eLearning content. Use it to simplify complex topics, create knowledge-check questions, or generate relatable examples that you can tweak for your audience.

The key is not to expect perfect results. Instead, use AI to help you get unstuck. Let it speed up the parts of your work that slow you down so you can spend more time on what really matters.

Good first tasks for AI

These use cases aren’t flashy or futuristic, but they’re practical. They give you back time and headspace, allowing you to focus on more in-depth design work:

• Rewriting learning objectives to match Bloom’s Taxonomy
• Generating quiz questions based on content outlines
• Creating dialogue starters for scenario-based learning
• Rephrasing content for different reading levels
• Summarizing long documents to find key points

These aren’t groundbreaking tasks, but they save time and help you work more efficiently, especially when you’re facing tight deadlines or repetitive requests.

Make it a two-way conversation

AI performs best when you treat it like any other work relationship. Be clear about what you want. Instead of saying, “Write a course about cybersecurity,” try:

“Create a short course outline on cybersecurity awareness for healthcare employees who need to complete it in 15 minutes. Include a short scenario and a knowledge check with three questions.”

If your prompt does not produce satisfactory results, ask follow-up questions or provide more direction. Refine your prompt like you’re editing a coworker’s draft. Be clear about what works, what needs fixing, and why. The better your input, the better your output.

Don’t be afraid to edit. The first draft from AI is just that: a draft. Take what’s useful, delete what isn’t, and shape it to fit your audience.

AI Is not a subject matter expert (SME)

Sure, it might be tempting to drop content into AI and hope it generates a course. But, that’s not how good instructional design works. You still need human insight to make it meaningful.

Use AI after you’ve worked with your SMEs. Once you’ve gathered the right information—like key messages, process steps, common learner questions, or examples—you can ask AI to help draft content, suggest assessments, or brainstorm visuals. Just don’t skip the human review.

Even better, think of AI as a behind-the-scenes partner while you work with SMEs. Your SMEs don’t need to use AI themselves. Instead, elicit their responses to shape your AI inputs. This keeps you in control of the process and saves time for both you and your SMEs where it matters most.

Keep the human element

Instructional design involves more than just delivering information: It is about crafting an experience that helps someone learn, grow, and succeed. Learners are real people. They have emotions, challenges, goals, and preferences.

AI can write a lesson and suggest a scenario, but it cannot replace the connection, empathy, and insight that a human instructional designer brings to the table. You know how to design content that makes someone feel seen and supported. You know when to add humor, when to encourage reflection, and when to simplify.

Your role as an instructional designer is more important than ever. As AI helps you move faster, your job is to make the content better. That might mean adding deeper interactivity, more meaningful assessments, or more inclusive examples. AI can help with speed. You can help with substance.

Don’t lose your voice

One of the risks of using AI for writing is losing your tone. AI tends to write in polished, but sometimes generic, language. It might sound smart, but not human. That’s why your editing matters.

Your learners respond to content that feels real and relatable. Don’t let AI strip that away. If the writing sounds robotic, rewrite it. Speak directly to your learners. Infuse your tone. Use contractions and short sentences. Be yourself.

AI is not your brand voice. You are.

The real value of AI in L&D

AI’s real value in L&D isn’t about helping you rush through your to-do list. AI gives you the time and energy for thoughtful, creative, and learner-centered work that actually makes a difference.

When you no longer need to handle repetitive tasks like drafting outlines or simplifying complex language, you can focus on deeper conversations with SMEs, testing learning experiences, and trying new ideas. AI frees you up to focus on content that truly connects with people and drives meaningful learning.

Instructional design has always been about people and how they learn. AI can help you move faster, but it can’t replace the insight and human connection you bring to the table.

Final thoughts

Remember, you are not being replaced. You are being empowered.

AI can help you move faster, write smarter, and think bigger. But only if you use it with intention. Only if you stay rooted in what matters most: the people for whom you’re designing learning.

The calculator didn’t take away the need for math teachers. Computers didn’t eliminate graphic designers. And AI won’t erase instructional designers. Instead, these tools made room for more strategy, more creativity, and more value.

The same is true here. Use AI to accomplish tedious work. Then, use your human insight to craft experiences that inspire, teach, and support real change.

The future of L&D isn’t artificial. It’s deeply human.

Image credit: bagira22

The post Tips for Getting the Most Out of Your AI Writing Partner appeared first on Learning Guild.

In this session, you will learn to: 

• Use AI tools and add-ins to customize PowerPoint and make it work for you.
• Save time and effort by automating complex and manual tasks.
• Find the best add-ins for your needs, including free ones—and those that justify their cost.

The post Using AI & Add-Ins to Transform PowerPoint’s Capabilities appeared first on Learning Guild.

Agentic AI is the biggest buzzword in the technology and any AI community. In order to utilize and explore the area of Agentic AI, we have to set some parameters on what it is and what is it not.

There are three main types of AI: Non-Generative/Traditional, Generative, and Agentic.

Non-Generative/Traditional

Non-Generative/Traditional AI focuses around the inputting of specific data and information where the tool cannot go and explore options other than what it’s been given.

This type of AI has the most guardrails and structure. Think of when you’re trying to troubleshoot an issue with your internet connection: You end up talking with a bot before you can get to a person. Those bots are non-generative AI. They have been fed with information, questions with answers, policies, procedures, and much more. The bot searches this information to (hopefully) find you what you need to self-service. The downsides are the limitations of the content and that it’s searching this content based only on those parameters.

Another part of Traditional AI is that it’s fully self-created. The people who program these bots and similar functionalities have to program and think of any iteration to a question, spelling, phrasing, and so forth for the tool to try to give a response. If you’ve been frustrated with a bot before, this would be why.

Generative

Generative AI is free to search large amounts of information, internal and external, in order to “predict” what it should give you back.

For example, when you enter a prompt about wanting to find a recipe for chocolate-chip cookies, it’ll give you a recipe it predicts based on its ability to search all the information on the internet for chocolate-chip cookies.

Will the recipe work? It absolutely could, but it’s only predictive. When you’re looking at the recipe, and it says “3 eggs,” that’s based on its search results, whereas a traditional recipe would call for 2 eggs. These “hallucinations” are very common, which is why we need to validate the output of Generative AI to ensure we’re getting the right information. Generative AI never knows what it’s going to create two to five words or items ahead; it’s only looking at what comes next, like each next word, based on its own programming and understanding. It’s extremely helpful in brainstorming and ideation, but for effective execution, there still has to be that human-in-the-loop.

Agentic

Think of Agentic AI as the bridge between Non-Generative/Traditional and Generative AI. Agentic AI uses the same parameters of Traditional, as in you’re usually telling it generally what resources and boundaries it has to have, but you’re giving it a specific goal to meet. It will generate and adjust its information in order to reach that goal, or when the goal is adjusted.

Agentic means “agency” in that you’re giving parameters and boundaries to the tool, but you’re allowing it to have the agency to predict and make decisions around that ultimate goal for the task at hand.

Think about an Excel Macro. They break so easily, but they are built around a process and steps. In a macro, those steps have to be followed exactly—no changes to the data, no additional rows of information or columns—otherwise the whole thing breaks down.

Agentic AI would be a “smart macro” where you still have these parameters and guidelines, but knowing the goal is to take this data and transform it into a dashboard with certain criteria based on the information, the agent will expand and contract the inputs as necessary—without breaking.

Use Cases for Agentic AI

Agentic AI can be a super useful tool in individual and leadership performance support. You can use Generative AI to create the background of certain personas (i.e., an angry customer, high performer, struggling leader, etc.). These personas can then be used by the Agentic side to be an “actor” where you set the goal of “I want you to take on the persona of Bill (here is all of Bill’s persona information from the AI), and the goal of today’s talk will be to help identify some skill growth opportunities. I will be Bill’s manager.”

We have given the Agentic prompt the guidelines and the goal, but everything else requires it to think through and seek to meet our goal, adjusting as our conversation happens. This is a great practicing opportunity for any crucial conversations or even performance support, allowing learners to ideate ahead of time.

Another use case for Agentic AI is around marketing of learning offerings and materials. Give the AI a goal of skills or values you’re trying to build, have it run through your learning library, and it will go through the content, descriptions, titles, etc. and help develop a plan based on the criteria.

This agent can be used over and over again, or set up in a way that it constantly is curating this skill path as things adapt and change. This agent can also draft the communications around these new AI-built programs and send them out to learners, based on employee criteria you’ve given it boundaries for.

AI from A to Z

Don’t miss your chance to explore AI from every angle at DevLearn 2025 Conference & Expo, November 12-14 at the MGM Grand in Las Vegas! Join conversations about Agentic AI, see demos, and participate in hands-on BYOD sessions to try your hand at working with AI, Register today!

Image credit: Vertigo3d

The post The Agentic AI Buzz & Simple Ways to Harness It appeared first on Learning Guild.

“You should really be using AI.”

If you’re an instructional designer, you’ve probably heard that from a boss, a vendor, or a gushing article on LinkedIn. The pressure is real. This article is my pushback.

I’m not against technology. I’ve been excited about its potential for learning since I wrote my first educational game in 1982 (you earned a point for every multiplication problem you got right; not much of a game, but I was nine and the computer had 16KB of memory).

I’m pushing back against AI for one reason: AI isn’t the future—it’s the present and the past. We’ve been using the parts that work for years—we just called it “predictive text” or “adaptive feedback” (or “Furby”). What’s being hyped as AI today is usually a loose collection of features looking for a purpose and investors.

Microsoft’s Immersive Reader is a perfect example. It was created at a 2015 hackathon by mashing up existing Microsoft tools. The tech never changed, but in 2019—right when early GPT chatbots were making headlines—it was suddenly rebranded as an “Azure Cognitive Service,” which was later renamed the “Azure AI Foundry.” Same code; new costume.

Educators aren’t here to impress investors. We’re here to build learning experiences that make a difference. To help us cut through the hype surrounding AI, I’d like to share four rules I derived from human-centered design (HCD) to guide my own decisions about using AI (or VR, AR, etc.). This isn’t a new framework or a provocation. The rules are a lens that cross-functional teams can use to focus design decisions on learners, rather than flashy features.

1. Solve the real problem

AI isn’t snake oil, but when something is being pitched as a solution for everything from low engagement to high development costs, it’s prudent to ask, “What really is our problem?” What I’ve noticed is that AI tools typically promise to solve business problems, not learning problems. While both matter, we contribute to the bottom line and the quarterly growth target by solving learning problems: helping people gain the knowledge or skills they need to do something they care about.

Some confusion about the real problem comes from the way we often measure outcomes. Because measuring learning directly is so tricky, we regularly measure proxies instead (such as engagement, satisfaction, and completion). These proxies yield critical insights into the health and efficacy of the learning environment, but are not evidence of learning itself.

What’s counterintuitive is that addressing a proxy directly will likely muck it up. For example, if you try to bump up your engagement numbers by adding an AI chatbot, you no longer know if the bump reflects genuine improvement in the module’s relevance, clarity, and challenge, or just the chatbot’s novelty.

Instead, solve the real problem by finding out what is getting in the way of your learners’ engagement. It’s the difference between solving a business problem with learning and merely improving a business metric.

2. Prefer needs over requirements

Remember the hype around the Segway, the scooter that would reshape cities the way the PC reshaped computing? It had cutting-edge technology, heavyweight backing, and lots of buzz.

The only thing it didn’t have was a reason to buy one. It was an innovative product that nobody needed because the Segway was designed to meet producers’ requirements, not the users’ needs.

AI is similarly an innovation looking for someone to need it. The hype is about how powerful AI is becoming and how you can’t afford not to use the latest AI tools. There is less talk about which needs are being addressed. The result is an ill-defined fear of missing out that is increasingly causing AI to be listed, usually in some nebulous form, among the requirements for new learning products (and job descriptions) without considering the needs of learners.

For educators, though, “considering the needs of learners” is central to our work. Learning occurs in one place and time only: inside the learner, when learning meets one of the learner’s needs.

No requirement can mandate learning. Hopefully, your team can work together to align requirements with needs. However, if you find ambiguity or conflict between the two, verify that the requirement is actually required and not merely a hedge against missing out.

3. Apply systems thinking

The world is more complex than we like to admit. We naturally limit our thinking to simple cause-and-effect relationships: you take an allergy pill and the sneezing stops. But you may also fall asleep at your desk.

One action will have multiple, often unintended, effects. Learning is a complex system. Adding something to a learning experience over here causes something that feels unrelated to pop up over there. Applying systems thinking means including as many of those effects as possible when making decisions.

AI only makes the system more complex, especially when vendors can’t tell you how their AI works. It may help you personalize content for more effective learning, but it may also inadvertently perpetuate the biases in the AI’s training or reduce accessibility.

Complexity is unavoidable, but if you aren’t thinking about the side effects of a feature, you end up playing whack-a-mole with learning problems.

4. Prioritize the audience over the show

This rule is a gut check. Who benefits from this feature: the learner or us? There’s no shame in wanting to create something impressive, but don’t confuse that with the mania surrounding anything “powered by AI.” Designing for affect over effect is just theatrics.

I call this the “Avatar Trap.” Yes, it was ground- and record-breaking, but has anyone ever quoted Avatar to you? Meanwhile, my kids yell, “I’m walking here!” without even knowing Midnight Cowboy exists, and finishing the quote, “Life is like a box of…” is practically a reflex.  Spectacle fades no matter how many awards it receives. To make a lasting impression, you need to resonate with the audience.

Learning works the same way. Adding AI-generated variations of ‘correct’ or ‘wrong’ might impress a committee, but not a learner. Microsoft’s Immersive Reader, on the other hand, quickly became the Princess Bride of edtech—no flashy features and no marketing at all, just creators laser-focused on an audience they understood and cared about.

How to use these rules

If your job is solving business problems with learning, the value of your work begins and ends with the change it facilitates in your audience. Human-centered design (HCD) is a mindset for making decisions, whenever possible, based on the needs and values of the human beings we are designing for.

These four rules are a handy HCD lens that cross-functional teams can use to cut through hype and reduce pressure from more peripheral factors. The next time you’re asked to use an AI tool or your team’s discussion turns to using the latest tech, pause and unpack your reasoning by asking yourself and your team:

• Are we addressing the real learning problem?
• Does adding this requirement interfere with any of the learner’s needs?
• Have we considered the effects throughout the system?
• Is this something our learners want, or is it something we want?

There are no simple answers to any of these questions, and that’s okay. The insights you or your team gains by wrestling with them will make you better at your job.

People love to accuse our field of chasing tech fads only to see them fizzle. In reality, we’ve been looting other fields for their tech since the “magic lantern” was used to project anatomy slides in 1685. We don’t need the hype around AI to be excited about its possibilities. When we find a tool that works, we make it work in ways its creators never imagined.

Disclaimer: This content is solely the responsibility of the author and does not necessarily represent the official views of the University of Pittsburgh or the National Institutes of Health.

Image credit: askinkamberoglu

The post AI Is NOT the Future of Learning & Development appeared first on Learning Guild.