Hello, friends, It’s Sunday, so I’m ruminating on some thoughts that have swirled around this week… I’ve been reading a lot this week about how many folks are now calling themselves “AI Experts,” and some pushback has been coming from folks in spaces ranging from digital learning companies to higher educations. I am especially interested in how conversations in higher education often center on single authorship and silo-ing our work in an odd, protectionist way. I want to share two frameworks that I have been working on since December 2022 and then invite you to not only give me feedback but also take these frameworks and try them out as you and your students engage in prompt engineering collaborations with AI-Assistants. I want to think about how we can make our students the experts in human-AI collaboration through intentional and ethical teaching and learning. And while we think, I want to share why this is important stuff: Top 10 Skills Employers Want
First, I want to focus our discussion by beginning with a topic that is so “done’ that it’s almost “already done.” — integration of generative AI in English Studies within higher education. You may already know that I am English Professor, specializing in digital writing, linguistics, and literacy at a large public university. You may also know that I am advocating for changes in how we teach our students writing processes and products. Whether you agree or disagree, I invite you to keep reading and let’s start a conversation.
My argument: as AI becomes ubiquitous in our workplaces, it’s crucial for us to re-conceptualize our educational strategies to prepare students for thriving and leading in these AI-infused environments. This calls for a radical re-engineering of our pedagogical approaches and a keen sense of our students as our audience. It also requires us to step out of our comfort zones and learn alongside our students (Dweck’s Growth Mindset).
I’ve devised a strategy known as “Rhetorical ‘Rhet’ Shot Engineering,” which offers a blueprint for a thoughtful writing process that aligns seamlessly with generative AI collaboration (prompt engineering) and recursive conversational process. Let’s explore how we can apply this to English Studies. If you think of more, hit me up:
- Purpose & Audience: Every piece of writing starts with intention. In writing studies, we need to teach students to articulate the purpose of their AI collaboration clearly – is it to generate ideas, solve a problem, or something else? And who’s the intended audience? This clarity will guide the AI to deliver relevant content.
Fact-Inclusion & Context: Generative AI is only as good as the data it’s fed. Students must learn to input specific, factual information and provide the right context to the AI, ensuring the output is both accurate and appropriate.
Tone & Genre: Just like us, AI can adapt its tone to suit different audiences. English Studies must emphasize the importance of choosing the right tone and writing genre for AI to follow, whether it’s formal for an academic paper or conversational for a blog post.
Style & Refinement: The final output from AI should reflect the stylistic choices befitting the genre. Students must master the art of tweaking AI-generated content through minor editing, such as adjusting sentence length, structure, and grammar, to fit the desired style.
Fact-Checking: In the spirit of rigorous academic integrity, students must be taught the vital skill of fact-checking AI-generated content. AI is a powerful tool, but it’s not infallible. We must instill a habit of verification to ensure the information provided by AI aligns with factual accuracy and is credible.n
Integrating AI into English Studies isn’t just about learning to command a tool; it’s about fostering a collaborative relationship. Here are some ways we can nurture this in any classroom at any level of learning, from middle school to college:
Peer Review: Teach students to use generative AI as a peer reviewer to critique and provide feedback on their work.
Collaborative Behaviors for Business: In group projects, Generative AI can be used to simulate real-world business collaborations, helping students understand the nuances of working with intelligent systems.
Editing & Brainstorming: Leverage generative AI for iterative writing processes, using it as a co-editor or brainstorming partner to enhance creativity and efficiency.
Social Media: Show students how generative AI can manage the tone and style for various platforms, teaching them the differences in communication strategies across media. Each platform is rhetorically unique, and writers need to know how to appeal to each one.
By adopting this “Rhet” Shot (as opposed to single shot) approach and embracing the ethical use of AI, we can empower students to be not just consumers of AI but also savvy collaborators. It’s time for English Studies to lead the charge in integrating generative AI into our curriculums, ensuring our students are equipped to thrive and lead in the AI-infused workplaces of today and tomorrow. So, I’ve given a snapshot of the rhetorical prompt engineering framework, now I want to introduce a framework for doing all of these cool AI collaborations, while also sustaining and cultivating ethical behaviors. Actually, I would submit to you that rhetorical prompt engineering is a phenomenal way to teach students the value of ethical language and content use.
For me it is important to demonstrate a human-centered ethical model for collaboration with generative AI and our use of outputs from our AI Assistants. I call it the “Four Qualifiers for Human-AI Collaboration” framework.
Output Ethics: Navigating the Human-AI Collaborative Space
In human-AI collaboration, we must consider my “Four Qualifiers for Human-AI Collaboration” to ensure ethical outputs:
Usefulness of Output: The AI-generated content must serve a practical purpose and add value to the intended task. It should also have a useful purpose for the human prompter.
Relevance of Output: The information should be pertinent to the context and audience, meeting the specific requirements of the project.
Accuracy of Output: Factual correctness is non-negotiable; the AI must provide information that is truthful and verifiable.
Harmlessness and Ethics of Output: The output must not cause harm and should adhere to ethical standards, reflecting moral responsibility.
We need to always be mindful that humans are the ethical compass guiding AI. It is our usage that determines the ethical standing of the outputs. This human-centric approach to generative AI ensures that as we leverage this powerful tool, we do so with a conscious commitment to doing good and avoiding harm.
So as we guide and follow our students into the AI-infusions of NOW, let’s remind ourselves and our students that with “great power comes great responsibility.” (with attribution to Voltaire and Spiderman’s Uncle Ben). It’s not the AI that holds ethical accountability; it’s us – the educators, the students, the writers, the human collaborators. How we choose to use AI is what imbues our outputs with ethical significance.
I encourage us to champion this understanding in our classrooms and beyond, ensuring that as we harness the capabilities of AI, we remain steadfast in our ethical considerations, shaping a future where technology amplifies our humanity, not undermines it. I encourage you to keep pushing boundaries and think ethically, critically, and intentionally. AND….encourage your students to do so as well!
P.S.: How are you ensuring ethical considerations in your AI endeavors? Join the conversation below. I hope you found this post helpful.