AI can generate course content in minutes, but it can also produce confident nonsense, biased visuals, and nonexistent citations. We close out our mini-series with Dr. Dwayne Wood by zooming out on how quickly AI has evolved from basic text chatbots to multimodal tools that draft e-learning, images, and more, and why that speed raises the stakes for educators and instructional designers.

We break ethics down into four practical areas of concern you can use in your workflow: accuracy, privacy, intellectual property, and accountability. Dwayne shares real examples of how AI can hallucinate sources (even producing fake DOIs), distort nuance when summarizing, and quietly reinforce stereotypes depending on training data and the way prompts are written. We also dig into copyright and fair use in course development, including why “just upload the PDF” can turn into a compliance problem for you, your school, or your client.

From there, we get tactical about balancing efficiency with quality. We talk about using AI for low-risk first drafts, matching your review effort to the risk level, and keeping a human in the loop through SME validation and QA. We also cover transparency and attribution, why it matters even when AI is only used for grammar, and how disclosure changes the trust and review process. The biggest takeaway is a simple ethical stop sign: if no accountable human can explain, verify, or defend the AI-generated content, do not publish it.

If you found this helpful, subscribe, share it with a colleague, and leave a review so more instructional designers can use AI responsibly without losing the human responsibility at the heart of learning design.

📢 Call-to-Action: To continue learning from Dr. Dwayne Wood, visit the links in the show notes and connect with him online. And as you move forward with AI-supported content creation, remember to pause, review, and lead with ethics, transparency, and care for your learners.

📖 Additional Resource: You can also visit National University to learn more about its programs and resources for adult learners, working professionals, and military-connected students.

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AI can write a quiz question in seconds, but it cannot tell you what your learners truly need or what’s ethical, accessible, and aligned. That’s the tension Jackie's unpacking as we continue the series Future Ready Learning: Designing Education for What Comes Next. If generative AI feels exciting and overwhelming at the same time, you’re not alone, and you’re not doing it wrong. The goal is not to ban AI or hand over the design process, but to use it with intention while keeping human judgment in the lead.

In this episode, Jackie walks through what changes when AI shifts from an occasional tool to a daily workflow partner for instructional designers, educators, and learning leaders. We talk about why “polished” output can still be wrong, misaligned to learning objectives, or a poor fit for your audience. Then, Jackie shares a practical design shift: move from AI as a content generator to AI as a co-design partner. That means using AI to explore options, challenge drafts, identify gaps, simplify language, and strengthen practice, not to make final decisions for you.

Subscribe for the rest of the series, share this with a colleague who’s navigating AI in education, and leave a review so more designers can find it.

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Future-Ready Design Brief: AI as Your Co-Designer

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AI can make you faster, but it can also make you lazy. That tension sits at the center of Jackie's conversation with Dr. Dwayne Wood as we bring together the big ideas from our mini-series and get practical about using generative AI for innovative instructional design.

We dig into rapid prototyping as one of the clearest wins: using AI to generate multiple starting points for activities, scenarios, and assessments so we can iterate quickly with stakeholders who are still figuring out what they want. Dwayne also shares why the first attempts often take longer, and how the payoff comes when you build prompt templates, create simple “assistants,” and set tight parameters for output format and length. If you have ever lost an hour to prompting with nothing to show for it, this will feel familiar.

From there we zoom out to the full instructional design process. We talk about using AI to analyze de-identified survey data and quiz results, identify themes, and spot learning pain points. We explore branching scenarios and personalized learning experiences that respond to learner decisions, plus “Socratic engine” scaffolding that asks questions instead of handing over answers. We also get honest about risks: hallucinations, over-trust, data privacy, and the need for guardrails in authoring tools and LMS chat features so AI augments learning outcomes rather than eroding domain knowledge.

If you want a simple experiment, we end with a concrete activity you can try this week using one objective from a course you already know. Subscribe for part five, share this with a designer friend, and leave a review so more educators can find the show.

📢 Call-to-Action: Ready to experiment with AI in your own instructional design workflow? Visit the links in the show notes to connect with Dr. Dwayne Wood, learn more about his work, and choose one small AI-supported design task to try this week.

📖 Additional Resource: You can also visit National University to learn more about its programs and resources for adult learners, working professionals, and military-connected students.

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👍🏼 Please make sure to like and share this episode with others. Here's to great learning!

2027 is closer than it feels, and the future of learning is already showing up in the tools we use, the questions learners ask, and the pressure to prove real value in education and training. Jackie is kicking off a new 14-part series, Future-Ready Learning, with a practical challenge for instructional designers and educators: stop treating “the future” like a shiny tech wishlist and start designing learning that helps people grow, adapt, and apply what they know in real contexts.

We dig into three shifts that define future-ready learning design. First, we move from information delivery to skill development by building practice into the experience: problem-solving, communication, collaboration, critical thinking, ethical decision-making, digital literacy, and AI literacy. Second, we move from passive assessment to meaningful evidence of learning. In a world where AI can generate polished work fast, we need assessment strategies that show process, reasoning, choices, revision, and growth, not just a final product.

Third, we move from technology-first design to human-centered design. Tools can personalize practice, increase access, and save time, but they should never matter more than the learner. Jackie shares a simple three-question “2027 design lens” you can use immediately to check any course, lesson, training, or activity: what capability are we building, how will learners practice it, and how will we know they’re progressing?

If you want learning experiences that stay ethical, accessible, and useful as AI reshapes the landscape, this is your starting point. Subscribe, share this with a fellow educator or designer, and leave a review so more people can design what comes next.

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Future-Ready Design Brief: The 2027 Design Lens

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👍🏼 Please make sure to like and share this episode with others. Here's to great learning!

The fastest way to ruin an adult learning experience is to start with a shiny tool and hope an outcome appears later. Jackie sat down with Dr. Dwayne Wood to get back to the foundations that keep instructional design effective, especially in online and asynchronous learning where motivation, time pressure, and cognitive load can make or break engagement.

We dig into adult learning principles through a practical lens: problem-based and application-based outcomes that give learners a clear end state, relevance placed right up front (and ideally explained by learners themselves), and autonomy that offers real choice without creating analysis paralysis. We also talk honestly about discussion forums, why they turn into “check the box” activities, and what to look for in learning analytics when grades alone do not tell the whole story.

Then we zoom out to the human side of learning design in an AI era. We share why “start with the learning outcome” is still the best rule for educational technology integration, how AI can add value when it supports reflection and feedback, and why self-direction is often misunderstood. Adult learners may be independent, but they still benefit from scaffolding, pathways, and simple supports like checklists that help them monitor progress.

Subscribe for the rest of the mini-series, share this with an instructional designer or educator you know, and leave a review so more people can find the show. What adult learning principle do you think course designs overlook most?

📢 Call-to-Action: To keep learning from Dr. Dwayne Wood, visit the links in the show notes and connect with him online. And as you reflect on today’s episode, consider one adult learning principle you can apply more intentionally in your next course, training, or learning experience.

📖 Additional Resource: You can also visit National University to learn more about its programs and resources for adult learners, working professionals, and military-connected students.

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🗣️ Want to be a guest on Designing with Love? Send Jackie Pelegrin a message on PodMatch, here: Be a guest on the show

🌐 Check out the show's website here: Designing with Love

📱 Send a text to the show by clicking the Send Jackie a Text link above.

👍🏼 Please make sure to like and share this episode with others. Here's to great learning!

Slides are the artifact everyone recognizes, and that’s exactly why the myth survives: if a stakeholder can only see the deck, they assume the work is decoration. Jackie closes out the Myths in Instructional Design and Education series by pushing back on “instructional design is just making slides” and naming what professional instructional designers actually do long before anyone picks a template.

We walk through the hidden core of learning experience design: needs analysis, clarifying learning objectives, organizing content around performance, planning practice, building assessments, creating feedback loops, and checking accessibility. Jackie also unpacks why this misunderstanding shows up so often, especially when we’re invited in late and asked to “make it look nice,” or when information delivery gets mistaken for learning. A polished slide deck can still fail if it doesn’t help learners apply, transfer, and grow.

Then, Jackie offers a simple myth reset challenge you can use immediately. Pick one deck, module, lesson, or training you’ve created or supported and ask: what is the learning goal, what should learners be able to do, and where do they get practice and feedback? From scenarios to job aids to performance support, we explore how to let the goal guide the format, so form follows function and the learning actually works.

If this gives you a new lens on instructional design, subscribe to the podcast, share the episode with a colleague, and leave a review so more educators and learning leaders can find it.

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Myth Reset Case File: Beyond the Slides

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👍🏼 Please make sure to like and share this episode with others. Here's to great learning!

If your learners are exhausted before they even reach the content, the problem might not be motivation. It might be the mode. Jackie welcomes Dr. Dwayne Wood back for part two of the mini-series to unpack multimodal learning for adult learners and what it really means to design with intention instead of stacking more media on top of a course.

We dig into cognitive load theory and why the same concept can become easier or harder depending on how you present it. Dwayne shares concrete examples of “tool friction” in online learning and blended learning, where unfamiliar tech quietly steals attention from the objective. From there, we connect multimodal learning to Universal Design for Learning (UDL): giving learners smart options like transcripts alongside video, accurate captions, and accessible resources that work with screen readers. We also talk about common AI-related pitfalls, like infographics that look great but fail basic accessibility due to contrast, image-only text, or missing alternatives.

You will hear practical instructional design strategies you can apply without overwhelming yourself or your learners: pairing explanation with application, using dual coding effectively, and building discussion forums with a real purpose so they foster social learning rather than checkbox posts. We also name the mistakes to avoid, including trend-driven modalities, too many clicks, and assessments that inadvertently measure technical confidence rather than mastery.

Subscribe to Designing with Love, share this episode with a colleague, and leave a review so more educators and instructional designers can build learning that is clear, flexible, and accessible. What is one modality you rely on that might be adding friction?

📢 Call-to-Action: If today’s conversation gave you ideas for making your learning experiences more flexible and engaging, be sure to visit the links in the show notes to connect with Dr. Dwayne Wood and learn more about his work in adult education, curriculum development, and instructional design.

📖 Additional Resource: You can also visit National University to learn more about its programs and resources for adult learners, working professionals, and military-connected students.

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💟 Designing with Love + allows you to support the show by keeping the mic on and the ideas flowing. Click on the link above to provide your support.

☕ Buy Me a Coffee is another way you can support the show, either as a one-time gift or through a monthly subscription.

🗣️ Want to be a guest on Designing with Love? Send Jackie Pelegrin a message on PodMatch, here: Be a guest on the show

🌐 Check out the show's website here: Designing with Love

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👍🏼 Please make sure to like and share this episode with others. Here's to great learning!

Class size is not a harmless detail; it’s a force that quietly rewrites the learning experience. When a course jumps from 15 learners to 75, the same discussion prompt can turn into noise, feedback timelines can stretch, and learners can feel invisible even when the content is strong. In this episode, Jackie is tackling a stubborn myth in instructional design and education: “Class size doesn’t affect learning quality.”

She breaks down why this belief sticks around, including how class size is framed as an administrative problem rather than a learning design problem, and how technology makes scale look deceptively easy. Yes, your LMS can enroll hundreds. Yes, automated quizzes can grade instantly. But scalable delivery is not the same as scalable support, and learning quality depends on interaction, communication, and timely guidance, especially in online learning and blended learning environments.

Then, Jackie shifts into what to do about it with a scale-aware design approach. We talk through concrete supports that make large classes work better without pretending they’re small: clearer instructions, smaller discussion groups, peer review with criteria, rubrics and exemplars, FAQ spaces, stronger navigation, multiple ways to ask questions, and layered feedback plans that combine automated feedback, peer feedback, and instructor feedback where it matters most.

If you want a practical way to audit your own course, workshop, or training, Jackie gives you a set of questions to run this week and a simple “choose one improvement” challenge. Subscribe to the podcast, share this with a colleague, and leave a review so more educators and instructional designers can design for scale with care.

🔗 Episode Links

Please check out the resources mentioned in the episode. Enjoy!

Myth Reset Case File: Class Size and Learning Quality

Quality Matters: Class Size in Online Courses

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🗣️ Want to be a guest on Designing with Love? Send Jackie Pelegrin a message on PodMatch, here: Be a guest on the show

🌐 Check out the show's website here: Designing with Love

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👍🏼 Please make sure to like and share this episode with others. Here's to great learning!