DESIGN SCIENCE RESEARCH AND ARTIFICIAL INTELLIGENCE IN EDUCATION: PRINCIPLES FOR THE DEVELOPMENT AND EVALUATION OF INTELLIGENT EDUCATIONAL ARTIFACTS

Abstract

The expansion of Artificial Intelligence (AI) in education has fostered the development of systems, platforms, adaptive environments, and other artifacts capable of supporting teaching, learning, management, and knowledge production processes. However, the incorporation of intelligent functionalities alone does not ensure educational relevance, scientific rigor, or the ethical adequacy of the solutions developed. In this context, Design Science Research (DSR) emerges as a particularly relevant approach because it connects the identification of real-world problems, artifact design, systematic evaluation, and the production of scientific knowledge derived from the design process. This article aims to systematize theoretical and methodological principles capable of guiding the development and evaluation of intelligent educational artifacts grounded in the articulation between DSR and AI. Methodologically, this is a qualitative, theoretical-bibliographic, and documentary study based on the analysis of publications on Design Science, DSR, educational technologies, and Artificial Intelligence in Education, while also establishing an analytical dialogue with a previous applied research experience conducted in the context of graduate education. As a result of the theoretical articulation, eight principles are proposed: contextual relevance; theoretical and methodological rigor; educational intentionality; user participation and iterativity; interdisciplinary integration; multidimensional evaluation; ethics, transparency, and data governance; and documentation, learning, and continuous evolution. The study argues that intelligent educational artifacts should not be assessed solely on the basis of technical performance, but also according to their capacity to address concrete educational problems, generate pedagogical and institutional value, and preserve human agency in AI-mediated processes. It concludes that the convergence of DSR and AI broadens the possibilities for educational research that is simultaneously design-oriented, scientific, critical, and committed to the responsible transformation of educational practices.