Training Program: AI for Research and Development
Overview
Artificial Intelligence (AI) is transforming how research is conducted across every discipline. This course is designed to equip participants with hands-on knowledge and tools to apply AI in research and development. From literature review and data analysis to predictive modeling and research planning, this training empowers researchers and professionals to integrate AI into every stage of the R&D process.
Through real-world examples and practical demonstrations, learners will gain a working understanding of AI techniques—including generative AI, machine learning, and automation—and how to apply them ethically and effectively within academic, industry, and interdisciplinary research contexts.
Instructors
I am Professor/Dr (Name), a dedicated academic/researcher/industry expert with over [number] years of experience in [specific field or area of expertise, e.g., healthcare, education, engineering]. I hold a [specific degree, e.g., PhD] in [specific field] from [University Name], where I focused my research on [brief description of research focus, e.g., innovative teaching methodologies, advanced healthcare solutions, sustainable engineering practices]. My key areas of expertise include: [Area of Expertise 1], [Area of Expertise 2], [Area of Expertise 3], [Area of Expertise 4], [Area of Expertise 5], [Area of Expertise 6], [Area of Expertise 7], [Area of Expertise 8], [Area of Expertise 9], [Area of Expertise 10], My research interests include: [Research Interest 1],[Research Interest 2],[Research Interest 3],[Research Interest 4],[Research Interest 5]Throughout my career, I have been comm... Read More
Outcomes
Upon completion of this course, participants will be able to:
- Apply generative AI tools to accelerate literature reviews and enhance research writing
- Use AI to automate qualitative and quantitative data analysis
- Implement basic machine learning models in R&D workflows
- Identify AI tools for research management and innovation tracking
- Understand ethical, legal, and governance issues related to AI in research
- Create a roadmap for integrating AI into their own research projects
Structure
The course is delivered in 7 practical modules over 3 hours. Each module includes live demonstrations, Q&A, and take-home resources.
- Introduction to AI in Research
- History and evolution of AI
- Key AI methods in R&D (NLP, ML, LLMs)
- History and evolution of AI
- Generative AI for Research Writing and Literature Review
- Using tools like ChatGPT and Scite.ai
- Synthesising articles and writing assistance
- Using tools like ChatGPT and Scite.ai
- AI for Data Analysis in Research
- Automating coding in qualitative research
- Using AI for thematic, sentiment, and trend analysis
- Automating coding in qualitative research
- Machine Learning in Scientific Research
- Basic ML models for predictive analysis
- Tools like Orange, RapidMiner, and Python notebooks
- Basic ML models for predictive analysis
- AI Tools for Research Planning and Project Management
- Automating project timelines, resource allocation, and reporting
- AI in research grant proposal development
- Automating project timelines, resource allocation, and reporting
- Case Studies and Global Applications
- How universities and R&D labs use AI
- Sector-specific examples: healthcare, agriculture, environment, and social science
- How universities and R&D labs use AI
- Responsible AI in R&D
- Ethics, IP issues, data protection, and human oversight
- Frameworks for responsible and inclusive innovation
- Ethics, IP issues, data protection, and human oversight
Assessment
Participants will be assessed through:
- A practical assignment: Create a short AI-enhanced research plan
- In-session quiz: 10 multiple-choice questions based on tools and ethics
- Peer feedback: Participants review each other’s research integration ideas
Certificates will be issued upon successful completion of the final assignment.
Target Audience
This course is designed for:
- Academic researchers and PhD/Master’s students
- Research officers, innovation managers, and consultants
- Professionals in R&D-intensive industries (e.g., biotech, education, public policy)
- Anyone involved in or supporting research and innovation activities
Recommended Resources
The State of AI in Research 2024 (Nature Research)
ChatGPT, Elicit, Scite, Connected Papers (AI tools)
Responsible AI in Research Handbook (OECD/UNESCO)
Orange Data Mining, RapidMiner, and IBM Watson Studio
GitHub repositories for academic AI workflows
Certificates
Upon successful completion of the course requirements, participants will receive a
certificate of completion from ExpertGate.
