Integrating artificial intelligence into K-12 school education through self-determination theory from a longitudinal study: opportunities, challenges, and future direction

Successful integration of artificial intelligence into K-12 school education depends on teacher motivation. This motivation can be explained by the three basic psychological needs (i.e. autonomy, competence, and relatedness) proposed by self-determination theory (SDT). This study used SDT to investigate how teachers experience psychological need satisfaction and frustration during AI integration. Using a two-year longitudinal qualitative design, seventy-four K-12 school teachers were interviewed, and data were analyzed via deductive thematic analysis. Findings revealed that the three SDT needs satisfaction fostered pedagogical innovation, efficiency, and professional community. Conversely, needs frustration undermined high-quality integration, which is caused by external control, skill gaps, isolation, and ethical disengagement. These results demonstrate that AI integration is fundamentally a motivational challenge, not merely a technical one. Implications call for needs-supportive environments that honor teacher agency, build genuine AI competence through sustained professional learning, and strengthen collaborative professional communities. Schools must move beyond tool-centric mandates to human-centered implementation strategies that proactively address teacher psychological needs. This transforms AI from a source of resistance into a catalyst for sustainable educational change.