Recent Post

Theory Of Knowledge
calendar 2025-10-14
Theory Of Knowledge
Developing 21st-Century Skills through AI
calendar 2025-10-14
Developing 21st-Century Skills through AI
Smart Learning Analytics
calendar 2025-10-14
Smart Learning Analytics
AI in Career Guidance for IGCSE Students
calendar 2025-10-14
AI in Career Guidance for IGCSE Students
Artistic Creativity, Authorship and Technology
calendar 2025-10-14
Artistic Creativity, Authorship and Technology
Categories
Humanities (Economics, Geography, and History)
2025-10-14
author

Humanities (Economics, Geography, and History)

 

1. AI in Economic Forecasting: Can Machine Learning Predict Market Behavior Better Than Humans?

Field: Economics

Why it’s strong:

  • Combines traditional economic modeling with data science.

  • Lets you evaluate predictive accuracy of AI vs. human-based models (e.g., econometric models or expert forecasts).

  • Connects to behavioral economics and information asymmetry.

Data ideas:

  • Compare historical predictions from IMF, World Bank, or private analysts with ML model outputs trained on macroeconomic indicators.

  • Evaluate accuracy using error metrics (RMSE, MAE).

Cautions:

  • Be realistic — focus on interpretability and limitations, not just performance.

  • EE-level analysis doesn’t require heavy coding; using published data or secondary sources is fine.

2. How AI Can Help Analyze Historical Data and Patterns

Field: History / Digital Humanities

Why it’s strong:

  • Explores how historians use machine learning to detect patterns (e.g., migration, trade, political sentiment).

  • Connects historical inquiry with digital methods.

Data ideas:

  • Use digitized archives (newspapers, census, trade records).

  • Analyze how AI uncovers trends human historians might overlook.

Cautions:

  • Emphasize methodological change (how AI changes historical interpretation), not just the tech itself.

3. The Impact of AI Automation on Global Economic Inequality

Field: Economics / Geography

Why it’s strong:

  • Deeply relevant — connects technological change to income distribution and development.

  • Lets you explore geographical disparities (e.g., between countries or regions).

Data ideas:

  • Use World Bank, IMF, or ILO data on employment, GDP per capita, and AI adoption rates.

  • Compare across regions or income brackets.

Cautions:

  • Make sure to isolate AI’s role from other automation or globalization effects.