Convenience
- “Make this easier for me.”
- Optimizes for speed.
- Hides complexity.
- Produces answers.
- Treats you as a consumer.
Sensemaking AI
Build computational agency for real life and work.
A mobile learning experience for adults who want to think clearly with AI — not just move faster because of it.
The core contrast
Most AI training teaches you to use tools. This teaches you to think while using them.
Most AI tools are designed to make things easier. That is not the same as making people more capable. Convenience and capability are different goals, and most AI literacy training optimizes for the wrong one.
Capability
- “Help me become more capable.”
- Optimizes for agency.
- Teaches you to navigate complexity.
- Builds judgment.
- Treats you as a decision-maker.
What you’ll practice
Six capabilities. Five pathways. Real scenarios.
Sensemaking AI is not a course about AI. It is practice for the kinds of decisions adults actually face when working with data, automation, and AI tools.
Problem framing
Clarify the real question before reaching for tools.
Representation
Choose useful structures for messy information.
Tool choice
Match tasks to methods, including when not to use AI.
Workflow debugging
Find where processes break down, and fix them.
AI judgment
Evaluate output, spot uncertainty, and preserve human agency.
Governance
Use data and AI responsibly: privacy, consent, accountability.
Each pathway uses short, scenario-based exercises with reflective feedback. No trivia. No gamification. No childish edtech aesthetics.
A sample of the experience
Practice the decisions you actually face.
A small nonprofit wants to respond to client questions faster. Which parts of the workflow should be automated? Which need human review? Which need investigation before anyone touches them?
This static preview is drawn from the MVP curriculum. It shows the shape of the learning experience without publishing the full activity bank.
See the Problem · 8 min
Is this really an AI problem?
Decide what should be automated, reviewed by a person, or investigated before building anything.
Decision task
Sort the workflow steps.
Best for automation Answer hours and location
Needs human review Handle crisis messages
Investigate first Flag high-risk wording
Reflective feedback
Nice work.
You separated low-risk factual tasks from sensitive decisions and ambiguous safety signals.
The goal is not to automate as much as possible. The goal is to design a better system for responding to people responsibly.
Founder note
Built by a cognitive scientist who studied how people decide where to look.
I’m Barbara Hidalgo-Sotelo. My PhD at MIT was on how humans deploy attention in familiar scenes: how we use context and prior knowledge to know where to look before we have finished processing what we are looking at.
That is not a side detail of this product. It is the foundation. Practical judgment with AI is the same kind of skill: knowing where to look, what to inspect, and when to slow down.
Sensemaking AI is what happens when cognitive science research meets deployed AI systems and a clear view of what AI literacy is missing.
PhD · MIT Cognitive Science AI/ML systems Knowledge architecture Austin, TX