COMP 191: Introduction to Programming with AI

Fall 2026 – Kenyon College

  • Instructors: 
    • James Skon and Ellis Cain
  • Meeting time: 
    • Monday, Wednesday, and Friday, 10:10–11:00 a.m.
  • Location: 
    • Chalmers 200
  • Office hours: 
    • Doctor Cain (Chalmers 356): Monday 3:30-5pm, Wednesday 1-2pm, Friday 1-2pm (starting 9/14)
    • Prof. Skon: Mon, Tues, Thurs: 2:00-3:00, Thursday 9:00-10:00 (Chalmers 357)
  • Tutoring (MSSC): Details to come

Course Overview

COMP 191 is an introduction to computer programming in which artificial intelligence is explicitly integrated into the learning and development process. Students will learn the same core programming concepts and skills expected in COMP 118, including algorithm development, Python, C++, data structures, files, and objects. At the same time, they will learn to use generative AI thoughtfully as a structured development partner.

The central goal is not to outsource programming to AI. Students must develop enough understanding to design solutions, read and explain code, detect errors, test behavior, and judge whether a proposed solution is correct. AI will be used to help generate, critique, debug, and refine programs, but students remain responsible for the reasoning and the finished work.

No prior programming experience is required.

Course Learning Objectives

By the end of the course, students will be able to:

  1. Design algorithmic solutions to computational problems.
  2. Write, trace, test, debug, and explain programs.
  3. Use variables, expressions, input and output, conditionals, loops, functions, strings, lists, dictionaries, files, and classes.
  4. Implement small programs in Python and C++.
  5. Write precise prompts that communicate programming goals, constraints, and testable requirements.
  6. Critically evaluate AI-generated code for correctness, clarity, efficiency, and conformance to requirements.
  7. Develop programs incrementally, using tests and evidence rather than trusting plausible-looking output.
  8. Document how AI contributed to a solution and explain the decisions they made.
  9. Build confidence in their ability to create useful working software.

Programming in the Age of AI

AI is a powerful tool, but it is not a replacement for understanding. In this course, students will learn a recurring workflow:

  1. Think: understand the problem, identify inputs and outputs, decompose the task, and design an approach.
  2. Prompt: communicate the goal and constraints clearly to an AI assistant.
  3. Evaluate: read the proposed code, trace it, test it, and identify weaknesses or mistakes.
  4. Refine: revise the prompt or code incrementally.
  5. Explain: demonstrate understanding of the final program and the decisions behind it.

A useful model is to treat AI as a junior developer who is fast, confident, and often wrong. Your job is to guide the work, recognize mistakes, and take responsibility for the result.

Texts/Online resources

This course uses several online tools for learning and assessing student progress. All of these resources are free of cost but some require the creation of a login account. It is essential that everyone participate in the associated activities as all are part of the learning process, and some are graded activities.

  • How To Think Like A Computer Scientist: Interactive edition (kenyoncollege_thinkcspy_Fall26) This is an interactive book. You must first sign up for this course. Follow this link and register using your Kenyon email. Use “kenyoncollege_thinkcspy_Fall26” as the course name. You will read the sections from text as assigned in the calendar below. I strongly recommend that you to do the included problems in the text as well.  The quizzes will largely be based on the readings from this text.
  • C++ for Python Programmers (kenyoncollege_thinkcpp_Fall26) This is for the C++ part of the course. Follow this link and register using your Kenyon email. Use “kenyoncollege_thinkcpp_Fall26″) as the course name. Like the Python book, you will be assigned readings from this text.
  • Kenyon coLearn-AI (https://csits.kenyon.edu) – This is a Kenyon created collaboritive learning system we will use in this course.  This is used for in-class activities including POGIL activities, quizzes and exams. You must sign up with your kenyon email address and actual Kenyon name.  Once logged in, you sign up for your section.  In the “Join a Course by Code” field enter “tba”.
  • coLearn-AI (colearn.ai.com) – This is the same platform as above, but running in the cloud. This allows access from off campus. This is used for assignments completed outside of class. Sign up with the same email and name as for csits.kenyon.edu.

Methodology

This course uses a variety of learning strategies in order to both enrich and enhance learning for every student of every background, as well as to keep the course interesting. Methods include:

  1. Group (collaborative) activities:
    • POGIL (Process Oriented Guided Inquiry Learning). Discussed below, this is a team oriented, discovery based approach to learning with small groups of students. Teams report back to the whole classroom and share their discoveries. The level of allowed AI assistance will be specified in the instructions for that particular lab.
    • In class small group programming. This is to allow learners to explore and solve a problem as a small group, such that each student engages with the material and each other, experimenting, teaching, and learning together.
  2. Individual activities:
    • Laboratory assignments. These programming assignments give each learner the opportunity to develop skill, experience, and confidence as programmers as individuals. There will be a lab assignment approximately every other week in the first half of the semester (3), and about once a week after the spring break (6). The level of allowed AI assistance will be specified in the instructions for that particular lab.
    • Reading Assignments and Daily Quizzes.  For every class, the students are expected to read certain sections from the textbook (online, interactive) BEFORE the class. For encouragement and accountability, there will be a short quiz in this class every day (except for the first day, but there is a survey and syllabus quiz that should be done before the first day that will count as quiz 1). A number of low quiz scores will be dropped.
    • History reflections – these small writing assignments, about one a week, give each learner the change to explore computer science in its larger historical context. We will have a brief discussion of these in class.
  3. Instructional Presentation and discussion. Occasionally the instructor will give a presentation related to the course topics. These will normally include discussion, and sometimes interleaved with in-class, hands on programming activities.

Attendance

Attendance and full engagement are essential because much of the learning occurs through collaborative work that cannot be reproduced by merely reading notes. Students are expected to attend every class unless they have a legitimate excuse, such as illness or participation in an official collegiate activity, and should contact an instructor before an anticipated absence whenever possible. Please note that up to 8 of your lowest quiz grades can be dropped.

Students who miss class are responsible for arranging to complete missed work when appropriate. A student who accumulates six absences may be required to withdraw or may fail the course. Tardiness or leaving during class may count as one-half of an absence. Ordinarily, credit for an in-class collaborative activity requires attendance and participation.

POGIL

Process Oriented Guided Inquiry Learning (POGIL) is a pedagogy that is based on research on how people learn and has been shown to lead to better student outcomes in many contexts and in a variety of academic disciplines. Beyond facilitating students’ mastery of a discipline, it promotes vital educational outcomes such as communication skills and critical thinking. Its active international community of practitioners provide accessible educational development and support for anyone developing related courses.

We will be learning about POGIL early in the course, and then use this method on a daily basis. You must be logged into your Kenyon account to access the activities.

POGIL Team Roles

Teams will normally have 3-4 students:

  • Spokesperson
  • Facilitator
  • Process Analyst
  • Quality Control

On a team of three, the roles will be Spokesperson/Facilitator, Process Analyst, and Quality Control.

POGIL Process Skills

Assignments

Due Date: All assignments are due as specified in the grading table below.

Missing Lab Assignments: Labs are an important part of this class; the effort spent on them is a crucial part of the learning process. Failure to submit labs is unacceptable: students earning 0s on two labs cannot receive a grade higher than a B- for the course; students earning three 0s on labs will receive an automatic F for the course.

Collaboration and Academic Honesty: In order to facilitate learning, students are encouraged to discuss assignments amongst themselves. Copying a solution is not, however, the same as “discussing.” A good rule of thumb is the “cup of coffee” rule. After discussing a problem, you should not take away any written record or notes of the discussion. Go have a cup of coffee or cocoa, and read the front page of the newspaper. If you can still re-create the problem solution afterward from memory, then you have learned something, and are not simply copying. (The in class assignments are exempt from this, as they are intended to be done together.) 

Academic Honesty and using code you did not write: Turning in code you did not write is cheating.

  • You should never receive code from other students, use code from the internet, or use instructor solutions from past semesters. Any code you submit must be written entirely by you. (See the “cup of coffee” rule under collaboration.)
  • Likewise, “facilitating academic dishonesty” is a violation of academic honesty. Thus sharing your code with other students is also forbidden.
  • The instructor has tools for checking the similarity of code, and will use them periodically to see if students’ code is too similar to be explained by coincidence.
  • If you suspect someone has used your code, you should report it.

Computer History Reflection: Once a week you will turn in a brief reflection on some computer history fact from either the Timeline of Women in Computing, the Computer History Museum Timeline, or How AI Works. One or two people people will be chosen each week to orally describe what they found in 1-2 minutes at the beginning of class. I will ask for volunteers, and everyone will speak at least a couple of times during the semester. The idea is give to us all an opportunity to explore the history of computer science, and to find something that interests each of us. Start by going to one of the timelines, and for each assignment explore the requested years until you find something interesting. Then write up a 200-300 word Reflection about what you found, what you found compelling, and why you think it is significant. Write the reflection in Moodle, and include a link to the item you found so it can be displayed while you share in class. These are due midnight before the day they will be presented (and appear in the calendar below). Late submissions will not be accepted on these assignments.

Expected Workload:

There will be more due dates in this course than in any you’ve likely taken. There will be work due several days per week in most weeks. Most of these assignments will take well under an hour, but working consistently and staying on top of what we’re doing is absolutely imperative. These assignments include readings required for daily quizzes, history reflections, and labs.  Tools for More Effective Studying.

Collaboration, Academic Honesty, and AI Use

Collaboration is encouraged during designated group design work and coLearn-AI activities. Individual work must reflect the student’s own understanding. Students may not copy another student’s code, share solutions to individual assignments, or misrepresent AI-generated work as their own reasoning.

This course is designed to scaffold your learning, so you can reach the point where tools like AI actually become helpful rather than misleading. AI use is expected when an activity or assignment permits or requires it. All such use must be transparent. Students must preserve requested prompt histories (when not using colearn-AI), identify substantive AI contributions, verify generated code, and remain responsible for every submitted line. Some exercises and assessments will restrict or prohibit AI so that students can demonstrate independent programming understanding. The instructions for each assignment determine what forms of collaboration and AI assistance are permitted.

Also, keep in mind: the final exam will be completed in class without access to AI or any digital tools. If you haven’t built real skills during the semester, that will become obvious.

AI-Assisted Lab Submissions (when not using colearn-AI)

Unless an assignment specifies otherwise, an AI-assisted lab submission must include:

  • Your initial pseudo-code
  • The transcript of interaction with AI
  • The final program
  • Tests and evidence that the program works

Students may be asked to explain or modify submitted code. Inability to explain a solution is evidence that the work does not yet meet the course objective, even if the program runs.

Grading

Grades will be entered in Moodle.

CategoryWeightCollaboration?AI?Notes
Computer History Reflection10%NoNot allowedDue by midnight the day before.
coLearn-AI POGIL Activities20%YesAllowed as specifiedYou must be in class to get credit for these, except in cases of excused absence.
AI-Assisted Labs30%NoAllowed as specifiedDue by midnight on the day due.
Quizzes10%NoNot allowedA short quiz (5 minutes) at the beginning of each class. The quiz opens 10 minutes before class, and you are encouraged to finish the quiz prior to class starting. If you are late, you will miss the quiz. You cannot make up quizzes, but the lowest 10 quiz scores will be dropped. Each quiz will include questions on the content of the reading assignment for that day, and possibly from the previous class.
Final Exam30%NoNot allowedIn-class, 3 hours long
Total100%

Programming Pretest and Post-test

Students will complete a programming pretest near the beginning of the semester and a corresponding post-test near the end. A common portion will also be administered in COMP 118. These assessments focus on programming understanding rather than AI use and will help us evaluate learning across the two course designs. The pretest is diagnostic and does not assume prior programming experience.

Late Work

There is no make-up for quizzes, period.  As a general rule, no assignment will be accepted late, with a single exception. Each student may request a free, one-time 24-hour extension during the semester on a lab assignment. The request must be made via email before the initial deadline.

Course Schedule

The schedule may be adjusted as the course develops; please check back regularly. Each listed coLearn-AI topic is planned as an approximately 35-minute activity. Readings and assignment deadlines will be listed in the table below.

DateTopicReadingcolearn-AI activitySlides/DocumentsAssignments due
Fri, Aug 28Course introduction

Signup for services
—POGIL introductionIntroductionCreate Accounts:
colearn in-class
colearn labs
Mon, Aug 31Intro Python, programming languagesPython Ch. 1.1-1.13Quiz 1 – The Way of the Program

POGIL – Introduction to Python

Python Demos
Introduction to PythonStudent Survey (ungraded quiz)
Wed, Sep 2Intro AI, Programming with Prompting, Pseudo-codeUsing AI to create Programs Intro

Pseudocode
Quiz 2- AI Prompting and Pseudocode

POGIL – The AI-Assisted Programming Cycle
Slides

AI and Computing
—
Fri, Sep 4Inputs, variables, and data typesPython Ch. 2.1-2.4Quiz 3 – Input and Variable

POGIL – Variables, Data Types, and User Inputs
Slides1930s from Comp History Timeline (due Thursday Midnight)

Lab 0 opens
Mon, Sep 7Variable names and keywords, statements and expressions, operators and operandsPython Ch. 2.5-2.7Quiz 4 – Operations

POGIL – Math Operations and Statements
Slides—
Wed, Sep 9Algorithms, formatting dataPython Ch. 2.8-2.11No quiz today

POGIL – Formatting Outputs with f-strings
Slides
Fri, Sep 11Decision making in PythonPython Ch. 7.1-7.3Quiz 6 – Booleans

Code Examples
Examples

POGIL – Boolean Expression
Slides1940s from Comp History Timeline
Mon, Sep 14Decision making in Python continuedPython Ch. 3.1-3.6Quiz 7 – Debugging and Booleans

Operator Precedence

POGIL – Complex Booleans
Slides
Wed, Sep 16Selection in PythonPython Ch. 7.4-7.5
f-Strings review
Quiz 8 – If-Then

POGIL – If-Then-Else
Slides

Selection
Lab 0 due

Lab 1 opens
Fri, Sep 18LoopsLoops (ONLY “While Loop”, “For Loop”, “While vs For Loops in Python”, and “break and continue Keywords: Creating Infinite Loops”)Quiz 9 – Loops

Triangle
NumberGuess
Circle

POGIL – While Loops
Slides

For Loops
1950s from Comp History Timeline
Mon, Sep 21Nested SelectionPython Ch. 7.6-7.7Quiz 10 – Nesting

POGIL – Nested If-Else statements
Slides

Nested If
Wed, Sep 23Nested loopsPython Ch. 8.1-8.3
Loop Animation
Quiz 11 – Looping loops

POGIL – Nested loops

More fun: Computing Loan Payoff
Slides

Nested Loops
Lab 1 Due
Fri, Sep 25Lab reviewLoop Demo 1
Nested Loop Demo
Quiz 12 – Strings

POGIL – Strings
Slides

Review Notebook (You will need to make a copy of this notebook.)
1960s from Comp History Timeline
Mon, Sep 28Turtles
Turtle Guide
Python Ch 4.1-4.6Quiz 13 – Turtles

POGIL – Vincent van Turtle
Slides
Turtle Example
Wed, Sep 30StringsPython Ch. 9.1-9.9Quiz 14 – Strings

POGIL – Working with Strings with AI
Slides

Strings
Fri, Oct 2Modules and built-in functionsPython Ch. 5.1-5.4Quiz 15 – Modules

POGIL – Python Modules with AI
Slides
1970s from Comp History Timeline
Mon, Oct 5FunctionsPython Ch. 6.1-6.5Quiz 16 – Functions

POGIL – Writing your own function
Slides
Wed, Oct 7Parameters & local variablesPython Ch. 6.6-6.10, 7.8Quiz 17 – Parameters

POGIL – Parameters and Local Variables with AI
Slides

Variable Scope
Lab 2 – Bouncing Balls due 10/9
Fri, Oct 9No class – October Break
Mon, Oct 12Nested Loops, StringsPython 8.1-8.3
9.10-9.19
Quiz 18 – Reviewing Loops

POGIL – Reviewing Loops and Strings
Slides

Wed, Oct 14Lists, passing listsPython Ch. 10.1-10.8Quiz 19 – Lists

POGIL – Lists
Slides

Lists
Fri, Oct 16Lists continuedPython Ch. 10.12-10.14, 10.16-10.17Quiz 20 – List Extravaganza

POGIL – Lists of Lists
Slides

List Functions
1980s from Comp History Timeline
Mon, Oct 19Reading FilesPython Ch. 11.1-11.5Quiz 21

POGIL – Computer Literacy
Slides
Reading Files
List Comprehension Demo
Wed, Oct 21Writing FilesPython Ch. 11.6-11.8Quiz 22

POGIL – Computer Authorship
Slides


FileWriteBasicExample
Fri, Oct 23DictionariesPython Ch. 12.1-12.3Quiz 23

POGIL – My Favorite Book: The Dictionary
Slides

Dictionaries
1990-1994 from Comp History Timeline
Mon, Oct 26RecursionPython Ch. 16.1-16.4Quiz 24

POGIL – Recursion
Slides

Wed, Oct 28Recursive recursionPython Ch. 16.5-16.6Quiz 25

POGIL – Recursion
SlidesLab 3 – Word Games Due 10/28

Set up github account
Fri, Oct 30Python Classes

IDE setup, introduction to Github
Python Ch. 17.1-17.4

Install Thonny, Create Github account
Quiz 26 – Github Basics

POGIL – Class-ic Python
—

Classes Tutorial
1995-1999 from Comp History Timeline
Mon, Nov 2AI IDEPython Ch. 17.5-17.9

Claude Code
——

Wed, Nov 4AI IDE———
Fri, Nov 6Python reviewSign up for C++ textbook: kenyoncollege_cpp4py-v2_fall26
—
Slides2000s from Comp History Timeline
Mon, Nov 9Introduction to C++, first programCPP4PY Ch. 1Quiz 27

POGIL – C++ Intro
SlidesLab 4 – Tomo-py-chi Sharing Code due 11/9
Wed, Nov 11C++ Data TypesCPP4PY Ch. 2.1-2.4Quiz 28

POGIL – C++ Data
Slides

Type Conversion
Fri, Nov 13C++ PointersCPP4PY Ch. 2.5-2.6Quiz 29

POGIL – C++ Pointers
Slides2010s from Comp History Timeline
Mon, Nov 16C++ Control StructuresCPP4PY Ch. 3Quiz 30

POGIL – Control
Slides

Wed, Nov 18C++ FunctionsCPP4PY Ch. 4.1-4.3Quiz 31

POGIL – Functions
Slides

C++ Functions
Fri, Nov 20C++ Feeding FunctionsCPP4PY Ch. 4.4-4.5Quiz 32

POGIL – Functional Overload!
SlidesLab 5 – Digital Rivers in C++ due 11/20
Nov 23–27No class – Thanksgiving Vacation
Mon, Nov 30C++ FilesCPP4PY Ch. 6.1-6.6Quiz 33

POGIL – C++ Files

Emily Dickinson Searches and Counts
Slides

C++ Reading and Writing Files
Wed, Dec 2C++ ClassesC++ Classes Reading

C++ Classes Tutorial
Quiz 34

POGIL – C++ Classes

Account Class
Slides

C++ Class Intro
Fri, Dec 4C++ VectorsC++ Vectors

C++ Vector Erasing Elements
Quiz 35

POGIL – C++ Vectors

Random Numbers in C++
Slides

C++ Arrays vs Vectors

C++ Vectors

Example using Vectors
Lab 6 – Word Games in C++ due 12/4
Mon, Dec 7C++ PairsC++ Pairs

C++ Sort Function
Quiz 36

POGIL – C++ Pairs 

Example:Naughty or Nice?
Slides
Wed, Dec 9C++ STL MapC++ STLQuiz 37

POGIL – C++ STL MAP
Slides

Several Map Examples

Word Count C++ Map Example
Fri, Dec 11Websites, final exam review—Creating your own website, coding portfolio——
Thursday, Dec 17FINAL EXAMThursday, December 17, at 1:30-4:30 pmLocation: The regular classroom——

Accessibility and Accommodations

Students who anticipate that they may need accommodations because of the impact of a learning, physical, or psychological disability are encouraged to meet privately with an instructor early in the semester. Students must also contact Student Accessibility and Support Services (SASS), 740-427-5041 or sass@kenyon.edu, as soon as possible to verify eligibility for reasonable academic accommodations. Except in extraordinary circumstances, accommodations must be certified and discussed with the instructors at least one week before they are to take effect.

Non-Discrimination, Civil Rights, and Title IX Compliance

Kenyon College does not discriminate in its educational programs and activities on the basis of race, color, national origin, ancestry, sex, gender, gender identity, gender expression, sexual orientation, disability, age, religion, medical condition, veteran status, marital status, genetic information, or any other characteristic protected by institutional policy or state, local, or federal law. The requirement of non-discrimination in educational programs and activities extends to employment and admission.

As faculty members, we are deeply invested in the well-being of each student we teach and will do our best to help with course-related and other concerns. Students should know, however, that faculty are mandated reporters of incidents of harassment, discrimination, intimate-partner violence, and stalking. We cannot keep information involving sexual harassment, sexual misconduct, interpersonal violence, or other harassment or discrimination based on a protected characteristic confidential. The Health and Counseling Center, College chaplains, and New Directions Domestic Abuse Shelter & Rape Crisis Center are confidential resources.

ADA & Section 504 Student Grievance Procedure

Sexual Misconduct & Harassment: Title IX, VAWA, Title VII

Discrimination & Discriminatory Harassment Policy

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