Scenario Based Learning | Katharine Johnston

Scenario Based Learning in Financial Literacy

Business Context

The Foundation for Financial Wellness (FFW) sought to scale access to its retirement support services. While 1:1 coaching outcomes were strong, demand consistently exceeded capacity thus limiting reach. Program evaluations revealed that participants struggled to translate financial knowledge into sound retirement decisions. This is was particularly apparent when faced with uncertainty, competing priorities, or emotional pressure.

FFW wanted a scalable learning solution that would improve decision quality while also serving as a bridge to appropriate coaching services.

Findings from existing evaluations:

Coaches were overloaded and spent a lot of time correcting foundational decision errors
Participants wanted a safe environment to test financial decisions in a test atmosphere
Learners struggled to understand how multiple variables interacted over time
Calculation accuracy and long-term planning judgment remained inconsistent

FFW partnered with Blue Sky eLearn to design a scenario-based eLearning solution. I served as the instructional designer responsible for the retirement decision-making curriculum, with emphasis on behavioral change, consequence awareness, and real-world application.

Learning Challenge

Participants were entering programs with:

Persistent misconceptions about retirement readiness
Limited opportunities to test decisions under realistic constraints
Low confidence navigating tradeoffs involving risk, debt, and income timing

The program needed to change how participants evaluated and adjusted retirement decisions while also promoting FFW’s broader services catalogue.

Learning Objectives

Upon completion, participants were able to:

Given projected retirement income, expenses, and market assumptions, the participant determines whether current retirement readiness is sufficient and selects a corrective action focuses on long-term sustainability without increasing overall risk exposure
When presented with multiple retirement income sources, the participant prioritizes and sequences withdrawals to maintain a sustainable withdrawal rate below 8% while minimizing depletion risk during market downturns.
When debt service exceeds 15% of projected retirement income, the participant chooses to rebalance savings and debt to preserve long-term stability.
Given market conditions, time horizon, and risk tolerance, the participant selects investment adjustments that aligns with retirement income needs
When evaluating withdrawal and contribution options across taxable, tax-deferred, and tax-free accounts, the participant selects a strategy that reduces lifetime tax burden without increasing future income volatility
When life events occur the participant can reevaluate and adjust retirement decisions to preserve long-term income viability

Instructional Strategy

Initial stakeholder conversations framed the problem as a lack of financial knowledge however through thoughtful analysis of coaching data and learner behavior, the core issue identified was decision-making under stress, ambiguity, and competing constraints.

Scenario-based learning was selected because it allowed learners to:

Experience realistic tradeoffs without real-world risk
See delayed and compounding consequences of decisions
Practice recovery from suboptimal choices

This approach aligned with how financial advisors actually work-tracking of client state over time rather than treating decisions as isolated events.

I was responsible for:

Creating realistic scenarios and branching logic design
Mimicking real work decision interaction modeling
Fully developing personas grounded in real client experiences and coaching challenges
Customizing a branching narrative using coaching-style feedback
Defining acceptable fidelity through appropriate knowledge checks, reflection prompts, and learner flow
Setting guardrails for SME input by aligning content with FFW instructional curriculum and resources
Establishing the decision model that prompted agency and personal responsibility

Building Personas

I developed three composite personas based on client data, coaching trends, and SME interviews.

Pre-Retiree Investor (primary example)- Stable income, limited investment strategy knowledge, time-constrained to recover from any financial mistakes.
Single Parent Rebuilding Finances
High debt load, limited financial literacy, strong emotional and practical constraints.
Low-Earning Couple Under Financial Strain
High fixed expenses, inconsistent income, relationship dynamics impacting decision-making.

Personas were designed as mechanics with distinct constraints, risk tolerances, and tradeoffs that directly altered available choices and consequences.

I created a branching experience focused on applying appropriate financial decision and provided reflection touchpoints for the learner.

Primary Decision Paths

Investment strategy and allocation information
Risk protection understanding
Understanding income sources

Secondary Decision Paths

Goal prioritization
Investment recalibration to budget
Promotion of FFW tools and services

Design Priorities

Decisions tied to real calculations
Immediate feedback on decisions
Short-term relief vs long-term pain
Forced recovery decisions
Promotion of FFW coaching services
Opportunities to go back to portfolio calculator and try new calculations
Strong coaching-style feedback to build financial confidence

How Persona Constraints Altered Decision Space

Each persona introduced hard constraints that limited or reshaped available options:

Income stability affected contribution flexibility
Debt levels altered risk tolerance thresholds
Household structure introduced secondary consequences beyond numeric outcomes

As a result, learners could not rely on a single “correct” strategy across all personas. They were required to adapt their decision-making thus mirroring real retirement planning complexity.

For this example, we will focus on Persona 1: The Pre- Retiree.

Decisions and Consequences

Increase retirement contributions- reduced liquidity, increased vulnerability to market volatility
Increase investment risk- accelerated growth potential with amplified downside close to retirement
Delay retirement- improved portfolio stability with lifestyle tradeoffs
Aggressively pay down debt- improved cash flow at the expense of investment growth

Emotional Drivers

Time compression and regret avoidance
Opportunity cost awareness
Balancing mathematical outcomes with lived reality

Interaction Model

To support application while managing cognitive load, the experience was built around:

Decision Screens
Learners applied strategies and immediately saw modeled outcomes, mirroring financial planning software.

Inline Knowledge Checks
Low-stakes confirmation of understanding without interrupting scenario flow.

Reflection Prompts
Placed at key inflection points to support metacognition, emotional awareness, and evaluation of tradeoffs.

Branching Design

FFW Retirement Module Scenario Based Learning.png

The scenario was intentionally designed as a bounded branching system. The goal was to simulate meaningful complexity without creating an unmanageable structure.

Why This Level of Branching

Retirement planning errors tend to result from patterns of thinking thus branching was limited to three primary decision domains:

Investment strategy and tax implications
Risk protection and income stability
Debt repayment versus retirement contributions

Each domain contained 2–3 high-impact decisions previously identified in the coaching data.

Branch Convergence

Paths diverged to deliver consequences and feedback, then reconverged before introducing new domains ensuring instructional coherence and realism.

Managing Complexity

Rather than branching on every decision, I used state-based variables to track:

Risk tolerance
Liquidity pressure
Retirement readiness confidence

These variables influenced feedback tone, warnings, and consequence timing, allowing personalization without exponential content growth.

Scalable Feedback Logic

Feedback was modular and pattern-based:

Risk decisions aligned to tracked risk tolerance
Liquidity strain triggered sustainability prompts
Confidence levels adjusted explanation depth

This maintained consistency across personas while supporting scale and maintainability.

Design Execution

Execution priorities included:

Progressive complexity to prevent tool overwhelm
Plain-language explanations of financial concepts
Consistent interaction patterns
Opportunities to revisit calculations and recalibrate decisions
Alignment with FFW brand and coaching philosophy

My focus was instructional clarity, scenario flow, and decision realism.

Design sample- Video made in Camtasia and used to introduce to the course.

Sample knowledge check reflection.

Feedback as a Driver

To move beyond simple corrective feedback, I designed consequence driven feedback to mimic a real-world coaching experience. Instead of labeling choices as "good" or "bad," I used state-based variables to show the immediate and long-term impact of decisions on the learner’s financial health. For the pre-retiree persona, this meant focusing on opportunity cost and time compression. By visualizing the trade-off between debt relief and retirement delays, the feedback forces learners to engage in metacognitive reflection, requiring them to "recover" from poor choices just as they would in real-life. This approach not only builds decision-making competence but also reinforces the value of FFW’s 1:1 coaching for navigating complex trade-offs. Below is an example of how this played out in the course.

Choice confirmation screen. Learners are prompted to review their choice before continuing. For this example, learners will confirm their choice.

Coaches feedback of the emotional consequence of the decision.

Coach shows how data has shifted.

Forced recovery decision.

Outcomes and Impact

Learner Outcomes

Decrease in the time coaches spent on "Foundational Errors" dropped from 40 minutes per session to 10 minutes
Improved accuracy in self-assessing retirement readiness resulting in participants using coaching for specific and tailor assistance.
Increase in purchasing affiliated services (whole life insurance, short term care plans) up 15% against previous year data
Increase in adoption of FFW services up 35% against previous year enrollment data

Organizational Outcomes

Increased conversion to 1:1 coaching (approximately one in every three online course participants went on to sign up for coaching).
Increased enrollment in other FFW classes- up 8% against previous year
Improved efficiency and quality of advisor interactions

These results supported FFW’s goal of scaling access while improving decision quality and service alignment.

Iteration and Refinement

The development of this program required balancing subject matter expertise with scalable logic. This led to two critical pivots during the design phase:

1. Reframing the "Coach vs. Course" Conflict- Initially, SMEs were resistant to this approach, fearing a digital solution would "replace" nuanced coaching. To build buy-in, I repositioned the eLearning as a pre-coaching sandbox. I demonstrated how the scenario would handle the repetitive, foundational decision-making errors which in turn would allow coaches to focus on high-level strategy during 1:1 session. This shift transformed the SMEs from skeptics into advocates. They saw this scenario-based eLearning as a way to increase the quality and quantity of their interactions.

2. Managing "logic bloat" and variable categorization- In the early build, the branching logic was hyper-specific, attempting to track every minor financial fluctuation. This created "logic bloat" that was difficult to maintain and confusing. I pivoted by moving from specific numeric tracking to broad state variables (categorizing "liquidity" as Low, Stable, or Surplus rather than exact dollar amounts). This simplified the backend architecture and allowed the feedback to focus on the principles of decision-making rather than getting bogged down in math. This ensured the instructional goals remained the primary focus.