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AI is growing and improving, and the quality of the data we use today plays a crucial role in its future accuracy. By prioritising reliable information, we can enhance AI’s effectiveness. Additionally, protecting privacy and confidentiality will help build trust and ensure integrity in its development. |
Generative AI is now part of the scientific process. It acts not just as a tool but as a partner that changes alongside human authorship. This relationship means both scientists and AI influence each other. As scientists adapt their work to include AI, the AI also improves by learning from these interactions (Watson et al., 2025).
Edith Cowan University has created guidelines to help academic staff and Higher Degree by Research (HDR) candidates responsibly use Generative AI (GenAI) tools in their research. These guidelines offer advice for effectively integrating GenAI into existing research practices.
Guideline for the Responsible use of Generative AI in Research
AI is now integrated across the entire research data lifecycle - from data collection to publishing - improving efficiency at every stage.
Research Data Management
Image adopted from: https://www.zi.uzh.ch/en/teaching-and-research/science-it/experts/expert-rdm.html
Enhance your knowledge with Elsevier's self-paced courses on the GenAI Literacy program. Use your ECU email address to access them.
The GenAI Literacy Program, part of Content Academy, offers a comprehensive suite of courses designed to deepen your understanding of generative AI (GenAI). The program includes:
- Foundations of AI
This course covers the fundamental concepts of GenAI. It helps participants understand AI literacy and demystifies the mechanics behind GenAI tools.
- Benefits & Risks of AI
This course explores the opportunities and challenges presented by GenAI. It highlights the academic activities enhanced by GenAI and identifies key pain points and limitations of the technology.
- Responsible AI
This course focuses on responsible practices in using and developing GenAI. It uses Elsevier's five responsible AI principles as a framework to explore what "responsible" means for different AI stakeholders.
AI has the potential to enhance various academic functions across six key domains
AI has the potential to enhance various academic functions across six key domains (Khalifa et al., 2024):
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Research and Analysis Domain |
Idea Development and Research Design |
Content Development and Structuring |
Literature Review and Synthesis |
Data Management and Analysis |
Editing, Review, and Publishing Support |
Communication, Outreach, and Ethical Compliance |
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AI's Contribution |
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How AI improves?
| Research and Planning | Data Collection | Data Processing and Analysis | Sharing and Publication | Preserving and Reusing | |
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Generates research topics by analysing keywords and providing suggestions, enhancing the research planning phase. Also, predicts future trends based on historical data. |
Automates data gathering, e.g., scraping websites. | Identifies outliers, standardises formats, detects missing data. Predictive modelling, statistical analysis, pattern recognition. | Drafting papers, generating text, improving grammar, brainstorming ideas. | The key to making data reusable is to ensure it is machine-actionable and preserved for future use; AI excels at this. Clear and standardised metadata enables both people and machines to utilise data more effectively (Bishop, 2023). AI predicts and can mitigate risks associated with data degradation and obsolescence. | |
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Tool Example: Research Rabbit, Aris AI, Scite, Consensus, ChatGPT |
Tool Example: OpenRefine, DataRobot, ChatGPT, DataCleaner |
Tool Example: ChatGPT, DataRobot, IBM Watson, Orange, KNIME |
Tool Example: Grammarly, Ref-N-Write, Hemingway Editor |
Tool Example: OpenRefine, Datadog, Pandas |
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