【AI x learning】 Conversations Aren’t Driftwood — They Move With Coordinates: Learn to Set Goals So AI Truly Knows Where You Want to Go

“Before asking AI anything, first know where you want to go — otherwise you’re just getting lost together.”

Published on: 2026/01/20

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Introduction

Is this you?

In a quiet afternoon café, Si-ying is rushing to finish a proposal deck for tomorrow’s client meeting.

Her mind is full of ideas about the project, yet she can’t organize them into a clear structure, and this has bothered her for days.

So she opens Copilot and types: “I’m preparing a proposal about product A’s sales. Can you give me some sales strategies?”

Copilot returns five or six possible strategies. They all look reasonable. “These all seem good… but why do I feel even more confused?”

Already overwhelmed by her own thoughts, Si-ying hoped AI would help her sort things out — but the conversation only made everything messier.

Later that night, the scene shifts to Yu-chen’s bedroom. He’s winding down, scrolling through his phone before sleep.

A few days ago, his crush casually mentioned the idea of “what kind of creature you might have been in a past life,” so he thinks it might be fun to ask ChatGPT and gather something interesting to talk about next time.

He opens ChatGPT and types: “Do you know anything about past lives?”

ChatGPT responds with religious perspectives, psychological interpretations, and cultural viewpoints. Yu-chen finds everything fascinating and starts diving deeper into religious topics.

An hour later, he pauses. “Wait… why did I ask this in the first place?” He realizes he has completely forgotten the original purpose.

The scene shifts again to Yu-ting, who is preparing a short story for an upcoming creator competition.

Gemini has always been her reliable partner during the creative process. Feeling comfortable with it, she opens a new chat and types an 800‑word description: “I want to write an isekai reincarnation short story. The protagonist’s name is…”

Gemini generates a smooth, well‑structured outline with an engaging plot. Yu-ting feels satisfied at first.

But then she hesitates. If Gemini already built the entire structure for her, what is left for her to write? And… why did she open Gemini in the first place?

Give your conversation a navigation route

Some people place a clear coordinate in their mind before speaking to AI: “That is the destination I want this conversation to reach.”

They don’t rush to type the first sentence. Instead, they confirm what outcome they truly want from the conversation.

When they finally speak, every sentence becomes a steady line drawn on a map, allowing AI to immediately understand the destination and move the conversation toward it.

Their conversations with AI aren’t random questions — they feel like a guided journey.

AI’s responses align precisely with their needs, and they can adjust the direction mid‑way to stay on course.

In these interactions, AI becomes more than a tool — it becomes a companion walking alongside them. And they become the navigator of the entire dialogue.

A Moment of Reflection Before You Begin

Before starting any conversation with AI, clarifying your task, constraints, and motivation — and forming a clear goal — isn’t just a formality. It’s the key to improving the quality and value of the interaction.

One of the theories we often reference in the previous articles, Self‑Determination Theory (SDT)_[1], states that when three basic psychological needs are met — autonomy (feeling in control of your actions), competence (feeling capable), and relatedness (feeling connected and understood) — high‑quality motivation naturally emerges.

Once you define the goal of your conversation, you feel that the interaction begins from your own choice rather than passive information intake.

A clear goal also strengthens your sense of competence — the feeling that you can effectively obtain the outcome you want from the conversation.

When autonomy and competence are both supported, you gain a stronger sense of control, the conversation becomes more meaningful, and AI’s responses feel more aligned with your needs. This also builds a sense of “moving forward together.”

On the other hand, when a conversation lacks a goal, it easily loses focus and becomes a pile of unrelated information. Not only does it fail to solve the original problem, it often creates more anxiety and frustration.

Another theory we referenced earlier, Expectancy‑Value Theory (EVT)_[2], explains the importance of goal‑setting from a different angle.

EVT suggests that whether a person takes action depends on two factors: expectancy (whether they believe they can do it) and value (whether the task feels meaningful).

When you set a clear goal before talking to AI, both expectancy and value increase. You can see the meaning and benefit of the interaction more clearly.

With high expectancy and high value, your willingness to engage naturally rises, and you become more proactive in moving toward your original goal.

Without a clear goal, both expectancy and value become blurry. This lowers your motivation to engage and reduces the quality of the conversation, making AI’s responses feel irrelevant or unhelpful.

In the context of generative AI’s impact on education, Chang et al._[3] observed that while AI can provide immediate, interactive, and personalized support, it may also lead students to rely on it without thinking — even using it for inappropriate shortcuts like plagiarism.

The same study integrates Zimmerman’s Self‑Regulated Learning (SRL) theory_[4][5][6] and suggests that educators shouldn’t ban AI. Instead, AI should be designed to guide learners to think rather than simply provide answers.

The study further emphasizes that learners should set learning goals at the beginning of the conversation, and AI should use reverse prompting to help clarify the problem.

AI should also guide learners to reflect on their understanding and adjust the difficulty and style of responses to support motivation and persistence.

From this research, we can see that goal‑setting is the core first step of the entire interaction. When learners know their goal, they can repeatedly align with it throughout the conversation.

AI can also understand what it should respond with, making the entire dialogue more focused.

Setting a goal is not only a chance for the user to clarify what they want from the conversation — it also gives AI a clear boundary for its responses.

Through the anchoring effect of goal‑setting, conversations gain direction. This isn’t just an AI skill — it’s a mental habit that keeps your thinking focused and your interactions meaningful.

Tone × Motivation × Goal

In the first article, we introduced four tone types: Tone Sprout Type, Tone Explorer Type, Tone Designer Type, and Tone Creator Type.

【AI x learning】 From Passive Use to Thinking Loops: Why You Must Learn to Ask Before Using AI

In the second article, we discussed different types of motivations — such as Problem‑Oriented, Curiosity‑Driven, Thought‑Organizing, Creative‑Support, Foggy‑exploration, and even Multi‑Mixed Motivation.

【AI x learning】 Asking Is More Than Input — It Is the External Shape of Your Inner Direction: Finding Your Starting Line

This chapter builds on those ideas by focusing on preparation before asking and the importance of setting a goal.

You might wonder: “How are these three articles different from each other?”

All three help you understand your state before talking to AI, but each addresses a different layer.

The first article focuses on tone — how much control you take in the conversation, whether you can guide the flow, interrupt AI, or steer the topic back to what you truly want to clarify.

In other words, it deals with **how you speak**.

The second article helps you identify your underlying motivation — the reason you’re asking in the first place.

It deals with **why you ask**.

This article helps you clarify what you want to gain from the conversation — the problem context, the framework, and the outcome you hope to reach.

It deals with **what you want to obtain**.

Together, these three layers help you understand yourself better before you start typing. With that clarity, you can express your needs more precisely and guide the conversation toward the result you want.

But remember: a goal is not a restriction. It’s the starting point that gives exploration direction. It helps you judge whether AI’s response is useful, decide when to ask follow‑up questions, and know when to shift direction. It also helps AI understand what to answer, keeping the conversation focused instead of drifting.

Marking Coordinates for the Journey Ahead: Finding Your Goal Anchor

Sometimes, it’s not that we don’t know what to ask — it’s that we haven’t clarified *why* we’re asking or *what* we want to gain.

Before filling out the “AI Conversation Goal Anchor Generator,” I recommend taking a moment to complete the motivation check from the previous article.

【AI x learning】 Asking Is More Than Input — It Is the External Shape of Your Inner Direction: Finding Your Starting Line

This isn’t a test. It’s a gentle self‑reflection. It helps you quickly identify your starting point — whether you’re task‑driven, curious, trying to organize your thoughts, or simply seeking companionship.

Once you have a sense of your motivation, filling out the form becomes easier and more aligned with what you truly want from the conversation.

If you already know your motivation, feel free to start directly. The form will automatically expand based on your choices.

And again — this isn’t a test. Take your time. Pour a glass of water, play some soft music, and gently ask yourself: “If I’m about to start a conversation with AI, what do I hope to gain from it?”

Alright then — let’s begin.

You’ve completed the AI Conversation Goal Anchor Generator — well done.

This isn’t just a form. It’s a map of your current thinking. It records your motivations, goals, constraints, and conversation framework. It’s not only for now — it’s a starting point you can revisit every time you begin a new conversation with AI.

Once you understand your conversational tone, your motivation for asking, and the goal you want to reach, the next article will help you design your opening line — your “first sentence.”

During the conversation, you can always return to this anchor map to check whether you’re still moving toward your original direction, and whether you still remember what you truly want to clarify.

If you’re already an experienced AI user, many of these anchors may already be internalized. You might not need to write them down each time — they’re already part of your thinking.

Even so, remember this: a goal isn’t a limitation. It’s a gravitational field that gives exploration direction. It helps you judge whether AI’s responses are useful and reminds you when to follow up or when to shift course.

Before starting any new AI conversation, take a breath and ask yourself: “What do I want to gain from this?”

You’ve already laid down a steady route. Now all that’s left is to set sail.

Summary

A clear and well‑defined goal is like a lighthouse guiding your AI conversation. You’ve already built a stable route — all that remains is to begin the journey.

From now on, every conversation with AI will feel less like random Q&A and more like a guided journey. AI’s responses won’t be a pile of information — they’ll align with your needs. And you won’t just be a question‑asker; you’ll be the navigator.

You’ll be able to adjust direction, redefine the problem, and revisit your anchor map whenever you feel lost.

AI, in turn, will respond more clearly because of your clarity. It becomes not just a tool, but a thinking partner walking beside you. And throughout the process, you’ll feel more and more that you’re the one steering the conversation — not the AI.

You already know where you want to go. Now it’s time to learn how to make AI see that direction from your very first sentence.

In the next chapter, we’ll design your “opening line” together — turning your goal into a signal AI can understand right from the start.

References

[1] Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.https://doi.org/10.1037/0003-066X.55.1.68

[2] Wigfield, A., Tonks, S., & Klauda, S. L. (2009). Expectancy-value theory. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 55–75). Routledge. https://doi.org/10.4324/9780203879498

[3] Chang, D. H., Lin, M. P.-C., Hajian, S., & Wang, Q. Q. (2023). Educational design principles of using AI chatbot that supports self-regulated learning in education: Goal setting, feedback, and personalization. Sustainability, 15(17), 12921. https://doi.org/10.3390/su151712921

[4] Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3–17. https://doi.org/10.1207/s15326985ep2501_2

[5] Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7

[6] Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

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