From Local to Global: Cross-Cultural Adaptation and Validation of the SAGAI Scale for Measuring Student Attitudes Toward Generative AI

Abstract

Generative artificial intelligence (GenAI) is becoming an increasingly visible component of higher education, raising the need for theoretically grounded instruments that can capture how students perceive and engage with these technologies. This study aims to adapt the Scale for Attitudes toward Generative Artificial Intelligence (SAGAI), originally developed in Turkish, into English and to examine its psychometric properties within a different linguistic context. The study was conducted with 350 higher education students enrolled at a UK-based open and distance learning institution. A systematic cross-cultural adaptation process was followed, including translation, back-translation, expert review, and pilot testing. Confirmatory factor analysis was conducted to examine the structural validity of the scale. The findings supported a four-factor structure—usefulness, anxiety, expectancy, and competency—consistent with the original model, with acceptable to strong model fit indices after minor modifications, resulting in a 22-item version. Reliability analyses indicated high internal consistency across all dimensions. Comparative analyses examined differences between the Turkish and English samples at both total and dimensional levels. Turkish participants reported higher levels of usefulness, expectancy, and anxiety, whereas English participants demonstrated higher perceived competency. Additional analyses within the UK sample indicated that prior experience with GenAI was strongly associated with attitudinal differences, pointing to the role of usage in shaping how these technologies are evaluated. The findings indicate that the underlying structure of attitudes toward GenAI remains stable across linguistic contexts, while the intensity and balance of these dimensions vary depending on contextual and experiential factors. The coexistence of positive expectations and concerns suggests that student attitudes toward GenAI are inherently multidimensional rather than uniformly positive or negative. The study provides evidence that SAGAI is a valid and reliable instrument for use in English-speaking contexts and offers a structured framework for examining student attitudes toward GenAI in cross-cultural research. The results also underline the importance of considering both context and experience when interpreting how students engage with emerging technologies.