This is one of a few PhD proposals I'm refining in the space of Artificial Intelligence and Human interaction. This one asks: to what extent can AI technologies, particularly machine learning and generative modeling, serve as effective collaboration and knowledge enhancing tools to enable non-experts to contribute meaningfully to various scientific disciplines?
This is very early stage and not refined. Lacks a robust literature review but gives a sense of the angle of exploration that interests me.
Leveraging Artificial Intelligence as a Collaborative Tool in Interdisciplinary Scientific Research: An Exploration for Non-Experts
This research aims to explore the vast potential of artificial intelligence (AI) to serve as a collaborator for individuals without specific expertise in a given scientific field. The rapid advancement of AI technologies, such as machine learning and generative modeling, presents an unprecedented opportunity to revolutionize scientific research by enabling non-experts to contribute to various scientific disciplines meaningfully. This study will investigate whether AI can serve as an effective collaborative tool in this context, harnessing its power to handle vast amounts of data and generate and improve data based on training data.
To what extent can AI technologies, particularly machine learning and generative modeling, serve as effective collaboration and knowledge enhancing tools to enable non-experts to contribute meaningfully to various scientific disciplines?
The literature review will examine past and current research on AI-assisted research methodologies, interdisciplinary collaboration, and studies on the efficacy of AI in supporting non-experts in diverse scientific domains. This section will delve into the transformative impact of AI on scientific research, exploring its potential to automate tasks traditionally performed by human scientists and its effectiveness in managing and interpreting enormous amounts of data. Theoretical perspectives on human-AI collaboration, the democratization of science, and the concept of citizen science will also be explored, underpinned by insights into the role of generative modeling in advancing our understanding of scientific phenomena.
The research will employ a mixed methods approach. The quantitative component will involve the use of AI, specifically techniques such as generative modeling and machine learning, to assist in developing several hypotheses in different scientific disciplines. The outcomes will be measured in terms of their scientific value, advancing our understanding of the potential for AI to automate various aspects of the scientific process. The qualitative component will involve case studies on AI-human collaboration in scientific research, interviews with non-experts to assess their experience using AI, and expert assessment of the research output facilitated by AI.
Significance and Impact:
This research will contribute to the ongoing discourse on the role of AI in interdisciplinary scientific research. It will provide insights into how AI tools can democratize access to scientific research and enhance the capacity of individuals to contribute meaningfully across various scientific disciplines, regardless of their specific expertise. It could be beneficial for scientists, educators, policymakers, and anyone interested in citizen science, AI in science, or interdisciplinary research.
Months 1-6: Literature Review
Months 6-12: Developing the AI-assisted research methodology
Months 12-24: Execution of AI-assisted hypotheses generation and testing
Months 24-30: Case studies and interviews
Months 30-36: Data Analysis
Months 36-42: Writing and Revisions
Month 42: Final Submission
Also for the the fun of it here are a couple of generative artworks I created exploring human and Ai collaboration.