Teachers' intentions to teach AI in K-12 settings are primarily driven by autonomous motivation, which is strengthened by basic psychological need satisfaction and moderated by self-efficacy and contextual factors.
Objective: The main goal of this study was to examine the factors influencing teacher intention to teach artificial intelligence in K-12 settings by integrating Self-Determination Theory (SDT) and the Motivation-Opportunity-Ability (MOA) framework. The researchers sought to address a significant gap in the literature, which has predominantly focused on student perspectives of AI learning while largely overlooking teacher perspectives on AI education implementation. The study aimed to understand how different types of motivation (autonomous, controlled, and amotivation) affect teachers' intentions to teach AI, how basic psychological needs satisfaction influences autonomous motivation, and how contextual factors such as self-efficacy, supportive resources, and student readiness moderate these relationships.
Methods: The study employed a quantitative research design using convenience sampling to collect data from 230 South Korean elementary school teachers with varying levels of AI education experience. Participants were recruited through online teacher community websites and provided written consent before participation. The research utilized a comprehensive survey instrument comprising 37 items designed to measure ten constructs using 7-point Likert scales. Before completing the survey, all participants received a standardized 30-minute introduction to AI curriculum and attended a 30-minute teaching demonstration to ensure uniform understanding of AI education concepts. The survey measured autonomous motivation, controlled motivation, amotivation, basic psychological needs satisfaction (autonomy, competence, relatedness), self-efficacy, supportive resources, student readiness, and intention to teach AI. Data analysis was conducted using SPSS and AMOS software, employing confirmatory factor analysis, structural equation modeling, and moderation analysis with interaction terms created through mean-centering procedures to examine the research hypotheses.
Key Findings: The study revealed several significant findings about teachers' intentions to teach AI. Teachers' intentions were generally comparable across demographic factors of gender and age but differed significantly based on prior experience in AI education, with those having more teaching experience and professional development training showing higher intentions. Autonomous motivation emerged as the strongest predictor of teaching intention (β=0.499, p<0.001), significantly outweighing controlled motivation (β=0.194, p<0.001), while amotivation had a strong negative impact (β=-0.509, p<0.001). The satisfaction of all three basic psychological needs significantly influenced autonomous motivation: competence (β=0.274, p<0.001), autonomy (β=0.343, p<0.001), and relatedness (β=0.182, p<0.01). The model explained 54.6% of variance in teachers' intentions and 37.7% of variance in autonomous motivation. Moderation analysis revealed that self-efficacy significantly moderated the relationship between autonomous motivation and teaching intention (β=0.141, p<0.01) and dampened the negative effects of amotivation (β=0.106, p<0.05). Supportive resources moderated the negative relationship between amotivation and teaching intention (β=0.150, p<0.01), while student readiness strengthened the relationship between controlled motivation and intention (β=0.089, p<0.05).
Implications: The findings provide significant theoretical and practical implications for AI education in K-12 contexts. Theoretically, the study contributes to the growing literature on AI education by being among the first to examine teacher perspectives systematically, expanding the application of SDT and MOA frameworks to AI education contexts, and demonstrating the complex interplay between motivational states, self-efficacy, and contextual factors. The research shows that autonomous motivation should be the primary focus when preparing teachers for AI instruction, as it is a more powerful and sustainable driver than external pressures or mandates. Practically, the findings suggest that teacher training programs should prioritize creating environments that support teachers' basic psychological needs for autonomy, competence, and relatedness. Schools and educational institutions should provide flexible approaches to AI curriculum integration, opportunities for professional achievement and recognition, and collaborative support networks. The study indicates that different motivational states require different support strategies: amotivated teachers benefit more from resource-rich environments and self-efficacy building, while autonomously motivated teachers need opportunities to enhance their confidence and pedagogical skills. The research also highlights the importance of ensuring student readiness for AI learning, particularly for teachers operating under external pressures.
Limitations: The study acknowledges several important limitations that affect its generalizability and scope. First, the research was conducted exclusively with elementary school teachers in South Korea, limiting direct applicability to other educational systems, cultural contexts, or grade levels. While South Korea's implementation of mandatory AI education provides valuable insights, the findings may not transfer to contexts with different educational policies or cultural attitudes toward technology. Second, the study employed convenience sampling and did not collect regional demographic information, which limits the ability to assess geographical variations and reduces representativeness. Third, the research relied solely on self-report measures, which may be subject to response bias and social desirability effects. Fourth, the cross-sectional design prevents establishment of causal relationships between variables. Fifth, the study focused specifically on elementary teachers' intentions rather than actual teaching behaviors, creating a gap between intention and implementation. Finally, the research was conducted during a period when AI education was voluntary rather than mandatory, which may have influenced motivation patterns and may not reflect responses under different policy contexts.
Future Directions: The researchers suggest several important avenues for future investigation to build upon these findings. Cross-cultural studies are needed to examine whether similar motivational patterns exist in different educational systems and cultural contexts, particularly in countries implementing or considering AI education policies. Longitudinal research should track the relationship between teacher intentions and actual implementation behaviors over time, as well as investigate how motivational factors change as AI education becomes more established. Studies should expand to include secondary teachers and explore whether motivational patterns differ across grade levels and subject areas. Future research should employ mixed-methods approaches combining surveys with interviews, classroom observations, and objective behavioral measures to provide richer insights into teacher experiences. Investigation is needed into the long-term effectiveness of different professional development approaches based on teachers' motivational profiles. Research should also examine how policy changes (such as transitioning from voluntary to mandatory AI education) affect teacher motivation and implementation. Additionally, studies could explore the role of administrative support, institutional culture, and community attitudes in shaping teacher motivation. Finally, research should investigate the relationship between teacher motivational factors and student learning outcomes in AI education.
Title and Authors: "Exploring teacher intention to teach AI: self-determination theory (SDT) and motivation-opportunity-ability (MOA) perspectives" by Seongyune Choi (Sunchon National University), Jaeho Jeon (University of Alabama), and Yeonju Jang (Korea University).
Published on: July 1, 2025 (accepted), received September 12, 2024
Published by: Education and Information Technologies, Springer Science+Business Media, LLC