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Beyond text boxes: Exploring a network-based user interface for AI-supported learning ​

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There are many ideas that inspired this essay, but these are the ones that most clearly stand out for me:

Abstract ​

This essay critically examines the predominant interface paradigm for AI interaction today—text-entry fields, chronological chat histories, and project folders—arguing that these interfaces reinforce outdated container-based knowledge metaphors that fundamentally misalign with how expertise develops in professional domains. Container-based approaches artificially segment knowledge that practitioners must mentally reintegrate, creating particular challenges in health professions education where practice demands integrative thinking across traditionally separated domains. The text-entry field, despite its ubiquity in AI interactions, simply recreates container thinking in conversational form, trapping information in linear streams that require scrolling rather than conceptual navigation.

I explore network-based interfaces as an alternative paradigm that better reflects how knowledge functions in professional contexts, and where AI serves as both conversational partner and network builder. In this environment, conversations occur within a visual knowledge landscape, spatially anchored to relevant concepts rather than isolated in chronological chat histories. Multimodal nodes represent knowledge across different modalities, while multi-dimensional navigation allows exploration beyond simple scrolling. Progressive complexity management addresses potential cognitive overload for novices while maintaining the network as the fundamental organising metaphor.

Implementation opportunities include web-based knowledge graph interfaces supported by current visualisation technologies and graph databases, with mobile extensions enabling contextual learning in practice environments. Current AI capabilities, particularly frontier language models, already demonstrate the pattern recognition needed for suggesting meaningful connections across knowledge domains. The barriers to implementing network-based interfaces are less technological than conceptual and institutional—our collective attachment to container-based thinking and the organisational structures built around it. This reconceptualisation of learning interfaces around networks rather than containers suggests an alternative that may better develop the integrative capabilities that define professional expertise and reduce the persistent gap between education and practice.

The container crisis in professional education ​

Our education systems are trapped in a metaphor that is so old we no longer see it. From scrolls to learning management systems, we've organised education into containers—neatly bounded spaces that separate and compartmentalise what should be connected. Courses, modules, folders, and even digital spaces fundamentally structure learning as a process of depositing information into separate containers, to be retrieved when needed. This container metaphor persists despite technological change that should have seen the introduction of more open learning environments. Modern learning management systems essentially recreate filing cabinets in digital form, with folders organised hierarchically to store learning resources efficiently. Container-based education is so deeply embedded that despite two decades of digital transformation, we've simply recreated pre-digital organisational structures in our online environments. For learners, this creates a fragmented educational journey as they move between separate knowledge containers without seeing how areas interconnect in practice.

The fragmentation is especially problematic in professional education, where practice demands integrated knowledge. Container models create artificial boundaries that practitioners must overcome through cognitive effort, with theory and practice becoming institutionally separated despite their inseparability in real situations. The connections between knowledge domains—particularly the complex, conditional relationships that characterise professional decision-making—typically remain invisible in container-based curricula. Knowledge learned in disconnected contexts is significantly less likely to transfer to practice environments. From the learner's perspective, this creates a disorienting disconnect between education and practice, with many reporting a sense of "starting over" when entering professional settings, despite years of preparation.

Health professions education highlights this challenge acutely. Healthcare professionals must integrate knowledge across traditionally separated domains for even routine patient encounters. A physiotherapist assessing knee pain simultaneously draws on anatomical knowledge, biomechanical principles, pain science, communication skills, and ethical frameworks, yet their education likely presented each in separate modules with distinct assessments and perhaps even without explicit connection. Patients present with complex, interconnected needs that rarely respect the boundaries of curriculum containers. A patient with diabetes, depression, and chronic pain requires integrated care that spans multiple knowledge domains. Emerging healthcare challenges—multimorbidity, social determinants of health, technology integration—further strain container-based education models that were designed for more straightforward, single-discipline approaches. Container models inadvertently prioritise testable knowledge over the complex, contextual competencies that practice values most. Assessment typically focuses on what can be easily measured within each container rather than the ability to integrate across containers. Not surprisingly, many health professional students report feeling unprepared for the integrative thinking required in practice despite excelling in containerised assessments.

Why do container-based approaches struggle so fundamentally with complexity, adaptation, and transfer? Several key limitations emerge. Containers prioritise completeness over connection; the focus is on "covering" content within the container rather than understanding how it relates to knowledge in other containers, leading to education that is comprehensive in breadth but lacks the connective tissue that gives knowledge meaning and utility. Containers create implied ownership and territoriality, with faculty becoming responsible for "their" modules, making adaptation difficult when knowledge evolves across traditional boundaries. "That's not covered in my module" becomes a defence against addressing emerging topics that don't fit neatly into existing containers. Updating knowledge in container models typically requires opening, modifying, and re-sealing containers—a slow process ill-suited to rapidly evolving fields like healthcare. Knowledge that doesn't fit neatly into existing categories is often relegated to "special topics" containers rather than integrated into core understanding. Finally, the boundaries between containers often become barriers to knowledge transfer. When containers are associated with different assessment methods, teaching styles, or disciplinary languages, students struggle to see connections across artificially imposed boundaries.

These limitations create a profound challenge for professional education. Container models made sense in an era of information scarcity, where the primary educational challenge was preserving and transmitting stable knowledge. But they fundamentally misalign with the realities of modern professional practice, where success depends on navigating complex, interconnected, and rapidly evolving knowledge landscapes. The container crisis thus represents not merely a pedagogical preference but a structural impediment to developing the integrative capabilities that define professional expertise.

The text-box problem in AI-supported learning ​

The rise of generative AI has seen the text-entry field, and associated chat history, emerge as a standard interface for interacting with powerful language models. At first glance, these interfaces appear to transcend container-based models through their conversational nature. Yet closer examination reveals that text-entry interfaces for AI interaction simply recreate container thinking in conversational form. Chat histories have become new types of containers, with information trapped in linear conversational streams that must be scrolled through rather than navigated conceptually. When platforms organise these chats into folders, projects, or topics, they create a meta-container structure that perpetuates rather than solves the fragmentation problem. The chronological structure of chat interfaces reinforces the idea that learning is a series of discrete information exchanges rather than an evolving network of understanding. From the learner's perspective, finding previously discussed concepts means searching through conversation histories rather than navigating relationships between ideas, recreating the limitations of container-based thinking in a new medium.

This problem stems from a fundamental mismatch between linear chat threads and the networked nature of professional knowledge. Professional knowledge is inherently networked and contextual, while chat interfaces are linear and acontextual. Ideas that are conceptually related may appear in different conversations separated by days or weeks, with no visual representation of their relationship. Chat histories create temporal containers that separate related ideas based on when they were discussed rather than how they relate to each other. The mental model suggested by chat interfaces—learning as a series of questions and answers—fundamentally misaligns with how expertise actually develops. Experts don't organise their knowledge as a sequence of conversation logs; they build rich, interconnected mental models where concepts gain meaning through their relationships with other concepts. Chat-based learning requires constant context-switching between different conversation threads, with no visual representation of how these threads relate to each other or to broader conceptual frameworks. This imposes significant cognitive load as learners attempt to mentally reconstruct the connections that the interface itself obscures.

The solution is not simply to build "better text boxes." Adding features to text interfaces—improved search, expanded memory, larger context windows—doesn't address the fundamental problem of container-based thinking. More sophisticated text-entry fields with better persistence still reinforce the question-answer paradigm rather than supporting exploration and connection-making. Even AI chatbots with perfect memory and retrieval still interact through a medium that fragments rather than integrates knowledge. "Improved" text interfaces might focus on making information retrieval more efficient rather than transforming how knowledge is structured and accessed. Features like chat history organisation, tagging, and search essentially apply patches to a fundamentally container-based approach. The problem isn't the quality of AI responses, but the organisational metaphor through which those responses are accessed and connected.

Perhaps most concerning is how text-based AI interfaces create an illusion of learning without necessarily supporting deeper understanding. The immediate, authoritative responses from AI in text interfaces can create an illusion of learning that masks gaps in understanding and application ability. The conversational format makes information feel personal and meaningful even when it's not being integrated into the learner's knowledge network. The apparent ease of information access can obscure the necessary cognitive struggle that leads to durable learning. Transcripts of AI conversations give the appearance of learning artefacts but often function more as external reference materials than integrated knowledge. The efficiency of text-based AI interactions can bypass the productive friction that helps encode learning more deeply. Students may find themselves with folders full of chat histories that feel like learning but function more as external knowledge stores, recreating the fundamental problem of container-based education in a new technological context.

This analysis is not a rejection of conversational interaction with AI, which can be valuable for learning. Rather, it highlights how the dominant interface paradigm for these interactions—the text-entry field, sequential chat history, and folders for projects—is fundamentally misaligned with how professional knowledge is structured and how expertise develops. As we continue integrating AI into education, we have to critically examine not just what these systems say, but how our interactions with them shape learning. Text-entry fields with their container-like qualities represent a missed opportunity to create educational interfaces informed by authentic models of knowledge organisation and expertise development. If large language models demonstrate remarkable capabilities for making connections across knowledge domains, we need a different organising metaphor that amplifies this potential.

Networks as alternative organising metaphors ​

If containers fundamentally misalign with the realities of professional knowledge, what alternative metaphor might better serve learning? Networks offer a compelling alternative organising principle that more accurately reflects how knowledge functions in practice. Knowledge in professional contexts operates as an interconnected web where concepts gain meaning through their relationships to other concepts. Understanding a medical diagnosis, for instance, isn't simply about retrieving its definition from a mental container but activating a complex network of relationships to symptoms, underlying mechanisms, differential diagnoses, treatment approaches, and contextual factors. Network-based thinking prioritises connections, relationships, and patterns rather than isolated facts or procedures. Professional expertise manifests not as mastery of separate domains but as the ability to navigate complex networks of knowledge in context-appropriate ways. Network-based approaches also make visible the context-dependent nature of knowledge—how the same concept takes on different significance in different situations, connecting to different concepts depending on the specific context of application.

Networks better reflect the reality of professional practice in several important ways. Healthcare professionals navigate knowledge networks rather than retrieve information from containers during actual practice. When assessing a patient with low back pain, a physiotherapist doesn't mentally access separate containers labelled "anatomy," "biomechanics," and "pain science," but instead activates an interconnected network of knowledge that spans these artificially separated domains. Clinical decision-making involves pattern recognition across complex, interconnected variables rather than sequential application of isolated facts. Expert practitioners develop knowledge networks that span traditional disciplinary boundaries, integrating biomedical, psychosocial, and systems perspectives. Healthcare professionals create connections that span institutional and disciplinary boundaries, following the contours of problems rather than the boundaries of disciplines. Network-based learning environments can therefore better prepare students for the ambiguity and complexity they will encounter in actual practice settings by making these connections visible and navigable during the learning process.

To give this idea a firmer shape, let's imagine something we might call a Clinical Pathway Network. Instead of organising nursing education around discrete subjects (anatomy, pharmacology, ethics), the Clinical Pathway Network organises learning around authentic clinical situations that serve as the primary navigation structure. Students move along interconnected pathways representing patient journeys or care scenarios, encountering relevant knowledge and skills in context rather than in artificial separation. Knowledge domains appear as interconnected nodes within these pathways rather than separate containers, making integration an inherent part of the learning experience rather than an after-the-fact challenge. Assessment could focus on successful navigation of the network and application of integrated knowledge rather than demonstration of mastery within isolated containers. This network-based approach to learning creates an environment that more closely mirrors how knowledge is actually used in practice, reducing the gap between education and application.

While the Clinical Pathway Network offers one instantiation of network-based learning, other complementary networks could address different dimensions of professional development. A Physiological Systems Network might connect body systems rather than isolating them, helping students understand interconnections between cardiovascular, respiratory, and other systems. A Decision-Making Network could map clinical reasoning processes, connecting assessment, diagnosis, intervention, and evaluation as an integrated flow. Community Health Networks might extend beyond clinical settings to encompass social determinants, resources, and population health trends. Professional Identity Networks could support the development of the practitioner's sense of self, connecting values, experiences, and career pathways. These complementary networks would overlap and interweave, creating a multi-dimensional learning space that more accurately represents the complexity of professional practice. The network metaphor thus offers not just an alternative to containers but a more authentic and flexible approach to organising learning that can adapt to the diverse dimensions of professional development.

A user interface for network-based learning ​

Knowledge graphs as the foundational interface metaphor ​

If we accept networks as a more appropriate metaphor for developing professional knowledge, we now have a foundation for thinking differently about the interfaces through which learners engage with AI-supported environments. Rather than text-entry fields, chat histories, and project folders, interfaces could instead be built around interactive knowledge graphs that visualise relationships and make connections explicit. The primary interface might resemble tools like Obsidian or Roam Research, but specifically designed for learning rather than note-taking. In such environments, the central visual metaphor shifts from linear conversations to spatial networks, with nodes representing concepts and edges representing relationships. Learners would move through knowledge spaces rather than search through chat histories, seeing their emerging understanding as a growing network rather than an accumulating transcript. The central dashboard would be a dynamic knowledge map rather than a list of courses or conversation threads. For beginners, simplified node-link diagrams focusing on essential relationships would provide scaffolding without overwhelming complexity.

Multi-dimensional navigation ​

This network-based approach requires multi-dimensional navigation beyond the simple scrolling that characterises text-based interfaces. Instead of scrolling through text, learners would navigate through knowledge spaces using zoom, pan, and focus operations. Zooming out would reveal broader patterns and connections across domains, helping learners understand how specific topics fit within larger conceptual frameworks, while zooming in would expose greater detail within specific concepts. Pathways through the network would represent learning journeys that connect concepts in pedagogically meaningful sequences, providing guided routes through complex knowledge landscapes. Focal points would allow learners to temporarily centre their view around specific concepts while maintaining awareness of surrounding relationships, preventing the tunnel vision that often accompanies text-based learning. And the relative size, colour, or transparency of those focal points might change, depending on contextual factors like learner competence, understanding, or interest. Navigation history would be visually represented as paths through the knowledge space rather than browser back-buttons, making the learning journey itself visible and reviewable. Filters would allow toggling different types of relationships or domains, revealing different dimensions of the knowledge network. Different lenses could change the visualisation to emphasise conceptual, procedural, or contextual relationships based on current learning needs. This multi-dimensional navigation would transform how learners move through knowledge spaces, replacing the linear progression of container-based approaches with a more flexible, contextually aware exploration that better mirrors professional reasoning.

AI as dialogue partner within the knowledge landscape ​

Within this network environment, AI would serve as both conversational partner and active weaver of knowledge networks, combining the benefits of dialogue with spatial relationship-building. Conversations with AI would occur within the context of the visual knowledge network, spatially anchored to relevant concepts rather than isolated in chat containers. When learners engage in dialogue with AI about a concept, that conversation would become embedded in the network node itself, maintaining context rather than disappearing into a separate chat history. As conversations develop, new connections emerge from the dialogue, making the knowledge-building process visible rather than implied. Instead of responding solely with text, AI responses might include visual extensions to the network that represent new connections, related concepts, or alternative perspectives. Learners could seamlessly transition between conversational exploration ("tell me more about this concept") and spatial navigation ("show me how this connects to what I learned last week"). The visual context would ensure that dialogues remain grounded in the broader knowledge landscape, preventing the isolated question-answer patterns of traditional chat interfaces. For complex topics, AI might suggest switching between conversation mode and a more visual exploration mode, choosing the medium that best supports the current learning need, or learner preference. AI tutoring interactions would remain available but spatially organised by concept rather than chronologically in conversation threads, allowing learners to revisit them in meaningful contexts. This hybrid approach would preserve the Vygotskian benefits of dialogue with a "more knowledgeable other" while embedding those interactions in a visible network that avoids container-based thinking.

Multimodal nodes in the network ​

Knowledge nodes within the network would be multimodal, containing text, images, video, audio, simulations, or interactive elements as appropriate to the concept. Rather than conversations about anatomical structures, nodes would contain manipulable 3D models that learners could examine from multiple perspectives. Instead of describing procedures, nodes would contain interactive simulations or video demonstrations that capture the dynamic nature of clinical skills. Clinical reasoning would be represented through interactive decision trees (possibly simplistic, but nonetheless useful) rather than textual explanations, allowing learners to explore different decision pathways and their consequences. Diagnostic patterns would be presented as visual comparison galleries rather than verbal descriptions, supporting the pattern recognition skills that characterise expert practice. AI would dynamically generate appropriate representations based on the nature of the knowledge and the learner's needs, selecting the modality that best communicates each concept. This multimodal approach acknowledges that professional knowledge encompasses multiple knowledge types—declarative, procedural, conditional, tacit—that require different representational forms. By moving beyond text as the primary knowledge representation, network interfaces could support the development of the multifaceted knowledge structures that professional practice demands.

Collaborative network building as social learning ​

Shared knowledge graphs would allow multiple learners to collaboratively build and navigate public networks, supporting the social dimensions of learning. Learners could see where their personal knowledge networks overlap with those of peers, identifying opportunities for shared exploration and complementary expertise. Collaborative annotation would enable socially constructed understanding of complex concepts, with multiple perspectives enriching the network beyond what any individual could contribute in isolation. Different perspectives on the same knowledge space could be toggled, revealing how different professional disciplines view the same concepts and supporting the development of interprofessional understanding. This kind of community-based, collective construction of understanding would draw on diverse experiences and insights, providing interprofessional education opportunities. Network visualisations would reveal patterns of consensus and productive disagreement that might remain invisible in container-based exchanges.

Progressive network complexity for developmental learning ​

Interface designs would adapt network complexity based on the learner's developmental stage, addressing the common concern that networks might overwhelm novice learners with excessive complexity. Novices would see simplified networks with core relationships prominently displayed and optional complexity temporarily hidden, providing a manageable entry point to the knowledge domain. As learners develop expertise, the visible network would progressively reveal greater complexity and nuance, gradually introducing the conditional relationships and contextual factors that characterise expert understanding. Visual indicators would help learners distinguish core disciplinary pathways from specialised extensions, allowing them to focus on fundamental concepts while maintaining awareness of potential depth. The system might gradually expose more complexity and depth, as learners demonstrate their understanding, providing developmental scaffolding that adapts to individual progress. This could even include elements of game mechanics to motivate progression (think, level unlocking) and provide a sense of achievement. The interface would support the transition from guided to self-directed learning through gradually increasing navigational freedom, mirroring the developmental trajectory from novice to expert practitioner. This progressive approach addresses the concern that network complexity might overwhelm beginners while still maintaining the network as the foundational metaphor, adapting its presentation rather than reverting to container-based approaches for novices.

Implementing with current technology ​

While this vision might appear aspirational, many components of network-based interfaces can be implemented with technologies available today. Contemporary web technologies like D3.js and Three.js already support sophisticated network visualisations, whilst modern graph databases provide the necessary back-end infrastructure for representing complex knowledge relationships. Progressive web applications can deliver consistent experiences across devices, and current AI capabilities—particularly frontier language models—demonstrate the pattern recognition needed for suggesting meaningful connections. Mobile extensions using existing capabilities like camera-based augmented reality could extend these networks into practice contexts, helping bridge the theory-practice gap. The barriers to implementation are less technological than conceptual and institutional—our collective attachment to container-based thinking and the organisational structures built around it.

Together, these interface elements would create learning environments that align with how knowledge development happens in practice, making visible the connections that container-based interfaces obscure and supporting the development of the integrative capabilities that define expertise. By reimagining interfaces around networks rather than containers, we can create learning experiences that better prepare students for the complex, interconnected challenges they will face in practice. This isn't merely an aesthetic or usability concern but a fundamental reconceptualisation of how professional learning interfaces should structure knowledge and support learning in complex domains.

The future of network-based learning interfaces ​

The shift from container-based to network-based interfaces represents a paradigm shift in education technology with far-reaching implications. As network interfaces demonstrate effectiveness, they will increasingly influence how learning content and relationships are conceptualised, moving away from self-contained modules toward interconnected knowledge structures. Educational content creation will evolve from isolated learning objects to richly connected knowledge nodes that explicitly incorporate relationships to other concepts. Assessment approaches will shift from measuring retention within containers to evaluating navigation through networks, focusing on how learners make connections rather than how much information they retain. Learning analytics will evolve beyond tracking completion metrics to analysing patterns of connection-making, identifying the emergent understanding that develops as learners navigate knowledge networks. Educational technology standards will expand to include relationship types, connection patterns, and network structures, formalising the network paradigm within digital learning frameworks. This evolution represents not merely a cosmetic change to interfaces but a fundamental reconceptualisation of how we organise and interact with knowledge in educational contexts. The container metaphor, after centuries of dominance, may finally give way to an approach more aligned with how knowledge functions in practice and how human cognition actually works.

Within this network paradigm, AI's role is focused on enhancing human connection-making rather than mere content generation. Network interfaces position AI as a guide through knowledge landscapes rather than the source of definitive answers, fundamentally altering the human-AI relationship in learning contexts. Visual network representations make AI's suggestions transparent and navigable rather than opaque and authoritative, allowing learners to see, evaluate, and potentially modify AI-suggested connections rather than accepting them as given. By externalising the knowledge network, interfaces allow learners to understand AI contributions in context rather than in isolation, seeing how suggested connections relate to their existing understanding. The network metaphor supports a collaborative relationship between learner and AI rather than a hierarchical one, with both contributing to the construction of understanding rather than one delivering knowledge to the other. AI can progressively recede as learners develop greater confidence in navigating knowledge networks independently, providing scaffolding that gradually fades rather than creating dependency. This approach addresses common concerns about AI in education—that it might replace human judgement or create passivity—by explicitly designing interfaces that position AI as an enhancer of human capabilities rather than a substitute for them. The network metaphor thus provides not just a better representation of knowledge but a more productive framework for human-AI collaboration in learning.

Network-based interfaces explicitly develop the connection-making skills that define professional expertise and that container-based approaches often neglect. By making knowledge relationships visible and navigable, these interfaces help learners develop pattern recognition capabilities that transfer directly to practice contexts. The ability to navigate complex knowledge spaces transfers directly to navigating complex practice situations, as the cognitive skills developed in exploring the knowledge network mirror those needed for clinical reasoning. Assessment can focus on the quality and usefulness of connections made rather than the quantity of information retained, aligning evaluation with the integrative capabilities that practice values most. Learning activities can explicitly develop skills in identifying meaningful patterns across seemingly disparate domains, preparing professionals for the cross-boundary thinking that complex problems require. Professional identity development occurs through growing confidence in navigating increasingly complex knowledge networks, with learners progressively expanding their navigational capabilities as they move from novice toward expert. These connection-making capabilities represent the essence of professional expertise yet often remain implicit in traditional education. Network interfaces make them explicit and central, focusing educational effort on developing the integrative thinking that defines professional competence rather than treating it as a secondary skill to be developed after mastering containerised content.

Perhaps most significantly, network-based learning interfaces can extend seamlessly into practice environments, reducing the theory-practice gap that plagues professional education. Knowledge networks used in education can connect directly to clinical decision support systems used in practice, creating continuity between learning and application rather than treating them as separate domains. The same network navigation skills developed during education apply directly to navigating complex clinical situations, eliminating the cognitive shift that occurs when moving between educational and practice contexts. Continuing professional development can build upon existing personal knowledge networks rather than existing separately, creating lifelong learning trajectories rather than episodic educational experiences. Network interfaces support the integration of new research findings into practice by making connections to existing knowledge explicit, facilitating the translation of research into practice that healthcare desperately needs. Practice-based observations can enrich educational knowledge networks, creating bidirectional flow between education and practice rather than treating practice as merely the application of educational content. This seamless connection between education and practice represents perhaps the most transformative potential of network-based interfaces, addressing the fundamental disconnect between how we educate professionals and how they actually work.

The vision presented here—moving beyond text boxes to network-based interfaces for AI-supported learning—offers a pathway toward educational experiences that better prepare professionals for the complex, interconnected challenges they will face in practice. By aligning our educational interfaces with the networked nature of knowledge, we can create learning environments that develop the integrative capabilities that define expertise. By federating intelligence across personal, institutional, and commercial AI systems, we can provide personalised support while preserving learner agency and privacy. By implementing these approaches with current technologies, we can begin the transformation today rather than waiting for speculative future developments. The choice between container-based and network-based approaches isn't merely aesthetic or technological but fundamentally shapes how learners understand knowledge and develop expertise. As AI becomes increasingly integrated into education, we face a crucial choice: will we simply add AI to existing container-based approaches, perpetuating their limitations in a new technological context? Or will we use this moment of technological transformation to reimagine our educational interfaces around more authentic models of knowledge organization and expertise development? The text box and the knowledge graph represent not just different interface designs but different conceptions of learning itself. The future of professional education may well depend on which we choose.

Conclusion ​

The container crisis in professional education represents not merely a pedagogical preference but a fundamental misalignment between how we structure learning and how expertise actually operates. Our persistent attachment to container-based metaphors—from physical textbooks to digital folders to chronological chat histories—continually reinforces artificial boundaries between knowledge domains that practitioners must mentally overcome. As artificial intelligence becomes increasingly integrated into education, we face a pivotal moment: will we simply reproduce these container metaphors in conversational form, or will we reimagine interfaces around more authentic models of knowledge organisation?

The network-based approaches outlined in this paper offer a pathway toward learning environments that develop the integrative capabilities defining professional expertise. By making connections visible and navigable, these interfaces support the development of pattern recognition and relationship-building that characterise expert practice. By positioning AI as a guide through knowledge landscapes rather than an authority delivering answers, they foster collaborative rather than dependent relationships with technology. By federating intelligence across personal, institutional, and commercial systems, they balance personalisation with privacy and learner agency.

Implementation requires addressing both technical and institutional challenges, yet many components can be developed with technologies available today. The greater barriers are conceptual and organisational—our collective attachment to container-based thinking and the institutional structures built around it. Overcoming these barriers requires not just technological innovation but conceptual transformation in how we understand the relationship between knowledge, interfaces, and learning.

The text box and the knowledge graph represent fundamentally different conceptions of learning. As we integrate AI into education, we must critically examine not just what these systems say, but how the interfaces through which we access them shape learning. By shifting from containers to networks as our primary organising metaphor, we can create learning environments that better prepare professionals for the complex, interconnected challenges of contemporary practice—environments where knowledge emerges through connection rather than transmission, where identity develops through community rather than isolation, and where meaning emerges through navigation rather than acquisition.