light-idea-generation

Generate structured research ideas by inputting project context and data to trigger level-aware ideation workflows, applying literature-backed novelty assessment, feasibility checks, and an evaluation rubric to output a ranked shortlist with supporting evidence.

Evaluating research feasibility and impact requires applying a rigorous evaluation rubric to your project context and data, producing a ranked shortlist of idea candidates with detailed justification, most-similar prior work, and data or compute estimates.

Prepare project context and data for research idea generation by defining your Level 1 clear direction or Level 2 data-driven direction, ensuring you outline background, data availability, tech stack, and timeline to generate actionable plans.

Yes, you can use OpenAlex for literature reviews to support novelty assessment during research design, integrating the findings with data feasibility and project constraints to produce structured ideation outputs.

Yes, this method supports both Level 1 clear direction and Level 2 data-driven or literature-driven ideation, applying level-agnostic justification and traceable evidence to generate governance-ready idea candidates.

Automated research idea generation outputs a ranked shortlist and supporting evidence formatted for critique, but it requires defined project context and data constraints, meaning it cannot replace independent validation during final project design.