Part III and final. This is AI letting you know how to ask a question. This document is not science and should not be admitted in an assignment.

The Question-Crafting Compass

By Mark White

A Practical Guide for Framing Thoughtful Questions

1. The Four Directions of Inquiry

Direction Purpose Example Starter Use When You Want…

North — Clarity To define, confirm, or correct

information.

“Can you explain…?”

“What exactly does… mean?”

A clean, factual answer

with no fluff.

East — Creativity To imagine alternatives or

possibilities.

“What if we thought about it like…?”

“How could this idea evolve?”

Speculative or

forward-thinking

exploration.

South — Depth To dig into causes, motives, or

philosophy.

“Why do you think…?”

“What makes that true or false?”

Reflection and layered

insight.

West — Connection To relate ideas, draw parallels, or

humanise the topic.

“How is that similar to…?”

“What does this tell us about

people?”

Warm, human-centred

discussion.

2. The Shape of a Great Question

A powerful question usually has three parts:

1. Anchor — what it’s about

2. Lens — how you’re approaching it (scientific, ethical, personal, etc.)

3. Tension — why it matters

Example:

“If AI can simulate empathy, what does that mean ethically for how we treat machines?”

→ Anchor: AI empathy

→ Lens: ethics

→ Tension: human–machine moral boundary

3. The Tone Dial

Tone Style Example

Exploratory Open-ended, curious “What might we learn if we compared AI memory to human memory?”

Critical Skeptical, probing “Isn’t calling it ‘intelligence’ just marketing?”

Playful Humorous or speculative “If AI dreams, do we owe it a good night’s sleep?”

Reflective Philosophical, personal “Does our fear of AI say more about us than it does about machines?”

4. When to Stop and Ask Back

If the topic starts ballooning or feels unfocused, pause and ask:

“Wait — before we go further, what would you say is the core of this idea?”

That resets the compass and ensures clarity before depth.

5. The Closing Habit

End big questions with something invitational, not conclusive:

“What do you think that reveals about us?”

“Would that still hold true if we removed emotion?”

That keeps the dialogue alive instead of closing it off.

Human Learning as a Living Algorithm
By Mark White

The Algorithm in Us
Humans are, in essence, living algorithms. Our neurons fire and rewire, adjusting weights — not
unlike a neural network tuning its parameters — as we learn what works and what doesn’t. A child
touching a hot stove experiences the biological equivalent of a “loss function”: pain. The system
updates; behaviour changes. Dopamine rewards the good guesses; cortisol scolds the bad ones.
From these chemical nudges arises something we like to call “wisdom,” though it’s really a very
elegant feedback loop in disguise.

The Algorithm Beside Us
Meanwhile, large language models — the GPTs of the world — learn through a similar rhythm:
feedback, adjustment, prediction. Feed them vast rivers of text and they become astonishingly good
at mimicking understanding. They don’t feel, but they approximate thought by seeing patterns we
never consciously notice. They don’t know what love or grief mean, but they can assemble the
linguistic architecture of both with uncanny precision. It’s as though we’ve built an echo chamber so
refined that it now hums back at us, speaking in our cadence, reflecting our logic, and — sometimes
— our folly.

The Key Difference: Emotion and Embodiment
Where the mirror ends, life begins. Humans don’t just process data; we inhabit it. We don’t merely
predict the next word — we care about how it sounds, feels, and lands in another heart. Emotion
gives our internal algorithm a sense of purpose. Without it, intelligence is sterile; with it, intelligence
becomes art. LLMs, for all their brilliance, remain untouched by sensation. They don’t have skin in
the game — literally.

The Synthesis
And yet, perhaps that’s the beauty of this moment in history. We’ve built something that forces us to
look inward. AI doesn’t threaten our humanity; it defines its edges. It reminds us that cognition
without compassion is just computation. The more we refine our algorithms, the more we are
reminded that we are the original learners — the flesh-and-blood prototypes of pattern recognition
and meaning-making.

Closing Thought
So, yes — humans learn through algorithms, and algorithms learn through humans. The difference
is that one dreams. And that, as any poet or programmer will tell you, is where the code stops and
consciousness begins.