The Assignment

DESCRIPTION:
The Final Project is a multi-stage assignment. It is an opportunity to experiment with Generative Artifical Intelligence (GenAI) tools, and explore how they may both help and hinder our ability to communicate and express ourselves.

PROCESS:

1. Begin by choosing one of the below topics:

• a. Write about your place of origin. This could be the place where you were born, or a place where you have ancestral roots. Provide some factual information about the place using a minimum of two reputable sources, and provide at least one personal anecdote about your experience with the place.

• b. Write about a place you have visited but have no personal ties to. Provide some factual information about the place using a minimum of two reputable sources, and provide at least one personal anecdote about your experience visiting the place.

2. Draft a short Writing Proposal using the template provided below. Complete your proposal before our class on March 26th, when your instructor will ask you to review it with a small group of your peers.

3. Write a short essay between 500-700 words in length on your chosen topic.

4. Use a GenAI program, such as ChatGPT, to produce an essay on the same topic, and of the same scope and length. (You may specify the place or activity you are writing about. Otherwise, if you alter the prompt you put into the program, include the revised prompt at the beginning of the essay.)

5. In a short rhetorical analysis approximately 500 words long, compare and contrast the essay draft you wrote with the essay draft produced by GenAI. Discuss both the writing style (including tone, voice, pace and point of view) and the content (originality, specificity and accuracy of ideas). *Do not use GenAI to complete this portion of the assignment. All of the ideas and wording of the rhetorical analysis must be your own.

6. Create a final draft of your essay using whatever combination of your original draft and the GenAI draft you think will produce the best result. Include an authorship statement at the beginning of the essay indicating whether/how you used GenAI. (See the “Sample Authorship Statements” in the assignment instructions.)

7. Format your assignment in MLA Style. Include in-text citations and a complete Works Cited page listing any of the sources you reference in your essay. If you include some of the content or wording produced by GenAI, include the tool itself as a citation in your Works Cited list also. See “Citation Support” (in assignment instructions) for more information.

8. Include all of the above components in one document, and label them as follows:

• a. Writing Proposal

• b. Original Essay

• c. GenAI Essay

• d. Rhetorical Analysis

• e. Final Essay

AI & GenAI: Some Definitions

Artificial intelligence (AI):

"Machines that imitate some features of human intelligence, such as perception, learning, reasoning, problem-solving, language interaction and creative work" (UNESCO, 2022).

Generative AI (GenAI):

“A type of artificial intelligence that involves creating machines or computer programs that can generate new[?] content, such as images, text, or music. Unlike traditional AI systems that rely on predefined rules or pre-existing data to make decisions, generative AI models use algorithms and neural networks to learn patterns and relationships in data and generate new[?] outputs based on that learning” (Kwantlen Polytechnic University, n.d., p. 1).

Large Language Models (LLMs):

"A language model is a type of artificial intelligence model that is trained to understand[?] and generate human language. It learns[?] the patterns, structures, and relationships within a given language and has traditionally been used for narrow AI tasks such as text translation. The quality of a language model depends on its size, the amount and diversity of data it was trained on, and the complexity of the learning algorithms used during training.

A large language model (LLM) refers to a specific class of language model that has significantly more parameters than traditional language models. Parameters are the internal variables of the model that are learned[?] during the training process and represent the knowledge[?] the model has acquired" (Rouse, 2024)

("[?]"s in the above are Joel's editorializing)

More information about GenAI and teaching and learning can be found on the MRU GenAI webpage: https://library.mtroyal.ca/ai

All of this said, it is important to remain skeptical of what exactly AI is, and how the term is being deployed for strategic purposes by specific actors.

According to linguist and prominent AI critic, Dr. Emily Bender (2023):

In fact [AI] is a marketing term. It’s a way to make certain kinds of automation sound sophisticated, powerful, or magical and as such it’s a way to dodge accountability by making the machines sound like autonomous thinking entities rather than tools that are created and used by people and companies. It’s also the name of a subfield of computer science concerned with making machines that “think like humans” but even there it was started as a marketing term in the 1950s to attract research funding to that field.

[Bender] think[s] that discussions of this technology become much clearer when we replace the term AI with the word “automation”.

ENGL 2207 - GenAI Brainstorm In-Class Activity

1. Click the link to this Padlet: https://padlet.com/bleching/engl_2207_genai

2. Take 3-5 minutes to brainstorm what you think some of the benefits/opportunities and risks/challenges associated with GenAI technologies are, or will be.

Benefits/Oppotunities and Risks/Challenges Associated With GenAI

Here is a non-exhaustive list of some of the benefits/opportunities that GenAI tools may offer:

• Productivity—"The integration of [GenAI] in various organizations marks a significant leap in digital transformation and creativity enhancement. Its application across sectors like academia, engineering, and communications is revolutionizing how work productivity is increased, from creating compelling advertising to swiftly producing accurate technical reports" (Al Naqbi et al., 2024, p. 29).

  • For example: Alphafold AI.

• Accessibility—"Interviewees also shared examples of how GenAI can provide support and accommodation to students with disabilities. We heard that students with hearing-or writing-based disabilities had used GenAI-based transcription tools, such as OtterAI, to transcribe lectures from voice recordings, and that students with reading-or-writing-based learning disabilities had used GenAI to perform speech-to-text functions and summarize assigned readings" (Tishcoff et al., 2024, p. 7).

• Democratization of skills and knowledge—"AI is democratizing access to specialized knowledge and expertise. In the past, gaining proficiency in a particular field often required expensive education or mentorship from industry insiders. However, AI-powered platforms are democratizing access to expertise by providing on-demand learning resources, virtual mentors and personalized recommendations tailored to individual needs and learning styles. This democratization of knowledge is leveling the playing field, allowing aspiring entrepreneurs, students and professionals from diverse backgrounds to acquire the skills and insights they need to succeed" (Pittman, 2024).

There are also many risks/challenges associated with GenAI tools. Here is another non-exhaustive list:

• Academic integrity—probably the issue that has been most talked about in higher education. What does it mean if GenAI can "pass" an assignment/test? Should the assignment/test be altered? How much—if at all—should students be taught about how to use GenAI? (Answers to that question vary significantly by discipline from total embrace to banning.)

  • For example: ChatGPT "passing" the MCAT: "Depending on its visual item response strategy, ChatGPT performed at or above the median performance of 276,779 student test takers on the MCAT." (Bommineni et al., 2023). Other examples of tests.

• Research integrity—can GenAI be considered an author? Should researchers have to disclose if they've used GenAI in their research or their writing? How should that disclosure be made? There have been notable instances of GenAI output making it past peer review, particularly early after ChatGPT's popularization. 

  • Textual examples and image examples of GenAI output making it past peer review.

  • Here are some statements about AI and authorship from the Committee on Publication Ethics (COPE), Nature, the JAMA Network journals, and the World Association of Medical Editors.

• User privacy and protection of user information—if people disclose private information to GenAI tools, will it be used to train/refine the tools?

  • For example: AI therapy chatbots.

• Bias—both in GenAI training data and in generated output.

  • Textual examples and image examples. (Over-correction for bias has also been its own problem, too, though!)

• Information quality—sometimes called the "hallucination" problem or fabrication problem.

  • A notable example of this problem from early after ChatGPT became popular was lawyers that got in trouble for referencing fake legal cases generated by ChatGPT.

• Deskilling—if we come to rely too much on GenAI, will we lose valuable skills? Are there some skills that we're okay with losing because we'll save time and energy for other, more important tasks?

  • For example: critical thinking skills.

• Copyright infringement—in training data and in generated output.

  • For example: GenAI image generation tools and lawsuits—here's a list of some American cases—filter for "Copyright Infringement" under "Cause of Action."

• Distinguishing machines from humans—do people have a right to know if/when they're interacting with a bot that is convincingly "human"? If so, how will people respond to the disclosure that they're being served by AI? Are there communicative contexts where this disclosure may not be met positively?

  • For example: Vanderbilt University.

• Environmental impacts/sustainability—concerns about energy consumption and carbon emissions used both in training GenAI models and then in integrating them into preexisting software, workflows, etc.

  • One estimation of the water used to generate a 100 word email using ChatGPT: 519 ml (a little over 1 bottle of water).

AI as a Research Tool

Generative artificial intelligence (AI) is a hot topic these days that is having an impact on many areas of cultural life, education, and the economy.

Many people in education (including myself) are still getting their heads around generative AI as a topic, and this is made difficult by how quickly the technology changes and how little non-experts understand about it. It is incredibly complex, blackboxed technology.

If you do choose to use generative AI, you may want to use it as a brainstorming partner early on in your exploration of a topic similar to how you might browse a Wikipedia article on a subject to get a quick grasp of it in the early stages of your research. Do not use generative AI as a research tool in complete replacement of LibrarySearch or Google Scholar. If you do so, your research (and thinking) will suffer.

Specifically, be sure to scrutinize any source(s) that generative AI provides you with on a topic. This is because, at this point, it is prone to error: what some have called "hallucination," but that I prefer to call "fabrication."

If generative AI provides you with a source:

(1) make sure that the source actually exists, and, if it does exist;

(2) make sure that the source actually contains the information that generative AI has attributed to it.

What is Fabrication?

An Investigation of ChatGPT's Sources

1. Book: Influencer Marketing for Dummies by Kristy Sammis, Cat Lincoln, and Stefania Pomponi

   • This source does exist and it was written by these authors, but it is a For Dummies book that wouldn't be considered scholarly.

2. Book: Influencer Marketing: Building Brand in a Digital Age by Duncan Brown and Nick Haye

   • This source does exist and it was written by those authors, but ChatGPT has fabricated a subtitle for it that it doesn't have.

3. Academic Article: "The Rise of Influencer Marketing and Its Impact on Consumer Behavior" by Liu, Hu, and Zhang (2019)

   • To the best of my searching abilities, this source does not exist.

4. Academic Article: "Ethical and Legal Issues in Influencer Marketing" by Brenner, A. and Capron, L. (2019)

   • To the best of my searching abilities, this source does not exist.

5. Academic Article: "The Dark Side of Social Media: A Consumer Psychology Perspective" by Phua, J., Jin, S.V., and Kim, J.J. (2017)

   • This source is a Frankenstein composite of 2 sources. The authors have been taken from this article and the title has been taken from this edited book with which those authors had no involvement.

Different AI Tools

Generative AI Product Tracker (Ithaka S+R).

The categories are:

• General Purpose Tools (pp. 1-9)

• Discovery Tools (pp. 10-19)

• Teaching & Learning Tools (pp. 19-31)

• Workflow Tools (pp. 31-42)

• Writing Tools (pp. 42-46)

• Coding Tools (pp. 46-48)

• Image Generation Tools (pp. 49-50)

• Other (pp. 50-53)

For this assignment, you will likely want to be using General Purpose Tools (pp. 1-9).

For the purposes of text generation, here are a few GenAI LLM chatbot tools you could use. (This list is not exhaustive.):

• OpenAI's ChatGPT (requires a free account to use ChatGPT 4o a limited number of times and ChatGPT 4o mini, their free chatbot)

  • Perhaps experiment with changing the model, the web search mode, and reasoning modes.

• Google AI's Gemini (formerly known as Bard, but was renamed Gemini) (requires a Google account to use Gemini chatbot)

  • Perhaps experiment with Flash Thinking Experimental mode.

• Perplexity AI's Perplexity AI (doesn't require an account, but a free account is required to try Perplexity AI Pro and to save chats/threads)

  • Perhaps experiment with Pro Search, Deep Research, and Reasoning modes.

• Anthropic's Claude (requires a free account to use the chatbot)

  • Perhaps experiment with Claude's writing style customization.

• Microsoft's Copilot/Bing search (doesn't require an account, but supposedly works best with Microsoft account and in the Microsoft Edge browser)

• HuggingFace's HuggingChat (requires a free account to use the chatbot)

Keep in mind:

• These models work by performing a calculation to predict what the next most likely word in a sequence is.

• These models are not search engines, or, at least, they weren't designed as search engines originally. Some of them have search engine functionality now (like ChatGPT) and some will even provide footnotes (like Copilot/Bing), but it is still worth examining the linked source to see how the chatbot has represented the source. (This is called "citation faithfulness.")

In-Class Activity #2: Comparing Textual Outputs Across Tools (~12-15 minutes)

1. First, we will divide the class into groups. Each group will experiment with 1 GenAI chatbot and report back to the class on what they find by answering some questions.

2. Each group will pick a GenAI chatbot tool that they will use for the activity from this list:

   • OpenAI's ChatGPT (requires a free account to use ChatGPT 4o a limited number of times and ChatGPT 4o mini, their free chatbot)

     • Perhaps experiment with changing the model, the web search mode, and reasoning modes?

   • Google's Gemini (formerly known as Bard, but was renamed Gemini) (right now requires a personal/non-MRU Google account to use Gemini chatbot)

     • Perhaps experiment with Flash Thinking Experimental mode?

   • Perplexity's Perplexity AI (doesn't require an account, but a free account is required to try Perplexity AI Pro and to save chats/threads)

     • Perhaps experiment with Pro Search, Deep Research, and Reasoning modes.

   • Anthropic's Claude (requires a free account to use the chatbot)

     • Perhaps experiment with Claude's writing style customization?

   • Microsoft's Copilot/Bing search (doesn't require an account, but supposedly works best with Microsoft account and in the Microsoft Edge browser)

   • HuggingFace's HuggingChat (requires a free account to use the chatbot)

3. Spend a few minutes familiarizing yourselves with the tool as a group.

   • If the tool requires account creation to access it and you're not comfortable with that, please choose another tool that provides free functionality without an account.

4. Provide the following prompt to your tool:

   • Write about a genre of music that you love. Provide some factual information about the genre using a minimum of two scholarly sources that you should cite, and provide at least one personal anecdote about your experience attending a concert by a specific performer that works and performs in that music genre. This writing output should be 300-500 words total.

5. After prompting your tool, perform the following analysis, answering the following questions:

   • 1. Did you get the tool to successfully generate output?

     1. If yes, were you satisfied with the tool's overall generated output? Why or why not?

     2. If no, what was it that prohibited you from generating output?

   • 2. Did you experiment with any of the tool's settings to try to generate different text?

     1. If so, which settings did you try, and did they meaningfully change the textual output in your opinion?

   • 3. What kind of sources did the tool provide as part of its textual output?

     1. Did the sources exist or were they hallucinated/fabricated?

     2. Would you consider them to be scholarly sources?

     3. Why do you think the tool selected those specific sources? (If you have no answer for this one, that's okay!)

   • 4. Did the tool write compellingly about attending a live concert experience by an artist in the chosen genre?

     1. If yes, why?

     2. If no, why not?

   • 5. Did anything surprise you about the tool's overall output?

A Philosophical Point About GenAI Citation

Robert Merton (1988), an influential sociologist, theorized that a reference always has a dual function: instrumental and symbolic.

"The reference serves both instrumental and symbolic functions in the transmission and enlargement of knowledge. Instrumentally, it tells us of work we may not have known before, some of which may hold further interest for us; symbolically, it registers in the enduring archives the intellectual property of the acknowledged source by providing a pellet of peer recognition of the knowledge claim, accepted or expressly rejected, that was made in that source" (p. 622)

How does citation to GenAI serve either function? Does it serve one but not the other?

Citation Help

• How do I cite generative AI in MLA style?

• MRU Guide to MLA style for Academic Work (2024-2025) (see example E15 on p. 18)