A FAIR and Free Prompt-based Research Assistant

Abstract: This demo will present the Research Assistant (RA) tool developed to assist with six main types of research tasks defined as standardized instruction templates, instantiated with user input, applied finally as prompts to well-known--for their sophisticated natural language processing abilities--AI tools, such as ChatGPT (https://chat.openai.com/) and Gemini (https://gemini.google.com/app). The six research tasks addressed by RA are: creating FAIR research comparisons, ideating research topics, drafting grant applications, writing scientific blogs, aiding preliminary peer reviews, and formulating enhanced literature search queries. RA's reliance on generative AI tools like ChatGPT or Gemini means the same research task assistance can be offered in any scientific discipline. We demonstrate its versatility by sharing RA outputs in Computer Science, Virology, and Climate Science, where the output with the RA tool assistance mirrored that from a domain expert who performed the same research task.

• The paper introduces a FAIR research assistant that streamlines six critical research tasks using standardized AI prompts.
• It leverages LLMs like ChatGPT and Gemini through a user-friendly React-based web application for efficient data management.
• The approach democratizes research by enhancing accessibility, interoperability, and reuse, thereby boosting productivity across disciplines.

An Overview of the FAIR and Free Prompt-based Research Assistant

The paper introduces a novel tool titled Research Assistant (RA), designed to enhance research productivity through the incorporation of LLMs, notably ChatGPT and Gemini. RA is engineered to aid researchers across various disciplines by effectively executing six distinct types of research tasks through standardized instruction templates, which are transformed into AI prompts. These tasks are tailored to generate comparable outputs alongside domain experts, crucially adhering to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. The versatility and interdisciplinary applicability of RA are demonstrated through examples spanning domains like Computer Science, Virology, and Climate Science.

Research Tasks and Implementation

RA supports six primary research tasks, each leveraging the natural language processing capabilities of AI tools:

1. FAIR Research Comparisons: This is the central feature, allowing fine-grained comparisons across research contexts.
2. Research Ideation: By providing a focused context, RA stimulates new research topics aligned with salient properties of a given issue.
3. Grant Drafting: Offers assistance in formulating grant proposals by outlining a preliminary structure, which researchers can further develop.
4. Scientific Blogging: Converts complex scientific findings into simplified narratives suitable for wider audiences.
5. Preliminary Peer Reviews: Aids researchers in drafting preliminary reviews by highlighting key aspects of manuscripts.
6. Enhanced Literature Searches: Optimizes search strategies by expanding keyword synonym sets, improving the comprehensiveness of literature retrieval.

The task scenarios are meticulously designed to ensure that researchers can harness the potential of AI in performing comprehensive and comparative analyses efficiently. These tasks are adaptable to a wide range of scientific inquiries, inherently demonstrating RA's domain-agnostic nature.

Architectural and Technical Implementation

RA is implemented as a user-friendly, web-based application using React. Its architecture comprises four main components that synergize to streamline user input, generate standardized prompts, and facilitate the comparison of AI-generated data. RA integrates various third-party libraries for tasks such as UI development, data exporting, and graphical enhancements, ensuring seamless user interaction and data management.

Implications and Future Prospects

The implications of RA are extensive both practically and theoretically. Practically, it allows for democratized access to research tools that are normally gated behind barriers such as cost and complexity, thereby potentially increasing the productivity and inclusivity of scientific inquiry. Theoretically, RA exemplifies a functional model of integrating AI with scholarly endeavors to enhance data visibility, replicability, and reuse, all crucial for fostering more connected and transparent scientific ecosystems.

Future prospects of RA could include enhancements driven by feedback from actual research use cases to ensure usability and effectiveness. Additionally, expanding the capabilities of RA to cover more nuanced aspects of research tasks, such as deeper integration with existing research knowledge graphs and databases, could further amplify its utility. As the dependence on AI-driven methodologies increases, tools like RA become increasingly essential, providing scalable, interdisciplinary solutions to evolving research challenges.

Overall, this paper delineates the value of combining LLMs with structured research assistance tasks, illuminating pathways for future research tool development in a rapidly evolving digital landscape.