Designing an integrated concept mapping and generative AI approach to develop pre-service teachers' digital storytelling project outcomes and critical thinking

This study designed an integrated concept mapping and generative artificial intelligence (generative AI) approach to enhance pre-service teachers' (PSTs) digital storytelling (DST) outcomes and critical thinking. A quasi-experimental study was conducted with 62 PSTs, where an experimental group (n = 30) employed the integrated concept mapping and generative AI approach and a control group (n = 32) used a standalone generative AI approach. Results indicated that the integrated approach produced broad improvements across most DST outcome dimensions, specifically enhancing ‘overall’ quality, ‘accuracy’, ‘completeness’, ‘innovation’ and ‘interaction’. However, the integrated approach did not yield a greater improvement in critical thinking tendencies compared to the control group; in fact, the control group demonstrated a significantly larger gain over time. Qualitative analysis of reflective reports revealed a key difference in PSTs' perceptions: the experimental group prioritised human–machine interaction as the most essential competency for DST creation with AI, whereas the control group emphasised critical thinking. This suggests that the pedagogical design, specifically the use of concept mapping as a structural scaffold, effectively shapes how PSTs perceive and engage with technology, guiding them towards more reflective and efficient learning with generative AI, even if its impact on critical thinking tendencies is more complex. The study offers relevant insights for educational practitioners seeking to utilise generative AI to develop PSTs' specific DST competencies and their understanding of human–AI collaboration.