<p>In this two-part position paper, we explore how instructional designers can reclaim the scientific foundations of instructional design (ID) by leveraging generative artificial intelligence (GenAI) to advance evidence-informed practice (EIP). In Part I, we argue that despite its benefits, EIP in ID remains constrained by persistent challenges: distinguishing scientific from anecdotal evidence, limited guidance in ID models for acquiring and appraising research, and insufficient time or resources for rigorous inquiry. To address the challenges, we begin by answering the foundational question, “What makes instructional design a science?” and argue that grounding design decisions in theory, research, and best practices may help ensure the effectiveness of resulting learning outcomes. We also examine how current academic and professional development pathways often fall short in preparing designers to engage in scientific reasoning, critical appraisal, and evidence-informed practice. We conclude by proposing that GenAI holds promise to support the acquisition, appraisal, and application of evidence. In Part II, we will outline conventional and AI-assisted methods for acquiring and appraising evidence.</p>