Work

Developing and validating a scale of empowerment in using artificial intelligence for problem-solving for senior secondary and university students

Year: 2024

Type: article

Abstract: Empowerment enables students to be psychologically and affectively ready to leverage the benefits of artificial intelligence (AI). However, a theory-driven scale to extend empowerment into the use of AI for problem-solving is lacking. This study developed and validated an 11-item scale of empowerment in using AI for problem-solving (EUAIPS) based on a proposed conceptual framework that synthesises empowerment and AI-related literature. The EUAIPS scale encompasses impact, self-efficacy, and meaningfulness in using AI for problem-solving. We collected data from a diverse sample of Hong Kong senior secondary and university students before (N = 477) and after the course (N = 409). Results demonstrated that the EUAIPS scale with a three-factor structure had good reliability and validity. Students also felt significantly more empowered to use AI for problem-solving after a 14-h course using AI for problem-solving. These findings empirically support the affective dimension of AI literacy and show that psychological control/competence is particularly important for students to harness AI to solve problems at the affective level. This study presents a valid instrument for researchers and practitioners to measure empowerment in using AI for problem-solving and informs AI literacy curriculum designers about including learning activities to help students realise its impact, self-efficacy, and meaningfulness. • A scale to assess students' empowerment in using AI for problem-solving is validated. • The three-factor 11-item EUAIPS showed good validity and reliability. • The EUAIPS is the first scale to measure empowerment in using AI for problem-solving. • Students' empowerment in using AI for problem-solving was enhanced after completing a PBL course. • AI literacy curricula should aid students to realise its impact, self-efficacy and meaningfulness.

Source: Computers and Education Artificial Intelligence

DOI: 10.1016/j.caeai.2024.100359

Authors: Siu Cheung Kong, Jinyu Zhu, Yin Nicole Yang

Institution: Education University of Hong Kong

Language: English

Topic: Artificial Intelligence in Education

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $2,210