A Field on the Brink of Disruption?

As I mentioned on LinkedIn, earlier this week Synthesia published the results of a global survey that we ran together the state of instructional design in 2024.

Having gathered a tonne of fascinating data from almost 500 people who design learning experiences, in this week’s post I’d like to share my take on what we learned, and what the data means both for the industry and for us instructional designers on the ground.

Here’s a teaser…

🎯 Almost one third of instructional designers are actually subject matter experts who design courses.

🤖 84% of instructional designers have tried ChatGPT, 57% use it daily – but this is yet to significantly impact productivity.

💼 Most IDs juggle 2-4 projects at once, with 38% turning down work due to capacity constraints.

🛠️ Traditional tools like Articulate still dominate, but IDs are frustrated with what some describe as “outdated tooling.”

💡 The biggest opportunity & challenge in the coming year is using AI to reduce the 37.7% of our time that we still spend on development tasks without compromising the quality of our work.

Let’s dive in!
Phil 🚀

The 2024 State of Instructional Design Survey reveals a window into an industry and profession which is potentially on the brink of disruption and transformation.

The disruption of an industry typically occurs when three conditions align:

1. Democratisation of previously specialised skills

2. The emergence of new enabling technologies

3. Growing frustration with status quo tools and processes

We’ve seen this pattern before, e.g. when website builders like Squarespace disrupted web design and when tools like Canva disrupted graphic design.

In each case, frustrations with slow, inefficient and unscalable processes collided with the rise of new technologies which made specialised skills more accessible. The result? Skills were democratised and professionals evolved into consultants, strategists and architects of more complex solutions.

In each case, frustrations with slow, inefficient and unscalable processes collided with the rise of new technologies which made specialised skills more accessible. The result? Skills were democratised and professionals evolved into consultants, strategists and architects of more complex solutions.

The findings from the survey suggest that the field of instructional design might be entering a similar phase of “disruptive professionalisation”. Our survey reveals that nearly a third of practitioners are subject matter experts who’ve added design to their toolkit, while 84% of respondents have adopted AI tools that automate core tasks.

Meanwhile, traditional “outdated” incumbent tools face growing criticism, not least because 38% of instructional designers still turn down work due in part to a lack of appropriate tooling.

With AI use among instructional designers dramatically rising, we appear to be entering a perfect storm and a potentially critical juncture for the industry and the people who work in it.

Let’s dive in to what I think are the four key findings from the survey:

One of the most striking findings from the survey is the growing role of Subject Matter Experts (SMEs) in instructional design. Nearly one-third of respondents identified as SMEs who actively design courses. ​

This trend aligns with broader patterns seen in other industries like those mentioned above, where advancements in tools and technology enable domain experts to take on specialised roles traditionally managed by others.

Much like how marketing teams now use platforms like Canva to independently create visual assets, SMEs are increasingly leveraging modern instructional design tools to produce effective learning experiences.

As one SME-respondent commented, “With AI I am able to create content from scratch and no longer rely on IDs. Their workload has decreased significantly, while the productivity of my department has improved.“

Our data reveals a telling story about where instructional designers spend their time and resources. The average project cost breaks down as:

The fact that nearly 38% of time and budget goes to development and implementation suggests this is where the greatest opportunity for innovation and automation lies.

While established tools like Articulate Rise & Storyline remain dominant, there’s growing recognition that current solutions may not fully serve evolving needs.

Many respondents shared the sentiment of this designer who commented that ,”our tools are outdated; they need to be improved – and be more cohesive.”

Despite the rise of a new wave of task-specific tools (e.g. text to video, text to quiz), there seems to be an appetite for more ambitious tools which increase the productivity and effectiveness of the end process (analysis, design, development, evaluation) rather than discrete tasks.

Which leads us nicely to my next observation…

While the adoption of generic AI is impressively high – with 84% trying ChatGPT and 57% using it daily – user satisfaction and the impact on the productivity of instructional designers remains unclear.

While the adoption of generic AI is impressively high – with 84% trying ChatGPT and 57% using it daily – user satisfaction and the impact on the productivity of instructional designers remains unclear.

Results from the survey show that the average instructional designer still completes around 16 projects annually, with 38% of us having to turn down work due to capacity constraints. This suggests that while AI is changing how we work, it hasn’t yet transformed how much we can deliver.

As one respondent noted: “AI has helped to build on ideas or frame them in a way that sparks new thoughts, but generating good-enough content with AI remains a huge challenge.”

As my research has shown, general-purpose AI models like ChaGPT often lack the domain expertise and nuanced understanding needed for effective instructional design. The TLDR is that these models reproduce common instructional design practices, not optimal ones. They also often fail to apply what they appear to “know” in theory in practice.

In a world where we are seeing the increasing “democratisation” of instructional design, the use of generic AI tools could inadvertently lead to the proliferation of ineffective practices, especially among a growing population of non-expert SME designers.

However, emerging research offers some hope. Early results from controlled tests of specialised AI tools built on robust instructional design knowledge and principles shows significant positive impact compared with generic AI models.

In blind scoring of instructional designs by experts:

• Human-only designs scored 2.37/5 on average

• ChatGPT-assisted designs reached 3.22/5

• Specialised AI-assisted designs achieved 4.08/5

Efficiency gains were equally striking, with specialised AI tools increasing human designer efficiency by:

• 72% compared with designing with ChatGPT

• 368% compared with manual, human-only design processes.

This research suggests that the path forward lies not in generic AI tools, but in specialised solutions built specifically for instructional design – much like how specialised AI copilots have transformed fields like coding (e.g. via Cursor) and medicine (e.g. via Hippocratic).

The survey suggests we should be on the look out for three key emerging trends:

1. Strategic Evolution: If we are indeed on the brink of the “productive disruption” of the ID industry, we should anticipate a world where ID is democratised. In this world, non-experts will be empowered to design learning experiences while professional IDs will evolve from content developers to domain experts & strategic learning architects.

2. Tool Integration: The rise of AI tools points to a future where AI will likely have both a positive and negative impact on the quality of ID. Generic tools which are not optimised for ID will perpetuate the most common (often not optimal) ID practices, while specialised AI tools will enable experts and non experts alike to make smart, data-informed decisions in the flow of their end to end process.

3. Quality Vs Speed: As AI becomes more of a reality in instructional design, IDs will need to increasingly focus on measuring and ensuring not just productivity but also learning impact. This is already reflected in changes in how success is measured, with learner performance and job-related results now the primary metrics for success.

The path forward likely involves:

• Developing robust frameworks for quality as design becomes more democratised.

• Finding ways to measure and communicate learning impact effectively.

• Building new skills in pedagogical theory, AI, data analysis, and strategic consulting.

• Embracing specialised AI tools which increase efficiency while also maintaining pedagogical excellence and driving impact.

The message from the field is clear: instructional designers need more than just faster ways to create the same old content. The opportunity – and responsibility – is to build tools that:

• Embed evidence-based instructional design principles.

• Support, rather than shortcut, pedagogical decision-making.

• Enable measurement of learning impact.

• Scale effective practices, not just common ones.

The data suggests we’re at a critical juncture. The next generation of tools will either amplify bad practices at scale (think: PDF > video + quiz) or help transform learning design for the better. The choice – and challenge – is ours.

The instructional design field is ripe for a period of significant disruptive transformation. While this brings uncertainty, it also creates opportunities for those willing to evolve their roles and embrace new ways of working.

The key to the positive disruption of instructional design will be finding the right balance between automation and expertise, democratisation and quality control, and between efficiency and effectiveness.

The key to the positive disruption of instructional design will be finding the right balance between automation and expertise, democratisation and quality control, and between efficiency and effectiveness.

As we navigate this transition, the goal remains unchanged: creating effective, engaging learning experiences that drive real results.

The tools and processes may change, but the need for deep instructional design expertise is perhaps more important than ever as we guide the development and implementation of AI-enhanced learning solutions.

Check out the full survey results here and let me know what you think!

Phil 👋

PS: If you want to get hands on and experimenting with AI with me and a group of people like you, check out my AI & Learning Design Bootcamp.