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Algorithmic drift in generative adaptive learning

Firuz Kamalov, Hana Sulieman, Ahmed El Sayed, Sivaraj R., Nitesh Sughnani

arXiv:2610.31041v1cs.AI

TL;DR

The long-term equity effects of adaptive learning systems remain under-theorized, and static bias audits do not capture cumulative learner–tutor feedback. The paper analyzes algorithmic drift through theory, simulation, and grader-noise measurement, then evaluates a targeted stabilizer. In the modeled environment, the conclusion supports TFS as a potential resilience measure, while real-platform effects remain to be established.

  • Problem

    The long-term equity effects of adaptive learning systems remain under-theorized despite attention to static bias and the potential for personalized tutoring to support access.

    Source: §1 · p. 2

  • Method

    The paper formalizes learner–tutor dynamics, simulates adaptive learning and measures grader noise, then evaluates the temporal fairness stabilizer against exploration baselines.

    Source: §1 · p. 3

  • Results

    Within the modeled assumptions, the findings indicate that adaptive feedback can turn minor observation noise into systemic deficits, while TFS succeeds in simulation; effects in deployed platforms remain unestablished.

    Source: §6 · pp. 31–32

  • Takeaways & Limitations

    The findings support treating fairness in adaptive education as a longitudinal property and considering re-verification of learner capability rather than retreat to remedial content.

    Source: §5 · p. 28, §6 · p. 32

    Learners are synthetic in a POMDP that excludes instructors, peers, and multimodal assessment, so real-learner validation remains necessary.

    Source: §5.4 · pp. 29–30

Abstract

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Generative artificial intelligence promises to democratize personalized tutoring, yet the longitudinal stability of adaptive systems remains under-theorized. Current ethical discourse focuses on static bias. This paper argues that a distinct threat lies in algorithmic drift: a feedback loop in which minor, demographically asymmetric observation errors compound over time into systemic inequities. We make three contributions. First, we formalize algorithmic drift as a coupled learner--tutor dynamical system. Second, we corroborate this theory with an agent-based Monte Carlo simulation in which either a transparent online tracker or an LSTM deep-knowledge-tracing network drives an IRT-based curriculum policy. Third, we introduce the temporal fairness stabilizer (TFS), a targeted intervention that periodically re-probes learner capability, and benchmark it against standard exploration baselines with explicit group-fairness metrics and replication-level inference. Within the modeled environment, TFS closes the achievement gap where generic exploration does not, and we characterize the conditions under which it succeeds. We separate what is established analytically, what is demonstrated in simulation, and what remains to be validated with real learners. Our results reframe algorithmic fairness in education as a temporal property of a closed loop rather than a deployment-time checkbox.

1 Introduction

The long-term equity effects of adaptive learning systems remain under-theorized, while ethical audits commonly focus on static bias. The paper frames algorithmic drift as cumulative feedback between assessment and instruction and examines its conditions, measurement, and potential mitigation.

1 Introduction

  • Minor assessment errors can compound through feedback into substantial demographic-group divergences in learning outcomes.The paper calls this process algorithmic drift.Source: §1 · p. 2
  • A learner’s performance updates the model, while the model’s proficiency estimate determines subsequent content, potentially reinforcing mistaken low-proficiency predictions.The paper describes linguistic friction being mistaken for weak conceptual understanding, followed by easier content that statistically reinforces the pessimistic prediction.Source: §1 · p. 2
  • Such misclassification can under-challenge capable learners and risk entrenching the resource disparities that educational AI is intended to address.The paper argues that fairness must be considered over a degree program, not only at static validation.Source: §1 · p. 2
  • The study asks when an initially fair tutoring policy diverges, whether contemporary graders exhibit asymmetric false negatives, and whether targeted regularization outperforms standard exploration.The questions address analytical characterization, measurement of grader error, and comparison with reinforcement-learning exploration strategies.Source: §1 · pp. 2–3
  • The paper combines dynamical-systems analysis, Monte Carlo simulation, grader-noise measurement, and comparisons of the temporal fairness stabilizer with exploration baselines.It also identifies failure conditions, tests sensitivity to learner states and group heterogeneity, and proposes longitudinal governance for adaptive systems.Source: §1 · p. 3

2 Related Work

Related work connects adaptive tutoring’s learner-state estimates and curriculum choices to concerns about demographic bias and feedback over time. The paper distinguishes targeted correction of asymmetric observation noise from standard exploration aimed at average reward.

2 Related Work

  • Knowledge tracing estimates hidden mastery from graded responses, and an instructional policy uses that estimate to select the next activity.Deep knowledge tracing uses recurrent networks and LSTM units to model learning trajectories, but is described as prioritizing prediction accuracy over interpretability and equitable calibration.Source: §2 · pp. 3–4
  • Research reports differential model performance across demographic groups and linguistic varieties, including grader errors affecting non-native English writers and dialect-based decisions.Source: §2 · p. 4
  • Static fairness audits assess a model at one time point, whereas educational interactions are continuous and cumulative.Source: §2 · p. 4
  • Prior work describes how repeated prediction errors can shift future data and reinforce disparities, including in knowledge-tracing curricula.Source: §2 · p. 4
  • Standard exploration methods target average reward, not demographic-specific observation noise, so persistent linguistic friction can prompt retreat from testing a learner’s upper bounds.The paper positions its framework as an intervention tailored to this temporal degradation rather than relying on generic exploration.Source: §2 · p. 4
  • GenAI tutors can dynamically generate learning paths, while related work raises concerns that biased content and decisions may compound educational disparities.Source: §2 · p. 5

3 Methodology

The methodology combines a reduced theoretical model with agent-based simulations of feedback between learner ability, noisy correctness observations, and adaptive curriculum decisions, including a targeted probe-based intervention. Using synthetic learners and tracker- and LSTM-based simulations, it measures estimation misalignment and group-level fairness contrasts across independent replications, with code and generated data supporting reproduction.

3 Methodology

  • The study uses a discrete-time agent-based Monte Carlo simulation to isolate the feedback between asymmetric observation noise and curriculum generation, with population results aggregated across learners and replications.The controlled computational setting isolates causal mechanisms that are confounded in human-subject research, while acknowledging that real educational settings include factors such as instructor intervention and peer support.Source: §3 · p. 5

3.1 Agent-Based Environment and Learning Dynamics

  • The simulation tracks 10,000 agents for 100 steps across 50 items, with 80/20 reference/protected groups initialized to identical ability distributions.The binary group split is a modeling abstraction; learners have heterogeneous positive learning rates and gain ability through a ZPD-based rule with forgetting and clipping.Source: §3.1 · pp. 5–6

3.2 Adaptive Architecture and Algorithmic Drift

  • The tutor estimates mastery from observed correctness and selects item difficulty to match that estimate, closing a loop in which asymmetric grading errors can depress both estimates and learning.The study uses a stochastic-gradient Rasch tracker and a frozen LSTM deep-knowledge-tracing estimator, with the LSTM pre-trained on noiseless synthetic logs.Source: §3.2 · pp. 7–8

3.3 Theoretical Analysis: A Bifurcation in the Learner–AI Dyad

  • The reduced model predicts that asymmetric false negatives make the protected group receive easier content, with a critical noise threshold beyond which learning stops and ability decays.The asymptotic ability gap is non-decreasing and discontinuous at the threshold, marking a shift from progress while under-challenged to being trapped and declining.Source: §3.3 · p. 9

3.4 Intervention: Temporal Fairness Stabilization (TFS)

  • TFS detects sustained relative underestimation, then probes just above the learner’s estimate and ratchets mastery upward when the probe is passed.Its reduced-model guarantee requires a probe to land inside the ZPD; the guarantee can weaken when probes are lost to noisy grading, no cohort reference exists, probes harm learners, or probes miss the ZPD.Source: §3.3 · pp. 9–10, §3.4 · pp. 11–12

3.5 Exploration Baselines

  • Unlike generic exploration, which seeks information or reward, TFS is triggered by prolonged remedial assignment to test whether a persistent deficit reflects grader friction.The implementation compares TFS with ϵ-greedy, UCB, and Thompson sampling under identical noise conditions and the same DKT estimate and environment.Source: §3.4 · p. 12, §3.5 · p. 12

3.6 Evaluation Metrics and Implementation

  • The evaluation uses three primary metrics for system divergence and learning outcomes, expressing true ability and estimated mastery in mastery-probability space before differencing.Source: §3.6 · p. 13
  • The evaluation tracks mastery-estimation drift, learning-gap widening, and false-negative remedial classifications, treating protected-versus-reference excess drift as the fairness-relevant quantity.Drift divergence is mean absolute misalignment between true and estimated mastery; the reference group can also show nonzero drift because the estimator lags as learners improve.Source: §3.6 · pp. 13–14
  • Five group-fairness contrasts measure served difficulty, ZPD access, estimation bias, remedial misclassification, and realized learning-gain parity.The gain ratio ρ equals 1 at parity; negative estimation-bias gap indicates relative underestimation of the protected group.Source: §3.6 · p. 14
  • The vectorized NumPy/SciPy tracker experiments use N = 10,000 agents, while LSTM-DKT runs use PyTorch on one GPU and repeat the main experiment.Full sweeps use the transparent online tracker because the drift mechanism is estimator-independent and the tracker supports interpretability and reproducibility.Source: §3.6 · p. 14

Statistical analysis.

  • Inference treats independent replications—not simulated learners—as the unit, using 50 replications per condition, 25 for one-dimensional sweeps, and 10 for two-dimensional grids.Results use 95% percentile-bootstrap intervals over replication means; policy contrasts are seed-paired and reported with bootstrap intervals, paired tests, and sign-consistency fractions.Source: Statistical analysis · p. 15
  • All findings use synthetic agents rather than human participants or personal data, and the released repository provides generated data, code, seeds, and regeneration scripts.The study reports that no ethical approval or consent was required.Source: §3.6 · p. 14, Statistical analysis · p. 15

4 Results

The results first measure whether LLM graders produce demographically asymmetric observation noise, then assess its potential longitudinal effects in simulation. On the constructed answer bank, the asymmetry was grader-dependent and appeared in confidence shifts even where hard false negatives were absent.

4 Results

  • The results section pairs grader-noise measurement with longitudinal simulations of cumulative divergence using an online tracker and LSTM-DKT.Source: §4 · p. 15

4.1 Phase 1: Measuring Observation Noise in LLM Graders (RQ2)

  • The matched-pair protocol held scientific content fixed while varying linguistic markers across 40 correct and 20 incorrect answers, and assessed three graders using paired verdicts and confidence measures.Three instruction phrasings directed graders to assess conceptual mastery while ignoring language form.Source: §4.1 · pp. 15–16
  • Qwen2.5-7B-Instruct rejected 5% of correct second-language answers accepted in Standard English (ϵ̂ = 0.05), while Phi-4-mini and Mistral produced no hard false negatives.The paired confidence was lower for second-language answers on 95% of Qwen items, 98% of Phi items, and 62% of Mistral items; the Qwen paired difference was ΔP = +0.055 (p = 0.043).Source: §4.1 · p. 16, Table 2 · p. 16
  • The measurement is a pilot using one author-constructed dialect manipulation and 40 items, so it establishes existence and order of magnitude rather than a population estimate.A population estimate would require an independently annotated bank spanning dialects, subjects, and response lengths.Source: §4.1 · pp. 16–17

4.2 Phase 2: Baseline Performance and Algorithmic Drift (Scenario A)

With baseline observation noise ϵ = 0.05, the control scenario shows protected-group learning diverging from the reference group, alongside a larger distortion in estimated mastery. The reported trajectories are means over 50 independent replications.

4.2 Phase 2: Baseline Performance and Algorithmic Drift (Scenario A)

  • The protected group ended at mean latent ability μ = 0.884, with gap widening Δ = 0.038 and gain ratio ρ = 0.911, while the reference group reached μfinal = 0.922.The gap widened to Δ = 0.147 at ϵ = 0.15.Source: §4.2 · p. 17
  • Protected-group estimated mastery was 0.593 against true ability 0.884, and its drift divergence D = 0.268 exceeded the reference group's D = 0.236.The estimation-bias gap was Gb = −0.047; the exposure gap was Gβ = 0.070 and the ZPD-access gap GZ = 0.059.Source: §4.2 · p. 18
  • The model attributes the drift to spurious deficit estimates that suppress curriculum difficulty and keep protected learners in a remedial loop with insufficient challenge.The simulation isolates the autonomous algorithmic loop from external correctives such as human instructors and multimodal assessment.Source: §4.2 · p. 18

4.3 Impact of Temporal Fairness Stabilization (Scenario B)

The temporal fairness stabilizer (TFS) uses periodic diagnostic probes and a capability ratchet to reduce estimation drift. Under the modeled conditions, it nearly eliminated the achievement gap, though the recovery depends partly on a stylized simulation assumption.

4.3 Impact of Temporal Fairness Stabilization (Scenario B)

  • TFS periodically probes after sustained remedial assignment and retains demonstrated capability through a ratchet, correcting most internal drift.Source: §4.3 · p. 18
  • TFS reduced gap widening to Δ = 0.003 (95% CI [0.002, 0.004]; gain ratio ρ = 0.994), while protected-group final mean latent ability reached μ = 0.972.Drift divergence fell from D = 0.268 to D = 0.203, and estimation-bias gap shifted from Gb = −0.047 to −0.026.Source: §4.3 · p. 18
  • The reference group was not harmed: its final ability rose from 0.922 to 0.975, a paired difference of +0.0530 (95% CI [+0.0528, +0.0533]).Source: §4.3 · p. 18
  • Near-complete recovery partly reflects a stylized design in which bypassing the noise channel immediately reveals the learner's true cognitive state.Real-world recovery may be noisier and more gradual because of human factors such as disengagement or frustration.Source: §4.3 · p. 18

4.4 Comparison Against Exploration Baselines (RQ3)

Under the modeled biased channel, generic exploration does not close the fairness gap, while TFS brings group contrasts near parity. With an LSTM-DKT estimator, the bias and trapping patterns persist, and TFS continues to control the gap.

4.4 Comparison Against Exploration Baselines (RQ3)

  • TFS brought all group-fairness contrasts close to parity (∆ = 0.003, GFN = +0.006, ρ = 0.994), while ϵ-greedy, Thompson sampling, and UCB did not substitute for targeted correction.Thompson sampling reduced the gap by lowering both groups’ learning outcomes; UCB helped partially but did not close the gap.Source: §4.4 · pp. 18–19, Table 4 · p. 19

4.5 Main Experiment with an LSTM-DKT Estimator in the Loop

  • The LSTM reproduced protected-group underestimation and easier content exposure across noise levels, showing these loop effects were not artifacts of the online tracker.At a = 0.7, Gb changed from −0.015 at ϵ = 0.05 to −0.091 at ϵ = 0.30, while Gβ changed from +0.022 to +0.169.Source: §4.5 · p. 21, Figure 4 · p. 21
  • With the LSTM, the ability gap remained at parity until about ϵ = 0.10, then turned positive at about ϵ = 0.15 for a = 0.7 and ϵ = 0.20 for a = 2.0.The better-calibrated estimator tolerated more grader noise before learning was harmed, although assessment-record and exposure corruption began at the smallest ϵ.Source: §4.5 · pp. 20–21, Table 5 · p. 20
  • LSTM-driven TFS kept the protected group at or above the reference group across noise levels and discrimination values, with at most 4.7 probes per learner.The reference-group outcome never fell below 0.964; the exploration baselines again failed to substitute for TFS.Source: §4.5 · pp. 21–22

4.6 Sensitivity Analysis: Impact of Noise Parameter (ϵ)

The simulated drift increases nonlinearly with observation noise, with harm accelerating after a trapping threshold. Even below that threshold, noise can leave an assessment-record deficit; TFS reduces gap widening in the lower-noise test.

4.6 Sensitivity Analysis: Impact of Noise Parameter (ϵ)

  • Noise below the learning-stall threshold still corrupted assessment records, separating protected learners’ true ability from estimated mastery; TFS reduced this subtler drift.The reported implication is that accuracy alone does not guarantee fairness and that reducing grader friction near the threshold can yield larger fairness gains.Source: §4.6 · pp. 22–23
  • At ϵ = 0.02, gap widening was 0.013 under control versus 0.001 under TFS.The figure reports mean trajectories over R = 50 online-tracker replications.Source: Figure 5 · p. 22
  • For the calibration used, the hard-indicator threshold was ϵ⋆ ≈ 0.040, while the smooth kernel delayed asymptotic ability collapse to ϵ ≈ 0.082.At ϵ = 0.01, the gap was 0.006 while agents remained within the ZPD.Source: §4.6 · p. 22, Table 6 · p. 23
  • Above the threshold, the gap rose steeply, with per-unit slopes of ≈0.79 across 1–5% and ≈1.17 across 10–15%.The paper attributes this accelerating profile to the protected group’s served difficulty moving outside the ZPD, where learning decays rather than merely lagging.Source: §4.6 · p. 22

4.7 Initial State and Group Heterogeneity

Across tested initial states and group differences, biased-channel drift remained present, and TFS reduced the gap or its channel-attributable excess. The ability-equivalence assumption isolates the mechanism rather than generating it.

4.7 Initial State and Group Heterogeneity

  • Across nine initial-ability distributions, the control gap ranged from 0.022 to 0.042, while TFS gaps ranged from −0.006 to +0.004.Higher starting ability compressed the gap as both groups approached the mastery ceiling; neither the initial mean nor spread changed its sign or order of magnitude.Source: §4.7 · p. 24, Table 7 · p. 24
  • Tutor initial-belief offsets up to ±0.2 left control gaps between 0.031 and 0.044, while TFS kept gaps at or below 0.007.The tracker forgets its initialization within a few tens of steps, while drift is driven by the persistent channel.Source: §4.7 · pp. 23–24, Table 7 · p. 24
  • When groups differed in prior ability or learning rate, the channel-attributable excess gap stayed near +0.034 to +0.039, and TFS removed the excess in every case.TFS gaps tracked the noiseless baseline gap ∆0 to within 0.018.Source: §4.7 · pp. 23–25, Table 7 · p. 24

4.8 Robustness, Ablations, and Partial Failure Conditions

Robustness analyses show that drift persists across learning rules, estimators, and noise channels, while TFS usually reduces the resulting gap. Under extreme noise, however, TFS leaves a residual gap.

Learning-Gain Rule

  • With soft gain, the capability gap nearly vanishes (∆= 0.008) while epistemic drift rises (D = 0.299); the hard-indicator rule yields ∆= 0.041 and D = 0.257.Under soft gain, estimated mastery is about 0.64 against a true mean of 0.97, indicating that assessment-record corruption persists even as students continue to learn.Source: §4.8 · p. 25

Estimator Family

  • Across η ∈{0.1, 0.2, 0.4}, control gaps are ∆= 0.056, 0.037, and 0.023, while TFS keeps each gap at ∆≤0.004.A moment-matching estimator also shows monotonically rising gap and drift as ϵ increases, from ∆= 0.004 and D = 0.36 at ϵ = 0.01 to ∆= 0.077 and D = 0.41 at ϵ = 0.15.Source: §4.8 · p. 25

Noise Process

  • At mean ϵ = 0.05, TFS reduces the control gap from 0.031 to 0.002 under difficulty-dependent noise and from 0.035 to 0.007 under bursty noise.With continuous per-learner friction, the most-affected 20% have mean friction about 0.16 and a control gap of ∆= 0.137, reduced to 0.033 by TFS; the memoryless-channel control gap is 0.038.Source: §4.8 · p. 25

Partial Failure Conditions

  • At ϵ = 0.20, TFS reduces the unmitigated gap of 0.206 to 0.043; residual gaps rise to 0.090 at ϵ = 0.30.With depressed learning rates at ϵ = 0.20, the residual gap is 0.050; probes themselves can register false negatives under sufficiently noisy observation.Source: §4.8 · p. 25

Hyperparameter Ablation

  • Across τ ∈{3, 5, 8, 12} and m ∈{0.05, 0.10, 0.15}, the residual gap remains within [−0.006, +0.026] of zero.At ϵ = 0.05 and 0.15, the absolute trigger yields ∆= 0.025 and 0.098, versus 0.003 and 0.024 for the cohort trigger.Source: §4.8 · p. 26, Table 8 · p. 26

4.9 Failure Conditions of TFS: Probe Side Effects

Probe side effects erode TFS's benefit, especially at higher observation noise, but the intervention retains substantial gap reduction under the tested penalties.

Probe Side Effects

  • Frustration penalties reduce learning after failed probes, while entrenchment penalties lower the tutor’s estimate; both erode TFS’s benefit monotonically.About 0.47 of probes fail at ϵ = 0.05 because probes are near the estimated level and use the same observation channel.Source: §4.9 · p. 26
  • In the worst tested cell, TFS leaves residual gaps of +0.008 at ϵ = 0.05 and +0.043 at ϵ = 0.15, removing 79% and 71% of the unmitigated gaps.The protected group’s final ability falls from 0.972 to 0.947 in that cell; the unmitigated gaps are 0.037 and 0.147, respectively.Source: §4.9 · p. 27, Table 9 · p. 27
  • The authors recommend pacing and framing probes to minimize frustration and preventing failed probes from lowering estimates below their pre-probe level.Entrenchment turns a diagnostic probe into an additional source of pessimism.Source: §4.9 · p. 27

4.10 Statistical Analysis

The revised analysis separates replication-level inference from learner-level effect size, emphasizing both uncertainty across seeds and practical magnitude across learners.

Statistical Analysis

  • TFS raises protected-group final ability by +0.088 over control (95% bootstrap CI [+0.087, +0.088]) across 50 paired replications, with a learner-level Cohen’s d = 0.83.The difference is positive in 100% of replications; learner-level significance is expected with thousands of learners, so the replication-level interval and learner-level effect size are the substantive quantities.Source: §4.10 · p. 27

5 Discussion

The discussion distinguishes analytical results, simulation findings, and open questions about real-world validation. It frames drift as a temporal property of the learner–tutor loop and draws implications for auditing, policy, and human oversight.

5.1 What the Evidence Establishes, and What Remains to be Validated

  • The analysis proves that asymmetric false negatives can drive easier content and trap affected learners above a computable threshold, while periodic ZPD re-entry prevents unbounded decay.These are results for a reduced mean-field model with a hard ZPD indicator, and hold for estimators consistent with the observed signal.Source: §5.1 · p. 28
  • Simulation shows near parity with TFS where ϵ-greedy, UCB, and Thompson sampling do not, across varied learners, estimators, noise processes, and TFS settings.The threshold occurs at larger ϵ for the better-calibrated LSTM; absolute magnitudes depend on Table 1 parameters.Source: §5.1 · p. 28
  • A matched-pair bank found a 5-point false-negative asymmetry for the most affected current open-weight grader, but this pilot is not a population estimate.Across graders, confidence shifted downward for second-language answers, with no false negatives on Standard-English answers.Source: §5.1 · p. 28

5.4 Limitations and External Validity

  • Real learners, deployed platforms, transfer of measured ϵ across contexts, and student tolerance of TFS probes remain unvalidated.The simulation uses synthetic learners and a stylized scalar noise channel; effect magnitudes depend on model coupling parameters, though the authors identify qualitative findings as robust.Source: §5.1 · p. 28, §5.4 · pp. 29–30

5.2 The Dynamics of Algorithmic Drift

  • The simulation’s recursive degradation makes fairness a dynamic property of the coupled system, and easier remedial tasks can reinforce the tutor’s depressed mastery estimate.Sensitivity analyses found the pattern across learning speeds, softer gain rules, initial states, and prior-ability group differences; short-term prediction accuracy can coexist with long-term trajectory decoupling.Source: §5.2 · pp. 28–29
  • The paper distinguishes its specific mechanism—adaptive difficulty coupled to an asymmetric grader—from prior feedback-loop work, and identifies a bifurcation at ϵ⋆.Below the threshold, learners are under-challenged but progress; above it, they become trapped and decline, motivating periodic ZPD re-entry rather than average-error reduction alone.Source: §5.2 · p. 29

5.3 Quantifying Instructional Misallocation

  • At ϵ = 0.05, protected learners’ true ability is θ = 0.884 while the AI estimate is ˆp = 0.593; remedial items occupy 40% of their steps versus 29% for the reference group.The discrepancy represents instructional resources spent on remedial content for learners capable of more advanced work, and grows as ϵ increases.Source: §5.3 · p. 29

5.5 Policy Implications for Institutional Adaptation

  • The authors propose longitudinal vendor audits, fairness-aware exploration, instructor-facing drift indicators, and centralized monitoring of group learning trajectories.These recommendations are conditional on the mechanism operating in deployed systems as it does in the model.Source: §5.5 · pp. 30–31

6 Conclusion

The conclusion identifies algorithmic drift as a potential vulnerability: minor observation noise can become systemic deficits through adaptive feedback loops. In simulation, TFS offers a resilience strategy, but whether the loop operates in deployed platforms remains to be established with real learner data.

6 Conclusion

  • Minor observation noise such as linguistic friction can become systemic deficits under learner-optimizing algorithms, analytically and in simulation.The conclusion describes this as a potential vulnerability in GenAI education systems.Source: §6 · p. 31
  • The study reports measuring the triggering asymmetry in current foundation-model graders.Source: §6 · p. 31
  • TFS simulation indicates feedback loops are not inevitable: uncertainty can trigger learner-capability re-verification rather than retreat to remedial content.The conclusion says this can decouple observation noise from curriculum generation.Source: §6 · p. 32
  • Whether and how strongly these feedback loops operate in deployed platforms remains to be established with real learner data.Source: §6 · p. 32

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