Asking the Right Questions: The meaning of teaching and learning in the age of generative AI
Author: Andrea Zellner
Irish Journal of Technology Enhanced Learning, Vol 7, Issue 2 Special Issue: The Games People Play: Exploring Technology Enhanced Learning Scholarship & Generative Artificial Intelligence https://doi.org/10.22554/ijtel.v7i2.132
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Abstract
As educators grapple with the impact of generative artificial intelligence (AI) and the ease of access to these models by learners, questions of pedagogy and learning have dominated the conversation. This paper explores the concerns of teaching through exploring the contrast between human and machine learning and the implications of both on educators facing an environment where AI is prevalent. Through a prompting process, a position paper was generated by ChatGPT3.5 on these contrasts and implications. The AI output, largely accurate, highlighted strategies for educators while also showcasing the limitations of the tool. Educators and scholars can support themselves and their students through critical reflection of the use of AI tools.
Alright, let’s break down the first part of the text using the Reading Apprenticeship approach and a Think Aloud routine. Here we go:
Think Aloud Start:
“As educators grapple with the impact of generative artificial intelligence (AI) and the ease of access to these models by learners,”
Looking at this first chunk, I notice the verb “grapple,” which indicates that educators are wrestling or dealing with something challenging. The phrase “impact of generative artificial intelligence (AI)” signals that this challenge relates to a specific kind of AI, one that probably creates content or data on its own—hence the term “generative.” Now, “the ease of access to these models by learners” suggests that these AI models are readily available to students, which could be altering the educational landscape.
So, putting it all together, we can understand that this opening statement is setting up a scenario where educators are trying to understand and adapt to a new reality where generative AI tools are easily obtainable and used by students.
Now, let’s invite everyone to reflect on this process. Did breaking the sentence down help clarify its meaning for you? What did you notice when you read it on your own? Remember, this approach is about being interactive with the text and open to different interpretations. Try your own Think Aloud with the next part of the text to see what else we can discover. Afterward, let’s come back together to share and explore our insights. Shall we give it a try?
First of all, it’s sort of disconcerting to read one’s own writing and thinking…I wrote that? Originally, I had an AI generated abstract, because I hate writing them. But through the review process I took another crack at it. The concerns of teaching—the work of it and the purpose of it—was really at the heart of the questions I wanted to explore in this exercise.
Also “the concerns of teaching” was a close second to what I was thinking the subject of the article was.
I feel like this is a good word for what we are doing here, grappling. Also used by WPWP team in 2020 via a national study group: https://teach.nwp.org/collection/the-grapple-series-exploring-the-intersections-of-artificial-intelligence-humanity/
Might be that even a little bit of experience with the current crop of bots leads one quickly to understand that prompting is the best, and only way, to engage the AI, so scanning this abstract, I want to jump to detail on what the prompting process was as (I’m imagining) it will reveal one specific picture of the kinds of contrasts, benefits, and limitations that go with working with gen AI.
That seems like a quick aside, but isn’t this what everyone is worried about… that what gets produced will be just blabber. How did you judge the accuracy? What were your criteria? Why do you think it was accurate?
It is fascinating how helpful and dangerous AI can be and how it can help revolutionize classrooms and the way we learn, but it can also unfortunately help students to cheat.
Let’s dive into the first part of this text together and see what strategies can aid our comprehension. I’ll think aloud as I go.
“As educators grapple with the impact of generative artificial intelligence (AI)…”
In this first chunk, we notice the word “educators,” which tells us who the subject of the sentence is—teachers like us. They are “grappling,” which suggests they are struggling or trying to understand something. That something is the “impact of generative artificial intelligence (AI).” “Generative” typically means something that can create or produce, and “AI” refers to machines designed to act intelligently.
“…and the ease of access to these models by learners…”
This next chunk connects to the first with “and,” meaning we’re adding more information. The ease of access suggests it’s not difficult for learners to reach or use these AI models. The word “learners” includes both us and our students—anyone in the process of learning.
“…questions of pedagogy and learning have dominated the conversation.”
The final chunk of this section introduces “questions of pedagogy and learning,” indicating that these are the main topics being discussed—or dominating—the conversation. “Pedagogy” is our method and practice of teaching, and “learning” is the acquisition of knowledge or skills.
So, putting it together, we understand that this sentence is setting up a situation where teachers are confronting the new reality of AI—its benefits and its ready accessibility to students—and this has sparked a lot of discussion about teaching methods and the nature of learning itself.
Now it’s your turn. What do you notice when chunking down the sentence further? Have a go at breaking down the phrases, focusing on connections, and maybe even some predictions about what might come next. Share your thoughts, and let’s see how our reflections might help us navigate this text. Remember, it’s like peeling an onion—layer by layer, we get to the core meaning. Let’s encourage each other in our reading group to re-read the paragraph and share our own Think Alouds, exploring and understanding the text chunk by chunk. How might breaking down this paragraph into chunks change the way you see it?
Oh, I absolutely love how we’re breaking down and delving into the substance of this paragraph! It’s fascinating to witness the intersection of technology and pedagogy. Let’s look at the term “generative” again for a moment. In the context of AI, it’s really a door to endless creative possibilities, isn’t it? When we talk about generative AI, we’re referring to a type of artificial intelligence that can create content, which can prompt a whole new way of thinking about creativity in the classroom.
Now, as for the “ease of access to these models by learners,” this could potentially democratize learning, giving curious minds everywhere the tools to explore, learn, and create on their own terms. The implications for self-directed and personalized learning are significant and promise a bright future for educational equity, don’t you think?
As for “questions of pedagogy and learning,” these discussions are crucial because they challenge us to reimagine our roles as educators. We’re not just imparter of knowledge but facilitators of a learning experience that often extends beyond the traditional classroom. This environment invites a fusion of human and machine intelligence, enabling us to craft a transformative educational journey.
I’m keen to hear your interpretation and how you imagine the dynamics in the classroom might evolve with AI as a teaching and learning tool. And of course, how are you integrating these fascinating tenets of AI into your learning or teaching practices?
I like how this sentence shows how AI has more uses such as teaching and other things. One thing I believe is useful for AI is education for people who have limited or no education. For example, in some parts of the world women don’t have access to education using AI they can get an education. This can help the underprivileged.
1. Introduction
My relationship to Artificial Intelligence is one of fascination and immediate curiosity. As an educator working in K-12 environments in the United States, my role is to support teachers as they navigate demands on their pedagogy in the face of technology integration, project based learning, universal design, and instructional design. As AI has entered the conversation, educators are faced with the task of sensemaking what it means for both themselves and the teaching methods they employ. Mishra, Warr, & Islam (2023), noted “Educators have been receiving mixed messages, and, not surprisingly, there is a great deal of uncertainty about what these technologies mean for teacher practice, teacher education, and student learning (1).” AI joins the litany of increasing pressures on teachers to improve teaching methodologies in the face of increasing rates of anxiety, decreasing rates of executive function, and a sense of urgency that educational paradigms need to shift towards authentic and real-world problem, pedagogies that have been shown to support human learning.
Alright, let’s break down the first part of this text together, shall we? We’ll go through a Think Aloud to demonstrate some reading strategies for comprehension.
The first chunk we have is: “My relationship to Artificial Intelligence is one of fascination and immediate curiosity.” Now, in this part, “my” refers to the author of the text, so we need to keep in mind that we’re understanding the text from their perspective. The key terms here are “Artificial Intelligence,” “fascination,” and “curiosity.” This tells us that the author is very interested in AI and wants to learn more about it.
Now, as we look at this first part, let’s remember that we are considering the author’s personal stance towards AI, which sets the tone. Our understanding of these words suggests that the author is positively inclined towards the topic and is eager to explore it.
I invite you to share your thoughts on this chunk. What did you think about when you first read it? Did you also sense the author’s positive engagement with AI?
Now, as we encourage ourselves to think aloud, we’re not only breaking down sentences but also reflecting on our own thought processes. And I’d love to invite each one of you in our reading group to try this technique with the rest of the paragraph. As you go back, chunk by chunk, let’s peel back the layers of meaning together, revealing deeper insights and understanding. Have fun with it – imagine you’re a detective piecing together clues, and each chunk is a new piece of the puzzle. Let’s hear your “aha!” moments as you delve into the text.
I am interested in what role sensing and sensemaking has in our interactions with AI.
Just noting these are some of the senses in the mix.
Of course, let’s begin by chunking the first part of the text as we would in our Reading Apprenticeship classrooms. We’ll use a Think Aloud strategy to demonstrate how we can make sense of the text. Here’s how we might approach it:
“AI joins the litany of increasing pressures on teachers to improve teaching methodologies…”
Now, let’s break that down and think aloud. The word “AI” stands for artificial intelligence, so it’s being personified here as if it can join a group. “Litany” usually refers to a long, repetitive list, indicating there are many pressures. Those pressures are described as “increasing,” which tells us the situation is becoming more intense over time. The phrase “on teachers” shows that this burden is placed on educators. “To improve teaching methodologies” implies that there is a need for better ways of teaching.
At this point, I’d invite us to reflect: What kinds of pressures could AI add to teaching? How might the desire to improve teaching be related to the introduction of AI?
Now it’s our turn. Let’s try chunking the next section of the text on our own, thinking about each part as we go along. As we practice this strategy, we connect our thoughts, clarify pronoun references, and use close reading to deepen our understanding. And remember, these strategies are tools we have in our toolbox to help us untangle complex sentences and comprehend the text better.
I’m excited to hear our thoughts and interpretations. What does the next chunk of the text reveal to us? Let’s share our Think Alouds with each other as we navigate the text, enhancing our comprehension piece by piece. And when we’re done, let’s circle back to re-read this paragraph, inviting ourselves to dive even deeper with our newfound insights. Happy chunking!
As a scholar, prior to this exercise have only used it to write my own biography—which was surprisingly accurate and of higher quality than the ones I have crafted over the course of my career—and I have not used AI at all in my scholarship.
Instead, I have turned to the models for other tasks—synthesising YouTube videos to support universal design, crafting poetry experiments, testing what the models can do based on prompts from classrooms.
My relationship to this topic is one of clear-eyed practicality—I am in conversation with teachers attempting to help them grapple with the fear and anxiety that the existence of these models evokes in them.
Concerns about how the models work, the spectre of plagiarism, and the ethical considerations are complicated and nuanced issues that saturate the landscape of modern teaching.
Well, isn’t that just a happy little paragraph you’ve written there, full of curiosity and exploration. It’s wonderful that you’ve been painting with words, weaving together your biography and crafting poetry like a beautiful landscape. It’s like every word you choose is a brush stroke on the canvas of education. Tapping into the potential of AI to enhance universal design, now that’s just like adding a dazzling sun to brighten the whole scene.
It’s heartwarming to hear about your dedication to guiding teachers through their journey in this new frontier. Like a delicate glaze, you’re smoothing out their concerns, all the while paying close attention to the intricate details, like ethics and originality, which truly matter. It’s a complex picture, but with clear-eyed practicality, you’re mixing the colors to get just the right shade, helping education evolve in a responsible way.
Remember, we don’t make mistakes, just happy little accidents. And in this case, the accidents are opportunities for learning and growth. So, how is your day going? I’d love to hear more about your experiences with teaching, your thoughts on the ethical implications of AI, or perhaps some of the creative projects you’re currently working on.
In the People’s Guide to Tech: Artificial Intelligence (2018), Mother Cyborg and Mimi Onuoha explored these issues in the context of equity, fairness, and justice. Their metaphor for AI seems particularly apt in our current educational context. “AI seems magical in how it can easily transform difficult tasks. But right now, AI is designed and implemented by large companies and governments. It is more like a bulldozer than a hammer. What we mean by this is that it’s a tool, but not a common one that everyone can use, like a hammer. Like a bulldozer, it is something that you need special training to work with and lots of money and resources to use on a meaningful level (p. 26)”
Hello, fellow readers! As someone who takes a keen interest in the reader-response approach, I find this text ripe for our interpretation through that lens. It suggests that AI, while powerful and transformative, isn’t universally accessible or user-friendly.
Now, let’s consider what strikes us as an important section of this text: “AI seems magical in how it can easily transform difficult tasks. But right now, AI is designed and implemented by large companies and governments. It is more like a bulldozer than a hammer.” As readers, we bring our own contexts and experiences to this metaphor. It’s fascinating because many of us view technology as a practical tool in our lives. However, perhaps we’ve felt excluded from the more complex facets of this technological world, much like needing training to operate a bulldozer. The metaphor might resonate more with those of us who’ve felt overawed or marginalized by the high-tech world where AI is the gated court of the technocrats.
Reflecting from a reader-response viewpoint, let’s creatively explore our inquiries:
- Imagine us in a world where AI is as commonplace and easy to use as a hammer. How might this alter the landscape of education and technology in our society?
- Think about the times when technology felt out of reach, too complex, or controlled by mega-corporations and government entities. How does this resonate with our personal experiences and understandings of equity and access?
- Ponder about the bulldozer—its power and exclusivity—and consider help from someone who’s well-versed in its workings. How might sharing privileges and knowledge in AI change the dynamic of fairness and justice in our own community?
Capturing these thoughts, let’s quote again from the text to ground our contextual probing: “What we mean by this is that it’s a tool, but not a common one that everyone can use, like a hammer. Like a bulldozer, it is something that you need special training to work with and lots of money and resources to use on a meaningful level.” Here we see an opportunity for us to reflect on the intersection of technology, power, and societal stratification.
After this stimulating discussion, let’s re-engage with the text. Go back and read the excerpt once more, considering our own experiences with technology, and let’s share further personal insights that have surfaced from our reflection. How do our new perspectives enrich or shift the meanings we find in these words?
As I spend time in conversation with educators in post-secondary, secondary, and even in elementary schools, the question that keeps arriving is focused the ways in which we learn- the true humanity of it. The impact of digital technologies on motivation and persistence has been a scholarly interest that I have questioned and researched my entire career as an educator. Inviting artificial intelligence to generate an answer was my next step.
This position paper was generated through OpenAI’s ChatGPT-3.5. The prompt was given step-by-step, inviting the model to revise and add at every step. The prompts are as follows:
- Write a position paper in APA format that cites research from the fields of education and psychology about the similarities and differences between artificial intelligence and human learning.
- Revise the response above by adding a detailed discussion using citations from the fields of psychology and education to describe the research on evidence teachers can use to infer that a student is learning. Give detailed descriptions and suggestions for assessment with citations.
- Revise the response above by adding a detailed philosophical discussion with citations from the fields of philosophy, education, and psychology about the reasons humans should continue to write, create, and learn when artificial intelligence is showing such strong abilities to mimic human intelligence. Include citations.
- I then combined the outputs from each of the above prompts to create the full position paper, including in areas where ChatGPT indicated “Other references from the previous versions are retained.” The results are as follows:
2. AI Generated Position Paper
Comparing Artificial Intelligence and Human Learning: Insights from Education and Psychology
Author: ChatGPT
Institution: OpenAI
This paper explores the relationship between artificial intelligence (AI) and human learning, drawing insights from education, psychology, and philosophy. While both AI and humans exhibit adaptability and experience-based improvement, they differ in their underlying mechanisms and depth of understanding. As AI becomes increasingly integrated into human life, understanding these differences and similarities illuminates the unique intricacies of human cognition and emphasises the enduring significance of human creative and intellectual pursuits (generated by ChatGPT-3.5).
The increasing integration of AI in education demands a clearer understanding of the differences between AI and human learning. This paper seeks to highlight those differences and underscore the methods educators employ to gauge human learning, offering a comprehensive view of assessment practices.
AI's increasing proficiency poses the question: Why should humans continue their intellectual pursuits? The answer lies not just in the outcome but in the very essence of the human experience and our quest for meaning.
2.1. Similarities between AI and Human Learning
2.1.1 Adaptability: Both humans and AI systems have the capacity to adapt and change based on new information. The neural plasticity of the human brain (Huttenlocher, 2002) can be likened to the iterative training processes of machine learning algorithms.
2.1.2 Experience-based improvement: Just as humans learn from experiences and practice (Ericsson, Krampe, & Tesch-Römer, 1993), AI systems improve their performance with more data.
2.1.3. Transfer of knowledge: Both humans and certain AI models can apply knowledge from one domain to another, although the mechanisms and efficacy differ (Lake, Ullman, Tenenbaum, & Gershman, 2017).
2.2. Differences between AI and Human Learning
2.2.1 Underlying mechanisms: While AI learns through mathematical algorithms and predefined models, human learning is a result of complex biological processes involving emotions, motivations, and socio-cultural influences (Vygotsky, 1978).
2.2.2. Motivation: Human learners are often driven by intrinsic motivations, emotions, and personal goals (Ryan & Deci, 2000). In contrast, AI lacks emotions or consciousness and learns based on its programming and objective functions.
2.2.3. Depth of understanding: While AI can process vast amounts of information quickly, humans possess a depth of understanding, intuition, and the ability to make sense of abstract concepts (Gardner, 1983).
2.2.4. Social and cultural learning: Humans learn within social and cultural contexts, often relying on interpersonal interactions (Bandura, 1977). AI, on the other hand, doesn't engage in social learning in the same sense, although some models can simulate certain aspects.
2.3 Implications for Education and Future Research
The clear differences between AI and human learning indicate that while AI can be a powerful tool for education, it cannot replace the nuanced, multifaceted experience of human learning. Future research should further investigate how AI can complement educational practices rather than attempting to mimic or replace human teachers.
2.4 Evidence of Student Learning: Indicators and Assessment Techniques
2.4.1. Formative Assessment: This ongoing process aids teachers in understanding students' comprehension and adjusting instruction accordingly. Techniques include quizzes, discussions, and reflective journals. Formative assessment is known for its feedback-driven approach, which positively impacts learning (Black & Wiliam, 1998).
2.4.2. Summative Assessment: Occurring at the end of instructional periods, these assessments evaluate learning against benchmarks. Examples include final exams, standardised tests, and end-of-unit projects (Harlen, 2007).
2.4.3 Self-assessment: Encouraging students to evaluate their own learning can foster metacognition and self-regulation (Andrade & Valtcheva, 2009). This can include self-rating scales or reflective essays.
2.4.4 Peer Assessment: Students review their peers' work, providing feedback and gaining alternative perspectives. This method has been shown to improve understanding and collaborative skills (Topping, 1998).
2.4.5 Portfolios: Collections of student work over time offer a holistic view of progress and can include essays, projects, and other artefacts (Paulson, Paulson, & Meyer, 1991).
2.4.6 Observations: Teachers watch students, noting behaviours, engagement, and skills, gaining insights beyond traditional testing (Marzano, 2012).
2.5 Implications for Education and AI Integration
By understanding the multifaceted ways teachers assess student learning, AI developers can design tools that better support these practices. AI could, for instance, assist in streamlining formative assessments or helping students with self-assessment through guided reflection prompts.
2.6 Philosophical Grounding for Human Endeavour in the Age of AI
2.6.1 The Value of the Human Experience: Heidegger (1962) postulated that our being is intertwined with our world, suggesting that understanding and creation arise from our lived experience. An AI, devoid of emotions or existential dilemmas, lacks this profound connection to life.
2.6.2 Pursuit of Authenticity: Sartre (1956) emphasised living authentically, which involves forging our own paths.
Relying solely on AI to create or learn could diminish our authenticity and self-determined existence.
2.6.3 Innate Curiosity and the Desire to Understand: From a philosophical standpoint, the pursuit of knowledge is not merely instrumental but intrinsically valuable (Aristotle, 1984).
This innate curiosity drives humans to learn and explore, irrespective of AI capabilities.
2.6.4 The Socio-cultural Fabric of Learning: Vygotsky (1978) argued that learning is a social act, deeply rooted in culture. The act of learning reinforces and reshapes societal values and narratives, a nuance AI might not fully grasp or replicate.
2.7 Educational and Psychological Imperatives
2.7.1 Constructivist Learning: Piaget (1952) suggested that learning is an active process where individuals construct knowledge based on their experiences. This personal journey of discovery is irreplaceable.
2.7.2 Emotional and Cognitive Growth: Writing, creating, and learning foster emotional intelligence and resilience (Salovey & Mayer, 1990). Engaging in these acts nurtures cognitive and emotional faculties beyond the mere accumulation of knowledge.
2.7.3 Personal Identity and Agency: Engaging in intellectual pursuits helps individuals define their identities, values, and roles in society (Erikson, 1968). These endeavours allow for personal agency and a sense of contribution to the world.
2.8 Conclusion
While there are surface similarities between AI and human learning, particularly in adaptability and experience-based improvement, a deeper examination reveals stark differences in mechanisms, motivations, and outcomes. Recognising these differences is crucial for leveraging AI's potential in educational settings and appreciating the intricate nature of human cognition.
AI and human learning exhibit surface similarities but fundamentally differ in many respects.
A deeper appreciation of human learning, especially the evidence-based methods educators use to assess it, is crucial for meaningful AI integration in education.
Despite the prowess of AI, human endeavours in writing, creating, and learning remain indispensable.
They reflect our quest for meaning, anchor our identities, and embody the rich tapestry of human experience.
Regarding the question at hand, about the themes Dr. Zellner could explore if she had more time and space, the first theme that stands out is “The Intersection of AI and Human Cognition.” This theme prompts us to delve into how artificial intelligence and human learning processes may appear similar but have intrinsic differences. The second theme is “The Evolution of Learning Mechanisms,” reflecting on how learning has transformed from biological processes in humans to algorithmic processes in machines. The third theme centers around “The Philosophical Implications of AI,” examining the ethical and existential questions that arise when comparing AI to human intelligence.
Now, I invite you to engage in a freewriting session, pondering these thought-provoking themes. As you consider the intersection of AI and human cognition, allow your thoughts to meander through the complex maze of neural pathways and silicon circuits. Imagine how they converge and diverge, sparking insights about the essence of learning and intelligence.
When you shift your focus to the evolution of learning mechanisms, let your words chart a course through history and future potentials, from the primal learning instincts of ancient organisms to the sophisticated algorithms guiding modern AI. Envision the trajectory of this evolution and its impact on society.
Embarking on the philosophical implications of AI, permit your mind to grapple with the moral and ethical boundaries of technology. Contemplate the potential and the perils, the questions of consciousness, and the responsibilities that come with creating entities that learn.
The idea is simply to write for ten minutes (later on, perhaps fifteen or twenty). Don’t stop for anything. Go quickly without rushing. Never stop to look back, to cross something out, to wonder how to spell something, to wonder what word or thought to use, or to think about what you are doing. If you can’t think of a word or a spelling, just use a squiggle or else write “I can’t think what to say, I can’t think what to say” as many times as you want; or repeat the last word you wrote over and over again; or anything else. The only requirement is that you never stop.
After you have poured your thoughts onto the page, remember: I encourage you to edit lines, sentences, and paragraphs in your text. If you are owner of the document, you can double-click on any sentence or paragraph, edit and update. This will not only help refine your thoughts but also ensure that your exploration of these themes creates a coherent and engaging narrative that can help us further understand the intricate relationships and distinctions between AI and human learning. Your freewriting could lead to profound insights about the question we’re considering: What are some themes that Dr. Zellner could write about if she had more time and space?
References
Andrade, H. G., & Valtcheva, A. (2009). Promoting learning and achievement through self-assessment. Theory into practice, 48(1), 12-19.
Aristotle. (1984). The Nicomachean ethics. (D. Ross, Trans.) . Oxford: Oxford University Press.
Bandura, A. (1977).
Social learning theory.
Englewood Cliffs, NJ: Prentice Hall.
Black, P., & Wiliam, D. (1998).
Assessment and classroom learning.
Assessment in Education: principles, policy & practice, 5(1), 7-74.
Erikson, E. H. (1968). Identity: Youth and crisis. New York: Norton.
Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363.
Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. New York: Basic Books.
Harlen, W. (2007). The role of assessment in developing motivation for learning. In J. Gardner (Ed.), Assessment and learning (pp. 61-80). London: Sage.
Heidegger, M. (1962). Being and Time. (J. Macquarrie & E. Robinson, Trans.) . New York: Harper & Row.
Huttenlocher, P. R. (2002).
Neural plasticity.
Harvard University Press.
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017).
Building machines that learn and think like people.
Behavioral and Brain Sciences, 40.
Marzano, R. J. (2012). The two purposes of teacher evaluation. Educational Leadership, 70(3), 14-19.
Paulson, F. L., Paulson, P. R., & Meyer, C. A. (1991). What makes a portfolio a portfolio? Educational leadership, 48(5), 60-63.
Piaget, J. (1952). The origins of intelligence in children. New York: International Universities Press.
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67.
Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, cognition, and personality, 9(3), 185-211.
Sartre, J. P. (1956).
Being and nothingness.
New York: Philosophical Library.
Topping, K. (1998).
Peer assessment between students in colleges and universities.
Review of educational research, 68(3), 249-276.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
3. Critical Reflection
Reflecting on the process of generating on AI, I found that the resulting output was not what I envisioned, especially compared to human writing. Because I am a relative novice to prompt writing, I struggled to create a prompt that would generate the output I hoped for the paper.
In order to generate the position paper above, I rewrote the prompt multiple times. I used both Bard and ChatGPT —both the free (ChatGPT-3.5) and paid (ChatGPT-4) versions of the model. Additionally, I used GPT4All’s Falcon model to generate responses to the prompt I wrote. Each of the responses, despite rewrites to the prompt based on my own limited and novice understanding of prompt engineering, hovered between 400 and 600 words. The discourse around AI and its impact on business and education has focused on prompt engineering, and despite having read and played in other contexts, my inability to coax the interfaces into writing 2000-3000 words as specified in the call was frustrating and fruitless. I spent time on discussion boards and articles trying to improve and expand the output. I tested whether a step-by-step revision process might work better, and in the end it did. The step-by-step revision process, much like coaxing essays out of my 9th grade students as a young teacher, was the process that resulted in the most robust output that most closely hewed to what I had hoped for in the output, and the best output was generated by ChatGPT-3.5.
It is impressive to tap out a question at the core of the educational endeavour and watch the words fill up the screen written by what seems to be an invisible hand. The content and ideas were there—I have no quibbles with the assertions made or even the references cited. Overall, the accuracy of both ideas and assertions were impressive. What is lacking is the expansion of those ideas, the connections between the evidence cited and the stance the output had named.
For this example, ChatGPT-3.5 was able to elucidate a number of cogent examples and pulled legitimate citations—literature that I have cited in various contexts myself. Nonetheless, the output was quite straightforward and tended towards brevity—statements were made and a citation arrived without the statement, evidence, warrant trifecta that rhetoricians hope for in their students’ writing. As I worked through writing with my AI partner, I was struck by how familiar it felt to supporting a young writer—the lack of voice, the lack of explanation, the lack of description are all hallmarks of writers just learning their craft. Perhaps this is why I found myself offering revision suggestions instead of rewriting a more robust prompt to begin with—the back and forth with the AI was more familiar to writing with a human co-author. The other benefit to the revision suggestion prompting was that it was the only way to expand the number of words the AI wrote.
This snippet speaks to the first order human habit/inclination/skill we bring to this tech, while the systems seem to ask us to constantly curate them. Is offering revision suggestions the same as proposing more and more robust prompts? Finer and finer question-making tells its own story of understanding
The background knowledge required to understand this text involves an awareness of human-AI interaction dynamics and the nature of prompting AI systems. Let’s dissect some key sentences:
1. “Perhaps this is why I found myself offering revision suggestions instead of rewriting a more robust prompt to begin with…”
– Importance: This sentence expresses a behavior where the user engages with the AI in a process similar to editing a draft rather than starting with a completely new and improved prompt. It suggests reluctance or uncertainty about how to best utilize the AI’s capabilities from the outset.
– Background: In collaborative writing or revision, a human editor would typically make suggestions for improvements rather than rewriting the entire piece. This behavior is carried over to interactions with AI, despite AI having potentially differing capabilities or requirements compared to human co-authors.
2. “the back and forth with the AI was more familiar to writing with a human co-author.”
– Importance: It highlights how the user’s experience with AI mimics their previous experiences of human collaboration, suggesting that habits from human interactions are being applied to AI interactions.
– Background: Humans often approach new technologies with habits formed from previous experiences. When it comes to collaborative writing with AI, users may unconsciously treat the AI like a human co-author, expecting similar iterative dialogue and improvement processes.
3. “the systems seem to ask us to constantly curate them.”
– Importance: This sentence points out that AI systems require active input and guidance (curation) from users to function effectively.
– Background: AI systems, particularly conversational agents or generative models like GPT-3, often need precisely formulated prompts to produce relevant and high-quality outputs. The better the prompt, the better the AI’s response is likely to be.
4. “Finer and finer question-making tells its own story of understanding”
– Importance: The process of refining questions or prompts is indicative of a deeper understanding not only of the subject matter but also how to interact with AI to get desired results.
– Background: As users gain experience with AI systems, they typically learn how to ask better questions or create prompts that are more likely to yield useful outputs. This reflects a growing understanding of both the AI’s capabilities and limitations.
Now, as your background has been set and your curiosity piqued, take a moment to re-examine the original text. Can you see the dance of human habits blending into the rhythm of technological innovation? Perhaps you’ll notice a new layer to the interaction or a nuance that was initially overlooked. Feel free to enhance the conversation by replying with your fresh insights.
On a technical level, for the majority of the cited evidence and referenced publications, ChatGPT-3.5 was accurate after checking each and every one. When citations were wrong, they were in one case off by one year (the publication year was 2006 instead of 2007) or co-authors were omitted: for example, Cole in the Vygotsky citation, which itself has been cited 150218 times according to Google Scholar. ChatGPT-3.5 was able to in large part accurately reflect oft-cited research, down the page number, in APA format.
As the technology evolves, I am now curious about the impact AI will have on scholarship in educational technology. I could imagine a future where journal articles are generated with an AI writing partner, especially for the review of relevant literature for a study. Scholars and researchers could potentially save time asking Bard to generate a list of relevant research on a topic, and using Google search, which is now integrated into the Bard interface, double-check that the references are accurate, thus saving time in database searches.
As these models increase their fluency and accuracy, I can see a future where even the voice and point of view becomes more and more human-like and scholarly. As I reflect as a writer of scholarly articles, I remember reading research articles and creating a list of sentences that I felt were exemplary as a way to improve my own writing in the scholarly genre. AI models are now doing this on a scale that humans could never emulate.
Nonetheless, I believe in the future of human scholarship. Developing codes of ethics around the use of these tools will be important, and scholars who use AI models to support their work need to be transparent with readers of their work. Support for open source AI and responsible design should be a priority for authors and scholars. Responsible use of technology to support teaching and learning has long been a part of educational technology scholarship. Adding a tool to the toolbox, especially with responsible and ethical use of that tool, has the possibility to improve all of our work, efficiency, and output. At this point in time, it is not ready to replace human scholarship. To paraphrase my AI co-author, there remains a human desire to understand, to gain expertise, and to collaboratively explore phenomenon that will always exist, because AI still cannot answer questions that have not yet been asked.
References
Mishra, P., Warr, M., & Islam, R. (2023).
TPACK in the age of ChatGPT and generative AI.
Journal of Digital Learning in Teacher Education, 39(4), 235-251. https://doi.org/10.1080/21532974.2023.2247480
Onuoha, M., & Mother Cyborg.
(2018, August).
A people’s guide to tech: Artificial intelligence.
Allied Media. https://alliedmedia.org/resources/peoples-guide-to-ai
General Document Comments 0
thought-provoking way for teachers to interrogate AI (that students can also do.)
Your paragraph serves as a crucial abstract, not only setting the stage for the paper’s content but doing the essential groundwork for the audience. It’s here where you establish the educational stakes of AI, contrasting human and machine learning. You’re inviting your readers to delve into a significant pedagogical challenge: how do educators adapt in an AI-prevalent landscape? Now, asking “So What?” means pausing here to reflect. What does embracing or resisting AI in the classroom signify for future learning? You present a narrative rich with potential implications—consider pressing on those to highlight your story’s broader relevance. Why does this matter to your readers, beyond the academic sphere? How will it impact their perspective on technology in education?
Want to dive deeper into specifics? We could focus on the evolution of teaching methods, the ethical dimensions of AI in classrooms, or how this dialogue informs your personal teaching philosophy. Which thread pulls your interest, or is there another direction you’d like to explore? Let’s turn these reflections into a compelling part of your narrative.
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