Generative AI-Assisted Competitive Microlearning Strategy: Development and Validation of the Plomp Model in Classroom Teacher Education in Finland

Abstract

Background. The rapid adoption of generative artificial intelligence (AI) in education has created challenges in designing responsible and adaptive learning strategies. Meanwhile, competitive elements in gamified learning remain debated due to inconsistent effects on learner engagement. Purpose. This research aimed to develop and evaluate a generative AI-assisted competitive microlearning strategy for improving instructional media development skills among Finnish Luokanopettajakoulutus (Class Teacher Education) students. Novelty. This study introduces a novel instructional design that integrates generative AI, microlearning, and optional quality-based competition. Unlike conventional competitive gamification approaches, the proposed strategy recognizes learner differences by examining whether competition produces varied outcomes across learning groups Method. Using the Plomp development model, the study involved 79 undergraduate students from four parallel classes. Data from expert validation, practicality questionnaires, and product assessments were analyzed using Aiken’s V and descriptive statistics. Results. The strategy achieved high validity (Aiken’s V = 0.87) and strong practicality (83.1% student response; 85.9% lecturer response). Variation in competition participation (41–62%) indicated different effects among learner groups. Conclusion. The findings contribute an adaptive AI-assisted microlearning framework that balances innovation, learner diversity, and educational equity in teacher education.