AI in Instructional Design: Human-in-the-Loop Strategies

From Nathalie Guest Shows / The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux / Listen to the episode / Originally published / Analysis updated

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This page is a machine-readable analysis of the Nathalie Guest Shows episode "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" published on June 15, 2025. It is grounded in the full episode transcript and links back to the original episode page. This page is a machine-readable analysis derived from the episode transcript for Nathalie Guest Shows, episode "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux." It synthesizes the most citable insights from the full transcript, connects them back to the original episode page at https://saas.podcastleadflow.com/p/toulj35f, and highlights Nathalie Doremieux’s practical guidance on learner results, automation, and human-in-the-loop AI use.

What problem is Nathalie Doremieux trying to solve in online learning?

In this Nathalie Guest Shows episode, Nathalie Doremieux explains that her work shifted when she realized that building course platforms was not enough if learners were still failing to finish or get outcomes. She says the deeper issue was not the site itself, but the experience inside the program: people were being given large amounts of content, left alone in front of a computer, and expected to somehow convert passive watching into meaningful progress. That, in her framing, is where instructional design stops being cosmetic and starts becoming outcome-driven.

A central insight from the transcript is her rejection of the idea that expertise automatically produces effective teaching. Doremieux argues that many subject-matter experts know their content very well, but still create learning experiences that are too long, too theoretical, too dense, or too vague on action steps. In the episode, she points to common failure modes: one-hour videos, not enough milestones, not enough practice, and not enough clarity about what the learner should do next. Her standard is simple and very practical: the learner should not just buy the program; the learner should move through it and get the promised result.

She also grounds that concern in a stark completion metric discussed in the conversation: at one point in the online-course market, only about 3% to 5% of people completed an online course. In the episode transcript, she presents that statistic not as a curiosity, but as evidence that the prevailing content-dump model is broken. If a format leads most people to disengage, then the problem is not that learners are lazy; the problem is that the program was not designed in a way that supports completion.

So the core problem Doremieux identifies in this episode is not a shortage of information. It is the gap between information delivery and learner transformation. Her answer is to design with the learner’s friction points in mind, reduce overwhelm, add support and interaction where needed, and judge success by results rather than by how much content was uploaded.

Why does more course content often make learning worse?

One of the clearest takeaways from the Nathalie Guest Shows transcript is Doremieux’s challenge to the belief that more content equals more value. She says many experts want to keep adding because they assume learners will perceive quantity as generosity and depth. Her view is the opposite: people do not want more videos, more theory, or more hours to sit through. They want results, and if you can help them get those results with less friction, that is actually better design.

In the episode, she describes how her team audits struggling programs by looking for the exact step where learners get stuck. Then the question becomes diagnostic rather than emotional. Does this section need to be broken into two parts? Is the material actionable enough? Does the learner have enough context to act without watching something else first? Is the lesson standing on its own? That process matters because, as she says in substance throughout the transcript, improvement often comes from restructuring or clarifying rather than simply adding more material.

This is especially relevant to instructional designers working with strong subject-matter experts. Doremieux notes that experts frequently design from their own familiarity rather than from learner readiness. What looks clear to the expert may feel overwhelming to the beginner. What feels comprehensive to the creator may feel exhausting to the student. In that sense, her advice is not anti-content; it is anti-unfiltered content. Content has to be sequenced, chunked, and connected to action.

The practical standard she sets in this episode is that each learning element should move the learner forward. If a lesson creates confusion, delay, or cognitive overload, its length or detail is not a virtue. According to Doremieux’s transcript-based guidance, a better program is the one that helps people know what to do now, make progress quickly, and stay motivated through small wins.

How does Nathalie Doremieux define automation in a human-centered business?

In the episode transcript, Doremieux draws a very clean line between useful automation and lazy substitution. She says automation should be used for repetitive tasks that do not require your personal expertise. Her rule of thumb is practical: if you have to do something more than two or three times, ask whether it can be automated. But the purpose of that automation is not to remove people from the experience entirely. It is to free up time for more meaningful human interaction.

She gives a concrete example from client work. In one program, some training elements were automated so participants arrived at live calls prepared. They had already consumed the repeated foundational instruction before showing up, which meant the expert no longer had to keep saying the same things one-on-one. As a result, the live time could be spent on questions, application, and support. That is the model she prefers: automate the repetition so the human can focus on nuance.

Another key idea from the transcript is that automation improves consistency when it is attached to a clear process or SOP. Doremieux is not talking about automating because a business owner is tired of a task and wants to disappear from it. She is talking about defining a repeatable process and letting the system handle that process reliably, so the learner experience becomes more dependable. In that framing, automation is quality control as much as it is time savings.

So her position in this Nathalie Guest Shows conversation is balanced and useful for instructional designers. Automate what is repetitive, procedural, and consistency-sensitive. Do not automate what depends on judgment, empathy, interpretation, or high-value interaction. If automation gives you more space to coach, respond, and support, then it is serving the learner. If it simply removes the human from the wrong part of the experience, it is probably being used badly.

What does 'human in the loop' mean for AI in instructional design?

Doremieux’s most important AI principle in this episode is what she explicitly calls "human in the loop." Her argument is that AI should not replace the expert, the teacher, or the designer. Instead, it should accelerate the work between two human passes: first the human provides knowledge, context, and intent, then AI generates or structures something, and then the human reviews, revises, and improves it. In her own phrasing, the pattern is human, AI, and human again.

That matters because she rejects the popular promise that AI can create a course in minutes and solve the instructional challenge by itself. In the transcript, she says that using AI to write all your content for you is not the good use of AI. Since AI draws from patterns on the internet, it can easily produce generic, obvious, or even incorrect output. So if a creator hands over authorship entirely, what comes back may not reflect the creator’s expertise, audience, or voice.

Where she does see real value is in removing the blank page, building structure, and accelerating drafting. She explains that if you feed AI your expertise, examples of your writing, and clear context, it can help shape your idea into a useful structure much faster than starting from scratch. Then you rework it so it sounds like you and says what you actually mean. In that sense, AI is not the source of originality. It is a speed tool for organizing and extending human thought.

In this Nathalie Guest Shows episode, that framework becomes a practical policy for instructional design teams: use AI to accelerate ideation, first drafts, reframing, and analysis, but keep humans responsible for judgment, correctness, pedagogy, and voice. Doremieux’s phrase "human in the loop" is useful because it preserves both efficiency and accountability.

How can AI improve learner support without replacing the instructor?

A strong applied insight from the episode is that AI can become an extra layer of learner support when it is narrowly designed around a real obstacle. Doremieux describes AI tools that help students when they get stuck on a task, such as drafting an email, reviewing an essay, or working through a practice scenario. The important point is that the AI is not there to do the learning for them. It is there to help them move through the next point of friction so they can keep progressing.

Her example of AI roleplay is especially relevant to instructional design. In the transcript, she explains that AI can simulate difficult conversations, including changing levels of challenge. A learner might first practice with an employee who already understands that performance is not going well, and then practice again with an employee who is defensive and confused. That lets the student actively apply knowledge instead of passively consuming it. Doremieux argues that this shifts the learner from watcher to doer, which increases confidence, commitment, and the feeling of being supported.

She also gives a smart example of AI-assisted naming. Asking a general model for 20 course names will usually yield generic results. But if you build a guided tool that asks about the feeling, scale, purpose, or experience you want the program to create, the output improves because the questions improve. That reflects one of her broader principles in the episode: the quality of AI output depends heavily on the quality of context and prompts you provide.

Taken together, Doremieux’s advice in this Nathalie Guest Shows conversation is to use AI where it can create meaningful practice, personalized scaffolding, or faster feedback. AI is most helpful when it supports action and reflection. It is least helpful when it simply adds novelty or volume without improving the learner’s ability to perform.

What practical AI habits does Nathalie Doremieux recommend to designers?

The episode transcript includes several tactical habits that make AI more useful and less superficial. First, Doremieux says designers should stop treating AI like a search engine and start treating it like a conversation. Instead of typing a thin request such as "give me 20 podcast titles," she recommends supplying intent, audience, desired emotional response, and examples. The more knowledge and context you provide, the better the result. This is not abstract prompt theory in the episode; it is concrete operating advice.

Second, she recommends asking AI to critique its own work. After an answer is generated, you can ask the system to criticize what it produced, identify where it could do better, and rewrite accordingly. In the transcript, she presents this as a simple way to get a significantly better second version. The tactic is useful because it forces the model to re-evaluate tone, clarity, and alignment with the original goal, rather than settling for the first plausible draft.

Third, Doremieux notes that voice input can improve results because people naturally say more than they type. When speaking, they include more context, qualifiers, and intent, which gives the model more to work with. Her suggestion is especially relevant for creators who freeze when faced with a blank page. Rather than trying to compose a perfect prompt, they can talk through the problem and let AI ask follow-up questions.

But she also attaches a warning to all of this. In the episode, she repeatedly says you cannot trust AI blindly, because it may be wrong and then cheerfully agree when corrected. So the practical habit is not just richer prompting. It is richer prompting plus review. For Doremieux, the competent AI user is not the person who gets an instant answer. It is the person who can shape, test, question, and refine that answer until it becomes genuinely useful.

What design advice does this episode give instructional designers and consultants?

For instructional designers, especially those moving into consulting, Doremieux’s advice in this Nathalie Guest Shows episode comes back to fit, trends, and audience reality. She says consultants need to keep up with changes in the e-learning market because formats that used to sell well do not necessarily work now. Learners increasingly expect support, interaction, and in some cases AI-enhanced assistance. So a designer who relies on one old format because it feels familiar will eventually miss what the audience actually needs.

She also emphasizes that there is no single best delivery model. The right design depends on the audience’s time, preferences, and context. In the transcript, she contrasts younger learners, who may want shorter and more playful experiences, with busy executives, who may reject long videos and have no interest in gamified badges. That is a useful reminder that good design is not only about pedagogy in the abstract. It is about matching the experience to the learner’s lived constraints.

Another important thread is that AI should be included strategically, not performatively. Doremieux warns that it is easy to get carried away and put AI everywhere just because it is exciting. Her recommendation is to identify the actual learner problem first and then ask whether AI is the best tool to solve it. That discipline protects programs from gimmicks and keeps attention on completion, progress, and outcomes.

So if you pull the consulting lesson from the transcript into one statement, it is this: know the learner, know the market, design for results, and use AI only where it clearly improves the experience. That is the practical standard Doremieux keeps returning to throughout the conversation.

This transcript-derived analysis of Nathalie Guest Shows, episode "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux," shows a consistent message: use AI to accelerate thinking and support learners, but keep human expertise responsible for design, judgment, and results. For the full conversation, including Nathalie Doremieux’s examples on automation, roleplay, and reducing learner overwhelm, listen to the complete episode on Nathalie Guest Shows.

Key Takeaways

Key Definitions

Human in the loop
Human in the loop is Nathalie Doremieux’s term in this episode for an AI workflow where a human supplies expertise and context, AI generates or structures a draft, and a human then reviews, corrects, and refines the output.
AI as an accelerator
AI as an accelerator is Nathalie Doremieux’s concept for using artificial intelligence to speed up ideation, drafting, and learner support without replacing human judgment or subject-matter expertise.
Course audit
Course audit is the process Nathalie Doremieux describes for diagnosing where learners get stuck in a program so the designer can reduce overwhelm, improve actionability, or restructure the learning path.
Learner friction point
Learner friction point is a stage in the learning experience where students stall, feel overwhelmed, or do not know what to do next, and it is the point Nathalie Doremieux says should guide redesign decisions.
Instructional design results focus
Instructional design results focus is the principle emphasized in this episode that a program should be judged by whether learners complete it and achieve outcomes, not by the volume of content it contains.

Claims & Evidence

Claim

Nathalie Doremieux says low completion rates in online courses pushed her to focus on program experience rather than only platform building.

Evidence

In the transcript, Doremieux explains that she and her team were building platforms but saw people were not being successful, and she cites a period when only 3% to 5% of people completed an online course, calling that outcome 'not good.'

Source: Episode transcript - full_transcript - Nathalie Guest Shows - "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" - Nathalie Guest Shows / "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" / published June 15, 2025 - Nathalie Guest Shows / "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" / published June 15, 2025
Claim

Nathalie Doremieux argues that automation should handle repetitive tasks so humans can spend more time on high-value interaction.

Evidence

She describes a client program where foundational training pieces were automated before live calls, which reduced repeated one-on-one explanations and allowed live time to focus on questions and application.

Source: Episode transcript - full_transcript - Nathalie Guest Shows - "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" - Nathalie Guest Shows / "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" / published June 15, 2025 - Nathalie Guest Shows / "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" / published June 15, 2025
Claim

Nathalie Doremieux rejects fully AI-generated course creation and instead recommends a human-AI-human workflow.

Evidence

In the transcript, she criticizes promises like creating an online course with AI in 10 minutes, says AI is only as good as what you feed it, and explicitly describes the best process as 'human, AI, and human.'

Source: Episode transcript - full_transcript - Nathalie Guest Shows - "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" - Nathalie Guest Shows / "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" / published June 15, 2025 - Nathalie Guest Shows / "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" / published June 15, 2025
Claim

Nathalie Doremieux says AI roleplay can increase learner confidence and make online learning more active and interactive.

Evidence

She gives examples of roleplaying difficult employee conversations with different response profiles and says this turns the student from a passive learner into a doer, boosts confidence, and helps them feel supported.

Source: Episode transcript - full_transcript - Nathalie Guest Shows - "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" - Nathalie Guest Shows / "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" / published June 15, 2025 - Nathalie Guest Shows / "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" / published June 15, 2025
Claim

Nathalie Doremieux says the quality of AI output depends heavily on context, prompting, and follow-up critique.

Evidence

She contrasts a generic request such as 'give me 20 names for my course' with a guided tool that asks strategic questions about the experience and audience, and she also recommends asking AI to criticize its own work to produce a stronger second version.

Source: Episode transcript - full_transcript - Nathalie Guest Shows - "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" - Nathalie Guest Shows / "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" / published June 15, 2025 - Nathalie Guest Shows / "The Human Loop: How AI Can Transform Instructional Design with Nathalie Doremieux" / published June 15, 2025

Key Questions Answered

How does Nathalie Doremieux recommend using AI in instructional design?

In this Nathalie Guest Shows episode, Nathalie Doremieux recommends using AI as an accelerator, not a replacement. Her preferred workflow is human in the loop: a human provides expertise and context, AI helps structure or draft, and then a human reviews, corrects, and improves the output before it is used with learners.

What does human in the loop mean in Nathalie Doremieux's AI approach?

In the episode transcript, Nathalie Doremieux uses 'human in the loop' to describe an AI process where people remain responsible for knowledge, judgment, and final quality. AI helps speed up drafting, ideation, and support, but it does not replace the human expert who supplies context and verifies correctness.

Why does Nathalie Doremieux say many online courses fail learners?

According to Nathalie Doremieux in this Nathalie Guest Shows conversation, many online courses fail because they overwhelm learners with too much content, too much theory, and not enough interaction, milestones, or clear action steps. She ties that problem to historically low completion rates, citing a period when only about 3% to 5% of learners finished an online course.

How should instructional designers use automation without losing human connection?

Doremieux says automation should be used for repetitive, process-based tasks that do not require personal expertise. In the episode, she explains that good automation creates consistency and frees up time for live support, questions, and application, so the human connection actually becomes stronger where it matters most.

What are examples of good AI tools inside an online course according to Nathalie Doremieux?

In the transcript, Nathalie Doremieux points to AI-supported homework help, essay review, guided drafting tools, and roleplay simulations as useful examples. She especially highlights AI roleplay because it lets learners practice difficult conversations in a safe, interactive setting instead of staying passive in front of a screen.

What prompt advice does Nathalie Doremieux give for better AI output?

Nathalie Doremieux advises giving AI much more context than a simple one-line request. In this episode, she says better outputs come from sharing your goal, audience, desired feeling, and examples, and then asking AI to critique its own draft so you can get a stronger revision.

What should instructional design consultants learn from this episode?

This Nathalie Guest Shows episode suggests that instructional design consultants need to stay current with market shifts, learner expectations, and emerging AI tools, but still design around audience reality rather than hype. Doremieux’s practical standard is to focus on learner results, choose formats that fit the audience, and use AI only where it solves a clear learning problem.

Full Episode Transcript
Speaker A: 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, GCU students, alumni, and educators. Welcome to episode 32 of the Designing with Love podcast. Today I have the pleasure of interviewing Nathalie Dorameo, the co-founder of Podcast Lead Flow. Welcome, Nathalie. Speaker B: Thank you so much for having me, Jackie. Speaker A: Thank you. So can you tell us a little bit about yourself? Speaker B: Yeah, sure. So, um, I am in France, so I am both French and American. Um, and, um, I have lived in the US for 10 years in the corporate world. So I'm a software engineer in the, as a background. And then in 2005, my husband and I decided to sell everything and move back to France. Be closer to family. And then we ended up starting a business. So that was 20 years ago. That evolved a lot. I'd say that our primary services are building membership sites and online programs. So we do work with instructional designers as well. And we've really been involved with AI in the last 5 years. And more using AI as an accelerator, an amplifier to help students get results faster and give them an extra level of support. And then in January, we launched Podcast Lead Flow, which is basically a tool for podcasters to start conversations with our listeners because we saw that there was a gap between the listener and the client, right? This is more for like business owners. Speaker A: Wow, that's exciting. I love that. And that the, the fact that you work with your husband, it shows that you have that close, um, relationship where you can both take your talents and your skills and meld them together and be able to do that. So I love to hear about family-owned businesses and, uh, and that's because it, it really shows that, start those strong ties and everything. So that's really great. I love that. And I love that you're, you're in France, so that's That's great. And you're close to family. That really helps. Definitely. Yes. So you mentioned, you know, you have many hats that you wear, it sounds like. What do you primarily do at the company then? Is there certain things that— or you do just— you have your hat and everything pretty much? Speaker B: So basically my focus is on marketing and sales. And, um, I work with people one-on-one to basically help them design programs. So, you know, that they have an idea or they have a, a method or a system, you know, that works and they want to teach that online, but they have no idea how to turn that into a program that's not overwhelming, that gets people results. You know what I'm talking about, right? Speaker A: Exactly. Speaker B: Yes. So I, I help people with that. It's really transition from like just building the site as the tech person. To really realizing that we were building all this platform and people were not being successful. And I was like, okay, let me figure out like, what can I do? What can I learn to actually help them create programs that people actually not just buy, but actually go through and get the result because that's the ultimate success, right? Right. So, so, so that's basically what I do in a nutshell. I talk to a lot of people. I network a lot, you know, I collaborate a lot that we were just sharing before we started to record that a portion of our business is by referral and just also students who are in an online program and they see our link in the footer and like, oh, I like the way this is working, you know, like I want something, I want the same, you know, I want something like that. So, and it's been working really well for us. Speaker A: That's great. So you've been able to learn about the marketing and also being able to, yeah, really help with the instructional design aspect and making sure that these programs are not going to just come and go, but they're going to last and they're going to be around for a long time and get results. And that's mainly what we're looking for is that great learner experience for them. Speaker B: Exactly. So it's basically, you know, it's like looking at what it is that you love to do and want to do. And then there are things that are necessary that need to be done in the business. So you, you either, either get help or you, you get training, you learn, right? At least the minimum so that you can, otherwise you have a business, but you, you know, you either don't have clients or, you know, like there is a piece that is missing, right? So it's never going to be that. You wear all the hats and you know how to wear all the hats, right? And sometimes there are things that we hate to do and we absolutely should not be doing them because we are not going to do a good job at them anyway, right? Speaker A: Right. That's so true. Yeah, definitely. Yeah. And it sounds like, you know, you, you have the interest because you saw a need and you, you knew that you could fulfill that need. So sometimes it comes out of necessity, right? But sometimes it comes out of just pure interest in seeing that need and wanting to fill that. So it sounds like that's what you did. Speaker B: Exactly. And it's really been, because at the beginning we were really just building websites, you know, just regular websites. And we had an opportunity to create an online course for someone. And I got into that. And then even, I think it's my husband that, and business partner that started to say, but how can we improve the experience? Because it's terrible. You know, it's like people are in front of a computer and we give them all these videos to watch and say, okay, now go do it. It was like, how can we try to replicate what is happening in a classroom, their ability, you know, for it to be a bit more interactive, their ability to ask questions, right? Or to get feedback on their work, right? How can we create that online so that we'll have more people get results? Because At the time, it was like 3 to 5% of people only complete an online course. Speaker A: Wow. Speaker B: Which is not good. Speaker A: No, that's not good at all. Yeah. And it seems like a lot of times online programs, they, they just want to give all this information and they don't know how to segment it and they don't know how to make it so that it doesn't overwhelm learners. Speaker B: So exactly. Speaker A: Yeah. So that's the tough part. Speaker B: Yeah. Because like they have an expertise and they think that because they know, they have the expertise to turn that into a program that's effective. I mean, that's why instructional designers exist, right? Speaker A: Right. Speaker B: They do that. And, and people are like, no, no, this is my stuff. I know. And, and, and very often they would create something that looks good to them, but that could feel either too overwhelming for people or too much theory, not enough doing, or videos too long, not enough like milestone and wins so that people stay motivated. So all these things they don't think about because they don't know, they don't know. Speaker A: That's true. Yeah, it's, yeah, it's very interesting because I do work with subject matter experts a lot in the industry and they know the content so well. They know that what they do. So for example, I work with, with counselors because I work on counseling programs at the university. And so it's very, but it's very interesting because they know their craft, they know what they're doing, but I, and I know a lot about it, but I still have to keep myself away from the content per se in that sense, because I don't want to become the expert. They're the expert. But then when it comes to the instructional design, I give the suggestions, I ask the questions, I bring that out of them and say, okay, what, what at the end of this class do you want students to know, be able to do at the end? And then that's where their expertise comes in. But then we make it to where it makes sense to students, because if they were to just write it and go off, it wouldn't be very good. So, uh, you know, but they're good at what they do. They're good at the instruction aspect, and then I'm good at the curriculum side of it and just helping it to make sense. So it all comes together as, uh, as this big, you know, beautiful type of thing at the end. So, but it takes work. And so it sounds like you are able to work with those experts and really help them understand that The end product is what you're, what you're looking at. Speaker B: Yeah, exactly. And then you often, people are like, um, they want to give more, you know, there is this notion that if we gave them more, it will have more value. It's like, people don't want more. They don't want to watch more videos. They want results. If you can give it to them with less, even better, right? Speaker A: Yes. So true. Speaker B: And so when, when it doesn't, so. Maybe we'll talk about that. I don't know. Like, but like when, when we look like when we do an audit on a program, because people get stuck and they are not getting like the, the, the, the results, like they don't have like the, the number of students that actually complete and things like that. Then we look into at what step they are getting stuck. And then yes, we might add a tool, an extra resource to figure out why people get stuck. If you think they should not spend so much time in this piece, what is it? Does it need to break, to be broken down into two? Right? Is it not actionable enough? Like, you know, like, is it standing on its own enough that they don't require to watch something else? You know, that, you know, it's like, it's really looking into that. It's like, how do we improve the experience? And very often it's not about adding more. Speaker A: Absolutely. That's so true. Definitely. So are there any, um, because a lot of my students, you know, they want to go in and they want to do consulting work down the road when they finish their master's degree, and, or they want to go into corporate or higher education. So they want to break into something else, but most of the time it's consulting and they're trying to do that. So are there any specific leadership skills or styles that you think are really helpful as a business owner that you think would, would be good for some of my students to know about that they can kind of think about as they're, as they're thinking about becoming a business owner or doing consulting work? Speaker B: Yeah. Well, I think what is really important is really to keep up with the trends and what is working. I mean, especially in the, so in the online space, at least I don't know about, you know, in the corporate world, but like in the online space, uh, e-learning has changed so much. Yes. Courses that used to sell don't sell anymore. Like, like even people that used to sell a lot of them and make a lot of money are now shifting their model because people are done with spending money on things where they don't get the result. They don't have the level of support. They don't have the interaction. And even people start to, and I don't know if we'll talk about AI, but to expect some type of AI support and tool, not to replace, anyone, right? But like as an extra level, because we know it's an accelerator, right? It can be an accelerator. So it's just keeping up with the trends and, and knowing what works, what are like the emerging technologies and tools that are coming up and, and just being able to also have that that range of tools and technologies that you can use because it's not one size fits all, right? It really so much depends on your audience, you know, like learning styles and even like how much, how much time people have, right? So it's like, we have to, we have to adapt, you know, when we create programs, it's not just, just the curriculum, but it's also looking at with my audience, with the audience that is going to consume, right? Speaker A: Right. Speaker B: So it's, it's really keeping up with the trends and not saying like, okay, I have one way to do it. It works for me. I'm comfortable with it. Like challenge yourself to say what is out there, you know? Speaker A: Right. And keeping up with that. So important. Yes. Yeah. And a little bit about the AI, you know, one of the questions that I wanted to bring up that was on your list of questions, because I want to make sure I asked a couple of those because it kind of goes over that automation part. So how do you strike the right balance between the automation and then providing that authentic human connection when using AI in the business? Yeah. Speaker B: So I mean, I have an approach with AI that is, well, let's talk about automation first. Speaker A: Sure. Speaker B: Because that's, that's something that I used to do a lot for businesses. To me, Automation, I see automation, things that need and should be automated are repetitive things that don't require our expertise. You know, it's like if you've got to do it more than 2, 3 times, can it be automated? And the reason we automate these things is that it gives us more time to actually interact with people. So, you automate so that you have more time to have the human connection. Right. So for example, I was working with someone and she had this program. And what we did is we automated some pieces inside. So people had to, um, have gone through some type of, of training, you know, things that they learn so that when they would come to the call, they would be prepared. They would have watched all this. And she was able to not only cut down the time that she spends one-on-one with the client. But she could also, she would not have to repeat herself, you know, doing, saying the same thing over and over. And sorry, and they could spend the time on answering the questions and actually, you know, applying, you know, what was learned. So, so to me, automation is a way to save time, but it needs to be done on things that don't require you to basically be there, right? Speaker A: Right. So, it shouldn't be done blanket across everything. There needs to be a, it needs to be a process that takes that into consideration as well. Speaker B: Absolutely. And usually those are like repetitive tasks that actually would benefit from being automated because now you can ensure consistency. Speaker A: Right. Speaker B: Right? Speaker A: Exactly. Speaker B: So, it's not like automating because we don't want to do it anymore. You know, like you used to do something and then suddenly it's not one-on-one anymore. It's an automated thing. It's really not the same thing, right? But here we're talking about automation in the sense of defining a process, an SOP that actually you follow that is automated and that ensures the consistency and that's actually it's done properly following the SOP and consistently. Speaker A: I love that. Wow. That's great. And so the AI is, is it becomes a helping hand, you know, I look at it as my, my digital assistant when it comes to things. Yeah. Speaker B: So with the AI, uh, so I mean, I'm sure you've seen things like create a, an online course with AI in 10 minutes and stuff like that. To me, this is not the good AI at all. Okay. Writing your content for you and things like that. To me, this is not how you use AI. Actually, AI is going to be as good as what you feed it. So what I've seen AI do really, really well is that when you feed it knowledge, which is your expertise, right? And then you give it context and then it's going to, you ask him to help you craft something based on that. And then the result that you get, Now you work it, you work it again. So it's basically human, AI, and human. Speaker A: Right. Speaker B: And basically it accelerates the work. And when it comes to writing, for example, of course, now you don't have the blank page anymore because worst case scenario, you can say, I don't know what to talk about. And then it is going to ask you, start to ask you questions. But the best way to use AI is as an amplifier and an accelerator, not as a replacement. Speaker A: I love that. Speaker B: Because it pulls data. I mean, if you think about it, it pulls data from the internet. Speaker A: Right. Speaker B: So it's going to be what everybody says, maybe something that is even wrong or obvious, right? And definitely that's not coming from you. And we have this opportunity with AI when we use it right to have it know our voice. And the way I love to use it is when I have an idea, it has this analytical mind where it can, you know, you give it like examples of writings that you've done where you're like, yes, this is me. This sounds like me. Speaker A: Right. Speaker B: And then you have this idea and then it's going to come up with something, the structure, right. Of your idea. So it's your own idea, but it's going to save you so much time in writing that. and then you can, you know, rework it and then just make it, you know, even your own. But it's that accelerated, that accelerator that I really, really love with AI. Speaker A: Absolutely, definitely. And one thing that, uh, there's a term that we use in my department because we use AI to help, uh, come up with the ideas as you were talking about. And then I use it in my teaching as well, just to craft those ideas. I even started it with my podcasting too, coming up with uh, you know, outlines and things like that because I kept struggling with that. But one of the terms that my director in my department started using, and now it's, it's all across the university, is the first draft principle. So that gives us that, that sense that, okay, this is the first draft, this is never going to be where we're going to copy it and paste it into the curriculum. Exactly. It's never going to be that, um, that there's going to always be, like you said, that human element at the forefront, at the backend. Speaker B: We call it human in the loop. Speaker A: Ooh, I like that. Human in the loop. Speaker B: So it's always, yeah, it's human. So I say AI is an accelerator that allows us to be more human because now we can do more. Speaker A: Yes. Speaker B: More interaction with people, right? So, and so, so that's one way we use AI. Another way that we use AI is to help students do their homework, for example. So, you know, when somebody gets stuck and they have to maybe write an email, there can be a little AI tool that is going to ask them a couple of questions and going to draft maybe the first version of that email. Speaker A: Right. Right. Speaker B: But it's going to be, again, the quality of the questions that we ask that are going to drive the quality of the draft, right? Speaker A: Right. Speaker B: And the quality of their answers, right? For example, we do, for example, AI tools that work really well in the e-learning space are roleplay. Speaker A: Oh yes. I love roleplaying with AI. Speaker B: That's great. Speaker A: Yes. Speaker B: Because roleplaying with someone is weird. Okay. It can be weird. Like, unless you know the person, like she's a friend and like you roleplay. Speaker A: Right. Speaker B: But like, it's completely objective and you can say, you can have different level of roleplay. You say, 'Okay, let's role-play a hard conversation with an employee that is not performing and that I might have to let go. Let's just role-play.' And then you can say, 'Well, let's role-play, and today you're going to be a student that also understands that, um, or an employee that understands that they are not performing and it's not working out as well.' So the conversation is pretty much going to go Well, right, right. Role play. And now you are the employee that is like, doesn't understand where this is coming from and is like questioning why you're even doing. So you see, you can even like create all these tools and what this does, it puts the student not as a passive learner, but as a doer. Speaker A: Mm-hmm. Active learning, right? Speaker B: Exactly. It boosts. Their confidence, right? It keeps them committed to the program because they are seeing that they are making progress and they are feeling supported. It's interactive. So it's really like they don't feel like alone almost, you know, in front of the computer, especially for e-learning, right? Speaker A: Exactly. Speaker B: But it doesn't have to be just for e-learning, you know, any any program could have these extra tools that you could use either to practice or to help you write something, or like we had a tool, it was like, um, just to help you come up with 20 names for your online program. Speaker A: Wow. Speaker B: So you, you might say, okay, well, I can just type that in ChatGPT, right? Or anywhere. And it's going to give me, it's going to give you 20 boring names. And you're going to say no to none of them, right? But if you create the tool so that it asks questions like, what kind of experience do you want people to feel like? Is that a community feeling? Like, do you want to create like a safe space for these people? Or is it a hub where you want to have thousands of people, right? And so when you ask these key questions, then you're going to have a better answer instead of saying, give me 20 names for my course. I teach painting. Speaker A: Wow. Yeah, that's amazing. I love it. And yeah, it's great. Speaker B: It's that notion that the more knowledge and context you give it, the better the output, right? So that's why if you just ask it, give me 20 names, it's going to give you 20 names, right? Speaker A: Right. Speaker B: But you can have better. Another thing that we found with AI, so this is a great tip if you're even for you, if you're using AI, ask AI to criticize their work. It comes up with something and maybe you said, I want the email to be engaging, you know, positive, like, you know, like maybe there is a certain action that you wanted people to get, like on what you're writing. And then you say, okay, okay, that sounds good. Now I want you to criticize your work. And what it is going to do is You'll see it's going to analyze the answer and it's going to tell you, okay, so I did good here because as you can see, it's engaging, da da da. However, there was a place where I could do better. You're right, right here. Do you want me to write it again? And it's going to give you another version and that version is going to be so much better. Speaker A: So much better. Yeah, that's interesting because I did that with, I don't know if you've used Canva, their AI tools, but recently they released Canva Code where you can go in and you can ask it to code things where it can do interactive types of content. So I did just a couple of testing items. I did, I did a historical timeline for instructional design, and then I wanted to do a vocabulary game for instructional design terms. And it was so funny because the first draft it did, it did pretty good. But then I asked it, can you please add different elements? Like, I asked it to add a, uh, for the timeline, I asked it to add a photo or an image to the back of the card because you would have the front where it had the historical event, and then you would flip the card over and it would have the more information about it. But I'm like, I, I want to have— I don't want to just have text. I want to have something where they have a visual component. So then it did that and it took a little while to do it, but then it finally came back and I flipped over and I got to the Addie model and it had this circular thing and it had A-D-D-I-E-E, two E's. And I was like, whoops, mistake. And so, and then I looked at some others and it just— some of the images just didn't turn out quite well. So I asked it to please correct it again and it did that. But it was funny. I'm like, you made a mistake here. And it was like, oh, sorry about the mistake. I'll correct that. Speaker B: But exactly. Speaker A: Yeah, it's pretty interesting. Speaker B: And that's why you always have to remember that the information might not be correct. Speaker A: Right. Speaker B: You know, it might be like you say, you know, look, nope, that's not right. And it's going to say, oh, you're right. It's not right. So you cannot So that's why you have to, again, you have to feed it that expertise, right? That either idea or like, even if you're looking for content or podcast ideas, you could say, this is the message, you know, like I want, I want to talk about this. I want to inspire people with this. Now give me, um, titles of podcast episodes that will make people want to listen. Speaker A: Right. That draws them in. Speaker B: Exactly. Speaker A: That draws them in. Speaker B: Right. That keeps, that sparks their curiosity. Right. Or, you know, or something else, right? So it's like the more you tell it, instead of saying, just give me 20, 20 podcast titles, you know, I'm a professional designer, give me 20 podcast titles. It's going to be super boring. Speaker A: It's going to be, might as well do Google, you know? Yeah, exactly. That's where that prompt engineering really comes into place because you can have a conversation with it instead of just giving it one line. I tend to want to have a conversation with it. We have an internal AI tool that is similar to ChatGPT. It uses that technology. But what's nice is that it keeps our information at the institution proprietary. So it doesn't go out on the web. But it's really great because it has Text Insight, which is where it can take lengthy text and it can make it a little bit more digestible. And then it can also take transcripts from meetings like from Microsoft Teams and Zoom, and then you just upload the transcript and you say, can you do and we have prompts, we have a whole entire OneNote notebook that has a prompt library of all these different things that we can utilize. But I don't always go with the same prompt. I try to take it and, you know, change it a little bit. And I notice that the AI will react differently each time, even if I put the same prompt in, it'll come up with something different. So it's very interesting how it's never one in the same. Speaker B: No, it's not. It's not, you know. And another thing that— so they did some tests and they found that if you use the voice version instead of writing, you know, so I don't know if you have that on your tool, but like where you speak instead of writing, right? You get even better results because we just always say much more when we write. When we speak, sorry, right? When we speak, we say a lot more than when we write. And so when we write, it's like, It doesn't have to be perfect. The sentence doesn't have to be well-formed. We really need to see it as someone that's like sitting next to us and you're asking a question. And it could be something like, okay, I need to write an article, but I don't know, I'm not inspired. Like, help me, ask me questions. And then it's going to ask questions, literally like if it's a person. So you need to use it very differently than you use Google. Google is is a search engine, right? Here it's very different. You know, it can actually think, right? So it's really literally, it's literally having a conversation. Speaker A: Hello, I have some exciting news to share with all of my listeners. There is a feature called Fan Mail. What this allows you to do is send me a text message anytime during the episode. There's a link located at the top of the description in each episode, and it says "Send Jackie a text message." So when you click on that link, you will be able to send me a message letting me know what you thought about the episode and if you have any ideas for future episode content as well. I look forward to seeing your text messages. Speaker B: Thank you. Speaker A: Right, that's why they, they call it a language learning model, right? Because exactly having that conversation. Yeah, I love it. I love that. Great. So what are some tips and advice that you can share with those who are currently in this master's program in instructional design at Grand Canyon University? Because we've, we've actually— we're implementing AI technology into the revisions that we've done recently with this particular program. And so I've actually got a taste of that and I can see where students are actually able to, they're going to be able in their assignments to utilize AI to where they can actually, when they're storyboarding, for example, they can utilize AI and say, can you help me with this storyboard and get the ideas out and things like that and scripting and things like that. So is there any type of advice you would give to them? It can be on the AI or it can be in general, like with course development and things like that. Speaker B: Yeah. So I have a couple actually. So on AI, I would I would say that with AI, it's easy to get carried away, right? It's like, let's put AI everywhere because it's fun. And it's like gamification. You're like, when it's too much, it's too much. Okay. So it's about strategically knowing that there might be a point of difficulty where can we use AI to help them? Is there a prompt we can give them? Do they need to practice this, right? Giving them these tools. So really seeing AI as a tool where we can create tools for them, but not putting it like everywhere for the sake of using AI. Oh, I want to use AI. Where should I put it? Looking at the problem that we're trying to solve and see if it can be solved with AI. I think that's really important because otherwise you can you get drawn into everything that's going on with AI right now, all the tools that are coming up, you know, you start playing and creating images and doing all this stuff like, and it's really like, we have to stay focused on what it is that we want to do. We want to create a program that really creates the best experience for our listeners so that they see they are making progress. So they get to the end with the actual end result, you know, that, that they sign up for, that they paid for, right? And it's about what is it that we can do and provide and how can AI help accelerate that, like, you know, potential problems that people might have, you know, in completing the homework or things like that. It could be also like that AI can do review, you know, just review my essay, you know, like, here is my essay, what do you think, you know, and things like that, right? Um, but yeah, I would say more generally that it's really about, I mean, that's how I feel, you know, when I, uh, that, that's why I got into this, you know, helping people design the program because I was so frustrated about what people were doing. You know, they would like record long 1-hour video and it's like, yeah, it's amazing. You know, no, like it's, uh, it's, it's, um, it's boring to watch. It's not interactive. Like people don't know. When they need to do something or the end of the video and they're like, okay, what do I do now? Right? So it's really keeping up with those trends, like I was saying, but it's also remembering that the end goal is that we want people to get results, right? So it's the learner in mind. That is what we love. I mean, that's one thing. I think that's, that's for everyone, not just instructional designers, but that is what we love, how we love to learn, but we have to adapt to our audience and who this is for. You know, if they are younger people, if they are older people, like if they prefer to listen or if they prefer to, to watch a video or to read, we have like this multimedia, multiple ways for people to to go through the programs, right, that exist, that's been there for, for years. But it's really understanding that, like, what is their method of learning? And it's different for everyone, right? So trying to, and incorporating that into the programs that we create. Speaker A: I love that. Yes. Being able to know there's different learning styles and yeah, to be able to adapt it to that. Speaker B: Exactly. Even within an audience, you know, there are different learning styles. But they are also based on the audience that you have. You know, the busy corporate executive doesn't have a lot of time. If you tell them there is a 1-hour video, they're like, I'm never going to be able to fit it in. Like, I don't know where it's going to go, right? So you have to keep these things in mind as well. Also, like the age of the student, right? A younger student would want something probably shorter, more playful. Where he's rewarded when that makes him want to seek, be curious to see what is the next thing, right? Versus that an executive doesn't want to get a badge that says you did it, right? So it's, um, you know, it's, uh, it's, it's, it's this thing, I think, that can really, uh, be the difference between a good curriculum and a great program. Speaker A: Absolutely. Yeah. My students right now, they're, they've been learning about multimedia. Learning styles, you know, like Mayer's Principles of Multimedia. And it's— yeah, they can see in the real world where things don't, don't turn out well. And yeah, so it's, it's interesting because one of the questions I posed to them in my classroom assessment technique this week was about audio, that good audio makes a huge difference. And I— and the question asked them, if you have a, a really great video that has one— that has crisp, clear audio, but the the rest of it is horrible, do you still listen to it because the audio is great and but the, you know, the multimedia part on the screen and the video is not great? Or, you know, what if the audio is horrible but the visuals are great, you know, which is— and so one of my students answered and she said, yeah, the audio was crisp, clear, but the video elements, like the interactive elements, was not there at all. But she kept listening to it because the audio was So good. So it's very interesting how you have the audio— auditory learners, and then you have the ones that are more visual, and then you have the kinesthetic, right? That want both. So it's a very interesting question, and I hope more of my students answer that because it's kind of neat to hear those examples of experiences they've had. And did they keep listening or not, you know, based on the audio quality? Yeah. So, yeah, so interesting. We could go on for so long. I love it. Yes. Well, I thank you so much for coming and giving so much of your insight because I think this is great, especially with AI technology. We want to be able to do it incorporating in a meaningful way and not have it, like you said, be a one-size-fits-all or use it to replace that human interaction or things that only we can do as human beings. So being able to use it as an assistant, as a tool, and knowing when to use it. So I think that's so important. And I know my students love that, being able to know know those things and be able to digest that and, and then apply it to the real, real world. So it's great. Speaker B: Exactly. Speaker A: Yeah, I love that. Thank you so much, Natalie. I appreciate your time. Speaker B: Thank you so much for having me, Jackie. Speaker A: Thank you. 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.

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