Generative AI has the potential to radically transform how people learn and conduct work. This resource provides information and suggestions on how to integrate generative AI into your teaching while maximizing its potential and minimizing the risks.  Watch the Fall 2023 Generative AI workshop recording offered by CFE.

We will update this resource with more guidance and examples as this emerging technology evolves and more information becomes available.

Visit the general syllabus information page

Topics:

WHAT is Generative AI and How Does it Work?

You may have read or seen AI be called or referred to as these several different names:

  • Open AI is an organization that focuses on developing advanced computer programs that can understand and generate human-like text. OpenAI works to ensure these models are used responsibly and for the benefit of humanity (OpenAI, 2023). ChatGPT is a product of Open AI.
  • Generative AI or Large Language Models (LLMs) is a computer program using large language models that learn by studying and connecting large amounts of text on the Internet. They use this information to answer questions, generate responses or content, create images, video, and audio, translate languages, and more.
  • ChatGPT, Dall-E, Google Bard and Microsoft Bing are some of the more common generative AI tools available in free and more advanced paid versions.

For purposes of simplicity, this resource will use Generative AI throughout each section and mention the ChatGPT tool when needed.

The training process for these models involves exposing them to vast amounts of text data from the internet. They learn patterns and relationships within the data, which enables them to generate coherent and contextually relevant responses to user queries.

The training process typically involves two stages: pre-training and fine-tuning. During pre-training, the model learns from a large corpus of publicly available text data to predict the next word in a sentence. This process helps the model gain a broad understanding of language. In the fine-tuning stage, the model is further trained on more specific datasets with human oversight and guidance to ensure its responses align with OpenAI's goals and values.

OpenAI's language models have a wide range of applications, including answering questions, generating text, providing summaries, translating languages, simulating characters, and much more. They can be integrated into various products and services to enhance user experiences and perform complex natural language processing tasks.

OpenAI also emphasizes the ethical use of AI and actively works on addressing potential risks and limitations associated with AI technologies. They promote responsible AI development, transparency, and collaborative efforts to ensure the technology is beneficial for humanity as a whole.

It's important to note that the information I provided is based on my training data available up until September 2021. OpenAI may have made further advancements and developments since then (OpenAI, 2023). 

Source: OpenAI. (2023). ChatGPT (Jun 14 version) [Large language model].

https://chat.openai.com/chat

WHY Allow the Use of Generative AI?

With the availability and evolving sophistication of generative AI, it may be increasingly impractical to prevent students from using generative AI on assignments. A March 2023 Best Colleges survey found that 43% of students reported using ChatGPT or a similar AI application and 61% of college students think AI tools like ChatGPT will become the new normal. Another survey by Study.com found that over 89% of students have used ChatGPT to help with a homework assignment. 

Furthermore, prohibiting student use of generative AI in courses may deprive them of valuable opportunities to develop skills and expertise in an emerging technology, which can benefit students in professions and industries that are leveraging generative AI to complete work and improve efficiency. For example, industries such as logistics and transportation, marketing, healthcare, news media, architecture, graphic design, and computer coding and programming are using generative AI in a myriad of ways. Generative AI is being used to create images, video, and audio in addition to text-based products in various industries.

HOWto Integrate Generative AI into Your Teaching

We want to know how you are using generative AI in your teaching and courses.

If you have an innovative use for how you or your students are using generative AI to advance and support learning, please submit a description and any materials such as student examples, AI generated teaching content, and assignment or activity prompts that incorporate generated AI use to cfe@montana.edu. These examples will be added to our resource to inspire and guide other faculty in its use.  

 

MSU does not currently have any policies prohibiting the use of generative AI, each faculty member will have the opportunity to determine how students can use the tool for their course. It is imperative that faculty set course expectations and policies around student use for if and when generative AI is permitted, how to show what pieces of content were AI generated, and how students arrived at that content.  

The pages below offer guidance on setting expectations and policies for no use, limited or partial use, and full use, as well as what to do if you suspect a student is misusing generative AI. Additionally, there are suggested activities and assignments to teach students how to use AI to effectively support learning. 

HOWto Integrate Generative AI into Your Research and Scholarship

As generative AI is being increasingly integrated into scholarly work, including graduate theses and doctoral dissertations, and journal articles, the following guidance can help faculty and graduate students with ethical and responsible use of AI in their scholarly work.  

To maintain the highest standards of academic quality and research integrity, graduate students and faculty advisors should make every attempt to practice full transparency. In achieving transparency, graduate students should declare how they plan to use generative AI to their faculty advisors to get approval for this use. Following good practices for other scholarly writing and disciplinary norms, students should cite when and describe how generative AI tools were used so that audiences of their writing can distinguish between the contributions of the writer and the generative AI tool. This, for example, may include citing and describing use of generative AI tools in searching, designing, outlining, drafting, writing, or editing the thesis, or in producing audio or visual content for the thesis, and may include other uses of generative AI.  

Note that departments and colleges may have different or additional specific requirements or restrictions on the use of generative AI during the phases of the research and writing cycle that correspond to their research methods and analytical processes. This could include, for example, guidance on use in writing text, conducting analytical work, reporting results (e.g., tables or figures) or writing computer code. To adhere to potential additional departmental requirements, check with your advisor and/or committee. 

Yes, generative AI tools and techniques can be integrated into your research. However, navigating the ethical and methodological considerations of incorporating generative AI into research requires careful planning and thoughtful implementation. Read on for a series of potential steps and guidelines to consider as you look to incorporate these technologies into your research process. 


Step 1: Seek Guidance from Your Advisor or Editor 

Before starting your research, it is crucial to seek the guidance of your advisor or editor. Their expertise and experience will prove invaluable as you assess the suitability of AI for your research question or scholarship, identify potential ethical concerns, and ensure compliance with institutional policies. Open communication will lay the foundation for a successful and ethically sound research endeavor.

 
Step 2:Identify the Right AI Tools and Techniques 

Generative AI encompasses a diverse spectrum of tools and techniques, each with its unique strengths and limitations. Carefully evaluate your research objectives and data characteristics to select the AI tools that best align with your needs. Consult with experts in the field and explore relevant literature to gain a comprehensive understanding of the available AI options. Through discussions with your advisor or editor, declare how you will be using the technology. 


Step 3: Gather and Prepare Your Data 

The quality of your data is paramount to the success of AI-driven research. Ensure that your data is accurate, consistent, and free from biases. Employ appropriate data cleaning and preprocessing techniques to prepare your data for analysis by AI algorithms. 


Step 4: Train and Validate Your AI Models 

Developing and refining AI models is an iterative process. Carefully select the training data and parameters to ensure that your models are robust and generalizable. Employ rigorous validation techniques to assess the performance of your models. 


Step 5: Interpret and Communicate Your Results 

AI algorithms can produce complex and sometimes counterintuitive results. Critically evaluate the outputs of your AI models and usage. As questions arise, seek expert guidance to ensure that your interpretations are sound and supported by evidence. Clearly communicate your findings in a manner that is accessible to both technical and non-technical audiences. 


Step 6: Cite AI Resources Appropriately 

Just as you would cite traditional research sources, it is essential to properly acknowledge the AI tools and resources that you have utilized. Follow established citation guidelines to ensure that your work is transparent and reproducible. (See section below on Describing and referencing generative AI tools and usage.) 


Step 7: Address Ethical Considerations 

AI applications raise a range of ethical concerns, including data privacy, biased representations, fairness, and transparency. Carefully consider these implications throughout your research process and implement appropriate safeguards to mitigate potential risks. 


Step 8: Document Your AI Methodology 

Provide detailed documentation of your AI methodology, including the specific tools, algorithms, and parameters employed. This documentation will facilitate reproducibility and allow others to assess the validity of your findings. 

There are several key reasons why generative AI tools and techniques should be appropriately cited when used within your research and scholarship: 


1. Transparency in your research methods is crucial for evaluating the quality and reproducibility of findings. Clearly attributing any use of generative AI provides transparency and necessary context. Moreover, citing implementation details enables accurate interpretation. 


2. If generative AI systems significantly contribute to your research, they should receive due credit like any other contributor. Citing AI recognizes valuable technological assistance and the hybrid nature of human/machine collaborations. 


3. Documenting AI use establishes provenance regarding generated content and accountability for outputs. Cataloging AI adoption through citation practices provides a record of its growing influence and patterns of substitution for human effort over time. Citations of AI tools and applications create valuable records which indicate and benchmark evolving technology use.

Generative AI Citation Examples – Multiple Styles 

APA style 

Currently, APA recommends that text generated from AI be formatted as "Personal Communication." As such, it receives an in-text citation but not an entry on the References list. 
Rule: (Communicator, personal communication, Month Date, Year) 
Examples:  
(OpenAI, personal communication, January 16, 2023). 
When asked to explain psychology's main schools of thought, OpenAI's ChatGPT's response included ... (personal communication, February 22, 2023). 

MLA style

The Modern Language Association provides detailed guidance on citing generative AI according to their template. 
MLA guidance on citing generative AI 

Chicago Style

Chicago Style with footnotes 
Personal communications are cited in a footnote, but are not listed in the bibliography. 
Rule: Number.Originator of the communication, medium, Day Month, Year. 
Example: 
1 OpenAI's ChatGPT AI language model, response to question from author, 7 February, 2023.    
Shortened note rule: NumberCorrespondent's last name, medium 
Example:  
1 ChatGPT, response to prompt from author 

Even when engaging in authorized generative AI use, faculty and graduate students must be aware of the risks in using such tools, some of which are discussed below.

Privacy and Secured Data

 It is unclear how companies that host and support generative AI use the input (prompts) and output data from these tools. This can potentially raise privacy and security concerns when using generative AI tools to analyze sensitive data collected for human-based research or a company’s proprietary technology. If a student or faculty plans to use generative AI tools to analyze data, they must include this use for approval in any applications for the institutional review board (IRB).  

Inaccurate and Biased Content 

Generative AI is prone to producing content that blends facts with false and biased information to make it appear true or valid. AI tools can reproduce biases that exist in the content it is trained on, including presenting untrue statements as facts the perpetuate biases from offensive content that discriminates against marginalized groups based on gender, race, and sexual orientation. Generative AI can at times make up references to scholarly work that does not exist. Students and faculty are ultimately responsible for all the content of their scholarly work, including content generated by AI tools.  


Note that Generative AI tools predict based on existing content meaning it may not be as capable of producing, or rearranging existing knowledge in ways to achieve, the type of new and novel content that meets the expectations and standards of research across fields and disciplines.  

Relevant Montana State University (MSU) Policies 

MSU Student Code of Contact 
MSU Dean of Students Plagiarism Policy? 
Other local policies or guidance? 
Other external policies 

Further Reading and Viewing 

  • Mohammad Hosseini, Lisa M. Rasmussen & David B. Resnik (2023). Using AI to write scholarly publications. Accountability in Research, DOI: 10.1080/08989621.2023.2168535
  • Nature (2023). Policy on Artificial Intelligence in Journal Submissions. [Online]. Available: https://www.nature.com/nature-portfolio/editorial-policies/ai [Accessed 4 December 2023]. 
  • White House Office of Science and Technology Policy. Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People. [Online]. Available: https://www.whitehouse.gov/ostp/ai-bill-of-rights/ [Accessed 04 December 2023]. 

Works Cited 

The School of Graduate Studies at the University of Toronto. Guidance on the Appropriate Use of Generative Artificial Intelligence in Graduate Theses. Available: https://www.sgs.utoronto.ca/about/guidance-on-the-use-of-generative-artificial-intelligence/ [Accessed October 3, 2023].

 

Establishing Expectations, Syllabus Languageand Policies

Instructors should set expectations for students in the syllabus, discussions in class and for assignments by defining when and how generative AI can be used from the beginning.

  • How to cite generative AI

Detection Tools and If You Suspect a Student is Misusing It

The Turnitin AI detection tool is incorporated into the similarity report that analyzes a student’s written work for plagiarism. It shows an overall percentage of the document that AI writing tools, such as ChatGPT, may have generated. 

Class Assignments

  • How to Make Assignments Less Susceptible to Generative AI Produced Content
  • Incorporating Generative AI Into Course Assignments and Activities

Incorporating Generative AI Into the Writing Process For Students

As with any assignment, make sure that you understand the goals of the assignment using generative AI and clearly articulate those goals to the student, what skills and knowledge should they develop or gain from completing the assignment using generative AI. 

How To Write Effective Prompts for Generative AI Tools

Developing skill with generative AI tools requires mastering creating effective prompts that produce the most useful outputs for completing a task or information search and then critically evaluating those prompts for accuracy, bias and misrepresentations.

Using AI as a Teaching Assistant

Generative AI can be used in a myriad of ways to increase your productivity as a teacher for generating ideas for class activities and assignments as well as for creating content for students to support their learning.