
Hands-On Learning
Apply generative AI to real research tasks with guided prompts, use cases, and interactive activities.

Research-Focused Instruction
Learn from in-depth, interdisciplinary examples rooted in academic and applied research practice.

Flexible, Self-Paced Study
Work through structured lessons at your own pace, with tools to support your current research projects.
About the Course
Our course on Responsible & Ethical AI in Research is designed to equip researchers with the practical insight and ethical foundations needed to navigate the evolving use of AI in scholarly and applied settings. Whether you're new to generative AI or already using it to support writing, analysis, or peer review, this course offers a structured, in-depth approach to ensuring your AI use remains fair, transparent, and aligned with the values of your field. Join us to develop critical skills that will help you work with AI thoughtfully, rigorously, and with integrity.

Meet Your Instructor
I'm Jonathan Wong, a Lead AI Scientist and researcher specialising in medical diagnostics, data science, and advanced research technologies. My work focuses on applying generative AI and machine learning to improve healthcare outcomes and support complex research workflows. In this course, I share practical tools, ethical strategies, and real-world insights developed through applied experience in research and development. Whether you're just beginning or looking to deepen your skills, you'll find structured guidance to help you use AI effectively, creatively, and responsibly in your research practice.
Curriculum
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1
Introduction
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6.1 Ethics of AI in Research and Academic Integrity
- 6.1.1 Ethical Considerations in AI-Assisted Research AI as an Academic Collaborator Free preview
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6.1.2 Ensuring Academic Integrity When Using Generative AI Tools
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6.1.3 Navigating Ethical Issues in AI’s Role in Writing, Reviewing, and Data Analysis
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6.1.4 How to Maintain Authorship and Originality When AI Tools Are Involved
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6.1.5 The Human Role in Validating and Taking Responsibility for AI-Generated Content
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6.1.6 Key Takeaways
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6.1.7 Knowledge Check
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6.1.A Practical Use Cases
- 6.1.B Learning Activities Free preview
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6.1.C Prompt Templates
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6.1.D Innovative Use Cases
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6.1.E Reflection & Discussion
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6.1.F Productivity Tips
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6.2 AI Hallucinations and Misinformation Risks
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6.2.1 Understanding the Risk of AI-Generated Hallucinations and Misinformation
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6.2.2 Managing Misinformation in AI-Generated Content and Ensuring Factual Accuracy
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6.2.3 Strategies for Identifying and Mitigating AI-Generated Errors in Research
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6.2.4 The Role of AI Verifiability in Academic Contexts Enhancing Trust and Accountability in Research
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6.2.5 Ethical Frameworks to Deal with AI-Generated Inaccuracies in Research Outputs
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6.2.6 Key Takeaways
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6.2.7 Knowledge Check
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6.2.A Practical Use Cases
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6.2.B Learning Activities
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6.2.C Prompt Templates
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6.2.D Innovative Use Cases
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6.2.E Reflection & Discussion
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6.2.F Productivity Tips
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6.3 Data Privacy and AI: What Academics Need to Know
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6.3.1 Data Privacy Concerns When Using AI Tools in Academic Research
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6.3.2 Best Practices for Maintaining Confidentiality and Security When Using AI Assistants
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6.3.3 Navigating GDPR and Other Privacy Regulations When Working with AI in Research
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6.3.4 How AI Can Impact Data Protection and What Precautions to Take
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6.3.5 Ensuring that AI Systems Adhere to Ethical Standards in Data Handling
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6.3.6 Key Takeaways
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6.3.7 Knowledge Check
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6.3.A Practical Use Cases
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6.3.B Learning Activities
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6.3.C Prompt Templates
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6.3.D Innovative Use Cases
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6.3.E Reflection & Discussion
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6.3.F Productivity Tips
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6.4 Bias and Fairness in AI-Generated Research Outputs
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6.4.1 Identifying and Addressing Bias in AI Algorithms and Outputs
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6.4.2 How AI Can Introduce Cultural, Racial, or Gender Biases in Academic Research
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6.4.3 Ensuring Fairness in AI-Generated Conclusions and Findings
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6.4.4 Mitigating Algorithmic Bias in the Research Workflow
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6.4.5 Strategies to Promote Diversity and Inclusivity in AI-Generated Research Outputs
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6.4.6 Key Takeaways
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6.4.7 Knowledge Check
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6.4.A Practical Use Cases
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6.4.B Learning Activities
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6.4.C Prompt Templates
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6.4.D Innovative Use Cases
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6.4.E Reflection & Discussion
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6.4.F Productivity Tips
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6.5 AI in Peer Review and Enhancing Research Transparency
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6.5.1 How AI Can Improve Peer Review Processes by Detecting Errors and Inconsistencies
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6.5.2 Using AI to Support Research Transparency by Providing Automatic Analysis and Recommendations
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6.5.3 The Role of AI in Ensuring Quality Control During the Peer Review Stage
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6.5.4 How AI Tools Can Improve Feedback Quality in Academic Research Reviews
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6.5.5 Enhancing Transparency and Objectivity in the Peer Review System Using AI
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6.5.6 Key Takeaways
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6.5.7 Knowledge Check
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6.5.A Practical Use Cases
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6.5.B Learning Activities
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6.5.C Prompt Templates
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6.5.D Innovative Use Cases
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6.5.E Reflection & Discussion
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6.5.F Productivity Tips
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Final Lesson Message
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Course Complete: Share Your Feedback
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8
Feedback
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End of Course Survey
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Unlock Your Research Potential
Join our course today and unlock the full potential of Generative AI in your research projects. Empower yourself with the knowledge and skills to drive innovation and make a significant impact in your field.
£97.00