Research Article
Motivation to shape the future of education with Artificial Intelligence: An international comparison between Switzerland and China
Synthesis: Martínez-Moreno and colleagues (2026) cross-culturally validated the (D)FIT-Choice (Digital Factors Influencing Teaching Choice) scale and examined motivational differences among 416 student teachers in Switzerland and China. Swiss student teachers reported stronger social utility values and intrinsic motivations, while Chinese student teachers showed higher perceived digital teaching competence and greater enthusiasm for integrating AI into education. The results reveal different levels of willingness to shape the future of education with AI, highlighting how cultural and systemic factors influence the motivation of future teachers and underscoring the need to adapt teacher preparation to local contexts for the digital transformation of education.
The (D)FIT-Choice scale and its cross-cultural validity
The study extends the FIT-Choice model — grounded in expectancy-value theory — into a digital era by adding AI-related items. The (D)FIT-Choice scale captures socialization influences, self-perceived teaching ability (including with technologies), task demand and return, fallback career, intrinsic/utilitarian/social values, and career-choice satisfaction. Most higher-order factors showed good to excellent internal consistency, but Salary, Fallback Career, and Job Transferability were less reliable, and Personal Utility Value and Task Return showed poor fit — both likely reflecting cultural differences in how practical benefits such as salary and job security are perceived in Switzerland versus China. Only Intrinsic Value reached metric invariance (equivalent loadings) but not scalar invariance, indicating the instrument is only partially equivalent across these contexts.
Swiss student teachers: social utility and intrinsic motivation
Swiss student teachers were more motivated by social utility values — enhancing social equity, making a social contribution, shaping the future of children and adolescents, and working with children and adolescents — and reported higher intrinsic motivation for teaching both generally and for their own subject. They also reported greater satisfaction with their career choice. This pattern aligns with an individualistic society that prioritizes personal growth and fulfillment, and the social-contribution motive is consistent with the strong civic engagement and volunteering associated with direct democracy. Swiss students also perceived the teaching profession as higher demand and requiring more expert knowledge than their Chinese counterparts.
Chinese student teachers: digital competence and AI enthusiasm
Chinese student teachers reported significantly higher perceived digital teaching competence and greater enthusiasm for incorporating AI into education, though general perceived teaching abilities did not differ. The authors attribute this to extensive technology exposure (the Ten-Year Development Plan for Education Informatization from 2011 versus Switzerland's Lehrplan21 beginning 2017), ubiquitous high-tech products, and strong government AI initiatives that provide robust facilitating conditions. Perceived lower task demands and higher task return reflect relatively accessible teaching qualifications in mainland China and teacher salaries in cities like Shenzhen and Hong Kong running three to four times local average income. The findings align with the Technology Acceptance Model and UTAUT, where perceived usefulness, social influence, and facilitating conditions drive adoption, and China's establishment of AI-education departments within faculties of education signals systemic integration that may confer a Technological Pedagogical Content Knowledge (TPACK) advantage.
Implications for teacher preparation
The study highlights how sociocultural, economic, and technological contexts shape the motivations of future teachers and their willingness to shape the future of education with AI. Because Swiss and Chinese student teachers are motivated by different value systems — social contribution and intrinsic fulfillment versus digital readiness and systemically supported AI integration — teacher-education programs should be tailored to local contexts, balancing traditional social-utility values with digital and AI preparation. Limitations include self-report measures and the partial cross-cultural invariance of the instrument, meaning several factors require item refinement before broad comparative use.
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Citation
Martínez-Moreno, J., Zhou, X., Petko, D., & Chiu, T. K. F. (2026). Motivation to shape the future of education with artificial intelligence: An international comparison between Switzerland and China. Computers and Education Open, 10, 100327.