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GNED 1401 - Fall 2025

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”.

 

GNED 1401 - GenAI Brainstorm In-Class Activity

  1. Click the link to this Padlet: https://padlet.com/bleching/gned1401_genai

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

Benefits/Opportunities and Risks/Challenges Associated With GenAI

Here is a non-exhaustive list of some of the benefits/opportunities that GenAI tools may offer. This list is general and not specific to the writing context. Which of these apply to writing?

  • 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).

  • 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. 

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

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

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

  • 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?

  • 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?

  • 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).

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 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's GPT-5 a limited number of times, with Plus and Pro tiers getting more GPT-5 access/features.)

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

  • 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)

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

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

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's GPT-5 a limited number of times, with Plus and Pro tiers getting more GPT-5 access/features)

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

    • 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)

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

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

  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. Bonus Question: Did anything surprise you about the tool's overall output?

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