Deceptive Overgeneralization: When Adaptive Learning Enables Systematic Misapplication

Synthesis: An, McLaren, and Stamper (2026) introduce and empirically test deceptive overgeneralization — a learning phenomenon in which learners acquire a skill with an incomplete subset of its necessary conditions, omitting a critical application constraint, yet still produce correct actions. Because observed correctness looks like mastery, Adaptive Learning systems that infer mastery from correct performance risk prematurely stopping practice before learners encounter cases where the action should be withheld, leaving the overgeneralization undetected. Across 11 experiments (N = 192) with Intelligent Tutoring Systems for Riichi Mahjong, learners systematically misapplied learned actions on first "do-not-act" detector items (61.5%–100% across skills and cohorts), far exceeding the error rates predicted by Bayesian Knowledge Tracing. Tailored "do-not-act" practice with feedback that names the missing constraint reduced misapplication to near-floor levels. A secondary analysis of K-12 Decimal Point data shows the framework also accounts for whole-number bias, suggesting it generalizes beyond adaptive systems to traditional instruction.

Key Findings

1. Correctness can mask incomplete conditional understanding. Learners can appear competent during practice while having compiled an overgeneralized production rule — performing the expected action but omitting the "when to withhold it" constraint. This "deceptive" pattern inflates learners' perceived competence and misleads any assessment that infers mastery from observed actions.
2. Deceptive overgeneralization is empirically prevalent. In 11 experiments using ITSs for composite-condition, single-action skills in Riichi Mahjong (score calculation, Pinfu, Kabe, Riichi judgement), learners misapplied the learned action on the first detector item at rates of 61.5%–100% across skills and cohorts — significantly exceeding both the BKT accurate-mastery baseline (12% expected error) and the new-KC baseline (40%) in nearly all comparisons.
3. Correctness-based mastery stopping rules can terminate practice too early. With BKT-based adaptivity (95% mastery threshold), the system stopped assigning practice before learners encountered any "do-not-act" case — so the overgeneralization went undetected in the adaptive round and only surfaced when learners were later given full exposure.
4. Targeted "do-not-act" remediation is highly effective. Short sequences of practice where the correct response is to refrain from the learned action, paired with feedback naming the missing application constraint, reduced misapplication from 78.6%–100% initially to 0.0%–23.1% post-remediation (Cohen's h 1.696–2.441).
5. The mechanism generalizes beyond adaptive learning. A secondary analysis of K-12 Decimal Point decimal-learning data (13 datasets, 2015–2025) showed whole-number bias — recast as a deceptive overgeneralization — persists even in an effective learning game, with 84%–88% of decimal-comparison errors consistent with the overgeneralized "longer-is-larger" rule.

Deceptive overgeneralization as a learning phenomenon

The authors ground the concept in ACT-R production systems and the Knowledge-Learning-Instruction (KLI) framework. A production is an IF(conditions)-THEN(action) pair; knowledge compilation merges chains of productions into efficient "macro-productions" whose condition sides grow, making it more likely some conditions are overlooked. Deceptive overgeneralization occurs when a learner acquires IF a subset of required conditions are met THEN perform action instead of the fully-constrained rule. It is "deceptive" because the learner still takes the correct action — until a scenario violates a missing constraint. The Crossair Flight 498 crash is offered as a domain-agnostic worked example: a commander's compiled attitude-display rule omitted the "Soviet-designed display only" constraint, and over 8,000 hours of experience did not correct it.

This distinguishes deceptive overgeneralization from negative transfer: while negative transfer typically corrects with experience, deceptive overgeneralization may persist because the overgeneralized rule competes with later knowledge during conflict resolution. It also differs from shallow-feature errors: here, all features the learner uses are part of the correct solution path, making the overgeneralization behaviorally indistinguishable from accurate understanding until a withheld-action context appears.

Implications for adaptive learning and ITS design

The findings directly challenge the assumption that correctness-based mastery inference is sufficient for Knowledge Tracing and Mastery Learning stopping rules. Because learner models and intelligent tutoring systems infer mastery from observed performance, a learner exhibiting deceptive overgeneralization can appear mastered while holding an incomplete production. The authors propose a four-step detection/remediation procedure: (1) represent skills as production rules; (2) identify composite-condition, single-action rules (most susceptible); (3) prioritize rules where misapplication is especially problematic; and (4) create tailored "do-not-act" detector items — scenarios violating a necessary condition where the correct response is to refrain — paired with Feedback naming the missing constraint. The practical implication for Adaptive Learning: treat streaks of correct actions as insufficient evidence of conditional understanding, include tasks that require withholding the learned action, and place them before mastery stopping rules trigger.

Cross-domain case study: whole-number bias in K-12 decimals

The framework is extended to a traditional, non-adaptive domain. Whole-number bias — students treating the longer decimal as larger because they apply whole-number comparison — is modeled as omitting the application constraint of the "longer-is-larger" production. Because positive whole-number comparisons provide no counterexamples, the rule is strengthened through repeated success; when decimals are introduced, the older overgeneralized production competes with the newer decimal rule during conflict resolution. In Decimal Point assessment data, error rates on the "do-not-act" decimal item fell only modestly after the intervention (53.11% → 47.47% delayed), and 84%–88% of errors were whole-number-bias aligned — suggesting simply teaching the correct procedure may be insufficient; the older, competing production must be explicitly refined. This has implications for K 12 mathematics instruction.

Connected Concepts

• Intelligent Tutoring
• Adaptive Learning
• Knowledge Tracing
• Mastery Learning
• Student Modeling
• Learning Theories
• Transfer Of Learning
• Misconceptions
• Feedback
• K 12

Connected Articles

• Adaptive Scaffolding Cognitive Engagement ITS — Adaptive ICAP scaffolding in an ITS (BKT vs DRL)
• Neural Symbolic Knowledge Tracing — Injecting mastery/non-mastery rules into deep learning learner modeling
• Stanbkt Bayesian Knowledge Tracing — Standardized Bayesian knowledge tracing
• Making AI Tutoring Productive Mastery Math 2026 — Making AI tutoring productive through mastery-based math practice
• GenAI Performance Vs Learning — The performance-vs-learning distinction in generative AI
• Correct Answer Trap Misconceptions — The correct answer trap and misconceptions in ITS

Citation

An, M., McLaren, B. M., & Stamper, J. (2026). Deceptive overgeneralization: When adaptive learning enables systematic misapplication. Journal of Computer Assisted Learning, 42, e70311.