Academic Integrity in ENGR 132

Academic integrity is a core value in Purdue engineering, which expects all its students to embody integrity and honesty, to embrace the community of trust within the classroom, and to live up to the ideals of the university and the engineering profession. Academic integrity is your pledge, as a member of the Purdue community, to uphold high standards of responsibility and honor in all your academic work. In return, the community will provide you with an ethical learning environment that promotes honesty and good collaboration.

A key part of your ENGR 132 experience is to learn how to be a good teammate and collaborator. This requires you to understand the difference between good teamwork and behaviors that cross the line into academic dishonesty. This document will help you understand the difference so that you can get a vibrant, collaborative learning experience in this course while maintaining the high academic integrity standards expected of all Purdue students.

There are four types of assignments that you will complete in ENGR 132.

  • Individual problems. You complete these problems on your own. You create your own code and/or solution independently. You can receive guidance and advice on these problems from the instructional team or your peers, but you are responsible for maintaining academic integrity while doing so. This means all work you submit must be your own.
    • Team Planning. This is when you work with teammates to develop a strategy to solve an individual problem. You can talk through the problem, its requirements, and how to address the requirements. You cannot work on code. You submit the team plan as a team, but you are responsible for submitting your own individual code submission. Problems with team planning are clearly marked.
  • Team assignments. On team assignments, your team submits one piece of work on which every team member will be graded. You are expected to work together on these assignments, including delegating specific tasks to each team member. All work originates within the team. As in all assignments, your team can always get assistance but must maintain academic integrity while doing so.  
  • Exams. You complete these problems individually without any assistance from any source. You may ask questions to your instructor or GTA. Class documentation will tell you what resources are allowed to be used.

Good Collaboration

Good collaboration facilitates learning while meeting academic integrity standards and ensures that the teaching team can trust the legitimacy of your work. On all code and assignment templates, you will see a space to list “contributors”. Contributors are people who help you on a problem in a way that affects the design of your code or your solution. You must list contributors if you receive this type of assistance. If you provide this kind of assistance to another student, you do not have to list that student on your solution. Only list contributors if they helped you.

General-information discussions – no need to cite contributors

This is sharing general information about course materials or using help materials to achieve a goal. It also includes speaking with your teaching team, which is absolutely encouraged throughout the course. You can discuss course topics and materials with peers, ask peers for clarification about an assignment’s expectations in a way that does not affect the design of your code or your solution, use MATLAB help resources, and search the internet for general MATLAB and programming information. At any time, you can and should ask your instructor, graduate teaching assistant, or peer teachers for assistance on any part of the assignment.

Assignment-specific discussions that require you to list your contributor(s)

Good collaboration allows you to get help from or give help to peers without affecting the integrity of your personal work and does not cross into academic dishonesty. When a peer helps you in a way that alters the design of your code or solution, then you must list them as a contributor on your submission. Here are some typical ENGR 132 examples of how you can seek such help from peers within acceptable bounds of academic integrity.

  • Discussing differences in how to solve the problem. You can discuss the expectations for a particular assignment and, in very general terms, potential solution approaches. But for individual assignments, you may not collaborate on any detailed solution planning or development, and certainly not on coding. Your solution should represent your own work. If those broad discussions are helpful, then you should list that person as a contributor on your submission. (Note: you do not need to cite your teammates when your work is based off a team plan)
  • Discussing how to debug code that has an error or bad result without working on the code together. You should not look through another person’s code to help debug, nor should you ask another person to look through your code. You can tell them the type of error the code produces, or you can show them an erroneous result. Do not share details of how the code works. If this discussion is helpful, then you should list that person as a contributor on your submission.

Team discussions about team assignments

Team members must each participate in the design of team solutions, and team members can discuss all aspects of the solutions. Your team should follow the guidelines above when soliciting help from people or sources outside the team.  If your team receives help from a peer that alters your design or solution, then list them as a contributor on your submission.

Generative AI

You are responsible for using generative AI in an ethical manner that supports and enhances your learning. Generative AI is a tool with strengths and weaknesses, just like any other tool. Good engineers use tools to strengthen their problem-solving and critical-thinking skills. They understand their tools and use those tools ethically. Before you use generative AI, you need to understand how it can help you and what its limitations are.

Generative-AI platforms are particularly helpful in the following situations:

  • improving equity by allowing access to personalized learning, spelling and grammar checking, and scaffolding;
  • providing a different perspective or support when brainstorming or troubleshooting;
  • motivating learners when they feel unsure of how to move forward with a certain task;
  • developing certain critical thinking skills.

But there are clear limitations.

  • AI platforms rely on language patterns to predict what an answer to a prompt should look like. They cannot think and are not concerned about correctness or problem context.
  • AI platforms excel at predictive text and pattern recognition but struggle with accuracy. AI will make up facts and data to fit the language pattern it is attempting to follow (“AI hallucintation”). Since AI replicates language patterns, these made-up things can sound very convincing but are NOT TRUE. Internet-connected AI platforms have not solved this problem. Assume the output includes information presented as fact that is false and/or just plain made-up. Know how to check the output the platform provides you.
  • AI platforms have bias. They have been trained on datasets that contain worldviews and assumptions and will replicate those ways of thinking about the world. Critical thinking strategies are especially important when engaging with AI-generated text. 
  • AI platforms depersonalize and weaken your writing. Good writing always has distinctive voice and style, which is crucial for effective communication. Sounding like a chatbot may harm your credibility and authority.

Never blindly trust the output of a generative AI platform. You must be able to independently verify any facts it states, check any programming suggestions it makes, and adapt its language suggestions to better match your voice and style.

Using Generative AI in this course

Generative AI tools can be powerful aids in learning, when used appropriately. You must use generative AI responsibly, in a manner that protects your ability to develop and understand your own solutions.

AI tools that may help you in this course: ChatGPT, Bing Chat, Google Bard, Quillbot, Grammarly, or Github Copilot.

Use generative AI in a manner that maintains academic integrity, enhances and promotes your learning, and does not make it appear as if you understand more than you do. The policies below outline how to do that in this course.

General policy outline:

  1. Always attempt a problem on your own before using AI.
  2. Prompt the AI using your own words
  3. Submit your own work on assignments – do not copy-paste AI output to pass off as your own.
  4. Never use AI on an exam.

After you have attempted an assignment or problem, you are ALLOWED to use generative AI to aid your understanding of programming concepts and assignment contexts.

  • Ask clarifying questions about general programming and engineering topics.
  • Correct language or debug coding that you have already written.
  • Create personalized study guides for exams.

You must document when you use generative AI in a way that concisely describes how you used the tool to aid your work and what changes you made at its suggestion.  

You are PROHIBITED from using generative AI to undermine or devalue the learning experience for yourself or your classmates.

  • Never ask generative AI to do a problem for you; always attempt your own solution before asking AI for help.
    • If you do not know where to start on a problem, ask questions about general programming techniques or engineering context to help you get ideas.  
  • Never plagiarize the output from a generative AI tool.
    • As with all sources in academic work, you must understand, adapt, and summarize any ethically generated AI output to authentically author your own work.
    • This applies to code as well as written language.
    • Submitting output from an AI platform as if it is your own work is plagiarism, just like copying an assignment off Chegg or from a classmate.
  • Never use generative AI during an exam.
    • This is considered unauthorized aid during the exam and will be reported to Office of Student Rights and Responsibilities along with other penalties.
  • Never submit any copyrighted or proprietary information to a generative AI platform.
    • All instructional course materials (assignments, exams, slides, study aids, notes, webpages, etc.) in ENGR 132 and their derivatives are copyrighted. Copyrighted material cannot be shared without express written permission from the copyright holder(s). You are prohibited from entering any ENGR 132 course material, whole or in part, into a generative AI platform.

Academic Dishonesty

Suspected cases of academic dishonesty may result in points being deducted from the assignment, a zero on the assignment, and/or reporting the case to the Office of Student Rights and Responsibilities.

Solution Sharing

Any type of solution, partial or whole, given to or received from another source is strictly prohibited and violates Purdue’s academic integrity standards. You must not

  • share your solutions directly with other students (including, but not limited to, email, GroupMe, printoffs, etc.),
  • receive solutions directly from another student,
  • receive solutions from a generative AI platform,
  • uploading course materials into a generative AI platform,
  • upload your solutions to an online site (such as CourseHero or Chegg), or
  • access others’ solutions from an online site.

This includes all graded work in the course, especially computer code.


Plagiarism is when one student attempts to pass off someone else’s work as their own. Plagiarizing code or solutions is as serious as plagiarizing an essay or paper.

  • Do not pass off generative AI results as your own work.
  • Do not jointly write or debug code on any individual problem. 
  • Do not use paired programming on individual programming problems. Even when properly cited, it is plagiarism and may result in penalties.
  • Do not share pseudocode or flowcharts to outline ideas about specific code solutions or techniques unless specifically directed by your instructor.
  • Do not copy another student’s work to complete or correct your own work.


Always be honest when discussing your work with your instructor or graduate teaching assistant. Misrepresenting or lying about your work or conduct exacerbates the consequences and betrays the university’s academic expectations. 


Be familiar with Purdue’s Academic Integrity and Student Conduct expectations.

Your instructor will support you in maintaining academic integrity. Contact your instructor or graduate teaching assistant for help if you observe cheating, have questions or concerns about your own work, or feel overwhelmed.