Introducing AI Horizons

By Paul Allison

Artificial intelligence is advancing at a pace that makes even attentive scholars feel left behind. New large language models appear with striking regularity. Features are added, refined, or replaced in a matter of months. Tools that once seemed experimental are now embedded in research workflows and classroom discussions. For those in the research community, the question is no longer whether AI will affect our work, but how we should respond.

That is why Code Horizons is becoming AI Horizons.

This change reflects more than a new label. It acknowledges that the center of gravity in research computing has shifted. AI systems are now intertwined with coding, data analysis, text processing, and experimental design. They are being used to draft survey items, classify text, debug programs, generate hypotheses, visualize data, and summarize complex findings. Used well, they can accelerate routine tasks and create space for deeper thinking.

But the risks are equally real. AI systems can fabricate references, reproduce biases, and present confident answers that collapse under scrutiny. They can tempt users to substitute automation for judgment. Pretending these tools do not exist will not make them safer. The only realistic path to mitigating their dangers is to understand them thoroughly and to use them critically.

That is precisely what Code Horizons has always tried to do—and what AI Horizons will continue to do. Our mission remains the same: rigorous, practical training taught exclusively by active university faculty who understand the methodological standards, professional values, and everyday realities of academic research. They approach AI not as hype, but as a set of tools to be evaluated, tested, and applied carefully.

Because the AI landscape evolves so rapidly, our seminars are delivered live via Zoom rather than pre-recorded. This allows us to address current models and emerging practices in real time, and to engage directly with participants’ questions. The goal is not a generic tutorial on how to use ChatGPT or other systems, but structured, instructor-led guidance grounded in real research problems.

The name change also reflects something practical: coding and AI have become increasingly difficult to separate. AI systems now help generate, refine, and explain code. They are becoming part of the standard toolkit for serious research, and familiarity with them is fast becoming a baseline expectation—not because AI replaces expertise, but because it reshapes how expertise is exercised.

Our current seminars cover AI-assisted data analysis, machine learning workflows, text analysis with large language models, qualitative data automation, experimental design, and advanced statistical modeling in R, Python, and Stata. We are actively developing additional courses across a wide range of disciplines and methodological areas, and new offerings will be announced as they become available.

AI Horizons builds on the same standards that have guided our work for years at Code Horizons and Statistical Horizons: clarity over hype, live instruction over canned content, and faculty-led training designed for academics and other research professionals. We invite you to join us in learning how to use these tools with rigor, care, and intellectual responsibility.