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 in Education departments within faculties of education signals systemic integration that may confer a Technological Pedagogical Content Knowledge (TPACK) advantage.

What this means for practice

• Teacher educators. Tailor AI preparation to local motivational profiles instead of importing one program wholesale: Swiss student teachers were motivated more strongly by social utility values and intrinsic value, while Chinese student teachers reported higher perceived digital teaching competence and greater enthusiasm for integrating AI.
• Teacher educators. Choose a different entry point in each context — in Switzerland, connect digital and AI competence to the social-contribution and intrinsic values that already drive career choice; in China, build on the systemic AI-education infrastructure that supports higher perceived digital competence.
• Faculty developers. Use the (D)FIT-Choice factors as a needs assessment for incoming cohorts, but rely on the well-behaved factors: most higher-order factors showed good to excellent internal consistency, while Salary, Fallback Career, and Job Transferability were less reliable.
• Researchers. Refine the instrument before running comparative studies: only Intrinsic Value reached metric invariance (and not scalar invariance); Self-Perception, Task Demand, and Social Utility Value lacked loading equivalence, and Task Return, Personal Utility Value, and Socialization Influences failed to establish even basic structural equivalence.

Limitations

• The sample of 416 student teachers (183 in Switzerland, 233 in China) was drawn from two institutions in each country and concentrated in specific cities — Zurich in Switzerland, Shenzhen and Hong Kong in China — so the authors caution the results may not generalize to other regions.
• Data are self-report questionnaires collected in 2023–2024; participation was voluntary and uncompensated, which the authors note may overrepresent students with higher motivation or greater interest in the topic, and no observational or objective competence measure was used.
• Measurement invariance held only partially: most motivational factors were not fully equivalent across the two groups, so the between-group mean comparisons (independent-samples t-tests) may be biased, and cross-cultural response styles on the 7-point scales may have contributed to the non-invariance.
• Sample size constraints made a confirmatory factor analysis of the full second-order model infeasible (single-factor CFAs were run per higher-order factor), and the cultural interpretation leans on Hofstede-type dimensions that the authors note have been criticized for oversimplifying variation within countries.

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.