
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 Generative AI for Evidence Synthesis & Systematic Reviews is designed to equip researchers with practical, structured guidance for integrating AI into review workflows. From literature screening to summary writing, you’ll learn where and how AI can support speed, consistency, and insight—while maintaining academic rigour and transparency. Whether you're new to systematic reviews or refining an established process, this course offers the tools and critical perspective needed to work effectively with generative AI.

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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2
4.1 AI-Assisted Literature Screening and Thematic Analysis
- 4.1.1 Using AI to Automatically Screen Research Papers for Systematic Reviews Free preview
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4.1.2 AI Tools for Conducting Thematic Analysis of Existing Research
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4.1.3 Identifying Key Patterns and Extracting Relevant Findings from Large Datasets
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4.1.4 Automating the Process of Categorising Research for Easier Review
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4.1.5 Using AI to Support Robust and Comprehensive Evidence Synthesis
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4.1.6 Key Takeaways
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4.1.7 Knowledge Check
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4.1.A Practical Use Cases
- 4.1.B Learning Activities Free preview
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4.1.C Prompt Templates
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4.1.D Innovative Use Cases
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4.1.E Reflection & Discussion
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4.1.F Productivity Tips
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4.2 Structuring a Systematic Review with AI Support
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4.2.1 How AI Helps in Structuring Systematic Reviews Using Pre-Built Templates
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4.2.2 AI’s Role in Summarising Research Findings and Identifying Areas of Consensus
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4.2.3 Automating the Organisation and Synthesis of Research Data for Reviews
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4.2.4 Using AI to Ensure Consistency and Accuracy in Systematic Review Formats
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4.2.5 AI Tools for Creating Clear and Reproducible Systematic Reviews with Minimal Human Input
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4.2.6 Key Takeaways
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4.2.7 Knowledge Check
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4.2.A Practical Use Cases
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4.2.B Learning Activities
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4.2.C Prompt Templates
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4.2.D Innovative Use Cases
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4.2.E Reflection & Discussion
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4.2.F Productivity Tips
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4.3 AI for Data Extraction and Critical Appraisal
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4.3.1 Leveraging AI Tools to Automate Data Extraction from Research Papers
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4.3.2 AI’s Role in Critical Appraisal of Study Quality and Evaluating Bias
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4.3.3 Using AI-Driven Insights to Assess the Credibility and Relevance of Studies
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4.3.4 Ensuring Methodological Rigour and Consistency in AI-Assisted Reviews
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4.3.5 How AI Enhances the Quality of Data Extraction from Large-Scale Studies
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4.3.6 Key Takeaways
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4.3.7 Knowledge Check
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4.3.A Practical Use Cases
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4.3.B Learning Activities
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4.3.C Prompt Templates
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4.3.D Innovative Use Cases
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4.3.E Reflection & Discussion
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4.3.F Productivity Tips
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4.4 Conversational AI for Writing Review Summaries
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4.4.1 How Conversational AI Generates Clear and Concise Summaries for Systematic Reviews
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4.4.2 Ensuring Accuracy and Coherence in AI-Generated Summaries of Research Findings
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4.4.3 Cross-Referencing AI-Generated Summaries with Primary Sources for Consistency
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4.4.4 Tools for Generating Accessible Summaries for a Non-Expert Audience
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4.4.5 Customising AI-Generated Content to Fit Review Scope and Intended Audience
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4.4.6 Key Takeaways
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4.4.7 Knowledge Check
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4.4.A Practical Use Cases
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4.4.B Learning Activities
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4.4.C Prompt Templates
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(Included in full purchase)
4.4.D Innovative Use Cases
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4.4.E Reflection & Discussion
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4.4.F Productivity Tips
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4.5 Addressing Reproducibility Concerns in AI-Assisted Reviews
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4.5.1 Best Practices for Ensuring Reproducibility When Using AI in Systematic Reviews
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4.5.2 How to Address Potential Biases in AI Outputs for Transparent Research
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4.5.3 Ensuring Transparency in AI-Assisted Reviews to Maintain Scientific Integrity
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4.5.4 Methods to Keep AI-Assisted Reviews Consistent and Reliable Across Different Datasets
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4.5.5 Documenting AI-Driven Processes to Allow Easy Replication of Reviews
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4.5.6 Key Takeaways
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4.5.7 Knowledge Check
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4.5.A Practical Use Cases
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(Included in full purchase)
4.5.B Learning Activities
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(Included in full purchase)
4.5.C Prompt Templates
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(Included in full purchase)
4.5.D Innovative Use Cases
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(Included in full purchase)
4.5.E Reflection & Discussion
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4.5.F Productivity Tips
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7
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