Why This Framework Now?
Slide Idea
The slide provides three justifications for teaching filmmaking fundamentals through an AI-mediated framework at this particular moment. AI tools force explicit specification of decisions that experts typically make intuitively, making tacit reasoning visible to novices. The framework teaches filmmaking fundamentals through a contemporary lens while remaining grounded in established film and design pedagogy. Students learn transferable decision-making skills rather than platform-specific tricks, emphasizing adaptive reasoning over tool-specific workflows.
Key Concepts & Definitions
Tacit Knowledge
Tacit knowledge encompasses understanding, skills, and judgments that practitioners possess but cannot easily articulate or transfer through verbal instruction alone. It includes embodied competencies developed through extensive practice, pattern recognition abilities that operate below conscious awareness, aesthetic sensibilities that guide choices without explicit reasoning, and procedural knowledge that manifests as "knowing how" rather than "knowing that." In filmmaking, tacit knowledge includes intuitions about when a shot composition "feels right," how to adjust blocking when something seems "off," or which lens choice will produce desired emotional effects. This knowledge resides in experts' minds and bodies but remains largely invisible to learners observing only final products or completed performances.
Source: Polanyi, M. (1966). The Tacit Dimension. University of Chicago Press.
Explicit Specification
Explicit specification is the process of articulating knowledge, requirements, or decisions in documented, communicable form that can be interpreted and acted upon by others—including collaborators, students, or computational systems. Specification transforms internal understanding into external representations: written descriptions, visual diagrams, parametric definitions, or executable code. When AI systems require prompts, parameters, and constraints to function, they force practitioners to make explicit what would otherwise remain tacit. This externalization serves dual pedagogical purposes: it makes expert thinking visible to learners, and it reveals to practitioners the extent and limits of their own understanding. What seems clear internally often proves ambiguous when articulated explicitly.
Source: Norman, D. A. (1991). "Cognitive Artifacts." In J. M. Carroll (Ed.), Designing Interaction: Psychology at the Human-Computer Interface (pp. 17-38). Cambridge University Press.
Transferable Skills
Transferable skills are competencies applicable across multiple contexts, domains, and technologies rather than being specific to particular tools or platforms. These include analytical abilities (problem diagnosis, requirement analysis, constraint identification), decision-making capacities (evaluating alternatives, making trade-offs, justifying choices), communication skills (articulating intent, documenting rationale, collaborative coordination), and adaptive reasoning (modifying approaches when situations change, applying principles in novel contexts). Transferable skills retain value as technologies evolve because they address fundamental challenges—translating intent to execution, working within constraints, coordinating collaborative effort—that persist regardless of which specific tools are used.
Source: National Research Council. (2012). Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century. National Academies Press.
Adaptive Reasoning
Adaptive reasoning is the capacity to think logically about relationships among concepts, situations, and solutions, particularly when encountering novel problems or changing conditions. It encompasses the ability to justify decisions with sound rationale, evaluate the reasonableness of proposed solutions, modify approaches when initial strategies prove inadequate, and apply fundamental principles in unfamiliar contexts. Unlike procedural fluency (efficient execution of established techniques), adaptive reasoning enables practitioners to determine when standard procedures are appropriate versus when situations require innovation. In creative and technical contexts, adaptive reasoning allows practitioners to maintain objectives while adjusting methods to accommodate constraints, opportunities, or unexpected complications.
Source: Kilpatrick, J., Swafford, J., & Findell, B. (Eds.). (2001). Adding It Up: Helping Children Learn Mathematics. National Academy Press.
Contemporary Lens / Established Pedagogy
This paired concept refers to teaching foundational principles through current practices and tools while grounding instruction in research-validated pedagogical frameworks. A contemporary lens means using AI systems, digital workflows, and contemporary production contexts as the material reality students engage with, acknowledging that these tools shape current professional practice. Established pedagogy means basing instructional design on learning sciences research about how expertise develops, how knowledge transfers across contexts, how collaboration functions, and how reflective practice supports improvement. The pairing prevents both outdated instruction disconnected from current realities and uncritical tool adoption lacking theoretical foundation. Students learn timeless principles through time-bound tools.
Source: Rabiger, M., & Hurbis-Cherrier, M. (2020). Directing: Film Techniques and Aesthetics (6th ed.). Routledge.
Platform-Specific Workflows vs. Fundamental Principles
Platform-specific workflows are sequences of operations optimized for particular software, services, or systems—button sequences, menu navigation patterns, feature-specific techniques that become obsolete when platforms update or alternatives emerge. Fundamental principles are enduring concepts about how creative work functions: how visual composition directs attention, how editing rhythm affects pacing, how sound design creates spatial impression, how narrative structure builds engagement. Principles remain applicable across changing technologies because they address human perception, cognition, and communication rather than particular implementation methods. Teaching workflows alone produces rapid skill obsolescence; teaching principles enables practitioners to learn new tools efficiently by applying underlying understanding.
Source: Schön, D. A. (1983). The Reflective Practitioner: How Professionals Think in Action. Basic Books.
Why This Matters for Students' Work
Understanding why this particular pedagogical approach is valuable at this historical moment fundamentally affects how students engage with contemporary tools and develop long-term professional capabilities. Many students approach AI tools either with uncritical enthusiasm ("this will do everything for me") or dismissive resistance ("real artists don't use AI"), missing the pedagogical opportunity these tools create. Recognizing that AI systems' requirement for explicit specification makes expert thinking visible transforms tool use from passive consumption to active learning about decision-making processes.
The visibility benefit addresses a persistent challenge in creative education. Students traditionally observe expert final products—completed films, polished designs, published writing—without access to the intermediate thinking that produced those outcomes. When experts work intuitively, their tacit reasoning remains hidden. AI tools that require explicit prompts, parameters, and specifications force practitioners to articulate reasoning that would otherwise stay internal. Students working with AI necessarily engage in specification work that makes decision-making explicit and reviewable. This externalization creates opportunities for reflection, critique, and improvement that pure intuitive practice does not provide.
For skill development, the transferability emphasis has direct implications for professional longevity. The contemporary technology landscape changes continuously—platforms update, new tools emerge, popular services disappear, workflows become obsolete. Students who learn platform-specific tricks master techniques with limited shelf lives, requiring constant retraining as tools evolve. Students who learn transferable decision-making skills—how to diagnose problems, articulate requirements, evaluate solutions, work within constraints—can apply those capabilities across changing technology contexts. The ability to quickly learn new tools by applying fundamental principles becomes more valuable than mastery of any particular current platform.
The adaptive reasoning dimension becomes particularly important as students encounter increasingly complex production contexts. Real professional work rarely matches textbook scenarios—budgets change mid-project, collaborators have different capabilities than expected, technical approaches prove infeasible, requirements shift based on stakeholder feedback. Procedural knowledge ("follow these steps") breaks down when situations deviate from established patterns. Adaptive reasoning enables students to maintain objectives while modifying methods, to recognize when standard approaches need adjustment, and to generate viable alternatives when preferred options become unavailable. This flexibility distinguishes effective practitioners from those who cannot function outside familiar contexts.
The grounding in established pedagogy prevents students from experiencing AI-mediated instruction as disconnected from traditional filmmaking or design principles. Some students worry that learning through contemporary tools means missing fundamental knowledge that earlier generations acquired. Recognizing that the framework teaches classic principles—visual composition, narrative structure, editing rhythm, sound design, collaborative workflow—through contemporary tools rather than replacing those principles with tool-specific knowledge addresses this concern. Students develop competencies recognized across the field while gaining familiarity with current production realities.
For collaborative work, understanding the framework's emphasis on explicit specification improves coordination effectiveness. When students must articulate their creative intentions precisely enough for AI systems to operationalize, they simultaneously develop the communication skills necessary for human collaboration. The same specification work that enables AI assistance—clear requirements, documented rationale, explicit success criteria—enables team coordination. Students who practice making their thinking visible to systems also become better at making their thinking visible to collaborators.
The contemporary lens dimension matters because students will enter professional contexts where AI tools are present regardless of individual preferences. Employers increasingly expect familiarity with AI-assisted workflows. Clients may request or prohibit AI use. Collaborators will have varying comfort levels and ethical frameworks regarding AI. Students who understand AI capabilities and limitations, who can work effectively with these tools while maintaining authorship and accountability, and who can make informed ethical choices about when and how to use AI possess more complete professional preparation than those who avoid engaging with these technologies during education.
How This Shows Up in Practice (Non-Tool-Specific)
Cinematography Instruction
Traditional cinematography teaching might demonstrate camera positioning, lighting setups, and lens choices through instructor examples that students observe and attempt to replicate. The instructor's tacit knowledge about why specific choices work remains largely invisible—students see the setup but not the reasoning. AI-mediated instruction requires students to specify their intentions explicitly: "I want to create claustrophobic tension using close framing, shallow depth of field, and low-key lighting with strong shadows." This specification makes visible the decision-making process connecting aesthetic intent to technical execution. Students learn transferable principles (how framing affects psychological space, how depth of field directs attention, how lighting creates mood) rather than memorizing instructor-specific setups. When technology changes—new camera sensors, different lighting instruments—principles transfer while specific workflows evolve.
Editing and Pacing
An editor might intuitively feel that a sequence's pacing "doesn't work yet" without initially articulating why. AI-assisted editing requires explicit specification: "This sequence should build tension through progressively shorter shot durations, maintaining spatial continuity through match cuts while fragmenting temporal flow." The specification process itself develops analytical capability—the ability to diagnose problems, articulate objectives, and connect formal choices to experiential effects. Students learn adaptive reasoning applicable across editing contexts: when to prioritize continuity versus discontinuity, how rhythm affects emotional engagement, how sound-image relationships create meaning. These principles remain relevant regardless of which editing platform currently dominates the market.
Production Design and Visual Development
A production designer developing visual concepts for a project may work through extensive sketching and iteration, much of which operates through tacit aesthetic judgment. AI-mediated design requires articulating those judgments: "The environment should convey institutional sterility through symmetrical composition, desaturated color palette dominated by grays and blues, minimal decoration, and fluorescent lighting quality." This explicit specification serves dual purposes: it makes the designer's reasoning visible to collaborators (and to instructional review), and it forces clarification of aesthetic principles guiding choices. Students learn transferable skills in translating emotional/conceptual objectives into formal visual characteristics—skills applicable whether working with AI generation, traditional concept art, or physical set construction.
Sound Design and Audio Production
Sound designers often work through experimentation and refinement guided by tacit judgment about what "feels right" for a scene. AI-assisted sound work requires explicit specification of objectives: "The soundscape should create urban isolation through sparse, distant traffic ambience, footstep reverb suggesting large empty spaces, and absence of human voices except the protagonist." This specification work develops analytical listening skills and the ability to articulate the relationship between sound characteristics and emotional effects. Students learn principles applicable across all sound production contexts: how frequency content affects spatial perception, how temporal density creates tension or calm, how sound-image relationships direct attention. These transferable insights remain valuable as audio tools and technologies evolve.
Collaborative Production Workflows
Traditional film production coordination might rely heavily on face-to-face communication and shared tacit understanding developed through working together. Contemporary distributed or asynchronous collaboration requires explicit specification of requirements, constraints, and decision rationale. Students must document creative intent, technical specifications, and evaluation criteria with precision sufficient that collaborators working remotely or at different times can coordinate effectively. This necessity develops transferable communication and project management skills: how to articulate requirements clearly, how to document decisions with supporting rationale, how to create shared understanding across distributed teams. These capabilities apply whether coordinating human collaborators or specifying requirements for AI-assisted elements.
Common Misunderstandings
"Making tacit knowledge explicit means expert intuition becomes unnecessary"
This fundamentally misunderstands the relationship between tacit and explicit knowledge. Expert intuition develops through extensive practice and pattern recognition that cannot be fully verbalized. Making some tacit knowledge explicit through specification serves pedagogical purposes—allowing learners to observe expert reasoning—but does not eliminate or replace intuitive judgment. Experts continue relying on tacit knowledge while also developing capacity to articulate reasoning when teaching, collaborating, or working with systems requiring explicit specification. The goal is expanding practitioners' ability to move between tacit and explicit modes as situations require, not replacing one with the other.
"Transferable skills are too abstract or general to be practically useful"
This confuses transferability with vagueness. Transferable skills are highly concrete—the ability to diagnose why a composition feels unbalanced, to articulate the relationship between shot duration and pacing, to identify which constraints are negotiable versus fixed. These skills transfer across contexts precisely because they address specific, recurring challenges in creative production rather than being platform-specific procedures. The difference is between "click this menu, select this option" (non-transferable procedure) and "analyze whether visual hierarchy matches narrative priorities" (transferable analytical skill). Transferable skills are practical capabilities, not abstract concepts.
"Learning through contemporary tools means abandoning established filmmaking principles"
This creates a false opposition between tools and principles. The framework uses contemporary tools to teach established principles—visual composition, narrative structure, editing rhythm, sound-image relationships—that have been recognized in film pedagogy for decades. The tools are the material students engage with; the principles are what students learn about. Using AI to explore cinematography teaches the same fundamental concepts about framing, lighting, and movement that traditional instruction teaches, while also preparing students for contemporary production realities. Tools change; principles persist.
"AI tools make decision-making easier by reducing the number of choices practitioners must make"
This reverses the actual cognitive demand. AI tools that require explicit specification increase the number of conscious decisions practitioners must make and articulate. Rather than working intuitively through established procedures, practitioners must specify objectives, constraints, evaluation criteria, and stylistic parameters. This specification work makes decision-making more deliberate and explicit, not easier or more automatic. The benefit is pedagogical—students develop decision-making capabilities through practice articulating requirements—not efficiency through automation of judgment.
Scholarly Foundations
Polanyi, M. (1966). The Tacit Dimension. University of Chicago Press.
Foundational analysis establishing that practitioners "know more than they can tell"—expert competence includes tacit knowledge that resists complete articulation. Explains why expert filmmakers can make effective decisions based on pattern recognition and embodied judgment without being able to fully verbalize their reasoning. Essential for understanding why making some tacit knowledge explicit through AI specification creates pedagogical opportunities.
Schön, D. A. (1983). The Reflective Practitioner: How Professionals Think in Action. Basic Books.
Analyzes how professionals develop expertise through reflection-in-action and reflection-on-action, continuously adjusting understanding as artifacts reveal implications of decisions. Demonstrates that professional competence requires both technical skill and reflective capacity. Provides theoretical foundation for teaching decision-making processes alongside technical execution. Critical for understanding adaptive reasoning in creative practice.
Rabiger, M., & Hurbis-Cherrier, M. (2020). Directing: Film Techniques and Aesthetics (6th ed.). Routledge.
Comprehensive filmmaking pedagogy text that grounds technical instruction in aesthetic principles and decision-making frameworks. Emphasizes understanding why choices produce particular effects rather than merely following procedures. Represents established film pedagogy that contemporary frameworks build upon. Updated editions address contemporary tools while maintaining focus on timeless principles.
National Research Council. (2012). Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century. National Academies Press.
Comprehensive synthesis of learning sciences research on transferable skills and deeper learning. Establishing that transfer requires teaching for understanding, providing practice in varied contexts, and developing metacognitive awareness. Demonstrates that narrowly procedural instruction produces inert knowledge that doesn't transfer. Provides empirical foundation for emphasizing transferable decision-making over platform-specific workflows.
Kilpatrick, J., Swafford, J., & Findell, B. (Eds.). (2001). Adding It Up: Helping Children Learn Mathematics. National Academy Press.
Defines five strands of mathematical proficiency including adaptive reasoning—the capacity to think logically about relationships among concepts and to justify conclusions. Demonstrates that procedural fluency without adaptive reasoning produces brittle knowledge that fails in novel situations. Applicable beyond mathematics to any domain requiring principled problem-solving. Establishes a theoretical foundation for emphasizing reasoning over procedure.
Collins, A., Brown, J. S., & Newman, S. E. (1989). "Cognitive Apprenticeship: Teaching the Crafts of Reading, Writing, and Mathematics." In L. B. Resnick (Ed.), Knowing, Learning, and Instruction: Essays in Honor of Robert Glaser (pp. 453-494). Erlbaum.
Establishes cognitive apprenticeship model emphasizing making expert thinking visible to learners through modeling, coaching, scaffolding, articulation, reflection, and exploration. Demonstrates that cognitive skills require pedagogical approaches that externalize mental processes. Provides framework for understanding how AI specification requirements create visibility of expert decision-making.
Bordwell, D., & Thompson, K. (2017). Film Art: An Introduction (11th ed.). McGraw-Hill Education.
Canonical film studies text establishing formal analysis frameworks for understanding how cinematic choices produce effects. Grounds filmmaking pedagogy in systematic analysis of visual composition, editing, sound, and narrative construction. Represents established film theory and pedagogy that contemporary frameworks build upon while using current tools and examples.
Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How People Learn: Brain, Mind, Experience, and School. National Academy Press.
Synthesizes learning sciences research on how people develop expertise, how knowledge transfers across contexts, and how metacognitive awareness supports learning. Demonstrates that transfer requires deep understanding of principles rather than surface knowledge of procedures. Establishes that learning environments should be learner-centered, knowledge-centered, assessment-centered, and community-centered.
Hutchins, E. (1995). Cognition in the Wild. MIT Press.
Ethnographic analysis demonstrating that complex cognitive work is distributed across practitioners, tools, and representational artifacts. Shows that modern technical work involves coordinating human judgment with tool capabilities rather than purely internal cognition. Relevant to understanding how AI-mediated creative work distributes decision-making across human specification and automated execution while maintaining human accountability.
Boundaries of the Claim
This slide does not claim that AI tools are pedagogically superior to traditional instruction in all contexts or that specification-based approaches should replace all other teaching methods. Different pedagogical approaches serve different learning objectives, and comprehensive film education requires multiple instructional strategies including hands-on production, critical analysis, historical study, and collaborative projects.
The slide does not claim that all tacit knowledge can or should be made explicit. Significant aspects of expert practice remain embodied, intuitive, and resistant to complete verbalization. The claim is that AI tools create opportunities to make some previously tacit reasoning visible in ways that support learning, not that all expertise can be reduced to explicit specification.
This slide does not claim that transferable skills eliminate the need for domain-specific technical knowledge or that principles alone suffice without practical execution capabilities. Professional competence requires both transferable decision-making abilities and technical proficiency with specific tools and techniques. The emphasis on transferability addresses the balance between these components, not their elimination.
The slide does not specify which AI tools should be used, which filmmaking contexts benefit most from this approach, or how this framework should relate to other components of film education. These remain pedagogical design decisions that vary based on institutional contexts, student populations, and learning objectives.
This slide intentionally leaves open questions about how this pedagogical approach should evolve as AI capabilities change, about cultural and contextual variations in what constitutes appropriate pedagogy, and about optimal sequencing of skill development across different learning stages. It presents rationale for the framework without prescribing complete implementation details.
Reflection / Reasoning Check
Reflection Question 1:
Think about a skill developed in filmmaking, design, writing, or another domain. How much of current competence operates through tacit knowledge—things "just known" without being able to explain fully—versus explicit understanding that could be articulated to teach someone else? If tacit knowledge had to be made explicit to specify it for an AI system or to teach a novice, which aspects would be straightforward to articulate and which would resist verbalization? What would that process of attempted articulation reveal about the nature of expertise?
Reflection Question 2:
Consider a tool or platform learned and used proficiently. How much of what was learned is transferable to other tools or contexts (understanding how visual hierarchy works, how narrative pacing functions, how constraints shape decisions) versus specific to that particular platform (menu locations, button sequences, feature-specific workflows)? If that tool became obsolete tomorrow, which capabilities would remain valuable and which would become irrelevant? How does this distinction affect how new tools or technologies might be approached in the future?