A well-designed lesson combines several of the elements below, each doing different instructional work. Choose what the content and learner need—not what fills space.
Micro-examples below are placeholders drawn from across our program areas. They will be replaced with annotated examples from actual Excelsior courses.
Activating Prior Knowledge
Opens by connecting new content to something students already know—from earlier in the course, from professional experience, or from common knowledge. Adult learners bring substantial real-world experience that course content can build on. Activating it reduces cognitive load and gives students a foothold before new material is introduced.
Example (Nursing)
“If you’ve worked in a clinical setting, you’ve probably seen patients respond very differently to the same medication. In this lesson, we’ll look at why. Specifically, we’ll consider how pharmacokinetics explains what your body does to a drug after you take it.”
Instructor Explanation
The instructor’s own voice making sense of the material—not a summary of the readings, but genuine teaching. This is where the SME’s expertise shows up directly: the specific framing, the perspective shaped by experience, the detail a textbook glosses over. Instructor explanation is most valuable when it does something no existing resource does as well.
Example (Cybersecurity)
“Zero-trust architecture sounds like a philosophy, but in practice it’s a specific set of decisions about where you put your verification checkpoints. Here’s how I think about the difference between a perimeter-based model and a zero-trust model—and why it matters when you’re working in a hybrid environment.”
Real-World Application
Shows how a concept operates outside the textbook—in an actual workplace, field, or professional context. For adult learners, this element often determines whether content feels worth learning. It answers the question students are always asking, even when they do not say it: “When would I actually use this?”
Example (Business)
A lesson on break-even analysis walks through a scenario in which a small restaurant owner uses break-even calculations to decide whether to add a catering service—working through the fixed costs, variable costs, and pricing logic with real numbers.
Worked Example or Step-by-Step Walkthrough
Walks students through a process, calculation, or procedure one step at a time—showing the thinking, not just the result. Valuable for procedural, quantitative, or technical content where students need to see how to do something before they can do it themselves. A worked example differs from a real-world application: the application shows the concept in context; the walkthrough shows the steps.
Example (Nuclear Engineering)
A lesson on reactor criticality walks through the four-factor formula step by step, showing how each variable is calculated and what it represents physically—before asking students to apply it to a problem set.
Analogy or Comparison
Uses something familiar to explain something unfamiliar, or compares two related concepts to sharpen understanding of both. Especially useful for abstract or technical concepts students have no prior frame of reference for. A good analogy creates a foothold students can build from—it does not flatten the concept, it opens it up.
(Computer Science)
“Think of a stack data structure like a stack of plates at a cafeteria: you can only add to the top and take from the top. That ‘last in, first out’ rule is exactly how a call stack works when your program executes a function.”
Addressing Common Misconceptions
Explicitly names and corrects a misunderstanding students are likely to bring to the content—from prior coursework, professional experience, or general assumptions. This element is high-value because it anticipates confusion rather than waiting for it to surface in assessments. Adult learners, in particular, may arrive with informal prior knowledge that is partially wrong and surprisingly resistant to correction if it is not directly addressed.
Example (Business)
“A common assumption is that a high profit margin always means a business is doing well. It doesn’t. A company can have excellent margins and still fail—if it can’t collect its receivables, if it’s burning through cash, or if it’s growing faster than it can fund. Margin is one signal, not the full picture.”
Media
Video, audio, infographics, simulations, interactive activities—formats that convey information in ways text cannot. Choose media because it serves the learning, not because it makes the lesson feel more produced. A three-minute video showing a clinical procedure is irreplaceable. A five-minute talking-head video summarizing a reading is not. Before commissioning new media, check whether a credible existing resource already does the job.
Example (Nursing)
A lesson on patient transfer technique embeds a short video demonstrating the two-person assist method—a skill that text description alone cannot convey.
Guiding Questions and Reflection Prompts
Questions that invite students to think rather than just read—to pause, apply, connect, or challenge. Guiding questions can frame a lesson at the start (“As you work through this lesson, consider…”), punctuate it in the middle, or close it with a prompt that bridges to the upcoming assessment. They do not need to be graded to be effective.
Example (Cybersecurity)
“Before you move on: think about the last time you received a phishing email. What made it suspicious—or what made it convincing? Keep that example in mind as we look at how social engineering exploits cognitive bias.”
Annotated Examples
A real artifact—a document, a piece of student work, a case, a report, a data set, a piece of writing—with instructor commentary pointing out what is happening and why it matters. Annotated examples make implicit standards explicit and give students a concrete model to orient to. They are particularly effective when the artifact being annotated is imperfect: analyzing what is wrong with something often teaches more than presenting a polished ideal.
One specific use of this element is annotating AI-generated content. Rather than using AI output as lesson content, the SME uses it as an object of analysis—showing students exactly where the AI is superficial, inaccurate, or misleading, and why a more expert understanding differs. This builds subject-matter knowledge and critical AI literacy simultaneously.
Example (Computer Science)
A lesson on secure coding presents an AI-generated code snippet alongside the instructor’s annotations identifying specific vulnerabilities—a SQL injection risk here, an unvalidated input there—explaining why each is a problem and what a secure version would look like.
Example (Nuclear Engineering)
A lesson on radiation safety presents a draft safety procedure written by AI. The instructor annotates where it is technically incomplete, where regulatory language has been oversimplified, and where the procedure would fail an NRC inspection—showing the gap between plausible-sounding text and field-accurate standards.