AI in Scientific Practice, Research and Innovation

About This Course

1. Describe how AI-enabled tools can support life sciences, research, laboratory, quality, regulatory, and scientific operations tasks such as literature review, data interpretation, documentation, workflow planning, reporting, communication, and process improvement.
2. Assess where Generative AI may add value in scientific workplaces, including research support, laboratory documentation, quality checks, regulatory-related drafting, knowledge management, operational efficiency, and innovation.
3. Demonstrate basic digital literacy in interacting with AI tools, including crafting clear and purposeful prompts, providing relevant context, refining outputs, and using AI-generated content appropriately for work-related tasks.
4. Review AI outputs critically to identify potential issues such as misinformation, unsupported claims, bias, incomplete information, lack of scientific context, flawed interpretation, or outputs that may affect quality, reproducibility, and decision-making.
5. Identify issues such as data privacy, confidentiality, intellectual property, bias, fairness, transparency, over-reliance, research integrity, reproducibility, and accountability when using AI and digital tools.
6. Recognise the importance of using AI in ways that are appropriate, evidence-informed, quality-conscious, and aligned with workplace expectations, data protection practices, and scientific standards.
7. Examine how AI may influence job roles, work processes, professional responsibilities, productivity, decision-making, collaboration, innovation, public trust, and future skills in digitally enabled scientific workplaces.
8. Explain how AI can enhance productivity, scientific quality, communication, operational efficiency, and innovation while maintaining professional judgement, human oversight, research integrity, and accountability.