District Academic Senate

Plagiarism and Artificial Intelligence Guidance

DTLC Guidance on Plagiarism and Artificial Intelligence
Approved by SMCCCD District Academic Senate on 9 December 2024

The guidance document admits that we do not have all the answers; it provides links to research and articles about the topic. It will be a webpage that is routinely updated by the DTLC; this is a draft of the information that will be going onto the webpage.

Last updated on 8 January 2025.

General take-aways about AI and Plagiarism

  • Skyline College, College of San Mateo and Cañada College already have academic integrity policies that prohibit the use of unauthorized resources. The use of AI content generators is covered by these academic honesty policies if supplemented by course-level policies that are explained in a course syllabus and/or assignment instructions.
  • Content produced by AI is often hard to detect, but there are strategies to create assignments and examine student work to mitigate this.
  • Using AI content generation to spark classroom discussion could help students understand potential dangers in using unauthorized AI content generation tools.

Based on Illinois State University’s Center for Integrated Professional Development, “Generative AI in the Classroom” (accessed 7 August 2024)

What is the purpose of this guidance?

Over the last couple of years, there has been a large amount of discussion and debate about the use of artificial intelligence, both the potential benefits and concerns, including concerns about academic integrity. These recent developments in the field of artificial intelligence (AI) have raised opportunities to reconsider academic honesty practices for faculty and students. As we all learn more about the possibilities and implications of AI in higher education, there is a need to develop guidance and recommendations around this topic. Therefore, this guidance aims to share relevant information, resources, and strategies that may help faculty and students navigate rapidly changing educational environments.

Please note that the strategies and resources provided in this guidance do not represent an exhaustive body of information. We recognize that developments and discussions around AI are rapidly evolving, and the implications may vary across disciplines and educational contexts. The District Teaching and Learning Committee (DTLC) will continue to update this document/website as new information becomes available. If you have resources that may be relevant to share, please contact Sarah Harmon (harmons@smccd.edu), Faculty Chair of the DAS Standing Committee on Teaching and Learning (DTLC). If you have any technical questions or resources to share, please contact Chris Collins (collinsc@smccd.edu), Faculty Chair of the District Distance Education Advisory Committee (D-DEAC).

Smart Devices in our Everyday Lives

Artificial Intelligence (AI) content generators use artificial intelligence to assemble content using natural human language processing. These services, like ChatGPT, utilize search engines and databases to generate a wide variety of written and/or symbolic documents, sometimes using real references and sometimes fabricating them. Other services, like DALL-E, produce multimedia content-like images. Even services like Grammarly incorporate Artificial Intelligence to aid in writing. These technologies continue to evolve at an exponential pace.

Because many of these services are able to produce content in a robust and realistic manner, there has been increasing concern in the broader higher education community that AI content generation may be used by students to complete assignments, essays, or other activities in a manner that would violate academic integrity and undermine student learning. Additionally, the expanding availability of these tools across computing devices, such as embedded within other software programs, increases the chances that students will not recognize their use as notable or even as a generative AI tool.  Understanding AI tools as well as their scope and application will facilitate engaging with these new technologies as informed scholars and educators.

Promoting Critical Literacy

Information literacy serves as a crucial foundation for AI literacy, equipping learners with the skills to evaluate AI-generated content critically. Just as information literacy emphasizes identifying credible sources, understanding context, and addressing bias, these principles extend to assessing AI tools and their outputs. Learners must not only differentiate between AI tools and traditional search engines but also evaluate AI-generated content for credibility, accuracy, and inherent biases. By integrating information literacy standards, such as evaluating sources, refining queries, and ethical information use, individuals can navigate the challenges of AI-generated content and leverage these tools responsibly and effectively.

  • Encourage revisiting course activities using the information in the tables/resources.
  • Facilitate connections between faculty, Instructional Designers, and Librarians to share ideas and develop student workshops.
  • Raise awareness of bias in AI and its usage;
  • Consult resources such as faculty guidance, articles, guides, and Canvas courses.

Guidance for faculty

The information below offers various resources for specific areas of inquiry with respect to plagiarism and artificial intelligence.

Last updated: 8 January 2025

Course statements and policies

Description and information

Incorporate clear and transparent statements about the role and implications of AI in your course(s). In drafting such statements, consider adopting compassionate (rather than punitive) language, helping students understand why and how AI tools will be incorporated into the context of your course. The syllabus and dedicated pages of your Canvas site are great places to share AI-related course policies

Suggested resources

Authentic assignment and assessment design

Description and Information

Many of the assignment design strategies recommended for deterring dishonest use of AI are consistent with good assignment design principles in general. Students are less likely to turn to all forms of academic dishonesty when assignments are authentic (applied to real-life circumstances), transparent (clearly and openly explained), and scaffolded (broken down into smaller, well-supported steps.

It's important to be clear with students as to the purpose of an assignment. It may be wise to consider the act of writing an essay as a way for students to strengthen their understanding of a topic or improve skill in presentation. In that way, the use of authentic assessments may entail a variety of products beyond traditional essays.

Use of a variety of media, in both assignment prompts and student demonstration of learning is consistent with Universal Design for Learning principles while also deterring the use of AI.

Finally, assignments that actually incorporate AI (asking students, for example, to analyze an AI-generated essay) also build information literacy, including student ability to detect AI.

Suggested resources

Spaces for inquiry and exploration

Description and information

Create opportunities for students to reflect on, discuss, and question the possibilities and limitations of AI tools in their disciplines, the workplace, the broader sociocultural context, and/or everyday events. Such spaces contribute not only to AI literacy but encourage critical analysis of AI tools.

Additionally, you can ask your students to submit a transcript of their interaction with many GenAI tools (particularly ChatGPT and Co-Pilot) as a means of showing how they interacted with the tool. This work can be cited in MLA and APA styles, among others.

Suggested resources

Use of AI Detection Tools

Description and information

There are multiple tools that screen text for whether it has been created by generative AI tools. This includes TurnItIn’s AI report, which as of Fall 2024 is part of SMCCD’s license with TurnItIn. These tools are significantly more unreliable than plagiarism detection tools, both in terms of false positives and false negatives, and probably will remain so. Based on this unreliability, any use of automated AI screening tools should be used with extreme caution or not at all.

Automated screening tools could be helpful as a starting point for discussing a submission with a student, including checking for student comprehension of the written content.

When making a determination that a student has submitted AI generated content as their own work in violation of the course and/or assignment requirements, it is recommended that the faculty does not rely on an automated report as their only evidence. In general, it is recommended to place greater emphasis on assignment design rather than detection methods.

Suggested resources

Taken from the San Diego Mesa College Artificial Intelligence in the Classroom LibGuide.

  • GPTZero: This is a free (with paid upgrade), non-commercial, tool. Documents are scored by how much may have been written by AI. Each sentence written by AI is highlighted. GPTZero Sources can do limited checking of sources and citations to point out fraudulent, contradictory or imaginary sources (EdScoop, 6 January 2025).
  • ChatGPT: You can actually use ChatGPT to check if something was generated by AI. Simply ask it "was this created by artificial intelligence?" and put in the piece of work.
  • AI Classifier: A resource that distinguishes between AI and human written text brought to you by the creators of ChatGPT.

Alternatives to AI detection tools

Example syllabus statement on use of AI detection tools:

“Be aware that coursework will be subject to plagiarism and AI generated content screening software, such as TurnItIn and ZeroGPT. If I believe that your submission has been generated by an AI tool without attribution, I might request you to meet with me in order to demonstrate comprehension of the material in order to earn credit on the assignment. Other possibilities include having to redo the assignment or not earning any credit on the assignment.”

Students Reporting AI Usage in Assignments

Encouraging students to report their usage of AI-backed tools is one way to foster critical thinking skills and information literacy skills with respect to their assignments. One strategy could be to require students to submit a brief submission survey or questionnaire to indicate which AI-based tools they used and how they used them. An example is shown below.

Question 1: Did you use any of the following tools to complete this assignment? (check all that apply)

  • ChatGPT, Bard/Gemini, Co-Pilot, Claude.ai
  • Grammarly
  • Midjourney, Adobe Firefly, OpenArt
  • ai, QuillBot
  • ... (insert whichever tools you think might pertain to the assignment)

Question 2: In which ways did you use these tools? (check all that apply)

  • Brainstorming ideas
  • Speech-to-text to help write my ideas
  • Assistance in composing my thoughts
  • ... (insert other potential uses you think might pertain to the assignment)

Resources

Below is a list of tools and resources that may be helpful as you explore this topic for your courses.

Last updated: 8 January 2025

Useful Tools

There are useful GenAI tools that students can interact and that have sources that accurately offer sources for the information that they provide. (More on citing sources in MLA and APA above.)

Articles

Canvas Courses

Guides

Webinars

Glossary

The terms listed below are taken from Academic Integrity Policies in the Age of Artificial Intelligence (AI) Resource Document Spring 2024  , published by the Academic Senate for California Community Colleges (2024).

  • Algorithm — the set of logical rules used to organize and act on a body of data to solve a problem or to accomplish a goal that is usually carried out by a machine. An algorithm is typically modeled, trained on a body of data, and then adjusted as the results are examined. Because algorithms are generally processed by computers and follow logical instructions, people often think of them as neutral or value-free, but the decisions made by humans as they design and tweak an algorithm and the data on which an algorithm is trained can introduce human biases that can be compounded at scale. Humans who interact with an algorithm may also find ways to influence the outcomes, as when a marketer finds ways to push a website up in the results of a search through search engine optimization (SEO).
  • Algorithmic justice — the application of principles of social justice and applied ethics to the design, deployment, regulation, and ongoing use of algorithmic systems so that the potential for harm is reduced. Algorithmic justice promotes awareness and sensitivity among coders and the general public about how data collection practices, machine learning, AI, and algorithms may encode and exacerbate inequality and discrimination.
  • Algorithmic literacy — a subset of information literacy, algorithmic literacy is a critical awareness of what algorithms are, how they interact with human behavioral data in information systems, and an understanding of the social and ethical issues related to their use.
  • Artificial intelligence (AI) — a branch of computer science that develops ways for computers to simulate human-like intelligent behavior, able to interpret and absorb new information for improved problem-solving and recognize patterns. Examples include training robots, speech recognition, facial recognition, and identifying objects such as traffic signs, trees, and human beings necessary for self-driving cars. AI relies on machine learning capabilities and training data. Humans are involved in creating or collecting sets of training data (e.g., employing low-wage workers abroad to identify objects on computer screens to provide data for autonomous vehicle navigation). Bias may be built into machine learning (e.g., by using criminal justice data sets for risk assessment in predictive policing). Machines can be trained to learn from experience but common sense and recognizing context are difficult, thus limiting the ability of computer programs to perform tasks such as distinguishing hate speech from colloquial humor or sarcasm.
  • Artificial Intelligence as a Service (AIaaS): Cloud-based AI services providing higher education institutions with access to AI tools, algorithms, and infrastructure, facilitating the development of AI-driven applications and research projects without significant upfront investments.
  • Artificial Intelligence Augmentation (AI Augmentation): The integration of AI technologies to enhance human capabilities in higher education, empowering educators and researchers with AI-driven tools for personalized learning, data analysis, and administrative decision-making.
  • Artificial Intelligence Bias Mitigation (AI Bias Mitigation): Strategies and policies for identifying, mitigating, and preventing biases in AI systems, critical in higher education for ensuring fairness, equity, and diversity in student assessment, admissions, and educational opportunities.
  • Artificial Intelligence Chipsets (AI Chipsets): Specialized hardware accelerating AI computations, utilized in higher education for research in AI algorithms, training large-scale models, and deploying AI applications with improved performance and energy efficiency.
  • Artificial Intelligence Ethics (AI Ethics): The development and deployment of AI systems in alignment with ethical principles and societal values, crucial in higher education for ensuring fairness, equity, and accountability in student assessment, admissions, and decision-making processes.
  • Artificial Intelligence Explainability (AI Explainability): Techniques ensuring transparency and interpretability of AI models, vital in higher education for explaining grading decisions, student feedback, and adaptive learning recommendations to students, instructors, and stakeholders.
  • Artificial Intelligence Governance (AI Governance): Policies and regulations governing the development, deployment, and use of AI technologies in higher education, ensuring ethical and responsible AI practices, data security, and compliance with legal requirements.
  • Artificial Intelligence Safety (AI Safety): Concerns and measures addressing potential risks and harms associated with AI technologies, guiding higher education institutions in the responsible development and deployment of AI systems to ensure student and staff well-being, data security, and regulatory compliance.
  • Attention economy — since our attention is a limited resource and every person only has so much of it, companies (both platforms and people who use the platforms to sell, entertain, or persuade) try to engage and keep people’s attention. This rewards clickbait and influences the design of algorithms and platforms to maximize time spent online.
  • Bias in AI: Systematic favoritism or prejudice in AI systems, posing challenges in higher education such as biased admissions algorithms and unfair grading systems, necessitating policies for bias detection, mitigation, and transparency.
  • Big data — a set of technological capabilities developed in recent years which, when used in combination, allows for the continuous gathering and processing of large volumes of fine-grained and exhaustive data drawn from multiple sources to be combined and analyzed continuously.
  • Computer Vision: An AI discipline enabling computers to interpret and analyze visual information, utilized in higher education for tasks such as facial recognition for campus security, content accessibility, and augmented reality applications.
  • Data exhaust — information incidentally generated as people use computers, carry cell phones, or have their behavior captured through surveillance which becomes valuable when acquired, combined, and analyzed in great detail at high velocity.
  • Deep Learning: A branch of machine learning involving neural networks with multiple layers, used in higher education for tasks such as personalized learning, predictive analytics, and natural language processing.
  • Edge AI: The deployment of AI algorithms on edge devices, enabling real-time processing and inference in higher education applications such as IoT-based campus management, personalized learning tools, and mobile educational apps.
  • Edge Computing: Decentralized processing of data near the source of generation, beneficial in higher education for low-latency AI applications, real-time analytics in remote locations, and efficient utilization of computing resources.
  • Ethical AI: The development and deployment of AI systems in alignment with ethical principles and societal values, crucial in higher education for ensuring fairness, equity, and accountability in student assessment, admissions, and decision-making processes.
  • Explainable AI (XAI): Techniques and methods ensuring transparency and interpretability of AI models and decisions, essential in higher education for maintaining trust, accountability, and regulatory compliance in academic and administrative AI systems.
  • Generative Adversarial Networks (GANs): AI frameworks where two neural networks compete to generate realistic data, utilized in higher education for creating synthetic datasets, generating educational content, and improving data privacy.
  • Hyperparameters: Parameters defining the configuration and behavior of AI models, requiring optimization and tuning in higher education applications for achieving optimal performance, reliability, and scalability.
  • Machine Learning (ML): A subset of AI focusing on algorithms and techniques that enable computers to learn from data and improve their performance over time without being explicitly programmed. Also, the use of algorithms, data sets, and statistical modeling to build models that can recognize patterns to make predictions and interpret new data. The purpose of machine learning is to enable computers to automate analytical model-building so computers can learn from data with little human intervention.
  • Model Interpretability: The ability to explain and understand AI models and their decisions, essential in higher education for transparent student assessment, research reproducibility, and accountability in automated decision-making systems.
  • Model Robustness: The capability of AI models to maintain high performance and reliability under varying conditions and inputs, critical in higher education for ensuring accurate student assessment, research findings, and administrative decision-making.
  • Natural Language Processing (NLP): The field of AI is concerned with enabling computers to understand, interpret, and generate human language, utilized in higher education for automated grading, language learning support, and virtual assistants.
  • Neural Network: A computational model inspired by the human brain's structure, employed in higher education for various applications including student performance prediction, adaptive learning systems, and data analysis.
  • Personalization — the process of displaying search results or modifying the behavior of an online platform to match an individual’s expressed or presumed preferences, established through creating digital profiles and using that data to predict whether and how an individual will act on algorithmically selected information. This process drives targeted digital advertising and has been blamed for exacerbating information silos, contributing to political polarization and the flow of disinformation. Ironically, to consider information “personal” implies it is private, but personalization systematically strips its targets of privacy.
  • Platform — an ambiguous term that means both software used on personal computers and software deployed online to provide a service, such as web search, video sharing, shopping, or social interaction. Often these systems use proprietary algorithms to mediate the flow of information while enabling third parties to develop apps, advertising, and content, thus becoming digital spaces for the individual performance of identity online, data-driven persuasion (commercial as well as political), and group formation through social interaction. In this report, we use the term to refer to “internet giants” such as Google, YouTube, Instagram, and Facebook and others mentioned by students in our focus group sessions.
  • Reinforcement Learning: An AI paradigm where algorithms learn by interacting with an environment and receiving feedback, applicable in higher education for adaptive learning environments and personalized feedback systems.
  • Semi-Supervised Learning: A combination of supervised and unsupervised learning techniques, employed in higher education for tasks such as student performance prediction with limited labeled data and large-scale data analysis.
  • Supervised Learning: A machine learning approach where models are trained on labeled data, used in higher education for predictive modeling, recommendation systems, and intelligent tutoring systems.
  • Transfer Learning: A machine learning technique where models trained on one task are adapted to perform related tasks, valuable in higher education for leveraging pre-trained models in educational content creation, student support systems, and academic research
  • Unsupervised Learning: A machine learning approach where models uncover patterns and structures from unlabeled data, relevant in higher education for clustering similar student cohorts, curriculum optimization, and anomaly detection.