Data Dashboard
Provides real-time insight into system trends, inefficiencies, and variable relationships.
Portfolio Case Study
Strategix
A systems-based instructional game designed to move students from answer-based math performance to strategic problem solving, decision-making, and systems thinking.
Overview
- Problem: Many math learning experiences reward procedural accuracy more than strategic reasoning.
- Solution: I designed a competitive instructional game that evaluates efficiency, decision quality, and system stability.
- Design Goal: Create a learning experience where gameplay directly reinforces analysis, strategy selection, and reflective improvement.
- Tools Used: ChatGPT, Google Gemini (Nano Banana), Microsoft Word, HTML/CSS.
The Problem
In many math classrooms, students are trained to get the correct answer as quickly as possible. While that supports procedural fluency, it often does not build the kind of thinking required for real-world problem solving. Students may succeed on isolated tasks while still struggling to analyze systems, compare solution paths, justify decisions, and understand the tradeoffs behind their choices.
I wanted to design an experience that moved beyond correctness alone and instead emphasized strategy, efficiency, and decision-making within a larger system.
The Solution
Strategix is a competitive, systems-based instructional game in which students participate in the Nexus Engineering Tournament as Junior Systems Engineers. Players solve increasingly complex challenges by optimizing dynamic systems under constraints. Instead of rewarding speed or correctness alone, the game evaluates how effectively players manage variables, allocate resources, and refine strategies over time.
The design challenge was to make the game mechanics themselves reinforce the instructional objective. In other words, the learning is not added on top of gameplay. The gameplay is the learning.
Design Process
Instructional Design Foundations
The project was grounded in whole-task learning, self-regulated learning, and higher-order cognitive processing. Each challenge requires learners to interpret system data, make strategic decisions, evaluate outcomes, and refine performance through feedback. This creates a complete problem-solving cycle rather than a fragmented set of isolated tasks.
Game System Design
At the core of Strategix is a repeating gameplay loop: challenge, analyze, decide, observe, and refine. Players work within dynamic systems where each decision affects stability, resource availability, and future options. This structure was intentionally designed to reinforce cause-and-effect reasoning and strategic thinking.
Gameplay Loop. Players encounter a challenge, analyze system variables, select a strategy, observe outcomes, and refine their decisions across repeated rounds.
AI-Assisted Workflow
AI was used as a design partner rather than a solution generator. I used ChatGPT to refine language, pressure-test structure, and compare design choices against assignment expectations. I used Google Gemini (Nano Banana) to generate visual prototypes for tools and interfaces. Just as important, I rejected many AI outputs that oversimplified the mechanics, introduced unnecessary complexity, or drifted away from the instructional intent of the project.
Key Features
The following features distinguish Strategix from more traditional mathematics games and show how the instructional design is embedded directly into the system.
| Feature | Description | Instructional Purpose |
|---|---|---|
| Strategy-First Scoring | Performance is evaluated based on efficiency and decision quality, not just correctness. | Encourages deeper thinking and more intentional problem solving. |
| Multiple Solution Pathways | Challenges support more than one valid strategic approach. | Promotes analysis, comparison, and flexibility in reasoning. |
| Dynamic Systems-Based Gameplay | Variables interact and respond to player decisions over time. | Reinforces cause-and-effect reasoning and systems thinking. |
| Constraint-Driven Decisions | Players work under limited resources and changing conditions. | Builds prioritization, planning, and adaptation. |
| Learning Analytics Dashboard | The system tracks performance trends and decision-making patterns. | Supports reflection and metacognitive awareness. |
| Iterative Feedback and Replay | Players receive immediate feedback and can revise strategies across attempts. | Strengthens learning through repetition, feedback, and refinement. |
Visual Design and Prototypes
The interface design was intentionally clean, analytical, and systems-focused. I wanted the visuals to reinforce the game’s instructional purpose by helping players interpret information, manage variables, and understand the impact of their decisions without distracting from the problem-solving process.
Resource Tokens
Represents limited assets such as time, energy, or budget that shape player decisions.
Optimization Modules
Allows players to adjust system variables, test approaches, and refine performance.
Constraint Modifiers
Introduces shifting limitations that require adaptation and deeper strategic planning.
Outcomes and Takeaways
Although this project was developed as a graduate instructional design artifact, it demonstrates the ability to design a learning experience in which pedagogy, gameplay, and feedback systems are fully aligned. It also shows how AI can be integrated responsibly into the design process as a collaborative tool for critique, refinement, and visual prototyping.
The biggest takeaway from this project was the importance of making the mechanics carry the learning load. Rather than layering game elements on top of academic content, I designed the system so that analysis, tradeoffs, and strategic refinement were built directly into how the player interacts with the game.