Abstract This study introduces SmartPD, an AI-assisted, micro-learning-based professional development (PD) model designed to enhance teachers' digital competence in line with the DigCompEdu framework. Existing PD initiatives are often generic, insufficiently contextualised, and lack mechanisms for personalisation, scalability, and iterative feedback, limiting their long-term impact. To address these gaps, this research adopts a Design-Based Research (DBR) methodology, following four iterative phases: (i) contextual exploration of teachers' digital competence using the DigCompEdu Check-In tool, (ii) design and development of SmartPD modules grounded in micro-learning and AI based feedback, (iii) short-term implementation with 32 in-service teachers, and (iv) evaluation and refinement based on analytics and participant feedback. The intervention integrated H5P for interactive content, Flipgrid for peer reflections, and ChatGPT for real-time formative feedback, with learner engagement tracked through xAPI dashboards. Data were collected through pre- and post-assessments, reflective journals, and platform analytics. Results indicated statistically significant improvements in digital competence across all five DigCompEdu domains, with the most substantial gains in "Facilitating Learners' Digital Competence" and "Teaching and Learning." Teachers also reported high levels of satisfaction and confidence, while engagement data confirmed strong usability and high completion rates. Despite limitations related to sample size and duration, SmartPD demonstrates the potential of combining DBR with AI-enabled micro-learning to provide scalable, context-sensitive, and evidence-based teacher professional development, aligned with SDG 4: Quality Education.
M. et al. (2026) studied this question.