Why these tech fundamentals matter, who created them, and the research and teaching at Willamette University that this series grew out of.
Origin
AI tools are reshaping the workplace, but they don't operate in a vacuum. They depend on hardware, software, operating systems, file structures, cloud infrastructure, and sound security practices. Students and professionals who lack confidence in these basics often struggle to get the most out of AI — not because the AI is hard, but because the underlying technology is unfamiliar.
This series was spec'd by Tim Johnson, who leads the AI management effort at Willamette University's Atkinson Graduate School of Management. But the need for computing and security fundamentals extends well beyond any single course. Multiple faculty at AGSM are teaching and researching at the intersection of AI, data, and management — and all of their students benefit from the same baseline tech literacy. This resource is designed to serve that broader effort: whether you're studying AI management, data analytics, statistics, or picking up AI tools in a professional role, the five modules here cover the shared essentials.
AI & Data at Atkinson
Tim Johnson's work on AI management is part of a broader commitment at the Atkinson Graduate School of Management to prepare students for a data-driven, AI-augmented workplace. Faculty across the school are integrating AI and analytics into their teaching and research.
Robert W. Walker · BUS 1301 · Spring 2026
Covers data types and storage, data visualization, probability, inference, and statistical reasoning — the quantitative foundations that complement the computing basics in this TechReady series.
Tim Johnson · AGSM
Students test AI models across business domains — operations, finance, accounting — and learn to evaluate performance, identify effective use cases, and deploy AI tools responsibly. Johnson's course treats AI use as a managerial task requiring workflow design, resource oversight, and structured testing.
The computing and cybersecurity basics in this TechReady series serve as shared preparation across these and other courses at AGSM. The modules are self-contained and not tied to any single course — take what you need.
Context
From a Willamette University news feature by Paul McKean, February 2026.
"When you're a manager, you're working with a team in order to execute some goal and achieve it. And that's basically analogous to what's going on with artificial intelligence — you're working with a series of models to perform some objective in the workplace you wouldn't just do by yourself."
— Tim Johnson
Willamette University professor Tim Johnson encourages students to view the use of AI as a managerial task: to leverage the technology, one must design workflows, oversee resources, organize tools, and communicate those plans via prompts. Using AI thus means managing AI.
In Johnson's classes, students don't just read about AI — they test models on specific management tasks and evaluate how well they perform. One week, students test AI in business operations, examining automation of scheduling or process mapping. The next week, they see how AI fares in finance and accounting. The goal is both the skill to use the tools and the judgment to assess when they fail.
Students excited about AI learn to channel that energy into rigorous testing rather than uncritical adoption — moving beyond "this is cool" to "this actually works for this specific task, and here's the evidence."
Skeptics are not just welcome — they're valuable. A critical eye helps identify constructive uses and poor uses more quickly. Healthy skepticism, grounded in structured testing, becomes part of a serious conversation about the technology.
"As with all new technologies, we should have healthy skepticism about AI and we should be judicious in our approach to it. By looking at the technology with a critical eye, folks will identify its constructive uses and poor uses more quickly. Accordingly, we need folks who are both critical of AI and willing to study it so that their skepticism can be part of a serious conversation about the technology."
— Tim Johnson
Johnson also sees a bigger picture. AI models can participate in their own improvement cycle — and as models get better, they can contribute to the development of subsequent models. This feedback loop sets the stage for a world where technological change accelerates faster than humans can easily track.
"Pretty soon we're going to start grappling with the challenges of a world that changes at a rate that is very difficult for a person to keep up with. AI models can be used in the AI development process, which means that as these models get better, they can participate in the improvement of subsequent models. I think this feedback loop sets the stage for the biggest challenge on the horizon for humanity overall: how do we cope with a world in which technological change gets faster and faster?"
— Tim Johnson
Building a better AI future requires informed communities to participate in decisions about the technology. That's the deeper reason Johnson is committed to helping students think critically about AI's implications — not just to use the tools, but to shape the conversation about what gets built and how it gets governed.
Series Creator
Tim Johnson set the specifications for this TechReady series and leads the AI management effort at Willamette University's Atkinson Graduate School of Management, where he serves as the Grace and Elmer Goudy Associate Professor of Public Management & Policy Analysis. He completed his PhD at Stanford University and served as a Predoctoral Fellow at the Max Planck Institute for Human Development's Center for Adaptive Behavior and Cognition in Berlin, Germany — the same research group founded by Gerd Gigerenzen that studies how humans and organisms make decisions under uncertainty. He is also a proud alumnus of the University of Oregon's Robert Donald Clark Honors College.
Long before ChatGPT made AI mainstream, Johnson was studying artificial intelligence — tracing back to his interest in Herbert Simon, an early AI pioneer and Nobel laureate, and his work using computer simulations and big data to model public policy outcomes and human cooperation. Now he brings that deep experience to research on how large language models behave in scenarios involving altruism, trust, and social decision-making.
Poets&Quants Best 40 Under 40 Professor
2020
Hudson Award for Excellence in Teaching
Willamette University
Aspen Institute Faculty Pioneer Award Finalist
2014
Atkinson Faculty Member of the Year
Willamette University
"Professor Tim Johnson likes to create an environment of discovery where students can engage and learn from discussion. He will start most classes with real-world examples of the concept he is discussing and draw students through the process of figuring out for themselves why the information they are learning is important and interesting. A class with Tim Johnson seems to fly by and you don't even realize how much you are learning."
— Student testimonial, Poets&Quants
Research
Johnson's research asks fundamental questions about AI behavior using tools from experimental economics and behavioral science — investigating whether AI agents exhibit behaviors consistent with self-interest, trust, and altruism, and whether real incentives change those behaviors.
Selected publications:
Johnson & Obradovich (2025). Testing for completions that simulate altruism in early language models. Nature Human Behaviour 9, 1861–1870.
Johnson & Obradovich. Measuring an artificial intelligence agent's trust in humans using machine incentives. Journal of Physics: Complexity.
Johnson & Salahshour (2026). Seventy-five years later, the prisoner's dilemma narrative continues to impart new wisdom. Europhysics Letters 153, 11001.
Johnson (2024). Managerial and Organizational Challenges in the Age of AI. JAMA Psychiatry.
Dawes, Fowler, Johnson, McElreath & Smirnov (2007). Egalitarian Motives in Humans. Nature 446, 794–796.
For more: faculty profile · full publications list · Willamette news feature
The TechReady modules cover the computing and security basics you need before working with AI tools. Start with Module 1 or take the condensed overview.
A note on this page: Tim spec'd the modules. Everything else on this page — the bios, the publications list, the links, the general atmosphere of self-promotion — is the work of Robert and Claude, who take full responsibility for the shameless marketing. Tim just wanted to teach students computing basics. We made it weird.