Using LIF for AI Portable Context: What Are You Trying to Make Possible (or Prevent)?

[kelhoyland]

This discussion focuses on one core question:

What problem are you trying to solve by using LIF to eliminate the "cold start" for AI tools or agents?

We’re especially interested in real-world constraints, not idealized AI scenarios.

Prompts to Get Started

You might respond to any of these:

• What context do AI tools not have today that they need?
• What information do learners get tired of re-entering?
• Where does personalization cross into risk, privacy, or mistrust?
• What would break trust if an AI agent “remembered” the wrong thing?

Scope Clarification

This is not about:

• Choosing a specific AI model
• Building a commercial product pitch
• Assuming LIF should store everything

This is about:

• Deciding what learner context should be portable
• Understanding what should persist vs. reset
• Identifying where AI + learner data becomes risky

Why This Matters

Your responses will help shape:

• What LIF should (and should not) provide to AI tools
• How persistent memory might work responsibly
• Where organizations may need configuration, not defaults

If you’ve experimented with AI tools—even informally—we’d love to hear what worked, what failed, or what felt uncomfortable.