1 credit
Fall 2026 Lecture Lower Division
This interdisciplinary course introduces students to the foundations of artificial intelligence within the broader context of human intelligence and social life. Students will explore how AI systems work, evaluate their societal impact, and learn to use AI tools to answer research questions in the sciences. Students will gain hands-on experience developing technical discernment in using contemporary AI tools for text, image, and data generation and analysis. Designed for non-specialists, this course develops AI literacy by building the ability to evaluate and use AI systems that increasingly shape our world. The course will be structured around a semester-long group project using AI tools supported by the Rosen Center for Advanced Computing. Course sessions will follow a colloquium model, comprising presentations by subject-matter experts on the week's topic, instructor-facilitated discussions and material processing, online preparatory assignments, individual reflections, and group work on the long-term project. Students will be expected to spend an additional hour per week with their groups.
Learning Outcomes1Analyze the pervasive integration of algorithmic systems in professional and societal contexts, distinguishing between deterministic software and probabilistic AI models to assess their influence on daily decision-making processes.
2Articulate the fundamental mechanics of the AI pipeline (including data curation, model training, and inference) and explain how specific design choices lead to ethical consequences such as bias, privacy erosion, or intellectual property disputes.
3Distinguish the operational boundaries of current AI technologies, identifying specific scenarios where AI implementation provides a measurable advantage versus those where it introduces unacceptable risk, unreliability, or inaccuracies (e.g., hallucinations)
4Evaluate the validity and trustworthiness of AI-generated outputs by auditing the underlying data sources for quality and testing the system for spurious correlations, logical inconsistencies, or unintended societal impacts.
5Employ iterative prompt engineering and algorithmic decomposition techniques to effectively steer AI tools, ensuring that generated solutions adhere to academic integrity standards, ethical norms, and logical soundness.
6Formulate transparent explanations of AI-assisted workflows, clearly documenting the human-machine division of labor to ensure accountability and reproducibility in scientific or professional communication.
7Synthesize a personal framework for human-AI collaboration, defining the evolving role of human expertise, oversight, and creativity within their specific field of study to navigate a shifting professional landscape.
made by Purdue students.