Both college educators and students increasingly use artificial intelligence (AI) and large language models (LLM) in the classroom, but without consistent levels of thoughtfulness. Much has been written about cheating, plagiarism, and other unethical uses; some have raised concerns about how such tools might reduce opportunities for deep learning. This calls for rethinking how we use GenAI to achieve our learning objectives and create course assessments. Interdisciplinary domains offer a useful context for testing AI learning supports. Here we take a relatively new interdisciplinary domain–data storytelling–and walk through the steps toward designing useful intelligent support. This paper argues that, given the prevalence of such tools, educators can adapt and achieve positive outcomes if they initially document how learning takes place without such tools, what challenges they face, and design ways that intelligent tools can support improved learning. For data storytelling, we used an evidence-centered design (ECD) method to understand how relevant teaching and learning unfold without intelligent technologies. Then we documented the types of intelligent instructional supports and interactions that educators and their students desired. The paper includes recommendations on how postsecondary educators, educational researchers, instructional designers, and professional developers may apply these ideas and methods in their courses. Students might still take shortcuts by asking GenAI to generate data stories, but if we can build an environment that allows AI to measure learning processes (formative assessment) and recommend alternative approaches, we may be able to combat the undesirable use of GenAI.