Teaching Technical Subjects Online? Tap Into the Brain’s Design Creativity Engine

Designing effective online courses—especially for technical disciplines like engineering, data science, and computer programming—requires more than organizing lectures, videos, and assignments. It demands creativity at every level, from course structure to learner engagement. But what kind of creativity are we talking about?

A fascinating 2018 paper by Leslee Lazar, "The Cognitive Neuroscience of Design Creativity," provides a roadmap. According to Lazar, design creativity is distinct from both artistic and scientific creativity. It’s uniquely tied to how humans solve complex, ambiguous, and evolving problems—what the paper calls “ill-structured tasks.” For instructional designers in the digital space, especially those working with technical subjects, this insight is profound. To truly prepare learners for the real world, our courses must engage their "design brains."

Embrace Ill-Structured Problems

In traditional education, especially in technical fields, we often rely on "well-structured" problems—those with clear parameters, predictable outcomes, and established solution paths. Think of solving an algebraic equation or calculating the flow rate through a pipe. While these tasks are useful for teaching fundamentals, they fall short of preparing students for the ambiguity and complexity of real-world challenges.

Lazar emphasizes the power of “ill-structured” problems—open-ended scenarios where both the problem and the solution evolve during the process. These are the kinds of problems that designers and engineers face daily: how to reduce waste in a city, optimize a software interface, or create a sustainable energy model. In online technical education, embracing this approach means offering scenarios that encourage learners to frame the problem themselves. Instead of handing students a tightly defined task, present them with a realistic challenge and ask, “Where would you begin?” This not only cultivates critical thinking but activates deeper brain networks associated with creativity and real-world problem solving.

Foster Divergent Thinking

One of the hallmarks of design creativity is the ability to generate many possible solutions to a problem. This process, known as divergent thinking, involves connecting seemingly unrelated ideas, drawing analogies, and pushing past conventional answers. It’s also associated with right-brain activation—particularly in the prefrontal cortex and medial temporal regions tied to memory and mental imagery.

To foster divergent thinking in an online technical course, instructors can build in brainstorming activities and reflection prompts that go beyond “what’s right?” to ask “what else could work?” For instance, in a course on systems design, pose a challenge like “design a water filtration system for a desert environment,” and invite students to submit five distinct conceptual sketches or approaches. Tools like digital whiteboards, collaboration platforms, and creative forums can provide the space for learners to explore without judgment. Emphasizing breadth before depth in the early stages of learning taps into this essential phase of the creative process and helps learners become flexible, innovative thinkers.

Balance with Convergent Thinking

While divergent thinking opens up possibilities, convergent thinking brings clarity. It is the process of narrowing down options, analyzing trade-offs, and making decisions. According to Lazar, this phase activates more analytical regions of the brain—primarily the executive control networks in the prefrontal cortex. Together, these processes form what researchers now view as a “dual-process model” of creativity: oscillating between the expansive and the focused, the imaginative and the evaluative.

In online learning, this means we shouldn’t stop at brainstorming. Learners also need structured opportunities to analyze and refine their ideas. For example, after generating a set of potential designs for a circuit or a software interface, students can be asked to evaluate each against a rubric that considers feasibility, efficiency, and user experience. Peer reviews, instructor feedback, and self-assessment tools can support this critical convergence stage, helping students internalize the skills needed to assess and refine their own solutions. Building this evaluative loop into course design teaches not only technical accuracy but the judgment needed for innovation.

Integrate Emotion and Intuition

An especially intriguing insight from Lazar’s review is the role of emotion in design decisions. During evaluation and final decision-making, brain areas like the medial prefrontal cortex and default mode network become active. These regions are associated with emotion, intuition, and personal preference—what designers often describe as a “gut feeling.”

This has profound implications for online learning. While we often focus on cognitive load and performance metrics, we shouldn’t overlook the emotional and intuitive dimensions of learning. Giving students space to reflect—through design journals, voice notes, or video reflections—can deepen their engagement. When students articulate why they chose a specific solution or how they felt about their learning process, they begin to integrate their analytical and emotional selves. This not only mirrors how real designers work but helps learners develop self-awareness and intrinsic motivation.

Use the “Design Brain” to Train Technical Brains

The neuroscience evidence is clear: expert designers think differently than novices. Their brains activate differently, especially in regions responsible for hypothesis generation, analogical reasoning, and mental imagery. Importantly, these skills can be taught—but not through lectures alone.

To help online learners move from novice to expert, instructors must model their thinking processes. Use screen recordings, narrated walkthroughs, or “design thinking in action” videos where experts tackle real problems. Make your own reasoning visible: how you define a problem, discard options, draw analogies, and iterate. This transparency helps learners build mental models of expert thought. Scaffold assignments with opportunities for learners to practice these same steps—first with support, then independently. Over time, learners will internalize the cognitive habits of expert designers, which are essential for mastering technical fields in the real world.

Conclusion: Teach Like a Designer

Teaching technical subjects online is a challenge—but also an opportunity. By drawing on insights from neuroscience and design cognition, we can create courses that mirror how real problem-solving happens. Instead of just transmitting content, we can build learning environments that activate the same brain systems used by innovative designers, engineers, and thinkers.

When we do this, our courses don't just inform—they transform. They help students become agile, creative, and confident problem solvers, ready to tackle the complex challenges of tomorrow.

So the next time you open your LMS or course builder, pause and ask: am I laying out a lecture... or designing an experience?

Reference

Lazar, L. (2018). The Cognitive Neuroscience of Design Creativity. Journal of Experimental Neuroscience, 12, 1–6. https://doi.org/10.1177/1179069518809664

Design for Learning in E-Learning: Making the Notion of "Quality" Concrete and Implementable

Design for Learning focuses on how to transform existing educational situations into desired situations where it is easier to achieve learning outcomes (Guislandi & Raffaguelli, 2015). The emphasis is on quality, and in doing so, the approach links the vision of how quality should be enacted in a program to the actual activities and procedures that are built into the learning program.

In the learning design, it is important to think of how the design of the course can affect ways of knowing, and also how to connect to improvements in practice.

Breaking Down "Quality" into Recognizable Elements

It is really all about breaking down quality into recognizable elements, and moving "quality" from an abstraction to something that can be recognized, measured, reviewed, and remediated (McNaught etal, 2012).

1. System: Make sure that the system used in learning is of high quality.  This means that it is necessary to review the learning goals and the potential users of a learning system (whether it be a learning management system, or a LMS-free approach) to assure that it can deliver what it needs to deliver.

2.  Experience:  Make sure that the learner / user experience is a positive one, and that it is friendly, not just for the learners, but also for the facilitators.

3.  Culture:  What are the institutional values? How and when are certain high-quality elements perceived?  What is the definition of quality?  How does it extend to a sense of respect for diversity, as well as efforts to build an authentic structure that can help learners and facilitators feel confident about their ability to achieve the mission of the organization and their goals as they relate to the institution.

4.  Flexible and Forward-Looking Vectors of Communication and Change:   Be willing to adapt existing structures to ones that are more flexible, and which accommodate changing technologies and locations. Ideally, learning organizations should be able to accommodate and even welcome individuals in all situations with a minimum of disruption. Further, the quality elements should extend to encouraging experimentation and innovation, with a high tolerance for failure (and success, which can bring about its own stresses and stressers).

University Degree Programs / Field Research Courses

Here's a concrete example. Let's say that we are a geology department in a state university, and we have a number of field courses. We've been intensely impacted by technology, not only in the way in which we communicate our findings, but also in the way in which field investigations are conducted.

We require all our graduate students to go out into the field and map outcrops and retrieve samples. However, our administration as well as our insurance providers have recently pulled the plug on the way that we were doing things in the past. They claim that there is not enough quality control in the design of the courses, so what the students bring back from the field are of dubious quality. Worse, they're considered dreadfully unsafe; only last month one student tumbled off a cliff and impaled herself on a cholla cactus. She was alone, and it was a minor miracle that she made it back alive. One might say it was only sheer luck that the escarpment was only 15 feet high, there was a ledge that partially broke her fall, and the cholla cactus plant was small and it broke apart upon impact. The weather was chilly and wet, so she was wearing pants and rain gear, which help minimize the impact of the cactus spines.

Details and luck notwithstanding, what happened to her was a clear indication that the department needs to go back and revisit Design for Learning and look at the four criteria:

1. System:  It's possible that the system itself is not giving people an opportunity to plan their research projects well. There may not be effective templates, and it may be important to customize the approach, given that each student's research project will be slightly different.

2.  Experience:  What is the user's experience? For the female student who fell down an escarpment and impaled herself on cholla cactus, it's less than ideal.  But it could have been worse.  Her experience in the field should not be confused with the learning design; the design should be developed so that she had a positive experience in planning and implementing her learning program.

3.  Culture: If the culture of the organization puts a high priority on eliminating all risk and all potential exposure to liability, then they may lose students. They will certainly lose innovative impulses, and many of their creative, inspired (and inspiring) thinkers will be drawn to different places, where they will potentially contribute transformative breakthroughs which could tangibly / substantially positively impact the institution itself and affect its persistence / viability.

4. Flexibility:  Communication could be improved. How about requiring digital inspections before going into the field and maintain an archive of photos of the sites (all of which are geotagged) and the gear.  Also, it would be possible to use low-cost satellite phones if there is no cell phone coverage. It's not always possible to work in teams, and so it's necessary to at least have a digital nanny.

Conclusions & Observations

Design for Learning articles are often cloaked in rather obtuse language which can be less than concrete. In order to really grasp the importance of the concept and the potential contribution to an organization, to learners, instructors, and a community, it's important to look at case studies. The concept of Design for Learning does, in fact, provide a powerful mechanism for operationalizing "quality" by breaking it into observable, measurable actions and by providing a platform for dissecting case studies with the idea of incorporating them in one's own learning programs.


REFERENCES


Ghislandi, Patrizia M. M.; Raffaghelli, Juliana E. Forward-oriented designing for learning as a means to achieve educational quality. British Journal of Educational Technology. (Mar2015)  Vol. 46 Issue 2, p280-299.

McNaught, Carmel, Paul Lam, and Kin Cheng. (2012)"Investigating Relationships Between Features Of Learning Designs And Student Learning Outcomes." Educational Technology Research & Development 60.2: 271-286. Professional Development Collection. Web. 11 June 2015.