> Source URL: /csc-105-theme.guide
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title: "CSC-105 Theme Proposal: Thinking With Machines"
description: "A prototype pitch reframing CSC-225 (Prompt Engineering for Large Language Models) as a themed section of CSC-105, in the format used on Furman's Introductory Courses page."
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# CSC-105 Theme Proposal: Thinking With Machines

A prototype reframing of [CSC-225: Prompt Engineering for Large Language Models](./index.path.md) as a themed section of **CSC-105: Introduction to Computer Science**.

## Why this theme, why now

- **AI literacy is a new computational literacy.** Every major the CS department serves, is already seeing LLMs adopted into the professional environment these students will enter. A CSC-105 theme on AI meets students where the world already is.
- **Prompt engineering makes CSC-105's learning outcomes concrete.** Decomposition, abstraction, iteration, evaluation, and debugging all map directly onto the practice of designing, testing, and evaluating computer programs.
- **Timeliness and recruiting.** A themed section on AI positions Furman alongside peer institutions that have added AI-focused intro courses and is a visible differentiator for prospective students comparing CSC-105 offerings.
- **Continuity with departmental research.** The theme draws directly on in-house work: Prof Johnson, Dr. Alvin are researching LLM-based agents for differentiated curricula using a "Curriculum-as-Code" methodology with [PathMX](https://pathmx.dev), the same platform that powers this site and a couple of other active CS courses. See [Prompt Engineering for Education](./education.guide.md) for the broader context.

> Note: This pitch is a prototype. Title, meeting time, and section number are placeholders for departmental planning.

## Proposed Theme

### FALL 2026

**CSC-105-0X – Thinking With Machines: Prompt Engineering and AI Fluency (with Prof Johnson)**
MWF @ TBD

A student pastes a prompt into ChatGPT, gets a confident answer with a plausible-looking citation, and hands it in... only to discover the citation was hallucinated. A small-business owner hooks an AI assistant up to their calendar and watches it quietly double-book three clients. A teacher builds a tutoring bot that works beautifully for the first ten students and starts giving away answers to the eleventh. None of these stories are about a broken AI. They are about people using a powerful tool without a mental model of how it works.

This course introduces students from all majors to the foundations of computer science through the lens of **large language models and the practice of building with them**. Students will learn how modern AI systems actually work — tokens, context, training, and the reasons they sometimes fail — while developing the computational-thinking skills to design, test, and critically evaluate AI tools they use in their own fields.

Potential topics could include:

- How LLMs actually work under the hood (tokens, context windows, training, and why models hallucinate)
- Prompt design as computational thinking: decomposition, iteration, and evaluation
- Building a simple chatbot for a specific domain (a class, a club, a workflow)
- Automating a real task with AI and measuring whether it actually works
- Retrieval-augmented generation (RAG): grounding AI answers in real data
- Ethics, bias, academic integrity, and thoughtful use of AI across disciplines

The course will emphasize **hands-on exploration**, including experimenting with current AI tools, writing and refining prompts against real evaluation criteria, and building small end-to-end projects — a tutoring assistant, a domain chatbot, or a workflow automation of the student's choosing. By the end of the semester, every student will leave with a portfolio of working projects and the vocabulary to discuss AI thoughtfully from their own major's perspective.

## Mapping to CSC-105 outcomes

This theme hits the standard computational-thinking outcomes expected of any CSC-105 section:

- **Problem decomposition.** Breaking an ambiguous goal ("write a tutoring bot") into prompts, tests, and sub-tasks.
- **Algorithmic thinking.** Designing multi-step prompt pipelines and simple RAG flows.
- **Abstraction and data representation.** Understanding tokens, embeddings, and structured outputs as the data layer underneath natural-language interfaces.
- **Evaluation and testing.** Writing evaluation rubrics and test cases for non-deterministic systems — a skill most students have never practiced explicitly.
- **Ethical reasoning about computing.** Bias, privacy, academic integrity, and the social consequences of deploying AI tools.

## Relationship to existing CSC-225

The existing [CSC-225](./index.path.md) curriculum is the source material for this theme. Moving it into CSC-105 would:

- Widen the audience from CS-adjacent students to every major the intro course already serves.
- Preserve the project-based structure and the cross-disciplinary guides already written for [Education](./education.guide.md), sciences, business, and the humanities.
- Require scaffolding more of the programming on-ramp inside the course itself, since CSC-105 assumes no prior coding experience.

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← [Back to Thinking With Machines](./index.path.md)


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## Backlinks

The following sources link to this document:

- [105-theme](/index.path.llm.md)