Understanding teens' self-beliefs when learning to construct and deconstruct AI/ML systems: Developing a survey instrument
Provides a validated tool for researchers and educators to assess affective factors in AI literacy interventions for youth.
The authors developed and validated a survey instrument measuring teenagers' self-beliefs in AI/ML learning contexts, confirming a six-factor structure with 124 participants and finding strong correlations between design justice beliefs and other constructs.
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.