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Rhetorical Strategies for Prompting: Developing Prompt Literacies

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Rhetorical Strategies for Prompting: Developing Prompt Literacies

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The thoughtfulness and pragmatism of the report on the experiment make me think about how I could iterate and update such an activity, in consideration of the devolution of Google Search!

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Submitter: Anna Mills, Cañada C

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The experiment:
As an exercise in critical AI literacy and information literacy, we probed Google search autopredict for bias. The exercise served as a bridge between previous discussions of large language models such as ChatGPT and the more familiar predictive text in search. First the students did homework assignments collaboratively annotating the introduction to Safiya Umoja Noble's book Algorithms of Oppression and Janelle Shane's explanation of large language models, "Let an Algorithm Choose Your Halloween Costume."

Then in class we watched a brief video of Noble explaining her research. We discussed how search behaves differently than when she first wrote the book, probably in part because of her advocacy. Projecting my laptop screen, I demonstrated a few search stems Noble had tried, such as "Why are black girls so..." and "black girls." I invited the class to suggest search stems they wanted to test and blended testing and discussion for fifteen minutes or so before splitting into groups where students performed their own search tests and reported back.
Results:
Students seemed very engaged. There was laughter and sometimes outrage in the room. "Why are athletes so" resulted in some stereotypes like "dumb" and "hot" For "why are black people so," the search refused to suggest any completions. I pointed out that Google provides no information about how it decides which completions to allow and connected this to Noble's critique of Google's power and lack of transparency. Students suggested "Why are Latinas so..." which resulted in some stereotypica, offensive phrases like "hard to date," "hot," and "attractive." "Why are Samoans so..." resulted in stereotypes like "big" and "fat." The group discussions were not as lively as I had hoped; I think I would assign students to do some probing for homework next time so they can focus and investigate on their own and then share back only the most interesting results in screenshots and reflections. Next time I would screenshare Google's process for reporting an inappropriate suggestion.

I'm still dissatisfied because this activity didn't help us understand the role of advertising and the profit motive in Google's algorithm, one of the key ideas in Noble's book. Students probably left with the impression that Google search predictions and results transparently reflected frequency of user queries. I have tried but have not been able to figure out whether ads still coordinate with stereotyped search predictions and if so, how to best showcase that coordination.

Relevant resources:

Contact:

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Submitter: Anna Mills, Cañada C ------------------ The experiment: As an exercise in critical AI literacy and information literacy, we probed Google search autopredict for bias. The exercise served as a bridge between previous discussions of large language models such as ChatGPT and the more familiar predictive text in search.

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Teaching Social Identity and Cultural Bias

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Teaching Social Identity and Cultural Bias

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I'm thinking about how this activity might be tailored to work in non-lit contexts, such as analyzing literacy narratives.

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Teaching Social Identity and Cultural Bias Using AI Text Generation – Exploring AI Pedagogy

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Submitter: Christopher D. Jimenez, Stetson U

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Submitter: Paul Fyfe, North Carolina State U

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The experiment:
I assigned students to "cheat" on their final paper by writing with the help of text-generating AI. Their task was to generate and blend any combination of human/machine-written text so that I could not tell which was which. Then students appended reflections on the experience (without AI) in context of several guiding questions: what was their process like? Did they feel they were "cheating"? Why or why not? What ideas about writing, critical thinking, authorship, plagiarism, &c. did this exercise test or challenge for them? Would or should you use these tools again and in what circumstances? Students used a web-based writing platform called Lex, or whatever text-generating AI they were comfortable with. I set no quotas about the amount of generated text required, nor rules about how it should be integrated with their work. Experimentation was strongly encouraged. Students were free to "write" as they pleased, so long as their paper engaged with our course discussions about AI and cited at least a few things from the syllabus. These included introductions to machine learning, articles about large language models and algorithmic bias, and journalistic commentary about the impact of AI on creativity, writing, and education. The goal was to elicit critical reflection through creative experiments.
Results:
Every student initially thinks that this assignment will be easy; they all eventually report the opposite. Students tend to realize that generating text is very different from "writing," which still requires their involvement across a cognitive spectrum of ideation, composition, selection and arrangement, editing, and revising. The assignment works well to break down myths and presumptions about generative AI, encouraging students to connect to the critiques we've read for class. At the same time, the assignment also makes room for students to experiment-importantly without the threat of punishment-in imagining how AI tools might be reasonably integrated in their own work. It invites students to participate in envisioning norms, rules, and guardrails for how AI should be used (or not) in education. Thus, the assignment takes a hands-on approach to developing principles for responsible AI and critical AI literacy.

Ultimately, this assignment moves the discussion beyond cheating to the array of complex issues and questions we have yet to settle about generative AI. For that reason, I have left the assignment open ended, and do not model for them in advance how to use these platforms. While disorientation is part of the point, this presents a challenge for some students unsure how to begin, or how their work will be assessed. In response, I have emphasized the importance of their process reflections and shared a rubric in advance.
Relevant resources:
Contact:

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Submitter: Paul Fyfe, North Carolina State U ------------------ The experiment: I assigned students to "cheat" on their final paper by writing with the help of text-generating AI. Their task was to generate and blend any combination of human/machine-written text so that I could not tell which was which.

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Submitter: Paul Fyfe, North Carolina State U

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The experiment:
I assigned students to "cheat" on their final paper by writing with the help of text-generating AI. Their task was to generate and blend any combination of human/machine-written text so that I could not tell which was which. Then students appended reflections on the experience (without AI) in context of several guiding questions: what was their process like? Did they feel they were "cheating"? Why or why not? What ideas about writing, critical thinking, authorship, plagiarism, &c. did this exercise test or challenge for them? Would or should you use these tools again and in what circumstances? Students used a web-based writing platform called Lex, or whatever text-generating AI they were comfortable with. I set no quotas about the amount of generated text required, nor rules about how it should be integrated with their work. Experimentation was strongly encouraged. Students were free to "write" as they pleased, so long as their paper engaged with our course discussions about AI and cited at least a few things from the syllabus. These included introductions to machine learning, articles about large language models and algorithmic bias, and journalistic commentary about the impact of AI on creativity, writing, and education. The goal was to elicit critical reflection through creative experiments.
Results:
Every student initially thinks that this assignment will be easy; they all eventually report the opposite. Students tend to realize that generating text is very different from "writing," which still requires their involvement across a cognitive spectrum of ideation, composition, selection and arrangement, editing, and revising. The assignment works well to break down myths and presumptions about generative AI, encouraging students to connect to the critiques we've read for class. At the same time, the assignment also makes room for students to experiment-importantly without the threat of punishment-in imagining how AI tools might be reasonably integrated in their own work. It invites students to participate in envisioning norms, rules, and guardrails for how AI should be used (or not) in education. Thus, the assignment takes a hands-on approach to developing principles for responsible AI and critical AI literacy.

Ultimately, this assignment moves the discussion beyond cheating to the array of complex issues and questions we have yet to settle about generative AI. For that reason, I have left the assignment open ended, and do not model for them in advance how to use these platforms. While disorientation is part of the point, this presents a challenge for some students unsure how to begin, or how their work will be assessed. In response, I have emphasized the importance of their process reflections and shared a rubric in advance.
Relevant resources:
Contact:

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Submitter: Paul Fyfe, North Carolina State U ------------------ The experiment: I assigned students to "cheat" on their final paper by writing with the help of text-generating AI. Their task was to generate and blend any combination of human/machine-written text so that I could not tell which was which.

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Teaching Social Identity and Cultural Bias Using AI Text Generation – Exploring AI Pedagogy

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Submitter: Christopher D. Jimenez, Stetson U

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Very interesting activity here having fyc students craft AI policy but I wonder how much initial exploration/front loading might be required. Or maybe that unfolds with the crafting?? (Jason Snart)

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Submitter: Brandee Easter, York U

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The experiment:
This in-class activity was part of a first-year writing course in the Spring of 2023. In this activity, students looked at the university's policy on academic honesty and discussed how AI may or may not fit into the pre-existing guidelines. Based on our previous experiments with text generation tools, students then worked in small groups to write a suggested update to the academic honesty policy.

When I introduced the topic of AI to this class, I was surprised to hear gasps from students. Perhaps because many were writing majors, students were reluctant to consider AI as anything other than plagiarism or cheating. The goal of this activity was to complicate that equation by looking directly at plagiarism policies and to ask what expressive, persuasive, and creative possibilities might be limited by that definition, including AI students already use frequently but don't think of as AI (such as grammar checkers).
Results:
This moment was ideal for this conversation because our university had yet to develop its own stance on text generation technologies. This meant that students were largely on their own to determine how they interacted with these tools, and I hoped this activity would help them think about possibilities and problems across various writing contexts.

If I teach this again, I would draw on a range of policies to compare approaches that provide a variety stances toward AI to help students imagine more complex relationships with these technologies. I would also spend more time on activities and readings beforehand that help students see the creative potential of AI to challenge an immediate conflation of AI with plagiarism.

Contact:

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Submitter: Brandee Easter, York U ------------------ The experiment: This in-class activity was part of a first-year writing course in the Spring of 2023. In this activity, students looked at the university's policy on academic honesty and discussed how AI may or may not fit into the pre-existing guidelines.

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Submitter: J. Seth Lee, Slippery Rock U

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The experiment:
This activity had three goals: introduce ChatGPT as a potential writing tool, discuss how it could work as a brainstorming resource, and prompt discussion about style and the need to revise AI generated text.

Classes began this activity by dividing up into groups of five. After signing up for a free OpenAI account, each group received a rhetorical technique (e.g. humor, narration, reiteration) which they needed to define for the class using their textbook. That done, each group choose from a series of questions I provided for the activity. These included such questions as "What is the correct orientation for toilet paper?" and "What is the greatest movie of all time?" Each group then formed a working thesis answering their question before turning to ChatGPT to brainstorm. For example, one group asked ChatGPT to "Help argue toilet paper should be placed over the top using humor." The groups then copied and pasted the question they asked ChatGPT, its response, and their group's substantive revision into a GoogleDoc.

Prior to the next class, I lightly edited the GoogleDoc's formatting to create a worksheet containing all the groups' prompts, ChatGPT suggestions, and the groups' revisions. During class each student received a copy of the worksheet and spent approximately 20 minutes reading and annotating the AI generated responses and the group written revisions. As a class we then discussed the differences in style between AI and human generated text.
Results:
I did not include a formal assessment of this assignment aside from class engagement. Based on class discussion, I believe the assignment was a success. Seeing multiple examples of AI-generated text, students rapidly picked up on the blandness of the text generator's style. One student described it as "soul-less" and another noted that ChatGPT tended to use the same sentence structure repeatedly. This led to further discussions about the human-written revisions.

In most cases, those revisions were substantive enough to avoid concerns about misusing ChatGPT. Serendipitously, each class had at least one revision that reused too much of the the generated text than was responsible. In two of the four classes, students did pick up on the revisions that were problematic. The other two classes did not, which required a good bit of prompting on my part before they saw the problem.

I will almost certainly use this assignment again with some tweaks. One of the rhetorical techniques I assigned was narrative. The groups given that technique all ended up with a text generated story, but the amount of time set aside for the assignment did not give them time to revise substantially. I will also turn the assignment into a three-day sequence for a fifty-minute class. The first day we'll create the worksheet. Day two will focus on style, and the final day will focus on revision. Covering style and revision related to this activity in one class period felt rushed.

Relevant resources: I have linked a sample worksheet from this assignment here: https://shorturl.at/bdOV3. Student names are redacted for privacy.

Contact: joshua.lee[AT]sru[DOT]edu

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Brainstorming and Style with ChatGPT – Exploring AI Pedagogy

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Submitter: Carly Schnitzler, Johns Hopkins U

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The experiment:
In my first-year writing classes, my students and I worked together to develop AI Standards of Conduct. We did this in-class during the first week, with a few pedagogical goals in mind: 1) to begin to articulate the value of writing for their education, for their personal growth, and for their professional development; 2) to establish a culture of collaboration and co-creation in the classroom environment; and 3) to set clear guidelines for the use of LLM-powered writing tools in the writing classroom, grounded in students' own values.

I gave a short overview of what LLMs are and how they work, followed by some questions (which often involved clearing up misconceptions) and discussion of past experiences. Then, small groups brainstormed and took notes session on two questions: 1) What's the point of being in a writing class, if language models can generate passable text and score well on exams? and 2) How can this translate into course policy? Student responses became the basis for our AI Standards of Conduct, which I then put into the syllabus.
Results:
This went really well! I have asked students to help with setting course expectations (i.e., be respectful, give constructive feedback, etc.) during the first week of class since I started teaching writing. This activity felt like a natural extension of that expectation-setting work. A beginning of semester intake survey demonstrated that students were reluctant to use and/or disclose use of LLMs because of their associations with cheating and plagiarism, so it felt important to have clear policies for the class. I imagine the suggestions will evolve over time, but I've copied the resulting policies from Fall 2023 below.
AI Standards of Conduct:

AI tool use should support-and not detract from-the goals of the course-developing critical thinking, analytical skills, personal voice, etc. To this end, AI generated output will not be permitted in any writing assignment (read: you can't copy/paste output and turn it in).

AI tools can be used for light brainstorming (think: getting unstuck from writer's block, working through an outline) and light editing or revision. All uses of AI tools in any assignment must be disclosed. If you use AI tools in any part of the writing process, please disclose via this form: [link redacted].

Conferences and revision are privileged over any disciplinary action in the case of over-reliance on AI tools in the course (read: if you're relying on AI tools too much, I'll ask to meet with you as a first step of intervention).

Relevant resources: Intake Survey
Contact: cschnit1[AT]jh[DOT]edu

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Developing AI Standards of Conduct as a Class

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Submitter: Carly Schnitzler, Johns Hopkins U ------------------ The experiment: In my first-year writing classes, my students and I worked together to develop AI Standards of Conduct.

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I love that the teacher found reading the essays about the AI-generated stories more interesting than the traditional essays.

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I love that the teacher found reading the essays about the AI-generated stories more interesting than the traditional essays.

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Submitter: Francesca Caparas, De Anza College

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The experiment:
In Spring quarter 2023, I taught a course on Literature and Composition that focused on the relationship between humans and non-human animals in literature. For our unit on Folklore, I gave students the option to write a more traditional literary analysis of a folktale we read for class, or they could do the following:

"Ask ChatGPT (or some other generative AI) to write you a story from the point of view of an alien landing on Earth and being appalled at the way humans treat animals. Keep asking the AI to refine the story until you are satisfied with it. Then write an essay (minimum 3 pages) analyzing why you think the AI made the choices that it made. Things to consider in your essay include:

Take into account the various ways you structured your prompts in order to get the story you wanted. Why did the prompts you entered produce certain results?

What might this writing process mean for the future of literature?

If we, as humans, have created an intelligent machine, what does this mean for our status as animals?"

Gen AI was used for developing the folktale, but the final written product was the students' reflection on the process. In addition to the written reflection, they also had to submit screenshots of their prompts. The goal was for them to engage in prompt engineering, reflect on it as a writing process, and analyze the larger implications for the future of human writing.
Results:
I was quite pleased with the outcome of this experiment. Since the generative AI activity was optional, the students who chose this option were more enthusiastic and curious about the tool.

As the teacher, I noticed that when grading the assignments, I was more eager to read the essays that reflected on the genAI activity. These essays provided fresh insights into the practice and creativity of writing. I was blown away by the students' varied approaches to the writing activity and the different meanings they took from the experience. One student noted that it seemed like ChatGPT was just doing the bare minimum to address the prompt, thus missing out on the sense of imagination one would hope for in a folktale. Another student, who also noted the banality of the genAI output, eventually pivoted toward more unconventional prompts such as "write dialogue that consists solely of Arnold Schwarzenegger movie quotes." This student reflected: "experimenting with ChatGPT allowed me to construct a narrative direction I was personally invested in."

Overall, students seemed genuinely engaged in this activity, and I was also engaged in reading their reflections. In the future, I would like to see how an assignment like this would fare outside of a literature class. In that case, I will have to adjust the starting prompt so it relates to a wider range of course topics, not just humans, animals, and aliens.

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Submitter: Francesca Caparas, De Anza College ------------------ The experiment: In Spring quarter 2023, I taught a course on Literature and Composition that focused on the relationship between humans and non-human animals in literature.

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I think this activity would be particularly helpful in something like a first year writing class as a general intro to what genAI can and cannot do.

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Rhetorical Strategies for Prompting: Developing Prompt Literacies – Exploring AI Pedagogy

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Submitter: Jon Ippolito with Greg Nelson and Troy Schotter, U of Maine

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The experiment:
In fall 2023 I taught a course designed to compare student experiences performing digital tasks with pre- and post-AI approaches in collaboration with Greg Nelson and Troy Schotter, a UMaine Computer Science professor and grad student respectively. In the first half of the semester, 50 students wrote essays, coded software, and created media using familiar digital tools; in the second half, they performed the same tasks using generative AI.

In Week 7, for example, students revisited their first writing assignment from Week 1-identifying winners and losers in a field disrupted by a contemporary technology-but were required to lean on GPT-4 for help brainstorming, drafting, and polishing a similar essay with a different choice of field and technology. Students then wrote a detailed comparison of the two drafts. The class repeated this process for designing logos, coding game avatars, creating illustrations, writing stories, recording soundscapes, and even grading homework.
Results:
My collaborators plan to analyze and share this study with the larger educational community, but we can already see trends in student responses. For the disruptive technology essay, students were impressed with GPT-4's essay structure, examples, and grammatical prose but felt citations were often unreliable and style sometimes overly elaborate or formal. One described the AI as a student trying really hard to sound smart. Having been coached on incremental prompting to improve results, another student remarked that it took about the same amount of time to coax a good enough essay from the chatbot as to write his original essay.

Even after the lesson, students still felt more confident writing with a traditional approach than with AI. Most felt low-to-moderate confidence about achieving their writing goals with AI, and even less confidence about how to use AI ethically. I hope with future research to figure out whether this insecurity is due to inexperience or endemic to AI tool use.

Relevant resources:

Contact:

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Asking AI To Write about Its Own Disruption – Exploring AI Pedagogy

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Submitter: Jon Ippolito with Greg Nelson and Troy Schotter, U of Maine

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Submitter: Carly Schnitzler, Johns Hopkins U

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The experiment:
In my first-year writing classes, my students and I worked together to develop AI Standards of Conduct. We did this in-class during the first week, with a few pedagogical goals in mind: 1) to begin to articulate the value of writing for their education, for their personal growth, and for their professional development; 2) to establish a culture of collaboration and co-creation in the classroom environment; and 3) to set clear guidelines for the use of LLM-powered writing tools in the writing classroom, grounded in students' own values.

I gave a short overview of what LLMs are and how they work, followed by some questions (which often involved clearing up misconceptions) and discussion of past experiences. Then, small groups brainstormed and took notes session on two questions: 1) What's the point of being in a writing class, if language models can generate passable text and score well on exams? and 2) How can this translate into course policy? Student responses became the basis for our AI Standards of Conduct, which I then put into the syllabus.
Results:
This went really well! I have asked students to help with setting course expectations (i.e., be respectful, give constructive feedback, etc.) during the first week of class since I started teaching writing. This activity felt like a natural extension of that expectation-setting work. A beginning of semester intake survey demonstrated that students were reluctant to use and/or disclose use of LLMs because of their associations with cheating and plagiarism, so it felt important to have clear policies for the class. I imagine the suggestions will evolve over time, but I've copied the resulting policies from Fall 2023 below.
AI Standards of Conduct:

AI tool use should support-and not detract from-the goals of the course-developing critical thinking, analytical skills, personal voice, etc. To this end, AI generated output will not be permitted in any writing assignment (read: you can't copy/paste output and turn it in).

AI tools can be used for light brainstorming (think: getting unstuck from writer's block, working through an outline) and light editing or revision. All uses of AI tools in any assignment must be disclosed. If you use AI tools in any part of the writing process, please disclose via this form: [link redacted].

Conferences and revision are privileged over any disciplinary action in the case of over-reliance on AI tools in the course (read: if you're relying on AI tools too much, I'll ask to meet with you as a first step of intervention).

Relevant resources: Intake Survey
Contact: cschnit1[AT]jh[DOT]edu

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Developing AI Standards of Conduct as a Class

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Submitter: Carly Schnitzler, Johns Hopkins U ------------------ The experiment: In my first-year writing classes, my students and I worked together to develop AI Standards of Conduct.

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https://exploringaipedagogy.hcommons.org/2023/11/13/clarifying-expectations/

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Submitter: Ryan Watkins, George Washington U

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The experiment:
Given that students often skim over (or skip entirely) the policy statements in my syllabus, I wanted to clearly communicate my expectations for student use of AI (i.e., LLMs such as ChatGPT) for each assignment in my courses. To achieve this I applied the common stoplight symbolism (i.e., green/yellow/red) to communicate when the use of AI was acceptable, questionable, or unacceptable for specific tasks within each assignment of my courses (see Figure 1).

Figure 1: Sample stoplight table for an assignment

After initial experimentation, I decided to create a free tool (using Google Forms, Sheets, Docs, Drive, and Sites) so that other instructors could also easily create similar stoplight tables for their assignments. The tool now includes customizable templates for Essay, Literature Review, Research Paper, and Coding/Programming assignments. Instructors complete a simple form, checking boxes for how students can (and cannot) use AI tools for the assignment. The form responses are then transformed into a stoplight table within a Google Doc that the instructor can copy/paste into their syllabus and further edit if desired. The tool deletes the created Google Doc files with the assignment boxes every 24 hours. It is free to use and if anyone wants the code that does the "magic" behind the scenes I am also happy to share it.
Results:
This semester I have included the stoplight tables for each of my assignments in each of my courses. Creating them with the tool was easy - as is creating them without the tool. The tool simply provides initial structure and tasks, that can then be edited and customized for the specifics of the assignment. While several of my assignments do request the use of AI for specific tasks, the use of AI is not required since some students may not be comfortable with using the free tools currently available due to data privacy and security concerns.

Up to this point in the semester, students have not asked questions about the appropriate task-level uses of AI on my assignments - which was my primary goal in creating the boxes. The lack of questions is one indicator, for me, that the additional clarity of expectations is working. I asked students for feedback on the boxes, here is one sample comment "... it gives a clear permission structure and even occasionally invites the use of AI in certain instances (when other courses/educators often just forbid it outright)." And another "I found it helpful and again appreciated the manner [in which] it was color coded and that it was included on each assignment, as a reminder." One recommendation was to include a link in the yellow light boxes that students could use to submit a form or email requesting to use AI in some creative way.

Contact: https://ryanrwatkins.com

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Submitter: Ryan Watkins, George Washington U ------------------ The experiment: Given that students often skim over (or skip entirely) the policy statements in my syllabus, I wanted to clearly communicate my expectations for student use of AI (i.e., LLMs such as ChatGPT) for each assignment in my courses.

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Submitter: Anna Mills, Cañada C

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The experiment:
As an exercise in critical AI literacy and information literacy, we probed Google search autopredict for bias. The exercise served as a bridge between previous discussions of large language models such as ChatGPT and the more familiar predictive text in search. First the students did homework assignments collaboratively annotating the introduction to Safiya Umoja Noble's book Algorithms of Oppression and Janelle Shane's explanation of large language models, "Let an Algorithm Choose Your Halloween Costume."

Then in class we watched a brief video of Noble explaining her research. We discussed how search behaves differently than when she first wrote the book, probably in part because of her advocacy. Projecting my laptop screen, I demonstrated a few search stems Noble had tried, such as "Why are black girls so..." and "black girls." I invited the class to suggest search stems they wanted to test and blended testing and discussion for fifteen minutes or so before splitting into groups where students performed their own search tests and reported back.
Results:
Students seemed very engaged. There was laughter and sometimes outrage in the room. "Why are athletes so" resulted in some stereotypes like "dumb" and "hot" For "why are black people so," the search refused to suggest any completions. I pointed out that Google provides no information about how it decides which completions to allow and connected this to Noble's critique of Google's power and lack of transparency. Students suggested "Why are Latinas so..." which resulted in some stereotypica, offensive phrases like "hard to date," "hot," and "attractive." "Why are Samoans so..." resulted in stereotypes like "big" and "fat." The group discussions were not as lively as I had hoped; I think I would assign students to do some probing for homework next time so they can focus and investigate on their own and then share back only the most interesting results in screenshots and reflections. Next time I would screenshare Google's process for reporting an inappropriate suggestion.

I'm still dissatisfied because this activity didn't help us understand the role of advertising and the profit motive in Google's algorithm, one of the key ideas in Noble's book. Students probably left with the impression that Google search predictions and results transparently reflected frequency of user queries. I have tried but have not been able to figure out whether ads still coordinate with stereotyped search predictions and if so, how to best showcase that coordination.

Relevant resources:

Contact:

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Submitter: Anna Mills, Cañada C ------------------ The experiment: As an exercise in critical AI literacy and information literacy, we probed Google search autopredict for bias. The exercise served as a bridge between previous discussions of large language models such as ChatGPT and the more familiar predictive text in search.

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Submitter: Yeqing Kong, Georgia Inst. of Tech.

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The experiment:
Voice and tone are two important concepts in technical communication, shaping a brand's personality and influencing users' content experience. This teaching experiment immerses students in the exploration of how rhetorical choices can modify the tone of digital products. I drew on Moran's (2023) framework, the four primary dimensions of tone-of-voice-humor, formality, respectfulness, and enthusiasm. I selected four websites to represent diverse combinations of tone-of-voice in commercial and non-profit contexts, including Starbucks, GEICO, MINI Cooper, and the Centers for Disease Control and Prevention (CDC). Students engage in the following multi-step process:

* Step 1: Evaluate each website's tone along the spectrum of four dimensions-formal vs. casual, serious vs. funny, respectful vs. irreverent, matter-of-fact vs. enthusiastic.

* Step 2: Craft prompts for ChatGPT to rewrite five messages on the websites by tweaking one of the four dimensions. Example prompts could include "Let's infuse a bit more informality into this message" or "Let's add some humor into the message." Refer to Follonier (2023) for additional examples. Record both the prompts and the original as well as the revised versions for each message.

* Step 3: Analyze and summarize the changes in each revision, reflecting on how these alterations contribute to a different tone.
Results:
The assessment of this activity has two aspects: revision and reflection. The revision component evaluates students' interaction with ChatGPT, focusing on the crafted prompts guiding the desired output. The reflection component assesses whether students can notice the subtle changes in the revised version and contemplate how these modifications influence the tone.

This activity proves productive in several ways. First, students were fascinated by AI's ability to generate content with varying tones by adjusting the words, phrasing, sentence structures, or punctuation. The incorporation of AI aids in conveying how a single message can carry different tones and impact readers' perceptions. Second, the activity helps students realize the importance of specifying tone when using ChatGPT for different purposes. The creation of prompts to constrain the tone of output text is a process of "prompt engineering," which can minimize the likelihood of generating undesired content and help in achieving communication goals (Open AI API, n.d.).

For instance, below is ChatGPT's revision of an error message on GEICO's website:

* Original message: "This is awkward, but we can't find that page!" (Tone: casual, funny, enthusiastic)

* Revised message: "We regret to inform you that the requested page is currently inaccessible, and we extend our apologies for any inconvenience caused." (Tone: formal, serious, respectful)

Relevant resources:

Follonier, F. (2023, March 19). ChatGPT style guide: Understanding voice and tone prompt options for engaging conversations. https://www.relataly.com/chatgpt-style-guide-understanding-voice-and-tone-options-for-engaging-conversations/13065/

Moran, K. (2023, August 16). The four dimensions of tone of voice. NN/g Nielsen Norman Group. https://www.nngroup.com/articles/tone-of-voice-dimensions/#four

Open AI API. (n.d). https://platform.openai.com/docs/guides/safety-best-practices

# data[].attributes.attachment_link.description
Submitter: Yeqing Kong, Georgia Inst. of Tech. ------------------ The experiment: Voice and tone are two important concepts in technical communication, shaping a brand's personality and influencing users' content experience. This teaching experiment immerses students in the exploration of how rhetorical choices can modify the tone of digital products.