I. Foundations & Purpose
Define the primary goal of using AI as a thinking partner.
Identify tasks where AI can enhance human reasoning.
Specify desired output types (ideas, plans, analyses).
Clarify assumptions about AI capabilities.
Determine scope and boundaries for AI collaboration.
Define success metrics for AI-human thinking partnerships.
Identify contexts where AI input is most valuable.
Specify constraints on AI guidance or suggestions.
Model interaction frequency between human and AI.
Determine ethical boundaries for AI involvement.
II. Cognitive Collaboration
Model AI assisting with brainstorming sessions.
Evaluate AI’s role in structured problem-solving.
Assess AI support in decision-making under uncertainty.
Model AI as a critical thinking partner.
Evaluate AI support in hypothesis generation.
Detect areas where AI can challenge assumptions.
Model AI assisting in idea prioritization.
Assess AI’s role in alternative scenario exploration.
Evaluate stepwise reasoning collaboration.
Prioritize tasks where AI collaboration has the highest impact.
III. Knowledge Integration
Model AI providing relevant knowledge to human reasoning.
Evaluate AI summarization of complex information.
Assess AI’s ability to connect cross-domain insights.
Model AI identification of knowledge gaps.
Evaluate AI’s capacity to propose evidence-based suggestions.
Detect inconsistencies in information provided by AI.
Assess AI’s contribution to research synthesis.
Model AI providing context-aware knowledge.
Evaluate AI-assisted literature mapping.
Prioritize knowledge integration points with highest human benefit.
IV. Idea Generation
Model AI-assisted brainstorming for creative solutions.
Assess AI ability to produce diverse perspectives.
Evaluate AI support for out-of-the-box thinking.
Detect patterns and themes in human-generated ideas.
Model stepwise refinement of AI-generated ideas.
Evaluate AI suggestion relevance to human goals.
Assess novelty vs practicality of AI proposals.
Model AI-assisted idea combination and synthesis.
Evaluate AI’s role in structured creativity exercises.
Prioritize AI contributions with high-value potential.
V. Scenario Exploration
Model AI-assisted scenario analysis.
Evaluate alternative outcomes proposed by AI.
Assess stepwise reasoning for long-term impact.
Model “what-if” explorations with AI support.
Detect cascading consequences of decisions.
Evaluate probability-based reasoning in scenario planning.
Assess AI’s ability to surface hidden dependencies.
Model cross-domain scenario connections.
Evaluate AI-generated risk assessments.
Prioritize scenarios with highest strategic relevance.
VI. Problem Structuring
Model AI assistance in decomposing complex problems.
Evaluate AI role in identifying sub-problems.
Assess AI’s ability to detect interdependencies.
Model stepwise problem-solving approaches.
Evaluate alternative problem decomposition strategies.
Detect gaps in problem understanding.
Assess AI suggestion for prioritizing sub-problems.
Model causal mapping with AI guidance.
Evaluate AI role in structured decision trees.
Prioritize high-impact sub-problems for AI focus.
VII. Hypothesis Generation & Testing
Model AI proposing hypotheses for exploration.
Evaluate AI-assisted refinement of hypotheses.
Assess AI support in designing experiments or tests.
Model stepwise testing of assumptions.
Evaluate AI suggestion of alternative explanations.
Detect bias or gaps in human reasoning.
Model AI-assisted data-driven hypothesis validation.
Assess probability estimation for competing hypotheses.
Evaluate iterative AI-human hypothesis development.
Prioritize high-stakes hypotheses for AI input.
VIII. Decision Support
Model AI providing structured decision options.
Evaluate AI-assisted pros and cons analysis.
Assess AI’s ability to highlight trade-offs.
Model stepwise evaluation of decision paths.
Evaluate probabilistic decision support.
Detect cognitive biases mitigated by AI suggestions.
Assess alignment of AI proposals with human goals.
Model prioritization of options based on impact.
Evaluate AI-generated scoring of alternatives.
Prioritize decisions with the highest potential for AI augmentation.
IX. Risk & Opportunity Analysis
Model AI-assisted identification of potential risks.
Evaluate AI detection of hidden opportunities.
Assess stepwise risk mitigation strategies.
Model probabilistic assessment of outcomes.
Evaluate AI suggestions for opportunity capitalization.
Detect early warning signals in complex scenarios.
Model AI contribution to scenario-based risk planning.
Assess AI support in sensitivity analysis.
Evaluate cumulative impact of AI insights on decisions.
Prioritize high-impact risk and opportunity analysis.
X. Iterative Thinking
Model AI support in stepwise reasoning loops.
Evaluate iterative refinement of ideas with AI.
Assess feedback integration from AI suggestions.
Model AI challenge to human assumptions.
Evaluate alternative reasoning paths with AI input.
Detect reasoning gaps revealed by AI.
Model iterative hypothesis testing cycles.
Assess AI contribution to continuous improvement.
Evaluate long-term learning from AI-human interaction.
Prioritize iterative processes with highest potential insights.
XI. Creativity & Innovation
Model AI-assisted design thinking exercises.
Evaluate AI role in lateral thinking.
Assess AI ability to combine diverse ideas.
Model innovation pipelines with AI input.
Evaluate AI contribution to radical vs incremental innovation.
Detect unexplored opportunities surfaced by AI.
Model AI suggestion for experimental approaches.
Assess stepwise creative idea refinement.
Evaluate AI support in prototyping and simulation.
Prioritize AI contributions to high-impact innovation areas.
XII. Knowledge Gap Identification
Model AI detection of missing information.
Evaluate AI role in surfacing implicit assumptions.
Assess stepwise identification of unknown variables.
Model AI-assisted prioritization of information needs.
Evaluate potential biases due to knowledge gaps.
Detect overlooked scenarios or factors.
Model cross-domain knowledge integration.
Assess AI ability to suggest data collection strategies.
Evaluate cumulative knowledge improvement.
Prioritize critical gaps for AI-assisted exploration.
XIII. Ethical & Value Considerations
Model AI highlighting ethical implications of ideas.
Evaluate AI detection of value conflicts.
Assess AI support in balancing stakeholder interests.
Model AI role in ethical trade-off analysis.
Evaluate alignment of AI suggestions with human values.
Detect unintended consequences through AI reasoning.
Model stepwise ethical risk assessment.
Assess fairness and equity in AI-assisted decisions.
Evaluate compliance with organizational or societal norms.
Prioritize ethical considerations for critical decisions.
XIV. Visualization & Concept Mapping
Model AI-assisted concept mapping.
Evaluate visualization of complex problem structures.
Assess AI support for relationship and dependency mapping.
Model idea clustering and pattern detection.
Evaluate scenario mapping with AI assistance.
Detect gaps or inconsistencies in conceptual maps.
Model iterative refinement of visual representations.
Assess clarity of AI-generated diagrams.
Evaluate AI assistance in cognitive load reduction.
Prioritize visualization for high-complexity areas.
XV. Human-AI Interaction Design
Model AI as an active discussion partner.
Evaluate clarity of AI prompts for reasoning support.
Assess AI suggestions for stepwise reflection.
Model feedback loops between human and AI.
Evaluate AI’s role in questioning assumptions.
Detect overreliance or underutilization of AI.
Model adaptive interaction strategies.
Assess transparency of AI reasoning.
Evaluate collaborative decision-making workflows.
Prioritize interaction mechanisms for maximizing thinking efficiency.
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