ai-agent-design-spec

name	ai-agent-design-spec
description	Turn dissertation evidence into an AI agent design specification for supporting active learning implementation, including user scenarios, functions, interaction flow, boundaries, failure modes, and design rationale.

AI Agent Design Spec

Use when the user asks to design, refine, evaluate, or document the AI agent proposed in the dissertation.

Inputs

Look for:

• DISSERTATION_BRIEF.md
• teacher interview findings, concept-card responses, design-elicitation outputs, or confirmed co-design outputs
• teacher interview/workshop findings
• research-wiki/DESIGN_RATIONALE.md
• research-wiki/FINDINGS_INDEX.md
• chapter drafts about design implications

Workflow

1. Identify the teaching problem the agent supports.
2. Define primary users:
   • university teachers
   • teaching assistants or learning designers, if relevant
   • students only if the dissertation explicitly includes them
3. Map teacher needs and concerns to agent capabilities.
4. Specify:
   • user scenarios
   • agent goals and non-goals
   • core functions
   • interaction flow
   • inputs and outputs
   • human control points
   • failure modes and recovery
   • evidence source for each design decision
5. Separate evidence-backed requirements from speculative features.
6. Produce a design rationale that links back to active learning and adoption conditions.

Output

Create or update:

• design-specs/AI_AGENT_DESIGN_SPEC.md
• design-specs/USER_SCENARIOS.md
• design-specs/DESIGN_RATIONALE.md

Required Sections

Use this structure unless the user asks otherwise:

1. Purpose
2. Intended users
3. Teaching scenarios
4. Core capabilities
5. Boundaries and non-goals
6. Interaction flow
7. Teacher control and override points
8. Data and privacy assumptions
9. Failure modes
10. Evidence-to-design traceability table

Guardrails

Read ../dissertation-shared/references/privacy-and-ethics.md when participant data is involved. Do not claim the agent is effective unless evaluation evidence exists.