By the end of this course, participants will:

• Understand AI’s role in academic research and data analysis.
• Learn how AI enhances literature reviews, research methodology, data visualization, and academic writing.
• Gain hands-on experience with AI-powered tools for research efficiency.
• Develop an AI-enhanced research workflow and publication strategy.

Topics Covered

• Overview of AI in research: Key concepts and applications
• AI for enhancing academic productivity and efficiency
• AI ethics in research: Bias, reproducibility, and transparency
• Case studies: AI-driven research breakthroughs in academia

Practical Exercise

• Explore AI-powered research tools (ChatGPT, Scite.ai, Semantic Scholar, Elicit)
• Discuss ethical concerns in AI-assisted research

Key Takeaways

• Understanding AI’s potential in academic research
• Identifying responsible AI usage in scholarly work

Topics Covered

• AI-assisted literature search and paper summarization
• Automated citation and reference management (Zotero, Mendeley, EndNote)
• AI for detecting research gaps and trends

Practical Exercise

• Use AI tools to generate summaries of key research papers
• Identify gaps in literature using AI-powered knowledge graphs

Key Takeaways

• AI-driven approaches to speeding up literature reviews
• Efficiently managing references with AI

Topics Covered

• AI for designing research experiments
• AI-based data collection and survey analysis
• Web scraping and AI-driven data extraction

Practical Exercise

• Use AI to design a survey with smart question recommendations
• Automate data collection using AI-driven scraping tools

Key Takeaways

• AI-enhanced methodologies for research design
• Ethical considerations in AI-powered data collection

Topics Covered

• AI for qualitative and quantitative data analysis
• Machine learning techniques for researchers (Regression, NLP, Clustering)
• AI-powered data visualization tools (Tableau, Power BI, Python Matplotlib)

Practical Exercise

• Apply machine learning techniques to analyze a dataset
• Use AI-powered tools for creating research graphs and visualizations

Key Takeaways

• Hands-on experience with AI-based data analysis
• Best practices for visualizing complex research data

Topics Covered

• AI-powered academic writing assistants (ChatGPT, Grammarly, Paperpile)
• AI for plagiarism detection and paraphrasing (Turnitin, Quillbot)
• Automating research paper formatting (LaTeX AI assistants)
• AI in journal selection and publication strategy

Practical Exercise

• Use AI tools to refine a research paper draft
• Identify suitable journals using AI-based journal recommendation tools

Key Takeaways

• Enhancing academic writing with AI
• AI-driven publication strategies

Topics Covered

• AI for research impact measurement (Altmetrics, Google Scholar AI tools)
• AI in grant writing and funding applications
• Future trends in AI for academic research

Practical Exercise

• Use AI to generate a research impact report
• Create a draft funding proposal using AI-assisted writing tools

Key Takeaways

• Understanding AI’s role in research evaluation
• Exploring future AI applications in academia

• Participants develop a research proposal incorporating AI-based methodologies.
• The project integrates AI tools for literature review, data analysis, and academic writing.

AI Tools for Research:

• Literature Review & Citations: Scite.ai, Elicit, Mendeley, Zotero
• Data Analysis & Visualization: IBM Watson, Power BI, Tableau, Python AI libraries
• Writing & Formatting: Grammarly, Paperpile, Turnitin, LaTeX AI assistants

Books:

• The AI Revolution in Scientific Research by Klaus Mainzer
• Deep Learning for the Life Sciences by Bharath Ramsundar
• Data Science for Business by Foster Provost

Online Learning Platforms:

• SIMI Swiss LMS
• SIMI Swiss eLearning Portal