5 AI Prompts Grounded in Motivation Science (Copy and Use These)

Generic AI prompts produce generic output. The prompts below are each grounded in a specific motivation science mechanism — SDT need theory, expectancy-value theory, implementation intention research, or Fredrickson’s broaden-and-build theory.

Copy them directly. Replace the brackets with your specifics. The theory behind each one is noted briefly so you can adapt when needed.

Prompt 1: The SDT Three-Need Diagnostic

When to use it: A goal has stalled for more than three days without an obvious reason. You still care about it but are not working on it.

The science: Self-Determination Theory predicts that motivation degrades when autonomy, competence, or relatedness is frustrated. Diagnosing which need is the problem determines the correct response.

The prompt:

“I’ve been avoiding working on [goal/project] for [timeframe] without a clear reason. I want to identify which of the three SDT psychological needs is frustrated: autonomy (does this feel like my choice?), competence (do I believe I can succeed?), or relatedness (is this connected to people or purposes I care about?).

Ask me five to seven diagnostic questions — at least two targeting each need — and then tell me which need seems most frustrated based on my answers. Be direct. If multiple needs are frustrated, rank them.”

What to do with the output: Address the most frustrated need specifically. Autonomy → reclaim ownership of how you approach the work. Competence → decompose to a smaller first step. Relatedness → name one person who benefits and have a real conversation with them.

Prompt 2: Outside-View Expectancy Calibration

When to use it: You are starting a new project or restarting a stalled one, and you want to build a realistic plan.

The science: Research on planning fallacy (Kahneman, Buehler) shows that people systematically underestimate completion time and overestimate their ability to navigate obstacles. The “outside view” — treating the current project as an instance of a class of similar projects — produces better calibration.

The prompt:

“I’m planning to [describe project] by [target date]. Apply the outside view: ask me about three to four similar projects I’ve attempted in the past and what actually happened with them. Use my answers to identify my systematic underestimation patterns — the specific types of complexity, obstacles, or scope creep I consistently fail to predict. Then help me build a revised plan that accounts for those patterns explicitly, including buffer time and pre-committed responses to the obstacles most likely to derail this project.”

What to do with the output: Add the identified obstacles to your plan as named risks with explicit if-then responses. Adjust the timeline to include buffers in the places where you typically underestimate.

Prompt 3: Implementation Intention Generator

When to use it: You have a clear goal but keep finding reasons not to start. Intentions are not translating to action.

The science: Gollwitzer’s implementation intention research across two decades shows that pre-specifying the when, where, and what of an action dramatically increases follow-through by offloading the start decision to context rather than requiring deliberate choice.

The prompt:

“I intend to [goal or task] but keep not starting it. Convert this intention into a set of implementation intentions using this structure: ‘When [specific situation — time, place, or cue], I will [specific action] in [specific location].’

Generate three to five implementation intentions for different likely contexts (morning routine, after a specific regular event, when a specific cue appears). Each specific action should take 20 to 30 minutes or less. Flag any that are still too vague to act on directly.”

What to do with the output: Enter the most applicable implementation intention into your calendar or task system as a recurring event linked to the cue. Do not rely on deciding to act; let the cue trigger it.

Prompt 4: Overjustification Risk Check

When to use it: You are considering adding an external accountability system, reward, or tracking mechanism to a goal you already care about intrinsically.

The science: The overjustification effect (Lepper, Greene, and Nisbett, 1973; extensively replicated) shows that introducing expected external rewards for activities already found intrinsically interesting reduces subsequent intrinsic motivation by shifting the perceived cause of the activity from internal to external.

The prompt:

“I’m thinking about adding [external system — streak tracking, financial commitment, accountability partner, points] to motivate myself on [goal]. Before I do, help me assess overjustification risk.

Ask me: Do I currently find this activity intrinsically interesting or was it already aversive before I tried to motivate it? If intrinsically interesting, help me think through whether adding this external system risks shifting my motivation from internal to external — and whether there is a lower-risk structural change that would address the same problem without introducing a controlling external reward.”

What to do with the output: If the goal was already intrinsically interesting, consider structural changes (reducing friction, implementing intentions) before adding external rewards. If the activity was already aversive, the overjustification risk is lower and external structure is more appropriate.

Prompt 5: Broaden-and-Build Emotional Substrate Check

When to use it: You are productive on paper but the dominant experience of your work is dread, anxiety, or low-grade stress. You want to distinguish between informative negative signals and habitual ones.

The science: Fredrickson’s broaden-and-build theory shows that chronic negative emotional states narrow cognitive resources and impair the flexible thinking that complex work requires. Distinguishing informative negative signals (something is actually wrong) from habitual ones (residue from past associations) determines the appropriate response.

The prompt:

“My honest emotional experience when I think about [project/work area] is [describe it]. Help me figure out whether this feeling is informative or habitual.

Informative: it is pointing to a real problem I need to address — something wrong with the goal, the plan, my conditions, or my relationships to the work. Habitual: it is residue from past negative associations that no longer accurately reflects the current situation.

Ask me questions that would help distinguish these two cases, then give me a clear assessment. If informative, help me identify what specifically needs to change. If habitual, help me identify what genuine positive element of this work I could acknowledge to start shifting the emotional baseline.”

What to do with the output: If informative — act on the signal. If habitual — do not try to manufacture positive feelings, but identify one genuine small win or meaningful element to acknowledge at the start of each work session. Fredrickson’s research does not recommend positive thinking; it recommends noticing genuine positive elements that already exist.

Using These Together

The five prompts form a diagnostic toolkit rather than a sequence. When motivation is specifically stalled: Prompt 1. When a plan is unrealistic: Prompt 2. When intentions are not translating to action: Prompt 3. When you are considering adding external incentives: Prompt 4. When the work feels chronically draining: Prompt 5.

Start with Prompt 1 on any goal that has been stalled for more than a week. The SDT diagnostic will usually point toward which other prompt to use next.

Related:

• The Complete Guide to Motivation Science and AI
• How to Apply Motivation Science with AI
• The Motivation Science Framework: DRIVE
• Beyond Time + Motivation Science Walkthrough

Tags: AI prompts, motivation science, SDT diagnostic, implementation intentions, expectancy-value theory