Understanding teens' self-beliefs when learning to construct and deconstruct AI/ML systems: Developing a survey instrument

TL;DR

This paper introduces and validates a survey instrument to assess teens' self-beliefs related to AI/ML learning activities, focusing on construction and deconstruction skills and attitudes.

Contribution

The paper develops and validates a new survey tool measuring teens' beliefs about AI/ML learning, including constructs like problem-solving, auditing, and design justice.

Findings

Confirmed a six-factor structure through factor analysis.

Design justice beliefs correlate strongly with problem-solving and self-efficacy.

Validated the survey with 124 teenagers.

Abstract

Despite growing calls to foster AI literacy, there are few available survey instruments designed for children and youth that study computational empowerment alongside construction and deconstruction activities. In such activities, learners' beliefs about their abilities and attributes can impact their engagement. In this paper, we introduce and validate a survey instrument with constructs related to construction (creative expression and problem-solving self-beliefs) and deconstruction (auditing self-efficacy and fascination with auditing), along with more general self-beliefs related to design justice and the value of learning about AI/ML. We administered the instrument to 124 teenagers and assessed the six-factor structure of the instrument using confirmatory factor analysis. In addition to confirming the structure, we found that design justice beliefs strongly correlated with problem-solving, auditing self-efficacy, and creative expression.