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These questions get progressively more specific. What are the learning objectives for this lesson? How do you know when students have achieved those objectives? What are some common misconceptions they have?

Eventually, the learning designer has built a clear enough mental model that they can draft a useful design document of some form or other.

The UI reinforces the idea of pattern matching and completion. The Output Template gives the AI the structure of the document it is trying to complete by the end of the chat. The General Instructions and Steps work together to define the interview pattern the system should imitate as it tries to complete the document.

Armed with the lesson and scaffolded by the template, participants got better over time at understanding how to think about asking the AI to do what they wanted it to do.

Using AI to improve AI

One of the biggest breakthroughs came with the release of a feature near the very end of the workshop series. It’s the “Improve” button at the bottom of the Template page.

When the user clicks on that button, it sends whatever is in the template to ChatGPT. It also sends any notes the user enters, along with some behind-the-scenes information about how ALDA templates are structured.

Template creators can start with a simple sentence or two in the General Instructions. Think of it as a starting prompt, e.g., “A learning design interview template for designing and drafting a project-based learning exercise.” The user can then tell “Improve” to create a full template based on that prompt. Because ALDA tells ChatGPT what a complete template looks like, the AI returns a full draft of all the fields ALDA needs to create a template. The user can then test that template and go back to the Improve window to ask for the AI to improve the template’s behavior or extend its functionality.

Building this cycle into the process created a massive jump in usage and creativity among the participants who used it. I started seeing more and more varied templates pop up quickly. User satisfaction also improved significantly.

So…what is it good for?

The usage patterns turned out to be very interesting. Keep in mind that this is a highly unscientific review; while I would have liked to conduct a study or even a well-designed survey, the realities of building this on the fly as a solo operator managing outsourced developers limited me to anecdata for this round.

The observations from the learning designers from large, well-orchestrated teams seem to line up with my theory that the big task will be to break down our design processes into chunks that are friendly to AI support. I don’t see a short-term scenario in which we can outsource all learning design—or replace it—with AI. (By the way, “air gapping” the AI, by which I mean conducting an experiment in which nothing the AI produced would reach students without human review, substantially reduced anxieties about AI and improved educators’ willingness to experiment and explore the boundaries.)

For the individual instructors, particularly in institutions with few or no learning designers, I was pleasantly surprised to discover how useful ALDA proved to be in the middle of the term and afterward. We tend to think about learning design as a pre-flight activity. The reality is that educators are constantly adjusting their courses on the fly and spending time at the end to tweak aspects that didn’t work the way they liked. I also noticed that educators seemed interested in using AI to make it safer for them to try newer, more challenging pedagogical experiments like project-based learning or AI-enabled teaching exercises if they had ALDA as a thought partner that could both accelerate the planning and bring in some additional expertise. I don’t know how much of this can be attributed to the pure speed of the AI-enabled template improvement loop and how much the holistic experience helped them feel they understood and had control over ALDA in a way that other tools may not offer them.

Possible futures for ALDA under 1EdTech

As for what comes next, nothing has been decided yet. I haven’t been blogging much lately because I’ve been intensely focused on helping the 1EdTech team think more holistically about the many things the organization does and many more that we could do. ALDA is a piece of that puzzle. We’re still putting the pieces in place to determine where ALDA fits in.

I’ll make a general remark about 1EdTech before exploring specific possible futures for ALDA. Historically, 1EdTech has solved problems that many of you don’t (and shouldn’t) know you could have. When your students magically appear in your LMS and you don’t have to think about how your roster got there, that was because of us. When you switch LMSs, and your students still magically appear, that’s 1EdTech. When you add one of the million billion learning applications to your LMS, that was us too. Most of those applications probably wouldn’t exist if we hadn’t made it easy for them to integrate with any LMS. In fact, the EdTech ecosystem as we know it wouldn’t exist. However much you may justifiably complain about the challenges of EdTech apps that don’t work well with each other, without 1EdTech, they mostly wouldn’t work with each other at all. A lot of EdTech apps simply wouldn’t exist for that reason.

Still. That’s not nearly enough. Getting tech out of your way is good. But it’s not good enough. We need to identify real, direct educational problems and help to make them easier and more affordable to solve. We must make it possible for educators to keep up with changing technology in a changing world. ALDA could play several roles in that work.

First, it could continue to function as a literacy teaching tool for educators. The ALDA workshops covered important aspects of understanding AI that I’ve not seen other efforts cover. We can’t know how we want AI to work in education without educators who understand and are experimenting with AI. I will be exploring with ALDA participants, 1EdTech members, and others whether there is the interest and funding we need to continue this aspect of the work. We could wrap some more structured analysis around future workshops to find out what the educators are learning and what we can learn from them.

Speaking of which, ALDA can continue to function as an experimental apparatus. Learning design is a process that is largely dark to us. It happens in interviews and word processor documents on individual hard drives. If we don’t know where people need the help—and if they don’t know either—then we’re stuck. Product developers and innovators can’t design AI-enabled products to solve problems they don’t understand.

Finally, we can learn the aspects of learning design—and teaching—that need to be taught to AI because the knowledge it needs isn’t written down in a form that’s accessible to it. As educators, we learn a lot of structure in the course of teaching that often isn’t written down and certainly isn’t formalized in most EdTech product data structures. How and when to probe for a misconception. What to do if we find one. How to give a hint or feedback if we want to get the student on track without giving away the answer. Whether you want your AI to be helping the educator or working directly with the student—which is not really an either/or question—we need AI to better understand how we teach and learn if we want it to get better at helping us with those tasks. Some of the learning design structures we need are related to deep aspects of how human brains work. Other structures evolve much more quickly, such as moving to skills-based learning. Many of these structures should be wired deep into our EdTech so you don’t have to think or worry about them. EdTech products should support them automatically. Something like ALDA could be an ongoing laboratory in which we test how educators design learning interventions, how those processes co-evolve with AI over time, and where feeding the AI evidence-based learning design structure could make it more helpful.

The first incarnation ALDA was meant to be an experiment in the entrepreneurial sense. I wanted to find out what people would find useful. It’s ready to become something else. And it’s now at a home where it can evolve. The most important question about ALDA hasn’t changed all that much:

What would you find ALDA at 1EdTech useful for?