5.00
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AI-Powered Systematic Literature Reviews and Structural Topic Modeling

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About Course

This advanced masterclass moves beyond foundational AI applications, empowering you to harness sophisticated artificial intelligence, including powerful topic modeling techniques like Latent Dirichlet Allocation (LDA) and Structural Topic Modeling (STM), to conduct systematic literature reviews with unparalleled depth and efficiency.

You will journey from crafting highly precise, AI-refined research questions to deploying intelligent data acquisition strategies using the latest AI tools for comprehensive searching, screening, and crucial metadata collection. Dive deep into the analytical core of modern research by mastering LDA and STM to uncover latent thematic structures and trends within vast literary corpora. Learn to synthesize these complex findings and develop robust theories, all augmented by AI-driven knowledge discovery.

The course culminates in mastering AI-assisted reporting, creating impactful data visualizations, and navigating the ethical landscape of AI in academic writing. As a capstone to your advanced learning, you will be introduced to the transformative concept of AI-powered Living Systematic Reviews, preparing you to maintain and contribute to continuously updated evidence syntheses in rapidly evolving fields.

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What Will You Learn?

  • Upon completing this masterclass, you will be equipped to:
  • Design AI-Sharpened Research Inquiries:
  • Formulate and refine research questions with exceptional precision using advanced AI assistants.
  • Develop robust PICO(S) frameworks with AI support to ensure focused and comprehensive reviews.
  • Strategically scope your reviews and verify novelty by leveraging AI to analyze the existing research landscape.
  • Execute Intelligent Data Acquisition:
  • Employ advanced AI-driven search techniques, including semantic search and visual literature exploration, to uncover a wider range of relevant studies.
  • Master AI-powered screening tools and methodologies for efficient and accurate study selection.
  • Systematically collect and manage comprehensive metadata crucial for advanced analytical techniques like Structural Topic Modeling.
  • Achieve Deep Synthesis with Topic Modeling:
  • Implement Latent Dirichlet Allocation (LDA) using tools like Gensim or scikit-learn to identify and interpret dominant thematic structures in large literature sets.
  • Utilize Structural Topic Modeling (STM) with the stm R package to analyze how research themes evolve and vary in relation to document metadata (e.g., publication year, journal type).
  • Leverage sophisticated AI tools for high-accuracy data extraction from diverse sources, including PDFs and tables, to feed into your analyses.
  • Synthesize quantitative insights from topic models with qualitative evidence to develop nuanced theories and discover novel research connections.
  • Communicate Your Research with Impact and Integrity:
  • Utilize advanced AI writing assistants for drafting clear, coherent, and high-quality research reports.
  • Generate compelling and informative visualizations from your topic models and literature network analyses to effectively communicate complex findings.
  • Critically evaluate and apply ethical principles for the responsible use of AI in all stages of research writing and reporting.
  • Pioneer Dynamic Evidence with Living Systematic Reviews:
  • Understand the principles and strategic importance of Living Systematic Reviews (LSRs) in rapidly advancing fields.
  • Explore AI tools and frameworks (e.g., DistillerSR, Elicit) that automate the continuous updating processes inherent in LSRs, including ongoing search, screening, and data integration.

Course Content

Module 1: AI-Assisted Inquiry: Advanced Research Question Design and Scoping
This module would focus on leveraging AI for more precise research question formulation, including PICO(S) development and ensuring novelty through AI-driven landscape analysis.

  • Fundamentals of Systematic Literature Review
    11:46
  • Understanding Basics of Research Question
    13:19
  • Live session 1: AI-Assisted Inquiry: Advanced Research Question Design and Scoping
    01:16:06
  • Designing AI-Powered Research Questions
    13:34
  • AI-Powered Research Question Tutorial (Phase 1)
    21:20
  • From Research Question to Keywords
    09:14

Module 2: Intelligent Data Acquisition: AI Tools for Comprehensive Search, Screening, and Metadata Collection
This module would cover advanced AI tools for literature discovery (e.g., Elicit, SciSpace, Litmaps, Scite.ai), AI-powered screening platforms (e.g., Rayyan, DistillerSR), and the critical importance of collecting rich metadata for advanced analyses.

Module 3: Deep Synthesis: Mastering Topic Modeling (LDA & STM) and AI-Driven Knowledge Discovery
This central module would introduce Latent Dirichlet Allocation (LDA) and Structural Topic Modeling (STM) for uncovering thematic structures and trends, alongside advanced AI tools for data extraction from literature.

Module 4: Communicating Impact: AI-Assisted Reporting, Advanced Visualization, and Ethical AI Use
This module would explore AI tools for enhanced drafting of research reports, creating compelling visualizations from topic models and literature networks, and critically address the ethical considerations of using AI in academic writing.

Module 5 (Extra): The Evolving Evidence Landscape: Mastering AI-Powered tools for Systematic Reviews
This new advanced module would introduce the concept and methodologies of Living Systematic Reviews, showcasing AI tools that enable continuous evidence updating and synthesis.

Student Ratings & Reviews

5.0
Total 2 Ratings
5
2 Ratings
4
0 Rating
3
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2
0 Rating
1
0 Rating
Mohammad Shaban
Mohammad Shaban
2 months ago
This course teaches me from basic to advanced and clears all my doubts.
VL
Viorel LUPU
2 months ago
Yes, I have a new vision. Thank you.