Expert training and strategic guidance for responsible AI adoption

Even with a strong grasp of how LLMs work, using them well requires a great deal of effort and intention. Getting reliable output means carefully structuring many prompts, providing the right context and files, checking results, and then repeating the whole process again for each task. This workshop introduces attendees to the more scalable approach of agent "orchestration." Instead of directly overseeing all tasks in one sustained conversation with an assistant, attendees will learn how to leverage an "orchestrator" agent that modularizes and distributes tasks across multiple parallel assistants ("subagents") on their behalf. This workshop helps attendees understand and navigate the tension between greater automation and auditability/interpretability of AI systems while firmly staying in control of the decisions that matter most. Attendees will also be introduced to the Data Analyst Augmentation Framework (DAAF), an open-source research orchestration framework built on Claude Code, as a concrete exemplar for this style of workflow in practice that they can leverage and build on themselves.

Once users can leverage AI orchestration frameworks, the core question of practice shifts from "how do I do this task?" to "how do I do this task most effectively and efficiently?" This workshop is about enhancing and extending on what attendees can already do -- learning the deeper mechanics that let them build more powerful, more reliable workflows and apply them to a wider range of problems. We cover best practices for "architectural" prompting (using AI to help design and improve the systems that govern its own behaviors and systems), advanced context engineering techniques, and the diagnostic tools (evaluation, chat logging, structured testing) that let users systematically identify what's working and fix what isn't. Attendees will also learn the more advanced version control and iteration practices that make these advanced workflows more testable, reproducible, and iterable over time. The goal is to move attendees from the beginner stages of developing these frameworks to someone who can confidently architect, evaluate, and extend them to fit whatever research problems they need to solve.

AI agents can now autonomously plan, write, review, and execute analytic code, raising urgent questions about their role in research given known risks like hallucinations and inaccuracies. This session provides researchers with an approachable framework for realizing the benefits of AI agents for more complex research workflows, while mitigating their very real risks, using DAAF: an open-source set of tools for Claude Code designed to accelerate data analysis while strictly enforcing transparency, reproducibility, and rigor. In addition to building familiarity with how to use and extend DAAF for their own purposes, attendees will leave with a strong understanding of the pros and cons of current AI agent frameworks for data analysis workflows, the main design considerations involved in robust "context engineering" practices, what these tools currently can and cannot do, as well as the intuition needed to evaluate their appropriateness for their own work.

Just as AI tools are rapidly reshaping what it means to practice data science and statistics, so too are they reshaping what it means to teach data science and statistics. Professors and instructors are now faced with an extremely difficult challenge: How can you redesign your curriculum to respond to the expanding role of AI in students' lives without knowing what kinds of jobs and careers will exist for them in the future? What skills and topics and experiences will be valuable for students beyond just the next few months? This workshop brings faculty together to work through those design tensions directly. We'll introduce practical heuristics for distinguishing "durable" skills in the era of AI, explore targeted opportunities to modernize curricula and assignments without abandoning the conceptual depth that makes graduates genuinely capable, and develop a more informed critical lens for evaluating AI developments as they continue to emerge. Participants come having pre-identified core "problems of practice" from reviewing their syllabi for the upcoming semester, so that the session's facilitated discussions center on real design tensions rather than hypotheticals -- questions like what trade-offs exist between productive struggle and AI assistance in the classroom, what role assessment should play when students can generate plausible-looking work effortlessly, and what opportunities AI presents for richer course materials and activities. Attendees will leave with concrete strategies for more agile curriculum adaptation and a stronger framework for making principled instructional decisions as the landscape continues to shift.