The Refraction Principle: How AI Bends (But Doesn't Break) Human Purpose
Human intention serves as the foundational force underlying all meaningful literacy interactions.
This post builds directly on Monday’s discussion about alterity and grounding in human-authored versus AI-generated texts. The framework below comes from an unpublished manuscript Terry Underwood, PhD and I am developing. In this short excerpt, we’re attempting to address what we see as a critical gap in current scholarship: the lack of a robust theory of how human intentionality operates in AI-mediated learning.
Monday I argued that AI and literature offer fundamentally different configurations of otherness. Literature grounds me in traceable relationship with specific human consciousness: Kawabata’s choices, his ethical universe, his purposeful construction. AI responses emerge from triangulation of my intentions, computational processes, and compressed training data. But recognizing this difference raises a practical question: How does human intention actually operate and transform through AI interaction? How do learners maintain intentional control while their purposes undergo genuine change?
We offer a tripartite model: Seminal Intention (the original human impulse), AI as Refractive Medium (bending and focusing without generating new intentions), and Hybrid Intention (the evolved form that remains fully human-owned). We also identify crucial metacognitive stances: centrifugal (exploratory, divergent) and centripetal (focused, convergent) that learners can strategically deploy.
The complete framework appears below. We offer it humbly, recognizing both its limitations and its potential value.
Human intention serves as the foundational force underlying all meaningful literacy interactions, whether in traditional text-based encounters, self-contained AI conversations, or hybrid human-AI collaborative exchanges. In every literacy event, intention functions as both catalyst and compass—initiating the cognitive work and directing its trajectory toward specific purposes. Even when learners engage with AI systems that appear to generate novel responses, intentionality remains anchored in human consciousness, manifesting through the questions asked, the problems posed, and the goals pursued.
This intentional primacy becomes evident in the phenomenon of reciprocal influences within hybrid chats, where human and AI contributions create feedback loops that adjust and refine initial purposes. While the AI processes information and generates responses that may surprise or redirect the human user, intentional architecture—the design behind the interaction—remains irreducibly human. The AI serves as a sophisticated refractive medium that can parse, analyze, and reorganize human intentions, but cannot originate them. This dynamic creates a unique form of reflexive intentionality where human purposes undergo revision without losing their essential human grounding in a physical, social, cultural, and institutional context.
The Tripartite Model of Intention: Seminal, Refractive, and Hybrid Intention
Part I: Seminal Intention
Seminal intention represents the original, undeveloped, human cognitive impulse that initiates a learning behavior of some sort. It exists as a binary state—either present or absent—and carries within it the essential DNA of a purpose, though often in embryonic form. Unlike mature intentions, seminal intentions are characterized by their potentiality rather than their specificity. They contain the energy and direction for learning but require development to achieve their full cognitive potential.
Examples of seminal intentions include:
“I want to understand why this historical event occurred”
“There’s something about this mathematical concept I’m not grasping”
“I need to improve my argument about climate policy”
“This literary passage seems to contain hidden meanings”
Part II: AI as Refractive Medium for Human Intention
AI functions as a refractive tool rather than a creative partner, serving to bend, focus, and amplify human intentions without generating new intentional content. Like light passing through a prism, seminal intentions encounter the AI’s analytical capabilities and emerge transformed in structure and clarity while maintaining their essential human origin. The AI’s role involves parsing linguistic nuances, identifying implicit assumptions, revealing logical gaps, and engineering optimal refinements—all under the continuous control and direction of the human user.
Specific examples of AI refraction include:
Assumption surfacing: An AI might respond to “I want to write about democracy” by asking, “Which conception of democracy—representative, deliberative, or direct?” thereby refracting the seminal intention through analytical precision.
Perspective multiplication: When a user states, “I need help understanding this poem,” the AI might respond by offering historical, biographical, formal, and thematic lenses, refracting the single intention into multiple investigative pathways.
Logical scaffolding: For the seminal intention “I want to argue against standardized testing,” AI refraction might involve unpacking hidden premises, identifying counterarguments, and suggesting evidential requirements.
Conceptual disambiguation: The intention “I’m confused about quantum mechanics” becomes refracted through clarifying questions about specific aspects—wave-particle duality, measurement problems, or mathematical formalism.
Part III: Hybrid Intention
Hybrid intention represents the transformed result of AI refraction—still fully owned by the human but no longer identical to its seminal form. It retains the essential purposefulness of the original while incorporating the structural clarity and analytical depth achieved through AI interaction. The hybrid intention is neither purely human (as it has been irreducibly changed by the AI encounter) nor AI-generated (as it maintains complete human ownership and control).
Examples of hybrid intentions emerging from AI refraction:
Original: “I want to understand the Civil War”
Hybrid: “I want to explore how economic, constitutional, and social factors intersected to make the Civil War inevitable, particularly examining how different historical methodologies interpret this causation”
Original: “I need to improve my writing”
Hybrid: “I want to develop my ability to construct compelling arguments by learning to anticipate reader objections, provide stronger evidence integration, and create more engaging transitions between complex ideas”
Model Synthesis
The complete tripartite model reveals a dynamic process where human intentionality maintains primacy throughout transformation. Seminal intentions provide the motivational energy and directional purpose. AI refraction serves as the analytical medium that reveals complexities, surfaces assumptions, and multiplies perspectives. Hybrid intentions emerge as enhanced versions of the original human purposes—more sophisticated, better articulated, and strategically developed, yet retaining complete human ownership and control.
This process is neither anthropomorphic (the AI doesn’t “think” or “create”) nor reductive (genuine transformation occurs). Instead, it represents a new form of reflexive intentionality where human purposes undergo development through systematic analytical enhancement.
Metacognitive Inversion and Metalinguistic Forms
The Inversion Principle
Metacognitive inversion describes the learner’s deliberate choice to adopt either a centripetal or centrifugal stance toward AI interaction, based on the nature of their hybrid intention and learning goals. This inversion is fundamentally metalinguistic—it involves conscious decisions about how to structure the language of inquiry itself. The learner must decide whether their hybrid intention calls for a binary stance, convergent focusing (centripetal) or divergent exploration (centrifugal), or a mixed stance. Once this instrumental intention is clarified, learners can start to design a strategy for a chat. Of course, the instrumental strategy is subject to ongoing revisions the chat unfolds.
Centrifugal Metalinguistic Stance
The centrifugal approach moves outward from a central concept, seeking to explore multiple perspectives, expand possibilities, and discover unexpected connections. This stance is appropriate when hybrid intentions involve creative exploration, comprehensive understanding, or systematic investigation of complex phenomena.
Classic centrifugal examples:
Exploratory analysis: “Help me understand all the different ways scholars have interpreted the fall of the Roman Empire, including economic, political, social, environmental, and cultural explanations. What are the strengths and limitations of each approach?”
Creative brainstorming: “I’m writing a novel about time travel. Generate multiple scenarios for how time travel might work, what problems it might create, and how different cultures might respond to its discovery.”
Comprehensive research: “I need to understand the complete ecosystem of factors that influence student motivation in mathematics. What psychological, social, educational, technological, and cultural elements should I consider?”
Centripetal Metalinguistic Stance
The centripetal approach moves inward toward a focal point, seeking to consolidate understanding, achieve precision, or solve specific problems. This stance suits hybrid intentions involving targeted skill development, specific problem-solving, or detailed analysis of particular elements.
Classic centripetal examples:
Focused problem-solving: “I’m struggling with this specific calculus problem involving related rates. Walk me through the solution step by step, explaining exactly why each mathematical operation is necessary.”
Precision writing: “Help me revise this paragraph to eliminate wordiness while maintaining my central argument about renewable energy policy. Focus specifically on sentence structure and word choice.”
Targeted skill development: “I need to master the use of semicolons in academic writing. Give me specific rules, common errors to avoid, and practice exercises focused solely on semicolon usage.
Mid-Range Examples with Variable Prompting Approaches
These examples demonstrate how learners might shift between centripetal and centrifugal approaches within a single interaction, or blend both approaches strategically:
Historical analysis with shifting focus:
Centrifugal opening: “What are all the major factors historians consider when analyzing the causes of World War I?”
Centripetal refinement: “Now help me focus specifically on how the alliance system functioned as a cause. I want to understand the precise mechanisms by which these alliances escalated the conflict.”
Blended synthesis: “How do these alliance mechanisms interact with the other causal factors you mentioned, and which scholarly interpretations best integrate these multiple causations?”
Literary interpretation with graduated specificity:
Centrifugal exploration: “What are the different ways critics have interpreted the symbolism in ‘The Great Gatsby’?”
Mixed approach: “I’m particularly interested in the green light symbol. Help me understand both its multiple possible meanings and how to construct a focused argument about its most significant function in the novel.”
Centripetal focus: “Now help me craft a thesis statement that makes a specific, arguable claim about how the green light symbol reveals Gatsby’s relationship to the American Dream.”
Scientific inquiry with methodological variation:
Centrifugal investigation: “What are all the current theories about consciousness in neuroscience, and what evidence supports each one?”
Strategic narrowing: “I want to focus on Integrated Information Theory. Help me understand both its core principles and its main criticisms.”
Centripetal application: “Now help me design a specific research question that could test one aspect of IIT using current neuroimaging technology.”
This metacognitive framework empowers learners to make strategic choices about how to structure their AI interactions, matching their intentional goals with appropriate metalinguistic approaches while maintaining full control over the learning process.
Terry Underwood and Nick Potkalitsky
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Michael Woudenberg’s Polymathic Being: Polymathic wisdom brought to you every Sunday morning with your first cup of coffee
Rob Nelson’s AI Log: Incredibly deep and insightful essay about AI’s impact on higher ed, society, and culture.
Michael Spencer’s AI Supremacy: The most comprehensive and current analysis of AI news and trends, featuring numerous intriguing guest posts
Daniel Bashir’s The Gradient Podcast: The top interviews with leading AI experts, researchers, developers, and linguists.
Daniel Nest’s Why Try AI?: The most amazing updates on AI tools and techniques
Jason Gulya’s The AI Edventure: An important exploration of cutting-edge innovations in AI-responsive curriculum and pedagogy.
Stephen Fitzpatrick’s Teaching in the Age of AI: Essential reflections from a veteran high school educator on the challenges and opportunities of generative AI in the classroom!!!
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Nick and Terry, exceptional framework you’ve proposed. While I’m cognizant this is an excerpt summery, I wish to share some additional thoughts to expand (if not done already) the model further which draws upon much of Nicks narrative in previous blogs: (1) after the hybrid approach, there could be representation of critical thinking that stems from the seminal mindset that challenges and/or accepts the output of the AI. (2) stemming from that includes checking sources for accuracy, delving deeper into a topic, and exploring new avenues with intentionality and (3) furthering each of the example prompts with the types of models used, such as thinking models versus a plain out of the box interactions. These recommendations combat and make agnostic the consumer offerings of large tech companies, complete with post training personality and guardrail additions, allowing freedom of choice with understanding to students in which model they use. Thank you for your deep scholarly work on this topic!
I really appreciate how this approach puts language to interactions. I often find myself prompting students, “Tell me about your use of AI on this assignment.” Instead of surface-level responses (or outright denial), this vocabulary and these visuals could help us engage in more critical, specific conversations—not only how they interacted with AI but also why they decided to. It seems to open opportunities to explore nuance, rather than defaulting to blanket yes/no statements about how students should and shouldn’t use AI.