Adolescents’ Motivation for AI-Enabled Environmental Actions: The Role of Supportive Environments and Expectancy–Value Theory

Climate change poses a major challenge to global sustainable development, and cultivating adolescents’ ability to use emerging technologies to understand and address environmental problems is receiving increasing attention. However, there is still a lack of systematic examination of adolescents’ motivation to use artificial intelligence (AI) to participate in environmental protection activities and the related factors, with research that simultaneously integrates contextual support factors and individual expectancy–value beliefs being especially lacking. Based on Situated Expectancy–Value Theory (SEVT), in this study, a questionnaire survey and covariance-based structural equation modeling (CB-SEM) were employed to examine the relationships between supportive social norms, facilitating conditions, expectancy, task value, and perceived cost and adolescents’ motivation to use AI to participate in environmental protection activities. A total of 602 valid questionnaires were collected, and the measurement structure was examined through exploratory factor analysis and confirmatory factor analysis. The results show that expectancy, attainment value, intrinsic value, utility value, and facilitating conditions are significantly positively correlated with motivation, while perceived cost is significantly negatively correlated with motivation. The direct path between supportive social norms and motivation is not significant, but there is a significant total indirect effect; facilitating conditions have both a significant direct effect and a significant total indirect effect. In addition, an exploratory follow-up survey including 30 adolescents and 30 adults suggested that the two groups may differ in their subjective perceptions of social norms, but the findings were not sufficient to establish an explanatory mechanism for age differences. This study provides empirical evidence of the applicability of SEVT in AI-supported environmental protection education contexts and offers a reference for designing educational activities that focus on adolescents’ motivational beliefs and contextual support conditions.