AI-Driven Task Generation for "Durable Skills"

During a high-intensity 4-day practicum at the Harvard Graduate School of Education, we collaborated with Imagine Learning (a leading US K-12 publisher) to solve a critical problem: 

How can AI responsibly generate high-quality, classroom-ready tasks that develop "Durable Skills"?

Using Evidence-Centered Design (ECD), we successfully prototyped a Custom GPT that translates abstract communication competencies into precise, observable instructional activities for Grades 9-12.

Moving from "Generic AI" to "Precision Pedagogy"

Initially, our scope was a broad K-12 framework. However, early testing revealed that LLMs tend to drift into "generic pedagogy" when the target audience is too wide.

The Decision: We narrowed our scope to Grades 9-12 and focused on a single, high-impact construct: "Communicating to be Understood." 

This allowed us to build a more robust logic gate for the AI, ensuring the output was sophisticated enough for high school students while maintaining rigorous alignment with the America Succeeds framework.

Applying Evidence-Centered Design (ECD)

To ensure the AI output wasn't just "flavor text," we built a four-stage engineering pipeline:

1. Construct Definition: We operationalized Communication not just as "talking," but as the sophisticated exchange of thoughts with acute audience awareness.

2. Student & Evidence Models: We defined the specific observable behaviors (e.g., adapting tone for a specific stakeholder) that prove a student is competent.

3. Task Models: We designed "constraints." For example, requiring the AI to design tasks for pairs or small groups to ensure the "communication" evidence stayed attributable to individual students.

4. Prompt Engineering: We synthesized these models into a multi-layered System Prompt, acting as a "Pedagogical Guardrail" for the LLM.

Engineering the "System Prompt"

One of my primary contributions was Constraint Engineering. We discovered that without explicit, step-by-step logic, the GPT would "spill outward"—creating interactions that were fun but impossible for a teacher to grade.

Key Technical Insights:

• Avoiding Pedagogical Drift: AI needs a "prioritization logic" to know which learning objectives matter most in a task.

• Reducing Teacher Burden: We refined the bot instructions to ensure the output included ready-to-use materials (handouts, prompts), reducing the "work" a teacher has to do to implement the AI's idea.

• Iterative Prompting: We used a "Human-in-the-loop" approach, testing 20+ iterations of the same task to find where the AI logic broke down.

Below is the presentation slides, and a full Technical Brief outlining our research, design pivots, and final outcomes.

"The HGSE students delivered while providing a blueprint for the process. The practicum will feed innovation at IL... providing growth for emerging educational leaders and innovators." — Imagine Learning Leadership

Outcome: Delivered a Proof of Concept (PoC) that demonstrated how LLMs, when constrained by Learning Science, can generate evidence-rich tasks that are far superior to standard "off-the-shelf" AI responses.