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On Conducting Interviews: A guide for UX and HAI Researchers
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What AI Sycophancy Means for K-12 Students
With great power comes great responsibility (risks)
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Prompting Techniques for Science Data Analysis and Sensemaking
A guide to choosing and applying the right prompting techniques for different levels of data tasks in science education.
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Prompt Engineering Framework for Data Sensemaking
Introducing SENSE, a structured framework for designing prompts that support scientific data sensemaking with Large Language Models.
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Intro to Large Language Models
Building a shared mental model of what Large Language Models actually are, and what they are not.
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Frame the Problem
Understanding how to anchor data investigations in real-world phenomena by framing problems before analysis begins.
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Data Wrangling (Processing & Transforming Data)
Learning to clean, organize, and transform data through iterative decision-making to prepare it for meaningful scientific analysis.
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Data Exploration and Visualization
Using visualization as a thinking tool to discover patterns, relationships, and uncertainties in science data investigations.
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Consider Models
Learning to select, evaluate, and interpret models as evidence-building tools that balance clarity, uncertainty, and real-world relevance.
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Consider Data
Learning to evaluate data sources, understand metadata, and make informed decisions about data quality and ethical implications in science investigations.