AI as Your Co-Designer: Collaboration, Not Replacement

Welcome & Series Context

Hello, and welcome to the Designing with Love Podcast. I am your host, Jackie Pellegrin, where my goal is to bring you information, tips, and tricks as an instructional designer. Hello, instructional designers and educators. Welcome to episode 157 of the Designing with Love Podcast. Today we are continuing our series, Future Ready Learning: Designing Education for What Comes Next. In the first episode of this series, we talked about how the future of learning starts now and how designers are being asked to move beyond content coverage toward capability building, meaningful practice, evidence of learning, and human-centered design. Today we are focusing on one of the biggest questions shaping our field right now. How do we work with AI without letting AI replace human judgment that makes learning design meaningful? Because for instructional designers, educators, and learning leaders, AI can feel exciting, helpful, overwhelming, and maybe even a little threatening all at the same time. It can help us brainstorm faster, organize ideas, draft examples, revise language, build scenarios, generate quiz questions, and think through design possibilities. But AI should not become the designer. It should not make the final decision about what learners need, what matters most, what is ethical, what is accessible, or what actually supports learning. That is still our work. In today's episode, we are talking about AI as your co-designer, not as your replacement, not as your shortcut around the design process, but as a collaborator that can support your thinking when you remain the human lead. So grab your notebook, a cup of coffee, and settle in as we explore this topic together.

AI Enters the Daily Workflow

Here's today's future ready signal. AI is moving from a tool we occasionally try to a partner many educators and designers are beginning to use inside their daily workflow. And that shift matters. A few years ago, many conversations about AI in education focused on whether students would use it, whether teachers should allow it, or whether institutions should ban it. Now the conversation is becoming more practical. Educators are asking, how can AI help me plan more efficiently? How can AI help me create examples? How can AI help me give clear feedback? How can AI help me design practice activities? How can AI help me improve accessibility, tone, or structure? And instructional designers are asking similar questions. Can AI help me analyze a roughed content draft? Can it help me turn SME notes into a cleaner outline? Can it help me generate scenario ideas? Can it help me check whether an activity aligns with the objective? Can it help me think of better ways for learners to practice? These are useful questions, but they also point to a bigger shift. AI is not just changing what we design, it is changing how we design. That does not mean AI should take over the process. It means we need to become more intentional about where AI belongs in the process, where it does not belong, and how we protect the human judgment that good design requires. The signal is not AI is replacing instructional designers. The signal is this. Designers who learn how to collaborate with AI thoughtfully will be better prepared to design learning that is clear, ethical, flexible, and future ready.

Speed Versus Quality in Learning

So why does this matter? It matters because AI can speed up parts of the design process, but speed is not the same thing as quality. And honestly, this is where we have to be careful. AI can produce a polished looking response very quickly. It can sound confident. It can organize ideas neatly. It can give us ten examples when we only ask for three. And sometimes that is helpful. But polished does not always mean accurate. Fast does not always mean aligned. Creative does not always mean appropriate, and efficient does not always mean effective. That is why AI collaboration always needs human leadership. As instructional designers, educators, and learning leaders, we understand context. We understand learning goals. We understand learner needs. We understand the difference between activity and learning, between engagement and entertainment, between content and capability. AI can help us generate possibilities, but it does not automatically know which possibility is best for our learners. It does not know the full story behind the emotional weight of a learning experience. And it does not carry the responsibility we carry when we design for real people. So when we talk about AI as a co-designer, what we are not saying is let AI do the work. We are saying let AI support the parts of the work while we stay responsible for the purpose, quality, ethics, and learning experience. That is the difference between using AI as a shortcut and using AI as a collaborator. A shortcut tries to skip thinking. A collaborator helps you think more clearly. And that is the kind of AI use future ready designers need to practice.

Co-Designer Mindset Over Content Generator

Today's design shift is this. Move from AI as a content generator to AI as a co-design partner. That shift may sound small, but it changes the way we use the tool. If we see AI only as a content generator, we might ask it to write the lesson, create the quiz, draft the discussion prompt, or build the activity. Then we copy, paste, and move on. That may save time in the moment, but it can also create problems down the road. For example, the activity may not align with the objective, the language may not sound like us, the examples may not fit our learners, the assessment may measure the wrong thing, and the design may look complete without actually supporting learning. But when we treat AI as a co-design partner, we use it differently. We ask it to help us explore options, not make final decisions. We ask it to challenge a draft, not simply produce one. We ask it to suggest alternatives, identify gaps, simplify language, or help us see a design from another angle. Then we decide what to keep, what to revise, what to question, and what to throw out completely. So let's look at three smaller shifts inside the larger design shift. Shift number one, from give me the answer to help me explore options. Instead of asking AI to give you the final version right away, ask it to generate possibilities. For example, instead of saying write a discussion prompt about ethical AI, you might say, give me three possible discussion prompt approaches for graduate students learning about ethical AI use in instructional design. One should focus on learner privacy, one on bias, and one on academic integrity. That kind of prompt keeps you in the role of the designer. You are not asking AI to decide everything. You are asking it to help you see options so you can choose the right direction that best fits your learners and goals. Shift number two, from create more content to improve the learning experience. AI can generate a lot of content very quickly, but more content is not always what learners need. Sometimes learners need a clearer example. Sometimes they need a practice opportunity. Sometimes they need a scenario. Sometimes they need feedback. Sometimes they need instructions to be less confusing. So instead of using AI only to create more material, use it to improve the learning experience. You might ask, how could I turn this explanation into a short practice activity? Or what part of this assignment instructions might confuse learners? Or how can I make this scenario more realistic for adult learners in a workplace setting? That's a stronger use of AI because it supports design thinking, not just content production. Finally, shift number three, from trust the output to review with human judgment. This might be the most important shift. AI output should always be reviewed, not skimmed, not assumed, reviewed. We need to check for accuracy, alignment, accessibility, tone, bias, clarity, and relevance. A helpful way to think about it is this. AI can draft, but humans decide. That means we remain responsible for the final learning experience. If the objective is weak, we revise it. If the example is inaccurate, we fix it. If the activity is too shallow, we deepen it. If the language is unclear, we clarify it. If the tone feels off, we make it sound more human. If the design does not support the learner, we redesign it. This is where our expertise matters. AI may help us move faster, but human judgment helps us move in the right direction.

The AI Co-Design Compass

Here is a simple activity you can try this week. I call it the AI Co Design Compass. Choose one small design task, not a whole course, not an entire module, just one task. Maybe you need to revise an assignment. Maybe you need to create a practice activity. Maybe you need to simplify instructions. Maybe you need to brainstorm examples. Maybe you need to turn content into a scenario. Then move through four steps. Step number one, set the direction. Before you ask AI for anything, define the learning goal, audience, context, and constraints. This helps you avoid vague outputs. For example, I am designing for adult learners in an online graduate course. The goal is for learners to evaluate whether AI generated responses are accurate, ethical, and useful. The activity should take about 15 minutes. That is much stronger than simply saying, create an AI activity. Step number two, ask for options. Ask AI for two or three possible approaches. Not one final answer, options. This gives you room to compare, combine, and revise. Step number three, apply the human filter. Review the output through four filters. Filter one, is it aligned? Does it support the learning goal? Filter two, is it accurate? Does anything need to be verified or corrected? Filter three, is it accessible? Will learners understand what to do? Filter four, is it human centered? Does it support real learners in a real context? Step number four, revise with intention. Finally, revise the output so it fits your learners, your voice, your course, and your purpose. That last step does matter. Because the goal is not to use AI output exactly as it appears. The goal is to use AI as part of a thoughtful design process.

Human Judgment Takeaways & Support

Before we close, here is one reflection question to carry with you. Where could AI help me think more clearly without taking over the thinking completely? That's an important question because AI collaboration is not an all or nothing. You do not have to use AI for every part of your design process. You also do not have to avoid it completely. The goal is to make intentional choices. Maybe it helps you brainstorm, maybe it helps you organize, maybe it helps you see gaps. Maybe it helps you revise. Maybe it helps you generate examples, you then adapt. But the deeper design decisions still belong to you. As we wrap up, here are the key takeaways from today's episode. First, AI is not just changing what we design, it is changing how we design. Second, AI should be treated as a co-design partner, not a replacement for instructional designers or educators. Third, the strongest use of AI is not simply generating more content, it is supporting better thinking, clearer structure, stronger practice, and more intentional learning experiences. Fourth, AI outputs need human review. We are responsible for checking alignment, accuracy, accessibility, ethics, tone, and learner fit. And finally, future ready designers do not hand over the design process to AI. They learn how to guide it, question it, refine it, and use it in service of meaningful learning. Here's an inspiring quote from Steve Jobs. Technology alone is not enough. I think that is such an important reminder for this conversation. AI alone is not enough. A prompt alone is not enough. A tool alone is not enough. A polish draft alone is not enough. What makes a difference is the human judgment, creativity, care, and responsibility we bring to the design process. When we use AI as a co-designer, we are not stepping away from our role. We are stepping more intentionally into it. We are deciding what matters. We are asking better questions. We are protecting the learner experience. We are making sure technology serves the learning, not the other way around. To help you apply what we discussed today, I've created an interactive companion resource called Future Ready Design Brief, AI as your co-designer. The brief will walk you through a simple process for choosing a design task, setting clear direction, asking AI for options, applying a human filter, and revising with intention. And if this episode helped you think differently about AI as a design partner, I encourage you to share it with another educator, instructional designer, trainer, or learning leader who is trying to use AI thoughtfully and responsibly. These conversations are better when we are learning and designing together. In the next episode of the series, we will continue exploring what future ready learning requires as we think about the skills learners need to navigate in a changing world. Thank you for joining me today. Until next time, keep learning, keep growing, and keep designing with love. Thank you for taking some time to listen to this podcast episode today. Your support means the world to me. If you'd like to help keep the podcast going, you can share it with a friend or colleague, leave a heartfelt review, or offer a monetary contribution. Every act of support, big or small, makes a difference, and I'm truly thankful for you.