AIIM for innovation.
AIIM for opportunity.
AIIM for leadership.

Award Distribution

Graduate student research assistants, Postdoctoral Research Fellows and non-Faculty Researchers recipients will receive their award via the payroll system. Faculty recipients will have their award transferred to their discretionary research account in their primary unit.

Criteria

Proposals are evaluated on:

• Innovation: Novelty and creativity in the application, development, or assessment of AI.
• Rigor and responsibility: The appropriateness of AI use within the specific research context.
• Impact: Demonstrated and potential of the work to inform the use of AI in research broadly, advance research or solve significant problems.
• Clarity: Well-organized, compelling narrative.
• Alignment: Relevance to AI

For an application to be considered, it must meet the following criteria:

• Relevance to Ongoing Research: The application must be directly related to an ongoing or finished project. This award is not for projects that are still in the planning stage.
• Significant Use of AI: AI must be considered as part of the project in a meaningful way, either as a research tool or as the core focus of the investigation. This includes, but is not limited to, projects in which:
  • AI serves as a primary tool for data analysis, modeling, simulation, prediction, or experimental design.
    Examples include using machine learning algorithms to analyze large datasets, deploying AI-driven models for hypothesis testing, employing natural language processing for text analysis, or leveraging computer vision for interpreting image or video data.
  • AI technologies are integrated into the development or enhancement of research methodologies, instruments, or protocols.
    Examples include creating new AI-based analytical techniques, incorporating AI tools into laboratory workflows, or developing custom AI applications or pipelines to address domain-specific research challenges.
  • The research aims to investigate, develop, or critique AI itself.
    Examples include advancing the theoretical understanding of AI developing novel AI algorithms or architectures; assessing the ethical, social, or policy impacts of AI; studying AI safety or fairness, or exploring human-AI collaboration.
  • AI is used to generate new insights, automate complex tasks, or enable innovative approaches that would be otherwise impractical or impossible.
    Examples include automating repetitive or labor-intensive research tasks, uncovering patterns and relationships in data at an unprecedented scale, or facilitating cross-disciplinary research through intelligent systems.