Enhancing AI Literacy Course Satisfaction Through Empowerment in AI Problem-Solving and Ethical Awareness: Development and Validation of an AI Project-Based Learning Scale

Synthesis: Zhu and Kong develop and validate a context-grounded AI project-based learning scale (AI-PBLS) for measuring students' perceived Project-Based Learning experiences when using AI to solve real-world problems, then test how those experiences relate to satisfaction with AI Literacy courses. Using data from 1,027 Hong Kong secondary and university students enrolled in an AI literacy program — 446 with complete data for structural equation modeling (SEM) — they show that empowerment in using AI for Problem Solving and AI ethical awareness mediate the relationship between perceived PBL and AI literacy course satisfaction. The study positions AI literacy as multidimensional, spanning cognitive, metacognitive, affective, and social dimensions, and grounds the mediation model in self-determination theory and social cognitive theory.

Key Findings

• The AI-PBLS was validated as a reliable 7-item, single-factor scale after exploratory (EFA, N = 513) and confirmatory (CFA, N = 514) factor analyses, with high internal consistency (Cronbach's α = 0.963) and a context-grounded design where the "AI" element was defined by the instructional setting rather than item wording — a contribution to Educational Measurement of AI learning.
• Perceived PBL was positively associated with AI literacy course satisfaction (direct β = 0.202), supporting PBL as a suitable Pedagogies and Teaching Strategies for AI literacy education.
• Empowerment in using AI for problem-solving was a stronger mediator than AI ethical awareness — the indirect effect through empowerment (β = 0.393) far exceeded that through ethical awareness (β = 0.029) — suggesting educators should prioritize fostering student empowerment.
• Empowerment strongly predicted ethical awareness (β = 0.668), and the final SEM model explained 58.4%, 66.1%, and 67.1% of variance in empowerment, ethical awareness, and course satisfaction respectively.
• All measures correlated strongly and positively with course satisfaction (r = 0.67–0.77, p < 0.001), underscoring the links among PBL, engagement, empowerment, and ethics.

Implications for Practice

• Design AI literacy courses around authentic PBL projects — including collaboration, hands-on artifact creation, and real-world problem contexts — over isolated AI tasks, allocating roughly 4–6 weeks per project cycle so students can define problems, revise ideas, and reflect on outcomes.
• Prioritize student empowerment over pure technical skill by giving learners meaningful choices, encouraging comparison of alternative AI tools or outputs, and asking them to justify how and why AI is used at each project stage.
• Embed ethical awareness throughout the project process rather than as a separate topic — for example, using prompts such as "What bias may exist in this AI output?" or "Who might be disadvantaged?" in proposals, peer discussions, reflection journals, and final presentations.
• For policymakers: promote guidelines for responsible and equitable AI use, and invest in professional development for teachers implementing PBL-based AI instruction.

Connected Concepts

• AI Literacy
• Project-Based Learning
• Ethics
• Educational Measurement
• Self-Efficacy
• Motivation
• Self-Determination Theory

Connected Articles

• AI-Supported Problem-Based Learning for Enhancing Computational Thinking — AI-supported problem-based learning for enhancing computational thinking
• A Systematic Review of Educators' Engagement with AI in Problem-Based Learning — systematic review of educators' engagement with AI in problem-based learning
• Design and Validation of a Questionnaire on Teachers' Uses of Generative Artificial Intelligence — design and validation of a questionnaire on AI/GenAI use
• The AI Literacy Heptagon: A Structured Approach to AI Literacy in Higher Education — structured framework for AI literacy in higher education

Citation

Enhancing AI literacy course satisfaction through empowerment in AI problem-solving and ethical awareness: Development and validation of an AI project-based learning scale — Zhu, J., & Kong, S. C. (2026). Computers and Education: Artificial Intelligence, 11, 100624.