AI Skill Report Card
Developing Research Roadmaps
Quick Start15 / 15
Create a research roadmap by addressing these four core elements:
- Problem Definition - What specific gap are you addressing?
- Technical Approach - How will you solve it systematically?
- Innovation Points - What's novel about your methodology?
- Impact Assessment - Why does this matter to the field?
Recommendation▾
Add more concrete input/output examples showing the actual roadmap structure with timelines, milestones, and resource allocation
Workflow13 / 15
Phase 1: Foundation Analysis
- Define the research problem with precision
- Review existing solutions and identify gaps
- Establish success criteria and measurable outcomes
Phase 2: Technical Architecture
- Break down the problem into manageable components
- Design the methodological framework
- Identify required resources and expertise
- Map dependencies between work packages
Phase 3: Innovation Framework
- Highlight novel contributions to the field
- Differentiate from existing approaches
- Validate technical feasibility
Phase 4: Impact Planning
- Define target beneficiaries
- Outline knowledge transfer mechanisms
- Plan dissemination strategy
Recommendation▾
Include template frameworks or standardized formats that researchers can directly copy and adapt
Examples17 / 20
Example 1: Input: AI-based medical diagnosis system Output:
- Problem: Current diagnostic accuracy limitations in rare diseases
- Approach: Multi-modal deep learning with federated training
- Innovation: Privacy-preserving cross-institutional learning
- Impact: 30% improvement in rare disease detection rates
Example 2: Input: Sustainable energy storage research Output:
- Problem: Grid-scale energy storage cost and efficiency barriers
- Approach: Novel electrode materials with AI-optimized synthesis
- Innovation: Bio-inspired hierarchical structures
- Impact: 50% cost reduction for renewable energy integration
Recommendation▾
Expand the workflow with specific deliverables and decision points for each phase to make it more actionable
Best Practices
- Start with clear problem boundaries - avoid overly broad scope
- Use quantifiable metrics for success criteria
- Build logical progression from fundamental research to applications
- Include risk mitigation strategies for each major milestone
- Align innovation claims with actual technical advances
- Connect research outcomes to societal or economic benefits
Common Pitfalls
- Confusing technical complexity with innovation quality
- Underestimating resource requirements for validation phases
- Creating roadmaps without clear go/no-go decision points
- Ignoring existing solutions when claiming novelty
- Overpromising impact without realistic timelines
- Focusing solely on technical metrics while ignoring practical constraints