Academic & Research Program
AI for Universities & Research Advancement (AURA)
Course Description
AURA is a program designed to empower researchers with the knowledge and skills to integrate AI into every stage of research. The program takes scholars on a structured journey, beginning with the foundations of AI in academia and moving through literature review, research design, data collection and analysis, writing and publishing, and finally dissemination and impact. Through hands-on exercises, students will learn to use AI tools for semantic search, bibliometric mapping, survey design, transcription, statistical modeling, qualitative coding, and academic writing. Special emphasis is placed on reproducibility, transparency, and ethical use of AI, ensuring that participants not only gain efficiency but also uphold scholarly integrity. The program blends theory with practice and culminates in a capstone project that demonstrates how AI can accelerate discovery, improve rigor, and expand research impact.
Modules
Module 1: AI Foundations for Research
Learning Objectives
- Understand the role of AI in modern research workflows.
- Learn the taxonomy of AI tools relevant to academic inquiry.
- Recognize opportunities and challenges in adopting AI for research.
Topics Covered
- AI vs. traditional research methods: complementarities and risks.
- Overview of AI tools for researchers: Gen AI, NLP, ML, and bibliometrics.
- How AI transforms the research process (end-to-end pipeline).
- Case examples: AI-assisted literature reviews in management research.
Hands-on Exercise
Group activity + demo of general-purpose AI (e.g., ChatGPT, Perplexity).
Module 2: AI for Literature Review & Idea Generation
Learning Objectives
- Use AI to systematically search, summarize, and synthesize literature.
- Generate and refine research questions using AI responsibly.
Topics Covered
- AI-enhanced semantic search and academic databases.
- Summarization and synthesis tools (Scholarcy, Elicit, ResearchRabbit).
- Concept mapping and bibliometric analysis with AI.
- Ethical considerations: avoiding plagiarism and over-reliance.
Hands-on Exercise
Students identify gaps in a chosen research domain using AI tools.
Module 3: AI in Research Design, Data Collection & Analysis
Learning Objectives
- Learn how AI supports quantitative and qualitative methodologies.
- Use AI for data gathering, cleaning, and analysis.
Topics Covered
- Survey design, text mining, and interview transcription.
- Secondary data scraping and sentiment analysis.
- Statistical modeling and forecasting with AI.
- Qualitative coding and thematic analysis using Gen AI.
Hands-on Exercise
Students analyze a small dataset using AI-assisted tools.
Module 4: AI in Writing, Reviewing & Publishing
Learning Objectives
- Explore AI’s role in academic writing and peer review.
- Understand ethical boundaries of AI in scholarly communication.
Topics Covered
- Drafting and refining manuscripts with AI support.
- Language editing and reference management tools.
- Peer-review simulation and journal matching using AI.
Hands-on Exercise
Students refine a short draft and discuss human oversight points.
Module 5: AI for Research Impact, Ethics & Capstone Project
Learning Objectives
- Learn how AI increases research visibility and impact.
- Evaluate ethical and professional implications.
- Apply AI skills to a real research challenge.
Topics Covered
- AI for dissemination: summaries, infographics, policy briefs.
- Altmetrics and AI-driven visibility strategies.
- Responsible AI, bias, transparency, and ownership.
- Future directions: multimodal AI, Gen AI, quantum + AI.
Hands-on Exercise
Group capstone: design a mini AI-driven research pipeline.