Louis NeJame spent years inside the edtech world, first as an analyst at Titan Partners, a strategy consultancy focused exclusively on education markets, then at McGraw-Hill leading AI product development, before conducting more than 200 interviews with higher ed instructional designers and arriving at a conclusion that stopped him cold.
The people who were most qualified to shape the future of online learning were spending the majority of their time on work that had nothing to do with learning. So he built Bevel, an AI-powered platform for higher ed instructional design teams, to do something about it.
Below is a story he tells, and it is the kind of story that is almost too on the nose to believe.
He was on a call with an instructional designer at a large online institution. She had a PhD in educational psychology. She spent the first fifteen or twenty minutes talking about her doctoral research with the kind of energy that makes you realize a person found their thing: the learning theory she had spent years developing, the ways it had genuinely moved the field.
Then NeJame asked how she was applying it in her current role.
She was not. She had spent the last two weeks going course by course through an accounting program, making sure the term deferred revenue was bolded in the right places and not bolded in the wrong ones, because that is how it appears in real accounting practice.
“My heart sank,” NeJame says. “That’s a misallocation of talent.”
That moment is the origin story of Bevel. But it is also a window into a much larger question about what our field is actually for, and what becomes possible when the work that should not require human expertise stops requiring it.
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The 60 to 70 Percent Problem
After conducting more than 200 interviews with higher ed instructional designers, NeJame landed on a number that he describes as the catalyst for everything. Most instructional designers, he found, spend 60 to 70 percent of their time on operational tasks: detailed course evaluations, accessibility fixes, content revisions, compliance checks, accreditation audits. Not learning design. Not pedagogy. Not the relational, strategic, deeply human work they were trained to do.
This is not a new problem. It is the problem that Jason Gorman describes in the opening of the episode: the gap between the meaningful work and the work that fills the calendar. The difference is that NeJame has decided to attack it from the supply side rather than the demand side. Instead of trying to protect time, the question he is asking is what if the operational burden could be substantially removed?
One early example makes the case in concrete terms. An institution Bevel is working with needed to complete 2,000 course audits for an accreditation review, checking each online course against a quality framework, verifying whether things like instructor welcome videos existed, whether accessibility standards were met, whether learning objectives were reflected in the actual course content. This was work the eight-person instructional design team was doing manually.
Bevel completed 2,000 reports in 90 minutes.
What happened next is the part NeJame finds most encouraging. The reports were shared with faculty and course builders. Suddenly, everyone could see exactly where their courses needed work. Demand for meetings with the instructional design team went through the roof. Faculty who had never thought much about accessibility were asking how to fix it. Designers who had flagged objective alignment issues were being sought out for their expertise.
“When we free the paperwork step as the bottleneck,” NeJame says, “we suddenly have more time and more demand for the relational work and real expertise.”
What the Technology Cannot Solve
NeJame is careful not to oversell what AI can do, and the care feels genuine rather than performative. He has thought seriously about what the technology is and what it is not, and he is direct about where the line sits.
The operational work, the audits, the accessibility checks, the skills mapping, the alignment to standards: AI handles this well and will handle it increasingly well. But the work that has always been the hardest part of the job is a different category entirely.
“Building the course alignment matrix was never the hardest part,” he says. “The hardest part was getting the faculty member who has taught the same course for 20 years to listen to you, to understand that we are going to change some things and it is not encroaching on their expertise.”
Change management. Internal politics. Trust building with a tenured professor who is terrified by an accreditation review and has not updated their course since before the pandemic. These are fundamentally human relational challenges, and NeJame is not in the business of pretending otherwise. His argument is not that AI replaces the human work. It is that clearing the operational fog creates more space for the human work to actually happen.
The Deeper Worry About AI and Learning
The most interesting part of the conversation is not about Bevel. It is about what NeJame worries AI is doing to the act of learning itself.
There is a version of the AI argument that treats knowledge transfer like plugging in a USB drive. You ask the question, you get the answer, you have the information. NeJame rejects this, and the rejection is grounded in something more substantive than nostalgia.
“There’s a deeper level of understanding you get when you’ve experimented with the wrong answers,” he says. “Finding an answer you think is right and then learning why it’s wrong, having a smart partner tell you good try, the actual answer is in this direction, and then going off to find it yourself: that type of fast iteration is what builds instinct.”
The concern connects to something he misses from his own career. He started as an analyst, spending years with Excel, SPSS, Stata, building models and analyzing data by hand. He has now largely outsourced that base-level work to AI agents. The speed is remarkable. But he notices a nostalgia for the hands-on process, a sense that something about deep familiarity with the model, the data, the problem is harder to develop when you are one abstraction layer removed.
This is not an argument against using AI. It is an argument for being honest about what the tool costs as well as what it delivers.
Looking Further Out
When NeJame looks at the longer arc, he keeps coming back to one variable: the entry-level white-collar job market. Higher education and the job market are deeply linked, and if AI significantly reshapes what entry-level professional work looks like, which he believes it will, that will send ripple effects through every institution offering degrees and credentials.
His read is that this will accelerate the shift toward shorter-form credentials, competency-based learning, and a different mix of skills: less technical execution, more critical thinking, adaptability, experiential judgment. He imagines a business class where the project is no longer writing a business plan but actually building something and getting users.
And in that shifting landscape, he sees instructional designers as what he calls a latent superhero class. The people who understand both the technology and the learning science. The people who know how to navigate institutional politics and translate between faculty expertise and pedagogical practice. The people, in other words, who are currently spending two weeks bolding the word deferred revenue.
The bet he is making with Bevel is that when you clear that work away, you find out what those people are actually capable of.
Listen to the full conversation with Louis NeJame on The LX Ops Show or wherever you listen to podcasts.
Podcast Transcript– Jason Gorman with Louis NeJame
Jason Gorman (00:00)
Welcome to the Learning Experience Ops Show, where we talk about making learning better that’s future ready. I’m Jason Gorman. I’m the founder and CEO of Jackrabbit Learning Experience. If I think back to my most idealistic, optimistic days as a learning experience designer, I can still feel how excited I was about certain aspects of the work. Like I was energized by the idea of co-designing with learners or by…
making a rough prototype and getting it to stakeholders and inviting real feedback and iterating quickly. ⁓ Or maybe stepping back from what I was doing and comparing it to the learning science to see where it really worked and where it was really failing and to really think about the high standards that we should have in our field. These were the kinds of things that always felt like the real meaningful work to me. But…
far too often ⁓ these are the things that I had the least time for, sometimes still had the least time for. And instead, days can get filled up with operational tasks, long to-do lists of necessary work, but often tedious work, and the kind of work that just crowds out the deeper thinking, the deeper work, the more meaningful work, because the deep work takes time, let’s face it.
And so my personal solution has always been to experiment with ways to carve out protected time, block time, block it on the calendar, defend that time, go away for the weekend just so I have time and space to think, to try sometimes unsuccessfully to just preserve space for the work that matters. And sometimes that worked and sometimes it didn’t work. But my guest today is approaching this exact challenge from a bit of a different angle.
Instead of trying to squeeze more time out of the calendar, he’s asking a different question. What if we could use AI to remove a meaningful portion of the operational burden altogether? Louis NeJame is the co-founder and CEO of Bevel. It’s an AI-powered platform built for higher ed instructional design teams. Louis has spent his whole career in ed tech. He started out at Titan Partners working on adaptive learning research, then at McGraw-Hill leading their AI.
product development, Lewis writes that after conducting over 200 interviews with higher ed instructional designers, he discovered that most spend 60 to 70 % of their time on the kinds of things that we’ve been talking about here so far, like detailed course evaluations or accessibility fixes or course revisions or maintenance, not actual learning design. so inspired by coding tools like Cursor and Cloud Code,
He set out to build Bevel to help instructional designers, instructional technologists, learning experience designers. Very excited for this conversation. Welcome to the podcast, Louis.
Louis NeJame (03:02)
Yeah, thanks so much for having me.
Jason Gorman (03:04)
All right, you ready to jump in with some lightning round questions? All right, what do you love the most about your work?
Louis NeJame (03:07)
Let’s do it.
Yeah, I, this sounds corny, right? But I love building creative, new, exciting solutions with people that I like that are passionate about the same things as me. I really get a lot of energy from building process, right? From like understanding the problem and then iterating and failing and trying new approaches until you get that light bulb moment with ideally a customer. And then the second part is really, you know,
Jason Gorman (03:25)
Mm-hmm.
Louis NeJame (03:40)
doing that process with people that I really like. So my co-founder, Brian, is amazing. The customers and instructional designers we get to work with every day are a blast to work with. And so I just derive a lot of energy and meaning from that whole creative process.
Jason Gorman (03:56)
That’s wonderful. When you’re absolutely dialed in at work, what are you most likely to be doing when you’re just in the zone?
Louis NeJame (04:03)
Yeah, it is a similar answer, but I’ll be more specific. I think it’s talking to a customer and it’s like drilling down to the sort of excruciating details of their problems. Like all the time I’ll have people pull up their screen and say like, show me how you’re doing this course mapping click by click. I need to understand it at that level of depth ⁓ so that we can, you and also talk to me about what the problems are. Like what is the parts that you like of this process? What are the parts that really are drudgery?
So I think drilling down and being curious and excited about course alignment, right? Like these things that people aren’t used to, used to people being excited about is a part where I’m really dialed in in my
Jason Gorman (04:48)
to talk a lot about the future and we’re going to talk a lot about ways in which you are designing for a different kind of future in our field. But let me just ask at the top here, what are you most anxious about for the future?
Louis NeJame (04:51)
Yes.
Yeah, a lot. ⁓ You know, there is a lot to be cautious about and nervous about with this technology, especially in a field like education, where it is sensitive. These are, you know, these are very social issues and institutions that we’re working on. ⁓ I think I feel a responsibility building with this technology. I think there’s a weight to it. ⁓ I’m I’m very cautious about not rushing to
the idea that just because the model can do something, it can generate you a course from a button. Does that mean that we want to be doing that? And I don’t know. I don’t want to take that course. ⁓ And so then sussing out what do we want the technology to do in the real world, not just sort of in a little bubble of technology. wow, it can do it. We should make it do it. No, we need to ask secondary questions of
Jason Gorman (05:41)
Mm-hmm. Yeah.
Louis NeJame (06:02)
Okay, great, it can do that. How do we wanna apply this? And so I am anxious because I see the markets all over the place right now, right? We’re trying to do that. There are other companies trying to be thoughtful about it, but there’s plenty that are just saying, great, the tech is there, we’re rushing in. And I think that ⁓ seems short-sighted to me.
Jason Gorman (06:21)
What is something in your own process, as I mentioned, you’ve had a few stops before this one, but something that you have done in the past that you like doing that you think you’re going to have to change because AI is just going to make you change it, but maybe you don’t want to change?
Louis NeJame (06:27)
Yeah.
Yeah, this is such a good question. I’m going to totally steal this for other people. ⁓ I started my career as an analyst. And ⁓ what that really means is I was taking a lot of notes and I did a lot of data analysis. That was really the role of an entry-level analyst. Note-taking, I’m totally good never doing that again. I’m so glad that Google Meets will do that for me. Take that off my plate. I don’t want to do notes. Data analysis, like,
Jason Gorman (06:42)
Good. Steal it.
Mm-hmm.
Louis NeJame (07:07)
I really built my early career on that skill being something I was good at. I enjoyed ⁓ Excel, SPSS, Stata, R. I loved that stuff. I still do love that stuff. And I love both the interpretation, but also ⁓ the manipulation of the data, whether it’s building a model or analyzing survey data or something like that. And at this point, just practically, I’ve almost entirely…
outsourced the base level ⁓ data analysis to cursor, Claude code, these agents, and I’ll have them write Python. And then I’ll just spend time interpreting. you know, from a speed perspective, it’s tremendous, right? I can do way more, way fast. And I can actually just get right to the actual, you know, interpretation and sort of implementation of whatever we decide. But I do miss it sometimes. Like there’s nostalgia for that.
Jason Gorman (07:44)
Mm-hmm.
Louis NeJame (08:05)
skill, like I did enjoy having my hands on the model. I think that does teach you something that you don’t get. You have a deeper level of understanding of, in that case, an Excel spreadsheet or an OSPSS file ⁓ that is hard to get when you are just sort of a layer abstracted away. So I think about that and I get a little bit nostalgic.
Jason Gorman (08:29)
I hear you. All right. Last lightning round question. ⁓ What is a book or a movie or a podcast or a piece of art, something that has nothing at all to do with your work, but actually shapes how you think at work?
Louis NeJame (08:39)
Yeah.
Yeah, there’s a book called, it’s called Finite and Infinite Games by James Kars. And it’s this, it’s weird, it’s weird book. Very peculiar. It’s not a novel. It’s not a business book. It reads almost like a, a game theory manual, almost. And it starts with this long list of definitions. It spends the first few chapters defining a game, a player, rules, judges, win conditions.
And what the point it makes is that it distinguishes between finite games, which are games to be played to win with fixed rules, fixed win conditions, fixed judges between that and finite or sorry, infinite games. And an infinite game is a game where the point of play is to continue play. And the point of play is that you can continue to play and you can change the rules and players can change and judges can change. more fluid.
Jason Gorman (09:23)
Hmm.
Huh.
Hmm.
Louis NeJame (09:41)
And you start reading the book thinking it’s like this weird economics book. And by the
Jason Gorman (09:46)
Yeah.
Louis NeJame (09:46)
end, think you really do realize, or I realize, like, this is a really profound philosophy book. ⁓ And, you know, it’s shaped how I approach our work because I have a tendency to drill down into, like, the finite game of, close this customer, ship this feature, beat this competitor, raise this round, like, these really narrow, you know, games. And I think it helps me
Jason Gorman (09:51)
Wow.
Yeah.
Louis NeJame (10:10)
pull out and say, actually the point of this is to keep building, it’s to keep learning, it’s to keep being able to iterate, right? And as long as that is true, then I’m still in this more infinite game. And I know that sounds fluffy, it is fluffy, but I think that that has really shaped how I try to remind myself to approach more.
Jason Gorman (10:15)
Hmm.
Okay.
I love that. That’s great. I’m going to read that book. I’m a big, big, big game player. And, ⁓ and I get, I get that. That’s, that’s awesome. All right. We’ll get into the other questions here. So I said a little bit at the top about your role and what you do, but ⁓ I’d love for you to go a little deeper. ⁓ Maybe just talk about your job, your company, and maybe what a week of work looks like for you.
Louis NeJame (10:35)
Yeah, I know it’s a deep cut. Yeah.
Yeah, so I’m a startup founder building an ed tech platform for higher ed. You know, we’re building software that, like you said, helps institutions and institutional leaders really transform how they manage online learning. So there’s all these operational tasks that we can help with with AI to have them, you know, be able to focus more on the meaningful stuff like what is the future of the pedagogy for this institution? What is the context of the learners? The things that folks are actually trained on, right?
So the types of things we’ve been helping with is accreditation, audits, course analysis, alignment to standards, skills mapping, ⁓ fast revision cycles on content that updates really quickly, compliance, these types of tasks that nobody’s really shouting from the rooftops about, but are chewing up meaningful amounts of time ⁓ from this really important band of folks in higher ed. So that’s what we’re doing. We’re nine months in. We’re a true startup.
every week is ⁓ chaotically different. My job right now at this stage is really, like I said, talking to customers and understanding what they need and then translating that to my great CTO, Brian, and iterating and getting their feedback on what’s working, what isn’t working, and how do we improve as fast as we can. And then of course, there’s all the other fun of contracts and lawyers and taxes and
Jason Gorman (11:59)
You
Louis NeJame (12:26)
marketing and all the many other functions,
Jason Gorman (12:27)
you
Louis NeJame (12:28)
but the primary function is really around ⁓ talking to customers and building something that really solves this set of problems.
Jason Gorman (12:36)
wonderful. You mentioned, or I guess I mentioned, but I mentioned you work at Titan Partners. You said that you started as an analyst. I assume you are an analyst at Titan Partners. ⁓ I imagine your work there was pretty different from what you’re doing now, and I’m kind curious to know what mindset or skill or process, what’s something that you picked up that was vital to what you did?
Louis NeJame (12:46)
Yes, yes.
Yeah.
Jason Gorman (13:04)
then but is still vital to your work now.
Louis NeJame (13:08)
A lot. ⁓ Titan is an incredible place that I have a deep amount of gratitude to that team for the opportunity and what I’ve learned from them. you for context, Titan is a strategy consulting firm focused exclusively on the education markets. ⁓ It was an incredible place to start my career and kind of grow for the first three or four years. ⁓ you know, I’d highlight a couple of things that I really took away from the experience. The first is a work ethic.
and pride in your craft. I know that maybe is simple, but like they worked hard, we worked hard and cared deeply about getting the details right. And there is no substitute for that. And I take the same thing into my work now. I mentioned going down to the click level. Like it doesn’t have to be kind of right. It has to be exactly right. And that matters. That level of precision and work ethic does, I think, really matter. So I certainly learned that early.
I think there’s also like the analytical rigor and expertise in the education markets is extremely helpful, right? Like I was deep in the numbers on very sort of esoteric data sets, know, NCES, iPads, ⁓ Lightcast, BLS, these like super education specific data sets that like I got to really learn. We got to do research on education. So it gave me both a good view of the market, but also I’m an analytical rigor that.
Jason Gorman (14:22)
Mm-hmm.
Louis NeJame (14:31)
It can be hard to replicate without being at that sort of over advisory layer. And then, again, it sounds a little ⁓ wooey, but the relationships and the way they operated, the culture, I want to replicate with Bevel. They were kind, they were supportive, and we had a lot of fun. And I need the same culture with the company we’re building. And I really am replicating a lot after that. So I learned an immense amount from Titan.
Jason Gorman (14:47)
Hmm.
We’re all about wooey here on this podcast. There is something you said at the beginning of that answer that ⁓ kind of sparked the thought for me, which ⁓ you talked about work ethic in that you learned a work ethic. And that is the kind of thing that I think people talk about learning at home, right? From their parents or from their community.
Louis NeJame (15:02)
Yeah, I should stop apologizing for it. I’m just winging it, guess, I think.
Jason Gorman (15:29)
And certainly on the job. I’ve certainly been in jobs where I have learned to work in a more rigorous way. I kind of wonder, and this little small rabbit hole we’re gonna go down here for a second, I kind of wonder, people haven’t said this, but I kind of wonder if work ethic is one of these things that AI, we’re at risk of AI chipping away for us. Because I don’t know if you feel this way, but sometimes if I find myself maybe in a little bit of a lazier mindset,
Louis NeJame (15:34)
Yeah.
Yo.
Jason Gorman (15:59)
I can go to AI and expect it to just give me the thing that I want without putting in the hard work of thinking it through and grinding through it and being very critical of what it says back to me. I’m curious to get your reflection on that given what you do.
Louis NeJame (16:16)
No, I think it connects to what I was saying about data analysis, right? Like there is, there’s a mindset, I think that the, you know, all in on AI sort of argument makes, which is that learning is like, you know, you have a USB drive and you need to plug it in your brain. And once we download the stuff that it’s good. And so if I can just ask, you know, GPT or perplexity or whatever, and I get the information, then I’ve done, I’ve got the information, right? ⁓ where.
Jason Gorman (16:19)
Mm-hmm.
Yeah.
Louis NeJame (16:43)
In reality, that works for some maybe lower levels of Bloom. Like there are some things that do work that way. Most is actually probably more on the side of like the process is part of the experience. so finding an answer that you think is right and then learning why it’s wrong is part, you know, having a smart partner tell you like, good try, Louis. No, the actual answer is probably in this direction. And then you go off and you find it yourself. Like.
Jason Gorman (16:47)
Yeah.
Louis NeJame (17:10)
That type of fast iteration with actual experts is what builds instinct. And ⁓ we could go down philosophical, what is knowledge versus what is known, and all these kind of definitional stuff. But I think there’s a deeper level of understanding you get when you’ve experimented with the wrong answers. And that all takes, work ethic, but also, B, not getting lazy and passing it to AI. think that. ⁓
Jason Gorman (17:14)
Mm-hmm. Yeah.
Louis NeJame (17:39)
think there’s something out there for sure.
Jason Gorman (17:42)
That’s great. I had a teacher, Latin teacher. I was hopeless, a hopeless Latin student in high school. And I really liked this guy, but I really did not enjoy the class. And his name was Dr. Ganong, one of my favorite, favorite teachers ever. And I remember Dr. Ganong saying to me multiple times over the course of our work together, I wish, you know, look, if it was as easy as me just cracking open your head and dumping Latin into it, I would do that. But
Louis NeJame (17:47)
Nice.
Jason Gorman (18:12)
Unfortunately, now we just need to put in the work. ⁓
Louis NeJame (18:14)
Exactly. I think that’s exactly right. And I think that the AI approach
can, at its worst, that is what it’s doing at its best. It can also be a way to facilitate the good work, the productive struggle, but it can be used in many ways.
Jason Gorman (18:22)
Yeah. Definitely.
definitely There was a question that I I have asked other guests on this podcast I’m gonna ask you the quest this question, but I’m gonna ask it to you twice and I want you to answer a little differently each time. So the question here’s a question the question is The world is changing fast for learning professionals. This means that how we work ⁓ also means that That how we work also needs to change quickly
Louis NeJame (18:43)
Okay.
Jason Gorman (19:01)
What do you believe are the implications for how learning professionals operate? And so the first time I want you to answer, I’d love for you to answer as it relates to your work at Bevel, because I know you were really honed in on this question. The second answer is a little different. Let’s start with that.
Louis NeJame (19:13)
Yeah.
Yeah, we’ll start very tactical, right? Like, I think we’re already seeing the work of instructional design is shifting, at least in high speaking, in higher ed. The work is shifting dramatically away from a lot of the tedium and kind of detailed execution towards more oversight of these agents we’re building. And then really truly the hardest part of the job, which is, has always been the hardest part of the job, which is change management, ⁓ internal politics.
Jason Gorman (19:43)
Yeah. Okay.
Louis NeJame (19:45)
⁓ navigating uncertainty, right? I think of instructional designers I’ve talked to in the many interviews and customers we’re working with now. building the course alignment matrix
was never the hardest part. It was one of the more time-consuming parts. The hardest part was getting the faculty member who’s now going through this scary accreditation review and they’ve had a course they teach the same way for 20 years to like…
Jason Gorman (20:04)
Hmm.
Louis NeJame (20:12)
listen to you and understand that this is okay, that we’re going to change some things and it’s not approaching on their expertise or whatnot. And so that to me is going to become more and more of the job, which is a bottleneck that, you know, I certainly, our technology certainly can’t solve and we don’t think other technologies can solve because they’re fundamentally very human relational challenges. And so we think that more and more and we’re seeing more and more of the job will shift to be away from changing the HTML on page 64 so that it’s
Jason Gorman (20:19)
Yeah.
Mm-hmm.
Louis NeJame (20:41)
It’s readable by a screen reader, like the AI does that, which is great. It’s exciting. And then it’s going to be more about working with the faculty to coach them through change.
Jason Gorman (20:43)
Mm-hmm.
The last person I spoke with on this podcast, we had a really great conversation about the relational nature of the work. And I feel like this is just something that’s echoed again and again in these conversations that at the end of the day, the work is really about people. It’s about trust building.
Louis NeJame (21:12)
Yes.
Yep.
Jason Gorman (21:14)
So I’m glad you said that, you know, as a technologist for some, and you’re clearly empathizing deeply with the people you’re talking to because I can say as somebody who’s done this work for long time, these are the things that people get stuck on ⁓ over and over again. ⁓ So I’m gonna ask the question again and I want you to answer differently. So the way that I want you to answer the second time is, and I’ll restate the question a second, the second time is,
Louis NeJame (21:29)
Yeah. Yeah.
Jason Gorman (21:40)
Setting aside the challenges that you’re solving for at Beville ⁓ Love to hear your answer again. So the question again is the world is changing fast for learning professionals This means that how we do the work also needs to change fast What do you believe are the implications for how how learning professionals operate?
Louis NeJame (21:44)
Yeah.
Yeah, it’s interesting to take the sort of what we’re doing hat off and answer it more zoomed out. ⁓ You know, obviously, we’re in a great period of change, ⁓ much more than just education system. And there’s totally warranted fear. At the same time, as somebody that’s like deep in the kind of technical stuff of how this technology is evolving and working, and Brian and I are building these, you kind of
Jason Gorman (22:07)
Yeah. Mm-hmm.
Louis NeJame (22:29)
sophisticated agent models. It doesn’t always seem clear to me that people are panicked about the right things. Sometimes they’re panicked about the wrong things. And then that erodes a lot of the productivity of the discourse. It means that we can’t have good conversations unless people have at least a baseline knowledge of what this technology is. What is a token? You don’t have to do machine learning. You just have to understand, does the model know anything?
Jason Gorman (22:36)
Hmm.
Mm. Mm.
Louis NeJame (22:55)
kind of, we should talk about it, right? Like these are questions that before we jump to, ⁓ you know, lot of the scarier stuff and the downstream stuff, which is important to talk about as well, I think that there just needs to be more baseline education so that we can have a sober conversation. And so I think that like getting curious and playing with this technology and breaking it and understanding why it broke and experimenting, like all things that you would, you know,
pedal in an inquiry-based learning, if you’re learning about inquiry-based learning, right, or experiential learning. Like, I think the same things apply with this technology. It’s not intuitive. ⁓ It should be experimented with. And ⁓ I think that there’s a lot of folks out there that are making policy decisions or making decisions for their business or their institution that haven’t done that work, and it shows. So more play, more experiential learning, and then we can have these big conversations. And the through line is that many things about our lives and our
jobs are changing fast and we need to be adaptable to it and we need to have that information.
Jason Gorman (24:00)
It feels hard to put our finger on any part of the…
of the work ⁓ that isn’t somehow impacted, even in places where, ⁓ even in the relational pieces, ⁓ even if we’re not using AI in the relational pieces, I think the assumption has to be is that
it’s really possible that if I’m working with a professor, that that professor could have an instructional design bot that they’ve set up on their computer and they’re kind of checking our conversations against instructional design bot. So there’s these indirect things too. ⁓ It’s quite mind bending when you really allow yourself to think about it enough. we talked just on this relational. ⁓
Louis NeJame (24:39)
Yeah.
If, if…
Jason Gorman (24:54)
on this relational piece. One of the things that you mentioned just now, I think, was change management and that this with the additional time that instructional designers have, learning professionals have, because they’re not doing these other operational tasks, that they will have more time to do this kind of bigger, more holistic, more directional, more strategic work. I’m curious, though, in your conversations, what evidence do you have
either from those conversations or from other places, that convinces you that the time that people save actually translates into better learning rather than just like other, you know, more meetings or other kinds of administrative work or more audits or just fills up with more of the stuff that we’re trying to weed out.
Louis NeJame (25:45)
Yeah, that’s a great question. ⁓ You know, we’re early. And so I don’t have ⁓ randomized controlled studies to show exactly. We want to get there, exactly prove it with evidence. I’ll tell you about the sort of what sparked it, a conversation that sparked it, and then early findings from our actual work with customers. So ⁓ what sparked it was I remember very vividly, I was speaking with an instructional designer at a large online institution. She was at
Jason Gorman (25:52)
Mm-hmm.
Yeah.
Louis NeJame (26:14)
very energized about learning theory. had a PhD in, I think, educational psychology or something like that. And she was telling me for 15, 20 minutes on the call, I’m not even sure how we got on the topic, ⁓ the sort of topic of her PhD thesis and this very interesting learning theory that she had kind of helped push the field forward in meaningful ways. And I think I at some point transitioned to, great, how are you playing it out? You’re in a great role to impact this
Jason Gorman (26:19)
Hmm.
Louis NeJame (26:43)
beliefs around education and how it should be on the actual ⁓ online learning higher ed market. How have you been, how’s it been going? And she goes, I don’t get to actually do this stuff. I just get to kind of, that was like my PhD program, right? And I was like, what do you spend your time on? Right. And it was more like, you know, accounting team needs all of the tables in the accounting program to be formatted this particular way.
I need to make sure that the deferred revenue is always bolded in this certain situation, and it’s not bolded in another scenario, because that mimics how they do it in real accounting. And I’ve spent the last two weeks going through course by course, highlighting, making sure they’re bold. And my heart sank. That’s a misallocation of talent. And the fact that I can use Claude Code at the same time to spin up, maybe not production grade yet, but surely working software.
Jason Gorman (27:11)
Yeah.
Louis NeJame (27:40)
In minutes and yet we have very smart people like this woman I was speaking with Spending their time doing that it feels like there’s a there’s an unlock is that if we can unlock that you worker and their expertise to focus on what she wrote her PhD about instead of What she’s doing right now, which is you know, not entirely that but but that’s a hyperbolic example But a lot of that type of work
Jason Gorman (27:41)
Mm-hmm.
Louis NeJame (28:09)
we’re excited and we think that sort of instructional designers, at least in higher ed, are this like sort of latent superhero class that is going to be unlocked as we get better with this technology. So that was really what sparked it. ⁓ Early evidence we’ve seen with institutions we’re working with, there’s certainly, of course, know, fear of like, well, I used to do this task. Now you’re doing it with your technology at a scale we couldn’t even really comprehend.
⁓ what is left for me. And what’s been promising is that so far it’s been kind of the opposite. So like one institution we work at, ⁓ they have to do 2000 course audits for their accreditation review. So they have to review all these online learning, all these online courses in Canvas, and they have to match them to a quality framework. This was work that the eight person instructional design team was doing. Right? Exactly, exactly.
Jason Gorman (28:42)
Hmm.
On the weekends and at midnight Yeah
Louis NeJame (29:04)
It’s very tedious work. Is there
an instructor welcome video? Yes or no. You got to go to the welcome page if there is one and check. That’s not work humans should be doing. And if there’s people that really want to defend that type of work, then send them my way. But we have it do 2,000 reports in 90 minutes.
then they share these reports with a lot of the faculty, the instructional designers that built them. And what they’ve seen is the demand for meetings with the instructional design team has gone through the roof because we’ve actually highlighted all the areas that courses needed work, right? Suddenly everybody’s aware of the fact that half their course is not accessible by WCAG 2.2 and they’re like, how do I fix this? And who do they go to? They go to the folks with the expertise. Suddenly they realize that, you know,
Jason Gorman (29:41)
Hmm.
Louis NeJame (29:57)
it flagged that there was content that was in the learning objectives that wasn’t properly covered in the courses. no, who do I turn to? I turn to my instructional design team. So I don’t think it’s always gonna work that cleanly, but I think that’s an example of when we free that as the bottleneck, the paperwork step as the bottleneck, we suddenly have more time and more demand for a lot of the, again, relational.
relational work and real expertise, right? So maybe some of that over time does shift to AI and that is something we should talk about. But I do think that at least in these early tests we’ve seen.
Jason Gorman (30:24)
Yeah.
So what you’re talking about, ⁓ one way to think about it, that’s really like a form of return on investment. And certainly anybody who’s buying a piece of software to do a job in their work, they really want to know the answer to that question. This is something that is somewhat hard to answer often for people who do the work that we do, because if you’re saying the ROI is
Is learning, that’s tough, right? That’s a difficult thing. You’re focused on something a little different though. You’re really focused on time and effort. what did you say about this PhD student, a misallocation of talent? I love that. So when you talk to clients about the ROI or when you think about how you can demonstrate how you’re measuring ROI or success, how do you frame that?
Louis NeJame (31:07)
It’s very tough to sell.
Yeah, it’s a great question. And it’s honestly different than I think normal software. think that this, these agents are different, right? Like is, what is chat GPT? Is it an email writer? No, no, it does a lot of things. And the point is the flexibility. And so how do you justify the ROI of chat GPT? It’s like, well, what are you using it for? Right. And that’s a, that’s been a challenge and an opportunity for us has been like, you know, oftentimes the early parts of conversations with customers is me just listening and being like, you know,
Jason Gorman (31:40)
Hmm.
you
Yeah, yeah.
Louis NeJame (32:04)
I’m going to kind of onboard you and you’re going to say, oh, accreditation is very painful. We really need that help. And then for them, we focus there for the first month or so of the engagement. And then all of a sudden they start discovering the bigger use cases. So it really varies, right? Like we’re having some institutions do giant skills mapping assessments where they map every single one of their programs to different, you know, licensure requirements or BLS job codes. That there’s a ton of ROI there, right? We’re having a lot to kind of these baseline course quality assessments, right?
⁓ And that can be something that their creditor requires, but they just want to do. And there’s ROI there because of course the quality of the average course goes up sort of at a scale that they couldn’t do before. And ideally the product is better and then better retention and better tuition. ⁓ I think that for me though, those are all like solid. Those will help deals move fast. They’re very practical. Longer term though, I’m really excited about like, how can we start to enable
Jason Gorman (32:45)
Yeah.
Louis NeJame (33:04)
institutions and designers to do things that they genuinely just couldn’t do before. And I don’t always know what those will be until the instructional designer tells me something and it like blows my mind. Right. And so like we have one institution who said, Hey, we want to use this to do all this tagging to skills and also learning objectives. And then we’re going to create like a modular curriculum so that students could weave through the curriculum in different ways. And that’s really cool. I didn’t think of that. That’s not my idea.
Jason Gorman (33:11)
Hmm.
That’s cool.
Louis NeJame (33:32)
I’m really excited about that, but we really think we’re building and enabling technology to say that suddenly all these ideas that we have, and there’s many of them and there’s no one best one, but there’s a lot of energy out there to change right now. And we think we want to be a part of that, a part of that motion.
Jason Gorman (33:49)
I
things that you said kind of buried in there ⁓ you said that you map you said a lot of amazing stuff but I just one thing just really caught my attention because
The ROI of the product on the efficiency of creating courses is one thing. And then there’s this kind of other ROI question that I would say ⁓ instructional designers, learning experience designers have pitched at us all the time, which is really, really hard to answer, which is, how do know this course is effective? How do we know that people are really learning? How do we know this is really helping people?
And if what you’re saying is one of the things that you can do, for instance, is map the alignment between the course and the job market, I mean, that is such a specific, concrete outcome of doing the learning, right, for a student. And that is, that’s all of what you said was exciting. I’m pointing that out in particular because that really gets to the value of the learning itself, which I think is so slippery.
Louis NeJame (34:34)
Yes.
Yes.
It is slippery and more and more institutions right now are trying to either launch new programs. see that oftentimes as they say, okay, well, we’ll just launch more programs and they build them from the ground up with, you know, with these, you know, kind of competency based learning or whatever the model is, which is great. I’m all for that. And we want to enable that as well. But what about your X number of thousands of courses that you have already got sitting in your Blackboard or Canvas or D2L, right? Like those courses have
Jason Gorman (34:58)
.
Yeah.
Yeah. Yeah.
Louis NeJame (35:25)
great rich content in them and also experts in the form of faculty and SMEs tied to them that know how to teach in that particular course, that particular content. And we need to bring that along to be competency-based as well. And we think that AI is exceptionally well ⁓ positioned as a technology, back to understanding what this technology is at reading really detailed large volumes of text and video and images.
and then translating that into something like, you we’ve had folks do it with Bureau of Labor Statistics, like really formal job, ⁓ you know, sort of frameworks and taxonomies. We’ve also had folks say, hey, I just have a PDF from PWC or from EY on like what they’re kind of looking for. And it’s very qualitative. I’m going to put that in and have it map against that. And so the model can handle this really, really flexible range.
Jason Gorman (36:12)
.
That’s awesome.
Louis NeJame (36:22)
And I’m excited to try and again enable that. I don’t think that’s right everywhere. I’m not trying to peddle any given pedagogy. think education is way too complicated to say that this is the one, but that is a big popular one that’s obviously growing and we’re excited to kind of support.
Jason Gorman (36:37)
I that. One of the things that’s so challenging as a designer is you have an architecture in your head. And then once you get into the details, and it’s like Tuesday, and maybe you’re not feeling so good, and you have to sit down and make the module. what did I? Well, I know we had a conversation about this three and a half weeks ago. I kind of remember that conversation. I’m just going to do my best. I mean, look, we’re humans, right? But it sounds like what you’re describing is that the bigger picture architect
Louis NeJame (36:48)
It’s a
Exactly.
Exactly. Yes.
Jason Gorman (37:05)
architecture is always easy to reference and go back to and you make a thing and then you are able to say, all right, if we compare this to what the bigger picture ideas and design is, we can actually validate whether or not we’re on the right path.
Louis NeJame (37:17)
Yes.
Yeah, and making that process not like, you know, in the case of accreditation, every four to five years, we do this giant, like, summative assessment. That’s what it is, right? And instead, we make it more formative, where I made some tweaks to this course, and I recheck it against whatever our quality standards are. you know, we have a belief about, you know, this program leads to this exact job. We want to add new content. We can always be constantly checking, because the cost of a check, the cost of a quality check has gone to
Jason Gorman (37:29)
Yeah.
Louis NeJame (37:50)
close to zero. So yeah, exactly right. We can have folks focusing more on the strategy of the larger curriculum than caught up in the execution.
Jason Gorman (38:00)
You’re ⁓ clearly focused on the short to medium term. Everything that we’ve been talking about are things that you’re doing, things that organizations are already using on their existing courses. ⁓ So I am curious to hear from you what your predictions are, not for the short or medium term, but for the long term. Where do you think?
Louis NeJame (38:25)
Hmm.
Jason Gorman (38:28)
things will change in our field.
Louis NeJame (38:31)
Yeah, you put me in a bad spot. Predicting is the worst. Predicting is hard, ⁓ especially long term predictions are hard. I will be. I will. Yeah, we can reference this in 10 years and see how right it was. ⁓ I if I just step back and think about like, where’s higher ed headed, right? And I think that can can signal where learning designers within the system are going. ⁓ I think pretty first principles about
Jason Gorman (38:35)
Nobody’s going to hold you to this. I promise. I promise.
That’s right.
Mm-hmm.
Louis NeJame (39:01)
why does a student go to college? And when we ask students that, when you look at the survey data, there’s many reasons to go to college. I fully understand that. The reason people do practically go that they cite most often is to get a job and to better their career prospects. And I am very closely monitoring the impacts of AI on the particular, the entry-level white-collar ⁓ job market.
And I suspect that that will be a very defining force in how higher ed as a system, because they’re already so deeply linked. ⁓ I think that as that market becomes incredibly turbulent, it already is, but I think we’re headed in a scary and turbulent direction. ⁓ How does that impact higher ed? And so it does put weight behind things like shorter form.
credentials and competency-based learning and also a shift in the type of skills that we’re teaching, A way, leaning away from the more technical skills, leaning more towards the, you know, durable skills around critical thinking, around adaptability, experiential learning. I think the ways in which we teach are changing that, you know, no longer in your business class, are you writing a business plan? No, you’re just, creating an app and getting users, right? Like that’s the, that’s the project now, right?
So the bar for that kind of thing is going up. So I’m throwing a lot in there, the most, ⁓ the biggest trend that I see is really how this is going to ripple through the job market it already is and how that’ll impact higher ed. And then the downstream is what does that mean for instructional designers? I really think it means that, ⁓ like I said, I think they’re really well positioned in some way to be a change agent because the change is going to come whether or not we like it.
⁓ And there’s going to be a lot of resistance. There’s going to be a lot of just, need the context of this institution, this set of learners, right? There’s not going to be a fix all. And we need smart people that are technologically sort of savvy and also really deeply embedded in learning theory to help make sense of it and help guide an institution to whatever is on the other side of this disruption. So I avoided being too specific so I can’t be held to it, but that’s my best bet.
Jason Gorman (41:17)
It’s a good bet and I appreciate it. And actually, I did not plan it this way, but the last question is always a question from the last guest. My last guest was Donald Taylor. Donald Taylor is UK-based. He’s a researcher and author of the LND Global Sentiment Survey. So what he does every year is he surveys about 3,500, 4,000 people-ish.
globally on what they are thinking about most for next year in learning and development. So what is most top of mind? What do they think is going to be hot? Because actually his word is hot. What’s going to be hot next year in L &D? And so ⁓ very interesting. He’s been doing this for 12 years. so kind of longitudinally, it tells a very interesting story about how we think about where we’re going. And then we get to see what happens. And then we get to see how we
Louis NeJame (41:57)
Yeah.
Jason Gorman (42:14)
how we think next year changes. It’s a very elegantly simple survey, but it yields a lot of very fascinating insights. So AI, no surprise, has been at the top of the heap the last couple of years. It shot way, way up in 24, I think it was. It stayed up there on 25. His latest was just released a couple of weeks ago. And it’s still there, but he’s starting to see a small shift downward.
as it becomes more of like a utility ⁓ type of thing, right? That isn’t just a hot thing anymore. It now becomes like Microsoft’s Word, which none of us could live without docs or Word, but it’s never gonna land on our list. anyway, that’s just context about the survey and Donald’s work, but his question, which is appropriate, because this is really what he’s asking the survey, do, and this is question for you.
Louis NeJame (42:51)
Yes. Exactly.
Jason Gorman (43:11)
Do you feel ready for the future?
Louis NeJame (43:15)
Mixed, right? Like, kind of, somewhat. But I’ll answer yes and no, and I’ll tell you what part of me feels each way, right? ⁓ On one hand, like, I’m an optimist, right? I believe that we can do stuff to make the world better. And this is the most profound technology I’ve ever encountered in my lifetime. you know, change is an opportunity. And way before AI,
Jason Gorman (43:30)
Hmm.
Louis NeJame (43:43)
there were plenty of challenges that we wanted to uproot, that there wasn’t maybe the momentum that we could change. And I’m just so energized by like how much is happening and how much is being uprooted and re-questioned. And I think that there’s opportunity to do a lot of good there. And I sure hope that what we’re doing with Bevel is in that camp of supporting, you know, high quality use cases that are really deeply tied to like what the actual user wants their future and their job and their work to be like.
so they can do more of the good stuff and less of the tedium, right? At the same time, like, I’m nervous too, right? I’m particularly worried about the pace of change compared to previous technological revolutions. You know, I think that this is moving way faster. I’m not the first, it’s not my idea, certainly. And I also think that the metabolism or the systems in society that we have
Jason Gorman (44:18)
Yeah.
Louis NeJame (44:41)
that are meant to be adaptive are not able to keep up with this technology. Like it’s moving at a speed that we really haven’t dealt with. And I’m very nervous about a lot of the effects of that. And so, you know, it’s a mix. It’s a mix. Like I sometimes am just so energized about the future and I think this is amazing. And when I’m deep in our work or deep in the technical stuff, I just get a lot of joy and excitement from it.
And I think about all the futures that we could have with this tech. And then at the same time, I look out into the world and see how our institutions are handling some of these things. And it makes me nervous.
Jason Gorman (45:20)
appreciate your honesty.
Louis NeJame (45:22)
Of course. Yeah, no, no. mean, it would be dishonest to say that we were like, yeah, this is all going to be ⁓ rosy, right? We’re trying to make sure that what we’re doing is really of a high ethical quality, but there’s certainly plenty to be worried about.
Jason Gorman (45:37)
Well, I really appreciate this conversation. I appreciate you coming on, sharing what you’re doing with Bevel, talking about your thoughts on what feels like a very optimistic ⁓ vision for the future of instructional design, which is very welcome on this podcast. So Louis, thank you so much for joining us today.
Louis NeJame (45:54)
Of course.
Thank you for having me.