Contemporary educational and AI-driven assessment systems predominantly evaluate learner performance at discrete moments, assuming that conceptual knowledge, once correctly acquired, remains stable over time. This assumption overlooks semantic drift — a distinct epistemic phenomenon characterized by the gradual, unintentional deformation of a learner’s internal conceptual representations, even in the absence of immediate performance errors, forgetting, or explicit misconceptions. In domains involving abstract, relational, or normative concepts, understanding can persist superficially while losing internal structure, coherence, boundary conditions, and constraint (e.g., compression of definitions, erosion of causal mechanisms into surface heuristics, or overgeneralization that reduces transferability and epistemic resilience). The result is brittle, structurally impoverished knowledge that evades detection under conventional paradigms focused on recall accuracy, misconception identification, or memory decay. This work introduces semantic drift as a previously under-recognized form of conceptual degradation and proposes the Cognitive Drift Regulator (CDR)—a closed-loop, high-level framework that reconceptualizes understanding as a dynamic state requiring ongoing epistemic maintenance rather than a terminal achievement or episodic evaluation. CDR models conceptual knowledge as inherently subject to entropylike structural degradation across time and context, necessitating continuous monitoring, detection, and restorative regulation to preserve depth and coherence. To distinguish semantic drift from related constructs (memory decay, performance variability, conceptual error), the framework emphasizes observable proxies for conceptual structure — such as representational similarity across learner outputs, structural changes in concept maps or knowledge networks, and rubric-based qualitative assessments—while characterizing temporal change through qualitative notions like drift velocity and departures from individually calibrated baselines. CDR remains deliberately implementation-agnostic at this stage, sketching core conceptual building blocks (to be formalized and validated in subsequent work) including Concept Signatures (CSig) as compressed representations of a learner’s current conceptual state, Semantic Distance (SD) metrics for comparing expressions over time, Drift Velocity (DV) as a measure of degradation rate, Critical Drift Thresholds (CDT) for triggering intervention, and Restorative Prompts (RP) such as spaced retrieval, refutation exercises, elaborative interrogation, or targeted analogical prompting. The paper includes an illustrative operationalization pathway and outlines a small-scale pilot design to test the feasibility of detecting and meaningfully regulating semantic drift in authentic learning contexts — serving as a validation precursor before larger empirical studies or system implementations. No quantitative thresholds, algorithmic recipes, or domain-specific parameters are committed to here; those will emerge from iterative refinement and empirical grounding in follow-on research. More broadly, this contribution reframes long-term learning as a process of active epistemic preservation, with implications extending beyond education to AI alignment (preventing drift in value learning or world models), human-AI collaboration, and societal epistemic resilience in an era of accelerating information change. By establishing this conceptual foundation, the work lays the groundwork for drift-aware educational tools, cognitive systems, and longitudinal models of understanding that prioritize structural integrity over surface correctness.
Murad Ahmadov (Thu,) studied this question.