Faculty Syllabus

Class Participation and Discussion -- 18% of your grade

Activities -- 18% of your grade

Exam 1  -- 10% of your grade

Exam 2  -- 14% of your grade

Exam 3  -- 18% of your grade

Exam 4  -- 22% of your grade

An overall grade will be assigned based on the following scale:

90% - 100% A 89% - 80% B 79% - 70% C 69% - 60% D 0% - 59% F

No textbook is required -- all readings will come from free resources!

A focus on discrete mathematical tools for the working computer scientist. An emphasis is placed on using logical notation to express rigorous mathematical arguments. Subjects may include introduction to graph theory, recurrences, sets, functions, and an introduction to program correctness.

This course is designed to provide students in the BAS Software Development program with a methodology for solving problems utilizing modern computing devices. This course includes both an overview of Computational Thinking tools (Abstraction, Decomposition, Pattern Recognition, and Algorithm Design) and an Introduction to the Discrete Mathematical topics of Logic, Proof, Sets, Functions, Relations, and Counting.

1. Utilize Computational Thinking tools such as Abstraction, Decomposition, Pattern Recognition, and Algorithmic Design to formulate problems, automate solution procedures, and analyze results

2. Make use of Logical Statements and associated operators to express mathematical concepts and relationships related to problem solving

3. Construct proofs of assertions by choosing appropriate techniques from your proof toolset

4. Apply correct mathematical terminology and notation to formulate problems

5. Model sequences as recurrence relations

6. Distinguish between and work with the definitions and properties of Sets, Functions, and Relations

7. Formulate and Solve problems using probability and counting techniques

Schedule for Computational Thinking (COSC 3302) – for the Fall semester of 2025

The instructor reserves the right to alter the planned schedule of activities as conditions warrant.

Professor Ralph E. Hooper

Office Phone: 512-223-2599

Office Location: Room 1300.25 San Gabriel Campus

Virtual Office Hours available: Tue, Thurs 12 pm -- 2 pm via Zoom (email for appt.)

ACC email: ralph.hooper@austincc.edu -- Zoom will be available for meetings

Instructor Website: https://hooper.accprofessors.com/ 

Instructor Bio: I have been teaching at the college level for many years in both mathematics and computer science. My research interests are computational thinking and educational technology. I enjoy travel and baseball.

Generative AI Policy for COSC 3302 -- Computational Thinking

1. Introduction

This course adopts a balanced approach to Generative Artificial Intelligence (GAI) use, recognizing both its potential as a learning tool and the importance of developing fundamental problem-solving skills independently. Students are permitted to use GAI for specific learning activities while prohibited from using it for assessment and core skill development exercises. This policy aims to prepare students for professional environments where they will need both the ability to leverage AI tools effectively and the foundational knowledge to work independently when needed.

2. Rationale

Permitted Use: GAI is allowed for exploratory learning, concept clarification, and generating practice problems because these activities enhance understanding without replacing the development of core competencies. In professional computing environments, practitioners regularly use AI tools for research, brainstorming, and preliminary exploration.

Prohibited Use: GAI is prohibited for homework assignments, discussions, exams, and proof construction because these activities are designed to build essential analytical thinking skills, mathematical reasoning abilities, and problem-solving strategies that form the foundation of computational thinking. These skills cannot be developed if students rely on AI to complete the cognitive work.

Required Use: Students will be required to use GAI in designated activities to develop AI literacy and learn how to effectively prompt, evaluate, and refine AI-generated content—skills essential for modern computational professionals.

3. Definition of GAI

For this course, Generative Artificial Intelligence (GAI) refers to any artificial intelligence system capable of creating text, code, mathematical solutions, or other content in response to prompts. This includes but is not limited to:

• Large Language Models (ChatGPT, Claude, Gemini, etc.)
• Code generation tools (GitHub Copilot, Tabnine, etc.)
• Mathematical problem solvers (Wolfram Alpha when used for step-by-step solutions)
• Any AI-powered tutoring or homework assistance platforms
• AI-enabled search tools that generate synthesized responses

4. Resources

Students are encouraged to use the following resources when GAI use is permitted:

• Effective Prompting Guide: Course materials on crafting clear, specific prompts for mathematical and computational problems will be provided
• AI Evaluation Checklist: Framework for assessing the accuracy and relevance of GAI-generated responses as discussed and presented in class
• Recommended Platforms: ChatGPT, Claude, or Gemini for conceptual discussions; Wolfram Alpha for computational verification
• Course AI Workshop Materials: Step-by-step guides for using GAI in permitted learning activities will be included with specific assignments
• Office Hours: Instructor support for questions about appropriate GAI use

5. Assessment

For Permitted Activities: GAI use will be assessed based on:

• Quality of questions/prompts submitted to AI systems
• Critical evaluation of AI-generated responses
• Ability to identify errors or limitations in AI output
• Integration of AI insights with course concepts

Documentation Requirement: When GAI is used in permitted activities, students must:

• Include a brief statement describing which tools were used and how
• Reflect on the accuracy and usefulness of the AI-generated content
• Demonstrate their own understanding through explanation or extension of AI-generated ideas

6. Penalties

Violations of this GAI policy will result in the following consequences:

• First Offense: Warning and required completion of AI ethics module
• Second Offense: Zero grade on the assignment and required meeting with instructor
• Third Offense: Failure of the course and report to academic integrity committee

Severe Violations (submitting AI-generated work as original on exams or major assignments): Immediate failure of the course and report to academic integrity committee.

All violations will be documented and may affect future academic standing and recommendation letters.

7. Exceptions

Exceptions to this policy may be granted under the following circumstances:

• Accessibility Needs: Students with documented disabilities may receive modified GAI permissions through the Office of Disability Services
• Technical Difficulties: If course-required GAI tools are unavailable during designated activities, alternative arrangements will be made
• Research Projects: Advanced students conducting independent research may petition for expanded GAI permissions with instructor approval
• Emergency Situations: Documented emergencies affecting a student's ability to complete work independently may warrant temporary policy modifications

Students seeking exceptions must submit a written request to the instructor at least 48 hours before the relevant deadline.

8. Usage Permissions

Prohibited GAI Activities

• All Homework Assignments: GAI may not be used to solve, check, or generate solutions for any graded homework problem
• Examinations: No GAI use during quizzes, midterms, or final examinations
• Mathematical Proofs: GAI may not be used to construct or verify proofs for any assignment
• Algorithm Design: GAI may not be used to create or optimize algorithms for graded submissions
• Code Debugging: GAI may not be used to identify or fix errors in programming assignments
• Problem Set Solutions: GAI may not be used to generate answers for any problem sets

Permitted GAI Activities

• Concept Exploration: Using GAI to explore definitions, ask clarifying questions about course topics, or request alternative explanations
• Practice Problem Generation: Requesting GAI to create additional practice problems (not for submission)
• Study Guide Creation: Using GAI to help organize study materials or create concept maps
• Background Research: Exploring historical context or applications of mathematical concepts
• Syntax Help: Using GAI for programming language syntax questions (not logic or algorithmic help)
• Brainstorming: Generating ideas for project topics or approaches (implementation must be original)

Required GAI Activities

• AI Literacy Activity: Students must complete hands-on exercises using GAI tools to understand their capabilities and limitations
• Prompt Engineering Assignment: Students must demonstrate ability to craft effective prompts for mathematical problem exploration
• AI Evaluation Exercise: Students must critically assess GAI-generated mathematical content for accuracy and completeness
• Professional Development Reflection: Students must research and reflect on the role of AI in their intended career fields

This policy is subject to revision based on evolving technology and pedagogical best practices. Students will be notified of any changes with at least one week's notice.