Strategic Competence in Sustainability Education: Conceptual Patterns Identified Through AI-Assisted Qualitative Analysis

This study investigates how participants conceptualise sustainability and sustainability citizenship, as well as how these conceptualisations relate to perceived agency. Drawing on two open-ended prompts, it analyses participants’ visions of a sustainable future and the roles they would like to play within it. The dataset was based on 1714 coded response segments from 164 participants. Methodologically, the study combines qualitative content analysis, independent human-AI double coding, manual validation, inter-rater reliability assessment, and residual-based co-occurrence analysis within a qualitatively grounded mixed-methods design. The results show that sustainability is predominantly framed in civic, symbolic, and ecological terms, whereas strategic competence and professionally articulated agency remain less visible. Sustainability meanings and role conceptions also vary systematically across disciplinary contexts. In addition, the analyses reveal patterned gaps between participants’ future visions and their self-attributed roles in sustainability transformations. The study contributes empirical insights into sustainability meaning-making and perceived agency and shows how LLM-assisted coding can be embedded in a transparent mixed-methods workflow. For sustainability education, the findings underline the importance of strengthening strategic and systemic dimensions of competence and linking civic engagement more closely to professional pathways of action.