The integration of artificial intelligence (AI) into remote laboratories presents sig-nificant pedagogical challenges that require empirically grounded design frame-works. This study is part of research conducted within the framework of a mas-ter's degree project, and the objective is to develop seven design principles for the use of AI-assisted remote laboratories in chemistry education, derived from a multiple triangulation methodology with an inductive approach. Three comple-mentary studies were conducted: a focus group with teachers, a student experi-ence survey, and an analysis of student-AI interactions in a remote acid-base titra-tion II laboratory implemented at the University of Buenos Aires. Seven princi-ples emerged from a systematic triangulation of data from previous studies. The conceptual structure of the principles, based on generalisable teaching design ra-ther than specific disciplinary content, allows for their transferability to remote la-boratories in various fields, including physics, biology, and engineering, contrib-uting to the democratisation of science education in contexts where traditional re-sources are limited.