Artificial-intelligence-based automated scoring is often an afterthought and is considered after assessments have been developed, resulting in nonoptimal possibility of implementing automated scoring solutions. In this article, we provide a review of Artificial intelligence (AI)-based methodologies for scoring in educational assessments. We then propose an evidence-centered design framework for developing assessments to align conceptualization, scoring, and ultimate assessment interpretation and use with the advantages and limitations of AI-based scoring in mind. We provide recommendations for defining construct, task, and evidence models to guide task and assessment design that optimize the development and implementation of AI-based automated scoring of constructed response items and support the validity of inferences from and uses of scores.