7 items with this tag.
Rather than cataloguing AI's failures, demonstrate thoughtful use, critique from practice, and amplify what matters to you.
Rich Sutton's 'Bitter Lesson' applies to education: AI reveals that artifact-based assessment never truly measured learning.
Despite the ethical concerns, generative AI represents an enormous opportunity for learning at scale. Here's why I'm optimistic.
Any claim that a course or programme of study develops AI literacy requires important qualifications—literacy develops through sustained practice, is developmental and contextual, and cannot be fully assessed at course completion.
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.
Traditional education systems are built on a paradox - institutions dedicated to learning are structured almost entirely around teaching. This fundamental misalignment has persisted for centuries, with higher education operating on the assumption that teaching inevitably produces learning. This paper argues that the traditional model, where knowledge flows unidirectionally from expert to novice, no longer serves a world of information abundance and technological disruption. Emergent scholarship offers an alternative approach that reconceptualises learning as a complex, networked process emerging from connections rather than transmission. By shifting from knowledge authority to learning facilitation, educators can create environments where diverse participants contribute to collective understanding, challenging hierarchies that position faculty as sole knowledge producers. This transformation is particularly urgent as artificial intelligence develops capabilities once exclusive to human experts, fundamentally altering the educational landscape. Rather than fighting these technological changes, emergent scholarship integrates them as participants in the learning ecosystem while focusing on uniquely human capabilities like critical thinking and collaborative problem-solving. The shift from teaching hierarchies to learning networks requires reimagining not just pedagogical approaches but institutional structures, potentially creating educational environments that better prepare graduates for complexity and uncertainty while fostering more engaging experiences for all participants.