Abstract Integrating computational thinking (CT) and artificial intelligence (AI) to foster interdisciplinary learning has received much-needed attention worldwide in educational research, though less emphasis has been placed on simultaneously developing CT and AI learning outcomes in disciplinary (e.g., mathematics) classroom contexts. CT and AI are closely linked to mathematics, where knowledge of data practices, mathematical modelling, and generic skills such as problem-solving are highly emphasized. This paper reports on a design-based research study with four design cycles, aiming to generate empirically grounded design principles for curricular and instructional innovations in school mathematics contexts that simultaneously support CT and AI learning. Drawing upon the theoretical perspectives of CT as modelling and programming as white-boxing, and taking exemplary tasks developed from our design-based study, we detail four design principles: (1) Encourage tinkering with computational artefacts; (2) Leverage CT as modelling for authentic problem-solving; (3) Consider tool-based developmental trajectories; and (4) Exploit the white-box effect to foster mathematical and AI literacy. From this, we conceive mathematics, CT, and AI as interconnected disciplines, where learning outcomes are not treated separately but rather as translatable across different disciplinary boundaries. We conclude by discussing the opportunities and challenges of implementing the curricular and instructional designs, offering insights to inform how CT and AI concepts and processes can be meaningfully embedded in mathematics education.
Ng et al. (Tue,) studied this question.