Research

Editing and Identifying Plagiarism

Using applications such as Grammarly, Paperpal, and iThenticate

Literature Review

Using applications such as Semantic Scholar, AI2 Paper and AI2 Scholar

Coastal and Marine Innovation:

AI allows researchers to process vast amounts of oceanographic data from sensors and satellites, detecting patterns in marine biodiversity, mapping coastal changes, and monitoring marine life.

• Example: AI in Support of Coastal Resilience, a NOAA project.

Environmental Science

AI accelerates the analysis of environmental data, enabling faster modeling and prediction of natural events such as storms, droughts, and biodiversity shifts. Machine learning supports resource management and optimizes conservation strategies.

• Example: NOAA Research develops an AI-powered sibling to its flagship weather model

Biomedical Science

AI advances diagnostic imaging (for example in radiology and  pathology), drug discovery, personalized medicine, and patient monitoring by analyzing complex medical data for more accurate detection and tailored treatments.

• Example: Managing Risks in AI-Powered Biomedical Research, Stanford University research project

Smart Resilient Communities

Well-resourced cities use AI for predictive traffic management, optimizing energy and waste systems, enhancing public safety, and improving disaster response. AI-driven decision support increases community resilience to crises such as extreme weather or infrastructure failures.

• Example: AI-powered Social Innovation to Build More Resilient Cities, an IBM project

Environmental Sustainability

AI aids in monitoring air and water quality, energy efficiency, waste reduction, and climate modeling. Predictive analytics help identify pollution sources and optimize resource management for greener infrastructure and agriculture.

• Example: Hydrologists to transform climate projections with microbial data and artificial intelligence, a research project at the University of Arizona

Education and Workforce Development

AI personalizes learning, bridges skills gaps, and provides adaptive training in STEM and beyond. Educational partnerships with industry help align curricula with workforce demands, preparing students for evolving technology careers.

• Example: INclusiVe Intelligent Technologies for Education, a project by researchers at the University of Illinois, Urbana-Champaign

Data Science and Remote Sensing

AI automates the analysis of data obtained via satellites, drones, and other sensors, enabling large-scale, timely detection of land, ocean, and atmospheric changes. Deep learning models classify imagery for ecosystem monitoring, disaster response, and urban development.

• Example: Earth Observation Data and Artificial Intelligence, a NASA project

Water Security and Infrastructure

AI allows monitoring water quality in real time, predicting equipment failures, and optimizing distribution and leak detection. Smart algorithms enhance operational efficiency for water utilities and predict supply-demand mismatches, supporting sustainable resource management.

• Example: AI Improves USGS Science and Efficiency, by USGS Earth Resources Observation and Science (EROS) Center

Researchers are encouraged to be aware of potential bias in AI algorithms and ensure that AI-powered research results are trustworthy and representative.

It is essential to acknowledge when AI is used in producing research results or manuscripts. Researchers are highly encouraged to study the guidelines provided by publications regarding the use of generative AI. Some, such as IEEE, require that authors acknowledge the level the AI system is used to generate the content such as text, image, figures, or code.

Researchers must be cognizant of how AI tools handle intellectual property rights, as by using contents generated by such tools, they could be violating copyright, patent, or trademark infringement laws.

• AI Tools: Annotated list of AI tools used specifically for research, writing, and summarizing tasks, as well as image, video and voice audio generation.
  
  
• Citing AI: Provides citation guidelines in APA, MLA, and Chicago formats.
  
  
• Literature Review: Offers guidance on using AI tools to assist with creating a literature review.
  
  
• Ethical considerations: Infographic that outlines the ethical implications of ai-assisted research.
  
  
• What is Prompt Engineering? This guide explains the value of prompt engineering and offers practical strategies, such as the CLEAR framework and Zero-, One-, and Few-shots prompting, to get effective results from Gen AI tools.