Investigating Youths' Everyday Understanding of Machine Learning Applications: a Knowledge-in-Pieces Perspective
Project Overview
The document explores the integration of generative AI in education, emphasizing the importance of AI literacy in K-12 settings. It reveals that many youths have a fragmented yet productive understanding of machine learning (ML), which can be harnessed to create effective learning environments that resonate with their everyday experiences. By adopting a knowledge-in-pieces approach, the study identifies how students' pre-existing notions about AI can be used to design educational tools that not only clarify misconceptions but also enhance engagement with AI concepts. Key applications include developing curricula that connect ML applications to real-world scenarios, ultimately fostering deeper comprehension and skills relevant to the digital age. The findings indicate that leveraging students' existing knowledge can facilitate meaningful learning experiences, equipping them with the necessary competencies to navigate an increasingly AI-driven world.
Key Applications
Knowledge-in-pieces approach to understanding ML applications
Context: K-12 education, specifically aimed at teenagers aged 14-16, in a workshop setting
Implementation: Conducted an in-person workshop where teens interacted with ML-powered applications and explained their functionality using cooperative inquiry methods.
Outcomes: Participants demonstrated understanding of how ML applications learn from training data and recognize patterns in input data, providing a foundation for further exploration of ML concepts.
Challenges: Limited understanding of learning algorithms and coherence in their knowledge about ML systems.
Implementation Barriers
Conceptual barriers
Youths may have misconceptions about how ML systems work, often attributing human-like characteristics to them or misunderstanding their complexity.
Proposed Solutions: Using knowledge-in-pieces perspective to leverage youths' existing fragmented knowledge and guide them towards more accurate understandings of ML.
Project Team
Luis Morales-Navarro
Researcher
Yasmin B. Kafai
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Luis Morales-Navarro, Yasmin B. Kafai
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai