CSC-105 Theme Proposal: Thinking With Machines

A prototype reframing of CSC-225: Prompt Engineering for Large Language Models 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, the same platform that powers this site and a couple of other active CS courses. See Prompt Engineering for Education 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 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, 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.