AI Research Support

🧩 Learning & Exploring

How conversational AIs support understanding, analysis, and exploration across disciplines.

📘 Concept Explanation

• What it does: Breaks down complex theories, models, or jargon into digestible explanations.
• How it helps: Adapts explanations to the user’s level—undergraduate, graduate, or expert.

Use case: Plain-language explanations of game theory or Pareto efficiency for students new to economics.

⚖️ Comparative Analysis

• What it does: Compares and contrasts theories, frameworks, or schools of thought.
• How it helps: Encourages critical thinking by highlighting differences, similarities, strengths, and limitations.

Use case: Compare qualitative vs. quantitative methods, or constructivism vs. behaviorism in education.

💬 Interactive Q&A

• What it does: Engages users in a Socratic-style dialogue to deepen understanding.
• How it helps: Promotes active learning by prompting reflection, questioning assumptions, and step-by-step knowledge building.

Use case: Explore ethics in AI through a back-and-forth on utilitarian vs. deontological perspectives.

🧠 Multimodal Learning

• What it does: Integrates text, images, diagrams, and code to explain concepts.
• How it helps: Supports diverse learning styles—visual, verbal, kinesthetic—making abstract ideas concrete.

Use case: Show a cell-cycle diagram with explanation, or annotated code to illustrate recursion.

📝 Annotation & Scaffolding

Especially valuable for interdisciplinary research or when entering a new field. Conversational AIs can scaffold learning by:

• Adjusting explanations based on prior knowledge.
• Offering layered insights—from basic definitions to advanced critiques.
• Providing just-in-time resources such as examples, analogies, and visual aids.