15 weeks | Tuesday/Thursday | 80 minutes | 28 class meetings
This course treats AI as a scientific, technological, economic, cultural, and political phenomenon rather than simply a business technology. Students from business, liberal arts, sciences, education, health, and other disciplines will connect the material to their own fields.
Every class includes four elements:
Historical material are woven throughout the semester rather than confined to a history unit. Historical anecdotes help you recognize recurring patterns involving technological competition, optimism, fear, regulation, military power, scientific collaboration, and unintended consequences.
Students will use OneNote to create and maintain a Digital Artifact Notebook, with a separate page for each class and topic.
This notebook will serve as the evidence base for two major papers: a midterm paper and a final paper.
Evidence form the 'Hands-on activity' from each class is collected and reflected upon in the notebook, along with notes on the core concept, historical connection, and social/controversial question for each class.
Core topics
Hands-on activity: Students interact with several current AI systems and develop their own tests for determining whether a system appears “intelligent.”
Social/controversial question: Who gets to define intelligence—and are human definitions biased toward the abilities humans value?
Core topics
Hands-on activity: Teams create an “AI capability challenge” and test multiple systems on the same tasks.
Social/controversial question: Are current AI systems genuinely reasoning, or are humans attributing intelligence to sophisticated pattern prediction?
Core topics
Historical connection: Why earlier generations repeatedly believed human-level AI was just around the corner.
Hands-on activity: Students experiment with a simple classification or prediction system and investigate how changing training examples changes results.
Social/controversial question: If historical data reflects inequality or discrimination, can an AI trained on that data avoid reproducing it?
Core topics
Hands-on activity: Experiment with prompts, context, source documents, and model settings to see how outputs change.
Social/controversial question: Does an LLM “know” anything in a meaningful sense?
Core topics
Hands-on activity: Students solve the same problem using progressively improved prompts and compare the results.
Social/controversial question: Will effective AI use become a new form of literacy, and what happens to people who lack access to it?
Core topics
Hands-on activity: Students choose a task from their own discipline and compare how several AI systems approach it.
Social/controversial question: When does AI assistance become unacceptable substitution for human work?
Core topics
Hands-on activity: Students give an AI a research question, verify its claims independently, and calculate an informal “trust score.”
Social/controversial question: Does easy access to plausible answers make society better informed—or easier to mislead?
Core topics
Hands-on activity: Students generate permitted synthetic media and then attempt to distinguish authentic from AI-generated examples.
Social/controversial question: What happens to democracy, journalism, courts, and personal relationships when audio and video can no longer automatically be trusted?
Core topics
Hands-on activity: Students create the same artifact with human-only, AI-only, and human-AI collaborative workflows.
Social/controversial question: Is AI-generated art actually art?
Core topics
Hands-on activity: Students examine several hypothetical creator/AI disputes and argue competing positions.
Social/controversial question: Should creators have the right to prevent their works from being used to train AI?
No Tuesday class
Core topics
Hands-on activity: Students ask AI to teach them an unfamiliar concept, then evaluate whether they actually learned it.
Social/controversial question: If AI can complete an assignment, should the assignment disappear—or should students be prohibited from using AI?
Midterm paper introduced.
Core topics
Hands-on activity: Students compare the economic cost of completing a task manually, with AI assistance, and primarily through AI.
Social/controversial question: Who captures the productivity gains from AI—the worker, employer, consumer, or AI provider?
Core topics
Historical connection: Compare AI investment with railroads, electrification, the Internet, and the dot-com era.
Hands-on activity: Students evaluate a hypothetical billion-dollar AI investment and identify what assumptions must be true for it to make economic sense.
Social/controversial question: Is current AI spending building the infrastructure of the future or creating an investment bubble?
Core topics
Hands-on activity: Students compare the same AI task running locally, through a cloud provider, and through a hosted model where available.
Social/controversial question: Should access to advanced AI depend on a small number of companies controlling enormous computing resources?
Core topics
Hands-on activity: Teams role-play a community hearing involving residents, utilities, environmental groups, local government, workers, and a company proposing an AI data center.
Social/controversial question: When do the economic benefits of an AI data center justify its local costs?
Core topics
Historical connection: Mechanization, industrial automation, ATMs, personal computers, and previous predictions of mass unemployment.
Hands-on activity: Students break an occupation into tasks and identify which tasks AI can automate, augment, or currently cannot perform.
Social/controversial question: If AI makes a worker twice as productive, should society expect half as many workers—or twice as much output?
Core topics
Hands-on activity: Teams investigate a profession outside their own major and demonstrate one current AI workflow.
Social/controversial question: Which decisions should always require accountable human judgment?
Midterm paper due - see D2L
Core topics
Hands-on activity: Students build or configure a simple agentic workflow using an accessible AI platform.
Social/controversial question: When should an AI be allowed to act without asking a human first?
Core topics
Hands-on activity: Install or configure Hermes Agent, establish a student workspace, select a model, and complete a bounded task.
Social/controversial question: How much access should an AI agent have to your computer, files, accounts, and communications?
Core topics
Hands-on activity: Students give Hermes or another agent a multi-step task requiring research, file creation, and verification.
Social/controversial question: Who is responsible when an autonomous agent makes a costly mistake?
Core topics
Hands-on activity: Students design a workflow in which several specialized agents divide responsibility for completing a larger task.
Social/controversial question: Could organizations eventually operate with very few human employees?
Core topics
Historical connection: Cold War nuclear competition, arms-control negotiations, scientific cooperation, and U.S.–Soviet efforts to prevent catastrophic escalation.
Hands-on activity: Students participate in a simulation involving countries negotiating limits on a powerful emerging AI capability.
Social/controversial question: Can nations cooperate to limit dangerous AI capabilities while simultaneously competing to develop them?
Core topics
Hands-on activity: Students conduct a threat-modeling exercise for an AI-enabled organization or campus.
Social/controversial question: How much privacy should society surrender for security, convenience, or efficiency?
Core topics
Hands-on activity: Students audit hypothetical AI decisions involving hiring, lending, admissions, insurance, or public services.
Social/controversial question: Should an algorithm ever make a consequential decision that its creators cannot fully explain?
Core topics
Hands-on activity: Student teams design five rules they would impose on advanced AI developers, then defend the tradeoffs.
Historical connection: Compare AI governance with nuclear technology, aviation safety, pharmaceuticals, automobiles, and the early Internet.
Social/controversial question: Which is the greater danger: regulating AI too slowly or regulating it too aggressively?
No Thursday class
Core topics
Hands-on activity: Students examine competing predictions about advanced AI and classify claims as:
Teams must provide evidence for their classifications.
Social/controversial question: Should society prepare now for AGI or ASI even if no one can demonstrate that either will occur?
Historical connection: Nuclear weapons provide a useful analogy: scientists and governments sometimes must make decisions about low-probability but extraordinarily consequential technologies before uncertainty has been resolved.
Core topics
Hands-on activity: Student teams construct alternative futures:
Students identify what evidence over the next several years would indicate which scenario is occurring.
Social/controversial question: Would substantially more capable AI make society more equal—or concentrate wealth and power even further?
Core topics
Hands-on activity: Students revisit predictions they made during Week 1 and identify which assumptions they would now change.
The final discussion separates three questions:
What do we know? Evidence supports it.
What do we reasonably expect? Evidence supports a forecast, but uncertainty remains.
What are we afraid of or hopeful about? These possibilities matter, but should not be mistaken for established scientific facts.
Social/controversial question: What kind of AI future should humans actually try to create?
Final paper serves as the student's evidence-based answer to that question.
Students maintain an AI Past, Present, and Future Log throughout the semester.
Each week they record:
The log becomes the evidence base for both major papers.
Suggested theme: What Have We Learned About AI So Far?
Students make and defend a thesis about the significance of current AI.
The paper should require them to distinguish:
Suggested theme: AI 2035: What Should We Expect, and What Should We Do About It?
Students develop an evidence-based forecast for how AI is likely to affect an area they care about.
They should address:
The goal is not to reward optimism or pessimism. The strongest papers should demonstrate that students can make a claim, examine conflicting evidence, acknowledge uncertainty, and revise their beliefs when evidence warrants it.
Weeks 1–3: What AI is and how humans interact with it Weeks 4–6: AI's effects on knowledge, creativity, and education Weeks 7–9: Economics, infrastructure, and employment Weeks 10–11: AI agents and increasing autonomy Weeks 12–13: Security, political power, ethics, and governance Weeks 14–15: AGI, ASI, alternative futures, and human choices
The recurring theme throughout the semester should be:
AI should be approached scientifically: test claims, demand evidence, compare competing explanations, recognize uncertainty, and be willing to change your mind. At the same time, recognize that questions involving employment, creativity, privacy, inequality, security, and human identity are deeply personal and emotional for many people.