RISE 360 Course Design Best Practices

Overview

When building courses in RISE 360, content block distribution and the balance between passive and interactive elements significantly affect learner experience and perceived quality. Over-reliance on interactive elements can make a course feel gimmicky or juvenile; under-use leaves learners disengaged. This article captures the distribution analysis and design principles applied during the ^[1] training course build.

Content Block Distribution

Based on analysis of RISE 360 best practices and review of existing high-quality courses, the recommended distribution is:

Observed Distribution (Agility Recovery Modules 1–3)

As a concrete reference point, the first three modules built for Agility Recovery landed at:

• 42 text blocks
• 22 knowledge checks
• 15 interactive tabs
• 12 accordions
• 12 statement blocks
• 8 quotes
• 12 lists
• 1 image, 1 gallery

This distribution was intentional and validated against the ~65% text-block target.

Design Principles

Less Is More with Interactive Elements

Flip cards, drag-and-drop interactions, and similar elements are engaging when used sparingly. When overused, they:

• Make the course feel childlike or toy-like
• Dilute the learning signal — learners focus on the mechanic rather than the content
• Increase production time without proportional learning benefit

Guideline: Reserve interactive elements for moments where the interaction genuinely reinforces the learning objective (e.g., matching terms to definitions, sequencing a process).

Match Block Type to Content Type

Different content types call for different block formats:

• Conceptual explanations → text blocks, statements
• Lists of features or steps → list blocks, accordions
• Quotes or testimonials → quote blocks
• Comprehension checks → knowledge check blocks
• Comparisons or categorization → interactive tabs or flip cards

Analyzing what other practitioners use for specific content types — not just defaulting to variety for variety's sake — produces more coherent courses.

Feedback Workflow

For modules already published to RISE:
- Reviewers should provide feedback directly in Review 360 so comments are anchored to specific content locations.

For modules still in the content planning/document stage:
- Feedback in the source document (e.g., Enhanced Course Modules Google Doc) allows corrections before build begins, reducing rework.

This two-track approach was established with Agility Recovery: document feedback for unpublished modules, Review 360 feedback for published ones.

AI-Assisted Content Development

The Agility Recovery build introduced a workflow where a custom GPT was trained on client-provided documents (197–277 source files including case studies, price lists, and presentations) before course content was drafted. Benefits observed:

• Accurate use of client terminology, product names, and customer references
• Reduced reliance on generic AI output ("ChatGPT garbage") that lacks domain specificity
• Ability to query the system for mind maps, quiz questions, and content summaries on demand

The GPT is a living asset — feeding it additional client documents over time increases its accuracy and utility.

"It's like the smartest guy in the company right now." — Gus Donelson, Agility Recovery

See also: ^[2]

Common Pitfalls

• Topic imbalance from source material: If the documents fed into the AI skew heavily toward one topic area (e.g., cybersecurity), the generated content will reflect that bias. Review early modules carefully for balance against the client's actual business priorities.
• Thin product documentation: When a client's collateral is sparse for specific products (e.g., tiered offerings like Ready Financial Standard / Essentials / Plus), the course content will be correspondingly thin. Flag these gaps early and request structured input from the client.
• Losing prior feedback during document revisions: When creating an enhanced/revised content document, explicitly migrate comments from the previous version (attributed to the original commenter) so no feedback is lost.

Related

• ^[3]
• ^[2]
• ^[4]