4 items with this tag.
A six-dimension framework that underlies all forms of literacy—information, media, digital, data, and AI literacy share the same structural pattern.
Higher education institutions have responded to AI technologies by emphasising prompt engineering—teaching students technical skills for crafting effective AI queries. This essay argues that a focus on interaction mechanics represents a fundamental misunderstanding of both AI engagement and learning itself. Rather than the outcome of technical skills, prompts emerge from students' personal meaning-making frameworks—their individual contexts for determining what questions matter and what constitutes intellectual exploration, or academic integrity. The institutional focus on prompt control reveals what I call the learning alignment problem, where educational systems optimise for measurable proxies (grades, compliance, technical proficiency) rather than authentic learning outcomes (for example, traits like curiosity, understanding, intellectual development). AI acts as a mirror, highlighting that students were already circumventing meaningful engagement in favour of strategic optimisation for institutional rewards. When students use AI to complete assignments without learning, they reveal that assignments were already completable without genuine intellectual work. This analysis draws parallels to the value alignment problem in AI safety research—the difficulty of creating systems that pursue intended goals instead of optimising for specified metrics. Educational institutions face similar challenges: we cannot directly measure what we say we value (learning), so we create proxies that students rationally optimise for, often moving further from authentic learning. The essay suggests that universities shift from control paradigms to cultivation paradigms—from teaching prompt engineering to fostering learning purpose, from managing student behaviour to creating conditions where thoughtful engagement emerges naturally. This means recognising that learning is inherently personal, contextual, and resistant to external specification. Educational environments must cultivate intellectual joy and curiosity rather than technical compliance, supporting students' meaning-making processes rather than standardising and seeking to control their interactions with AI.
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 graph-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 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 of concepts beyond simple scrolling. Progressive complexity management addresses potential cognitive overload for novices while maintaining the graph 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 graph-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.
This essay explores the idea of language as humanity's first general purpose technology—a system we developed to extend human capabilities across a range of domains, which enabled complementary innovations. Through this conceptual lens, large language models (LLMs) emerge not merely as new digital tools, but as a significant evolution in the continuum of language technologies that stretches from spoken language through writing, printing, and digital text. The essay explores how LLMs extend language's core capabilities through unprecedented scale, cross-domain synthesis, adaptability, and emerging multimodality. These extensions are particularly relevant to health professions education, where students face the dual challenge of information overload and inadequate preparation for complex practice environments. By viewing LLMs as an evolution of our most fundamental technology rather than simply new applications, we can better understand their implications for clinical education. This perspective suggests shifting educational emphasis from knowledge acquisition to clinical reasoning and adaptive expertise, developing new forms of AI literacy specific to healthcare contexts, and reimagining assessment approaches. Understanding LLMs as part of language's ongoing evolution offers a nuanced middle path between uncritical enthusiasm and reflexive resistance, informing thoughtful integration that enhances rather than diminishes the human dimensions of healthcare education.