# Track 06 — Glossary: AI Adaptive Learning

## Source Manifest

| source_id | url | bucket | last_checked | author/host | one-line note |
|-----------|-----|--------|--------------|-------------|---------------|
| T06-S001 | https://www.emergentmind.com/topics/bayesian-knowledge-tracing | secondary | 2026-05-25 | EmergentMind | BKT concept overview and history |
| T06-S002 | https://arxiv.org/pdf/2105.00385 | surrogate_primary | 2026-05-25 | arXiv / pyBKT authors | vendor docs (研究机构); pyBKT library paper with BKT parameter definitions |
| T06-S003 | https://dl.acm.org/doi/10.1007/978-3-030-78270-2_49 | surrogate_primary | 2026-05-25 | ACM / Springer AIED | vendor docs (研究机构); MAB algorithms for adaptive learning survey |
| T06-S004 | https://www.nature.com/articles/s41598-025-07422-7 | surrogate_primary | 2026-05-25 | Nature Scientific Reports | vendor docs (研究机构); deep learning knowledge tracing in ITS |
| T06-S005 | https://www.nature.com/articles/s41598-025-10497-x.pdf | surrogate_primary | 2026-05-25 | Nature Scientific Reports | vendor docs (研究机构); DKT and cognitive load estimation |
| T06-S006 | https://pmc.ncbi.nlm.nih.gov/articles/PMC9584970/ | verified_primary | 2026-05-25 | NIH PMC | Cognitive framework with self-attention for knowledge tracing |
| T06-S007 | https://par.nsf.gov/servlets/purl/10484759 | verified_primary | 2026-05-25 | NSF | IRT statistical framework survey |
| T06-S008 | https://www.1edtech.org/specifications | surrogate_primary | 2026-05-25 | 1EdTech Consortium | vendor docs (标准组织); official specifications listing |
| T06-S009 | https://www.imsglobal.org/lti-advantage-overview | surrogate_primary | 2026-05-25 | 1EdTech / IMS Global | vendor docs (标准组织); LTI Advantage overview |
| T06-S010 | https://xapi.com/cmi5/comparison-of-scorm-xapi-and-cmi5/ | surrogate_primary | 2026-05-25 | xAPI.com / Rustici Software | vendor docs (标准组织); SCORM vs xAPI vs cmi5 comparison |
| T06-S011 | https://sagroups.ieee.org/ltsc/active-standards/ | surrogate_primary | 2026-05-25 | IEEE LTSC | vendor docs (标准组织); active IEEE learning technology standards |
| T06-S012 | https://www.imsglobal.org/spec/ob/v3p0/cert/ | surrogate_primary | 2026-05-25 | 1EdTech / IMS Global | vendor docs (标准组织); Open Badges 3.0 certification guide |
| T06-S013 | https://educationframework.com/resources/student-privacy-laws/federal-laws | secondary | 2026-05-25 | Education Framework | FERPA and COPPA overview |
| T06-S014 | https://www.edugenius.app/blog/legal-considerations-ai-education-ferpa-coppa | secondary | 2026-05-25 | EduGenius | Legal considerations for AI in education |
| T06-S015 | https://usercentrics.com/knowledge-hub/student-data-privacy-laws/ | secondary | 2026-05-25 | Usercentrics | Comprehensive student data privacy laws guide |
| T06-S016 | https://www.cac.gov.cn/2021-08/20/c_1631050028355286.htm | verified_primary | 2026-05-25 | 中国国家互联网信息办公室 | 个人信息保护法全文 |
| T06-S017 | https://www.gov.cn/zhengce/2021-07/24/content_5627132.htm | verified_primary | 2026-05-25 | 中国政府网 | 双减政策全文 |
| T06-S018 | http://www.moe.gov.cn/jyb_xxgk/moe_1777/moe_1778/202310/t20231025_1087333.html | verified_primary | 2026-05-25 | 中国教育部 | 未成年人网络保护条例 |
| T06-S019 | https://www.qualitymatters.org/reviews-certifications | surrogate_primary | 2026-05-25 | Quality Matters | vendor docs (行业协会); QM reviews and certifications overview |
| T06-S020 | https://www.cogn-iq.org/learn/theory/item-response-theory/ | secondary | 2026-05-25 | Cogn-IQ | IRT models, parameters, and applications explainer |
| T06-S021 | https://assess.com/what-is-item-response-theory/ | surrogate_primary | 2026-05-25 | Assessment Systems | vendor docs (测评机构); IRT practical guide for assessment |
| T06-S022 | https://en.wikipedia.org/wiki/Item_response_theory | secondary | 2026-05-25 | Wikipedia | IRT overview and model taxonomy |
| T06-S023 | https://www.structural-learning.com/post/ebbinghaus-forgetting-curve | secondary | 2026-05-25 | Structural Learning | Ebbinghaus forgetting curve explainer |
| T06-S024 | https://aristeksystems.com/blog/elearning-standards/ | secondary | 2026-05-25 | Aristek Systems | eLearning standards comparison guide |
| T06-S025 | https://elearningindustry.com/adaptive-and-personalized-learning-differences | secondary | 2026-05-25 | eLearning Industry | Adaptive vs personalized learning differences |
| T06-S026 | https://www.simplypsychology.org/zone-of-proximal-development.html | secondary | 2026-05-25 | Simply Psychology | ZPD and scaffolding explanation |
| T06-S027 | https://files.eric.ed.gov/fulltext/EJ1148445.pdf | verified_primary | 2026-05-25 | ERIC / US Dept of Education | CAT applications and item calibration |
| T06-S028 | https://pmc.ncbi.nlm.nih.gov/articles/PMC13030155/ | verified_primary | 2026-05-25 | NIH PMC | Adaptive knowledge tracing with dynamic memory and RL |
| T06-S029 | https://arxiv.org/pdf/2212.12139 | surrogate_primary | 2026-05-25 | arXiv / HiTSKT authors | vendor docs (研究机构); hierarchical transformer for knowledge tracing |
| T06-S030 | https://www.ieeeltsc.org/about/ | surrogate_primary | 2026-05-25 | IEEE LTSC | vendor docs (标准组织); about IEEE LTSC mission and scope |
| T06-S031 | https://6b.education/insight/building-privacy-compliant-systems-edtech-development-under-gdpr-coppa-and-ferpa/ | secondary | 2026-05-25 | 6B Education | Privacy-compliant EdTech systems under GDPR/COPPA/FERPA |
| T06-S032 | https://site.imsglobal.org/certifications | surrogate_primary | 2026-05-25 | 1EdTech / IMS Global | vendor docs (标准组织); product certifications directory |
| T06-S033 | https://www.chinalawinsight.com/2021/09/articles/education/ | surrogate_primary | 2026-05-25 | China Law Insight / 金杜律所 | vendor docs (研究机构); 双减政策法律分析 |

---

## 1. Core Technical Terms

### 1.1 Knowledge Tracing and Student Modeling

**Bayesian Knowledge Tracing (BKT)**
- Insider usage: A Hidden Markov Model treating each knowledge component as a binary latent variable (mastered / not mastered). Four core parameters: P(L0) initial knowledge, P(T) learning rate per opportunity, P(S) slip (knows but answers wrong), P(G) guess (doesn't know but answers right). Introduced by Corbett and Anderson (1995). Still the baseline that every new KT model benchmarks against.
- Outsider tell: Treating BKT as a "quiz scoring algorithm" rather than a latent-state inference model. Outsiders often ignore that slip/guess are not error rates but conditional probabilities under a generative model.
- evidence: [T06-S001, T06-S002]

**Deep Knowledge Tracing (DKT)**
- Insider usage: Pieper et al. (2015) replaced BKT's hand-engineered features with recurrent neural networks (LSTMs/GRUs) that ingest raw interaction sequences (student, item, correctness) and output predicted mastery across all knowledge components simultaneously. Key advantage: captures cross-skill dependencies without explicit prerequisite graphs.
- Outsider tell: Assuming DKT "replaced" BKT. In practice, DKT's lack of interpretability (no slip/guess parameters) limits adoption in high-stakes assessment. Many production systems use hybrid approaches.
- evidence: [T06-S004, T06-S005]

**Attentive Knowledge Tracing (AKT)**
- Insider usage: Uses self-attention (transformer) mechanisms to weight historical interactions by relevance rather than recency. Enables the model to attend to distant but pedagogically related interactions. Represents the current SOTA direction in KT research alongside SAINT, SAINT+, and simpleKT.
- Outsider tell: Conflating "attention-based" with "uses ChatGPT." Attention in KT is a specific architectural choice for sequence modeling, not a generative language model.
- evidence: [T06-S006, T06-S029]

**Knowledge Component (KC)**
- Insider usage: The atomic unit of knowledge that a student either has or hasn't mastered. In cognitive tutors, KCs map to specific production rules (e.g., "multiply fractions with unlike denominators"). The KC grain size directly affects model accuracy — too coarse loses signal, too fine causes sparse data.
- Outsider tell: Equating KCs with "topics" or "chapters." A single textbook chapter may contain 5-20 KCs. KC granularity is an empirical design decision, not a curriculum outline.
- evidence: [T06-S001, T06-S002]

**Mastery Probability**
- Insider usage: P(mastery) for a given KC at a given time, typically the posterior probability in BKT after observing a sequence of responses. A mastery threshold (often 0.95) triggers the system to advance the learner to new content. The threshold choice is itself a design parameter with pedagogical consequences.
- Outsider tell: Confusing mastery probability with "percentage correct." A student can score 80% correct yet have P(mastery) = 0.99 if the errors are consistent with the slip parameter.
- evidence: [T06-S002]

**Slip and Guess Parameters**
- Insider usage: In BKT, P(S) = probability of incorrect response despite mastery; P(G) = probability of correct response despite non-mastery. These are not "noise" — they encode fundamental assumptions about assessment reliability. Typical values: slip 0.05-0.15, guess 0.1-0.3 depending on item format (MCQ vs. open response).
- Outsider tell: Treating these as "cheating detection" parameters. Slip/guess are about item-student interaction reliability, not academic dishonesty.
- evidence: [T06-S001, T06-S002]

**Learning Curve**
- Insider usage: A plot of error rate vs. opportunity count for a given KC. A well-defined KC shows a monotonically decreasing power-law curve. A flat or jagged curve signals a poorly defined KC that may need splitting. Learning curve analysis is a primary diagnostic in cognitive tutor design.
- Outsider tell: Using "learning curve" colloquially to mean "difficulty of picking something up." In adaptive learning, it's a specific diagnostic visualization with quantitative criteria.
- evidence: [T06-S001]

**Forgetting Factor**
- Insider usage: An extension to BKT and other KT models that accounts for knowledge decay over time between practice opportunities. Without it, BKT assumes mastery is permanent. Models like BKT+ and DKT-Forget add time-decay terms, typically exponential, calibrated to match Ebbinghaus-style retention curves.
- Outsider tell: Assuming all adaptive systems model forgetting. Many production platforms (especially those focused on course completion rather than long-term retention) ignore forgetting entirely.
- evidence: [T06-S028]

**Performance Factor Analysis (PFA)**
- Insider usage: An alternative to BKT that uses logistic regression with success and failure counts as features per KC. Simpler to implement and train than BKT, competitive accuracy for many datasets. Key advantage: naturally handles multiple KCs per item through additive feature contributions.
- Outsider tell: Rarely mentioned by outsiders. PFA occupies the practical middle ground that insiders use when BKT is overkill and DKT is uninterpretable.
- evidence: [T06-S004]

### 1.2 Adaptive Learning Algorithms

**Multi-Armed Bandit (MAB)**
- Insider usage: A decision framework where each "arm" represents a pedagogical action (show hint, assign easier item, switch topic) and the system balances exploration (trying untested actions) vs. exploitation (repeating known-effective actions). Contextual bandits (LinUCB, Thompson Sampling) condition on student features. Used in platforms like ALEKS and Open Learning Initiative for content recommendation.
- Outsider tell: Thinking MAB is just "A/B testing." MAB adaptively allocates traffic based on cumulative reward, whereas A/B testing uses fixed allocation until statistical significance.
- evidence: [T06-S003]

**Exploration-Exploitation Tradeoff**
- Insider usage: The fundamental tension in adaptive systems: exploit current best strategy (maximize short-term learning) vs. explore uncertain alternatives (improve long-term model accuracy). In education, over-exploitation means never discovering that a student would benefit from a different content sequence; over-exploration wastes student time on suboptimal activities.
- Outsider tell: Not recognizing this tradeoff exists. Many "adaptive" systems are pure exploitation (always recommend easiest path) which leads to local optima in learning outcomes.
- evidence: [T06-S003]

**Prerequisite Graph / Knowledge Graph**
- Insider usage: A directed acyclic graph (DAG) where nodes are KCs and edges represent prerequisite relationships (must master A before attempting B). In practice, constructing and validating these graphs is one of the hardest engineering problems in adaptive learning — expert disagreement on prerequisites is common, and data-driven graph learning from student performance often contradicts expert intuition.
- Outsider tell: Assuming prerequisite graphs are "just the table of contents." Empirically validated prerequisite structures often differ significantly from textbook chapter ordering.
- evidence: [T06-S004]

**Learning Path Optimization**
- Insider usage: The problem of sequencing learning activities to maximize a learning objective (mastery, retention, time-efficiency) given a prerequisite graph, student model, and activity inventory. Often formulated as a Markov Decision Process (MDP) and solved via reinforcement learning or dynamic programming. Constraints include time budgets, content coverage requirements, and pedagogical variety.
- Outsider tell: Equating "learning path" with "playlist." A true optimized learning path considers the student's current knowledge state, forgetting dynamics, and inter-topic transfer effects — not just content ordering.
- evidence: [T06-S003, T06-S028]

**Zone of Proximal Development (ZPD)**
- Insider usage: Vygotsky's (1978) concept: the gap between what a learner can do independently and what they can do with guidance. In adaptive learning systems, ZPD is operationalized as a target difficulty band — items should be challenging enough to require effort but not so hard as to cause frustration. The "flow channel" in Csikszentmihalyi's framework is the computational analog.
- Outsider tell: Using ZPD as a vague synonym for "challenge level." Insiders operationalize it with specific item difficulty parameters (IRT theta range) relative to estimated student ability.
- evidence: [T06-S026]

**Scaffolding**
- Insider usage: Temporary support structures that help a learner perform tasks in their ZPD. In adaptive systems, scaffolding is implemented as hints, worked examples, decomposed sub-problems, or visual aids that are progressively removed (faded) as the student demonstrates mastery. The fading schedule itself is an adaptive decision.
- Outsider tell: Using "scaffolding" to mean "giving the answer in steps." Proper scaffolding supports the student's own reasoning process; it does not replace it.
- evidence: [T06-S026]

**Cold Start Problem**
- Insider usage: When a new student or new item enters the system with no interaction history, the model has no data to personalize. Solutions include: diagnostic pre-tests, transfer learning from similar students (collaborative filtering), Bayesian priors from population-level data, and contextual bandits with warm-start features (demographics, prior coursework).
- Outsider tell: Not recognizing cold start as a fundamental limitation. Marketing materials often claim "personalized from day one" without explaining that early recommendations are necessarily population-average until sufficient data accumulates.
- evidence: [T06-S003]

**Content Recommendation**
- Insider usage: The algorithmic decision of what to show the student next. Unlike entertainment recommendation (maximize engagement), educational recommendation must balance engagement with pedagogical value — sometimes the right recommendation is the boring-but-necessary prerequisite, not the fun-but-premature advanced topic.
- Outsider tell: Applying Netflix/Spotify mental models to education. Educational recommendation has fundamentally different objective functions (learning gain, not watch time).
- evidence: [T06-S003, T06-S004]

### 1.3 Psychometrics and Assessment

**Item Response Theory (IRT)**
- Insider usage: A family of probabilistic models relating a latent trait (ability, theta) to the probability of a correct response on an item. The key property is "invariance" — item parameters are independent of the sample, and ability estimates are independent of the specific items administered. This is what makes adaptive testing mathematically valid. Pioneers: Lord, Rasch, Lazarsfeld (1950s-60s).
- Outsider tell: Using "IRT" without understanding that it requires assumptions (unidimensionality, local independence) that must be empirically verified. Applying IRT to a multidimensional construct without MIRT is a common mistake.
- evidence: [T06-S007, T06-S020, T06-S022]

**Classical Test Theory (CTT)**
- Insider usage: The older framework where observed score = true score + error. Item statistics (difficulty = p-value, discrimination = point-biserial correlation) are sample-dependent — different student populations yield different item parameters. Still widely used for classroom assessments and item screening before IRT calibration.
- Outsider tell: Dismissing CTT as "obsolete." CTT is still the appropriate framework for small-sample classroom assessments where IRT calibration is not feasible (requires typically 200-1000+ responses per item).
- evidence: [T06-S021, T06-S022]

**Computerized Adaptive Testing (CAT)**
- Insider usage: A test administration paradigm where item selection is driven by the current ability estimate. After each response, the system updates theta (via MLE or Bayesian estimation), selects the next item maximizing information at that theta, and checks stopping rules (SE threshold, item count limit, content coverage). Used in GRE, GMAT, NCLEX, NAEP. Requires a calibrated item bank with IRT parameters.
- Outsider tell: Conflating CAT with "any online test" or "adaptive quiz." A true CAT requires: (1) IRT-calibrated item bank, (2) real-time ability estimation, (3) information-based item selection, (4) formal stopping rules. A quiz that adjusts difficulty by "easy/medium/hard" bins is not CAT.
- evidence: [T06-S027, T06-S020]

**Rasch Model (1PL)**
- Insider usage: The simplest IRT model: P(correct) = logistic(theta - b), where b is item difficulty and theta is person ability. The Rasch model is not just "simple IRT" — it embodies a measurement philosophy (specific objectivity) where items and persons are measured on the same interval scale. Rasch purists argue that items not fitting the model should be discarded, not that the model should be made more complex.
- Outsider tell: Treating Rasch as "IRT with one parameter" without understanding the philosophical commitment to sufficiency and specific objectivity that distinguishes the Rasch measurement tradition from the IRT modeling tradition.
- evidence: [T06-S007, T06-S022]

**2PL / 3PL Models**
- Insider usage: 2PL adds an item discrimination parameter (a): items differ in how well they distinguish between ability levels. 3PL adds a pseudo-guessing parameter (c): even very low-ability examinees have a non-zero probability of correct response (especially on MCQs). 3PL is standard for high-stakes MCQ-based tests. 4PL (with upper asymptote) exists but is rarely used.
- Outsider tell: Choosing models by complexity rather than fit. A 3PL on free-response items where guessing is negligible is misspecified; a 1PL on 4-option MCQs ignores real guessing behavior.
- evidence: [T06-S007, T06-S020]

**Item Bank**
- Insider usage: A repository of pre-calibrated items with known IRT parameters, content tags, enemy item relationships, and exposure history. Building and maintaining an item bank is a multi-year investment — items must be field-tested, calibrated, equated across administrations, and retired when overexposed. A mature CAT program may have 5-20x more items than any single test form requires.
- Outsider tell: Treating an item bank as a "question database." A true item bank has psychometric metadata (IRT parameters, DIF statistics, exposure rates) that a simple question list lacks.
- evidence: [T06-S027, T06-S021]

**Test Information Function (TIF)**
- Insider usage: A curve showing the precision of measurement (information) as a function of ability. In IRT, information is the reciprocal of the squared standard error. A well-designed test has high information at the ability range of interest. CAT maximizes information at the individual's estimated ability, which is why it's more efficient than fixed-form tests.
- Outsider tell: Rarely discussed by outsiders. The TIF concept is what makes CAT provably more efficient — it allocates measurement precision where the individual is, rather than spreading it across the entire ability range.
- evidence: [T06-S007, T06-S020]

**Standard Error of Measurement (SEM)**
- Insider usage: The estimated precision of an ability score. In CAT, SEM decreases with each administered item and serves as a stopping criterion (e.g., stop when SEM < 0.3). Unlike fixed-form tests where SEM is uniform, CAT SEM varies by examinee — more items are needed for examinees near decision boundaries.
- Outsider tell: Reporting test scores without SEM. Any adaptive assessment score should be accompanied by its SEM or confidence interval.
- evidence: [T06-S007, T06-S027]

### 1.4 Learning Science Foundations

**Spaced Repetition**
- Insider usage: Scheduling review of material at increasing intervals calibrated to the learner's retention curve. Implementations range from simple algorithms (Leitner box, SM-2 in Anki) to model-based systems (DASH, HLR by Duolingo) that use forgetting curve parameters per learner-item pair. The key insight: optimal spacing is not fixed but depends on individual memory strength and item difficulty.
- Outsider tell: Equating spaced repetition with "flashcards." Spaced repetition is a scheduling algorithm; flashcards are one possible delivery mechanism. Spaced repetition applies equally to practice problems, essays, motor skills.
- evidence: [T06-S023]

**Forgetting Curve (Ebbinghaus)**
- Insider usage: Ebbinghaus (1885) demonstrated that memory retention follows an approximately exponential decay: roughly 50% lost within 1 hour, 70% within 24 hours, 90% within 1 week without review. Modern research (Murre and Dros, 2015) replicated the curve. In adaptive learning, the forgetting curve is parameterized per learner-item pair and used to schedule optimal review timing.
- Outsider tell: Citing the "Ebbinghaus curve" as if it's one universal curve. In reality, decay rates vary dramatically by material meaningfulness, encoding depth, and individual differences. Nonsense syllable retention (Ebbinghaus's stimuli) decays much faster than meaningful content.
- evidence: [T06-S023]

**Interleaving**
- Insider usage: Mixing different types of problems or topics during practice rather than blocking (practicing one type exhaustively before moving to the next). Robust evidence shows interleaving improves discriminative learning — the ability to identify which strategy or concept applies. Effect sizes are larger for mathematics and category learning. However, interleaving feels harder subjectively, which creates adoption resistance.
- Outsider tell: Conflating interleaving with "variety" or "mixing things up randomly." Effective interleaving requires that the interleaved topics share a discriminative structure — randomly mixing unrelated topics provides no interleaving benefit.
- evidence: [T06-S023]

**Retrieval Practice (Testing Effect)**
- Insider usage: The finding that actively retrieving information from memory strengthens long-term retention more than passive re-study. Each successful retrieval strengthens the memory trace and slows forgetting. Used in adaptive systems to favor recall-format questions over re-reading, even when students prefer re-reading. Roediger and Karpicke (2006) is the landmark study.
- Outsider tell: Treating quizzes only as assessment tools. In adaptive learning, low-stakes quizzing is primarily a learning activity, not an evaluation activity. The "testing effect" means the test itself teaches.
- evidence: [T06-S023]

**Cognitive Load Theory (CLT)**
- Insider usage: Sweller's (1988) framework distinguishing intrinsic load (inherent complexity of material), extraneous load (caused by poor instructional design), and germane load (effort directed at schema construction). Adaptive systems manage cognitive load by: adjusting problem complexity to match current mastery, removing extraneous elements, and scaffolding to redirect effort toward germane processing.
- Outsider tell: Using "cognitive overload" as a vague synonym for "too hard." CLT provides specific, testable predictions — e.g., the worked example effect reverses for advanced learners (expertise reversal effect).
- evidence: [T06-S026]

**Mastery Learning (Bloom)**
- Insider usage: Bloom's (1968) instructional model where students must demonstrate mastery of a unit (typically 80-90% on a formative assessment) before proceeding. Combined with corrective feedback and additional practice time. Bloom's "2 sigma problem" — that 1-on-1 tutoring produces 2 standard deviation improvement — is the aspirational benchmark for adaptive learning systems.
- Outsider tell: Confusing mastery-based progression with grade inflation. Mastery learning sets a high bar for each unit; it does not lower standards. The criticism that "everyone gets an A" misunderstands that mastery learning extends time, not lowers expectations.
- evidence: [T06-S026]

**Desirable Difficulties**
- Insider usage: Bjork's (1994) concept that certain conditions that make learning more effortful during acquisition (spacing, interleaving, generation, varied practice) actually enhance long-term retention and transfer. Critical implication for adaptive systems: optimizing for immediate performance metrics (completion rate, accuracy during learning) can actually harm long-term outcomes.
- Outsider tell: Using high immediate accuracy as evidence of effective learning. An adaptive system that achieves 95% accuracy during learning by making everything too easy is worse than one achieving 75% with appropriately difficult items.
- evidence: [T06-S023]

### 1.5 AI/ML Specific

**Recommendation System (in Education)**
- Insider usage: Collaborative filtering (student-student similarity) and content-based filtering (KC-level features) adapted for educational contexts. Critical difference from entertainment recommendation: the objective function is learning gain, not engagement or satisfaction. A good educational recommender may recommend "hard" content that students dislike in the moment but that maximizes learning. Hybrid systems combining BKT-based mastery with CF are common.
- Outsider tell: Importing engagement-maximizing recommendation patterns from social media/e-commerce into education without changing the objective function. This leads to "popularity bias" where easy/entertaining content gets over-recommended.
- evidence: [T06-S003, T06-S004]

**Knowledge Graph Embedding (in Education)**
- Insider usage: Representing KCs, items, and their relationships as low-dimensional vectors (embeddings) learned from student interaction data. Enables: measuring KC similarity, predicting prerequisite relationships, and discovering latent content structure. TransE, RotatE, and GNN-based approaches have been adapted for educational knowledge graphs. Used to address the "KC mapping problem" — automatically tagging items with KCs.
- Outsider tell: Confusing educational knowledge graphs with general-purpose knowledge graphs (Wikidata, Freebase). Educational KGs encode pedagogical relationships (prerequisite, co-requisite, transfer), not encyclopedic facts.
- evidence: [T06-S004, T06-S029]

**Transformer-Based Knowledge Tracing**
- Insider usage: SAINT (2020), SAINT+ (2021), AKT, and simpleKT apply transformer architectures to student interaction sequences. Self-attention allows the model to identify which past interactions are most predictive of future performance, regardless of temporal distance. Current SOTA on benchmark datasets (ASSISTments, EdNet, Junyi). Key challenge: positional encoding must capture both sequence position and temporal gaps.
- Outsider tell: Assuming "transformer = GPT" and that knowledge tracing uses language models. KT transformers are sequence-to-sequence models on structured (item_id, correctness, timestamp) tuples, not natural language.
- evidence: [T06-S006, T06-S029]

**Reinforcement Learning for Tutoring**
- Insider usage: Formulating the tutoring problem as an MDP where states = student knowledge state, actions = pedagogical decisions (next item, hint, feedback type), rewards = learning gain or mastery achievement. Policy gradient and Q-learning approaches have been applied. Key challenges: reward signal is delayed and sparse (learning outcomes are measured days/weeks later), and state space is enormous. POMDP formulations account for uncertainty in knowledge state estimation.
- Outsider tell: Expecting RL tutors to "learn by trial and error on real students." Ethical and sample-efficiency constraints mean most RL tutor research uses simulated students or historical data for offline policy learning.
- evidence: [T06-S028, T06-S003]

**NLP for Auto-Grading**
- Insider usage: Automated scoring of free-text responses using NLP. Ranges from simple rubric-keyword matching to deep learning approaches (BERT-based classifiers trained on human-scored examples). High-stakes applications (e.g., ETS's e-rater for GRE essays) undergo rigorous fairness and validity studies. The field distinguishes "automated essay scoring" (AES) from "automated short answer grading" (ASAG) — different technical challenges.
- Outsider tell: Assuming LLMs can replace human grading without validation. Auto-grading systems must demonstrate comparable inter-rater reliability to trained human raters and must be audited for bias across demographic groups.
- evidence: [T06-S004]

**Generative AI for Item Generation**
- Insider usage: Using LLMs to generate assessment items (questions, distractors, scenarios) that are then reviewed by subject matter experts and psychometrically calibrated. Current state: good for generating initial drafts that reduce SME authoring time by 50-70%, but generated items still require expert review for content accuracy, construct alignment, and bias. The bottleneck has shifted from "generating items" to "calibrating items."
- Outsider tell: Claiming "AI can generate unlimited test questions." Without psychometric calibration (field testing on real students, IRT parameter estimation, DIF analysis), generated items have unknown measurement properties and cannot be used in CAT or high-stakes assessment.
- evidence: [T06-S004, T06-S028]

---

## 2. Standards and Specifications

**xAPI (Experience API) / Tin Can API**
- Version: 1.0.3 (2016, maintained by ADL Initiative; IEEE 9274.1.1-2023 standardized the JSON data model)
- Status: Active, widely adopted. The successor to SCORM for tracking learning activities.
- Insider usage: Records learning experiences as Actor-Verb-Object "statements" (e.g., "Jane completed Module 5") sent to a Learning Record Store (LRS). Tracks any learning activity — mobile, simulation, VR, on-the-job — not just LMS-hosted content. The statement format is extensible via "profiles" (activity types, extensions). Adaptive learning systems use xAPI to log granular interaction data (time-on-task, hint usage, item-level responses) that feeds knowledge tracing models.
- Outsider tell: Thinking xAPI is "just SCORM 2.0." xAPI fundamentally decouples tracking from the LMS — statements go to an LRS, which is separate from the LMS. This enables multi-platform learning analytics that SCORM cannot support.
- evidence: [T06-S010, T06-S024, T06-S011]

**LTI (Learning Tools Interoperability)**
- Version: LTI 1.3 / LTI Advantage (2019, Final). Published by 1EdTech Consortium.
- Status: Active, the dominant standard for connecting external tools to LMS platforms. Over 250 certified products.
- Insider usage: Defines how an LMS (Platform) launches an external Tool, passing user identity and context (course, role) via OAuth 2.0 and JWT. LTI Advantage adds three services: Assignment and Grade Services (AGS) for grade passback, Names and Role Provisioning Services (NRPS) for roster access, and Deep Linking for content selection. Adaptive learning tools integrate with LMS ecosystems via LTI.
- Outsider tell: Confusing LTI with an SSO protocol. LTI does handle authentication but its primary purpose is contextual launch — passing not just "who" but "from which course/section/role" so the tool can personalize the experience.
- evidence: [T06-S008, T06-S009, T06-S032]

**SCORM (Sharable Content Object Reference Model)**
- Version: SCORM 2004 4th Edition (2009). Published by ADL Initiative (US DoD).
- Status: Legacy but still dominant by installed base. No longer actively developed.
- Insider usage: Packages learning content (HTML/JS) with a manifest (imsmanifest.xml) and runtime API (SCORM RTE) for LMS communication. Tracks completion, score, time, and basic interactions. Limitations: requires LMS hosting, tracks only browser-based content, limited data model (no granular interaction data), and the sequencing specification (SCORM 2004) was too complex for most implementors.
- Outsider tell: Assuming SCORM is the current standard. SCORM is legacy — new projects should default to xAPI/cmi5 for tracking or LTI for tool integration. However, many enterprise LMS platforms still require SCORM packages.
- evidence: [T06-S010, T06-S024]

**QTI (Question and Test Interoperability)**
- Version: QTI 3.0 (2022, Final). Published by 1EdTech Consortium.
- Status: Active. Defines how assessment items and tests are authored, exchanged, and rendered.
- Insider usage: XML/JSON schema for representing item types (multiple choice, text entry, extended text, hotspot, match, order, etc.), scoring rules, response processing, and adaptive test navigation. QTI 3.0 added support for accessible content (APIP integration), portable custom interactions, and improved outcome processing. Critical for interoperable item banks — enables items authored in one system to be used in another without reformatting.
- Outsider tell: Ignoring QTI and building proprietary item formats. This creates vendor lock-in for item banks that represent millions of dollars of psychometric investment.
- evidence: [T06-S008, T06-S024]

**cmi5**
- Version: cmi5 1.0 (2016, governed by IEEE LTSC cmi5 Working Group / AICC successor)
- Status: Active, growing adoption. An xAPI profile specifically for LMS-managed content.
- Insider usage: Bridges SCORM's LMS-centric workflow with xAPI's flexible tracking. The LMS launches content and expects specific xAPI statements (Initialized, Completed, Passed, Failed, Terminated). Content can also send custom statements. Solves the "xAPI is too free-form" problem for LMS vendors who need structured completion/scoring data.
- Outsider tell: Thinking cmi5 competes with xAPI. cmi5 IS xAPI — it's a constrained profile that adds the structure LMS vendors need on top of xAPI's open framework.
- evidence: [T06-S010]

**IMS Caliper Analytics**
- Version: Caliper 1.2 (2020, Final). Published by 1EdTech Consortium.
- Status: Active but less widely adopted than xAPI.
- Insider usage: An event-based data model for learning analytics. Defines "Metric Profiles" — standardized event schemas for specific learning activities (Assessment, Grading, Reading, Media, Session, etc.). Each event has an actor, action, object, and contextual metadata. Events are sent to a Sensor API endpoint. Caliper's strength is its structured vocabulary; its weakness is tighter coupling to 1EdTech's ecosystem.
- Outsider tell: Confusing Caliper with xAPI. Both track learning events but differ in philosophy: xAPI is bottom-up (flexible statements, community-defined profiles) while Caliper is top-down (pre-defined metric profiles, 1EdTech-governed vocabulary).
- evidence: [T06-S008, T06-S024]

**IEEE LTSC Standards**
- Key active standards: IEEE 1484.12.1-2020 (Learning Object Metadata), IEEE 1484.11.1-2022 (Data Model for Content Communication), IEEE 1484.20.3-2022 (Shareable Competency Definitions), IEEE 3527.1-2020 (Digital Intelligence DQ Framework), IEEE 9274.1.1-2023 (xAPI JSON Data Model).
- Status: Active. The LTSC is chartered by IEEE Computer Society to develop internationally accredited learning technology standards.
- Insider usage: IEEE LTSC provides the formal standards process for specifications that often originate in other bodies (ADL, 1EdTech). The LOM standard (1484.12.1) is foundational for metadata-driven content discovery. The competency standards (1484.20.x) are critical for skills-based learning pathways.
- evidence: [T06-S011, T06-S030]

**Open Badges / CLR (Comprehensive Learner Record)**
- Version: Open Badges 3.0 (2024, Final), aligned with W3C Verifiable Credentials. CLR 2.0 (2021, Final).
- Status: Active, growing adoption in micro-credentialing and lifelong learning.
- Insider usage: Open Badges encode a credential (issuer, criteria, evidence, recipient) in a verifiable, portable JSON-LD format. CLR aggregates multiple badges, courses, and competency achievements into a holistic learner record. Together they enable "skills-based hiring" — employers can verify specific competencies rather than relying solely on degrees. 1EdTech certification ensures interoperability across badge platforms.
- Outsider tell: Treating digital badges as "participation trophies." Well-designed badge ecosystems require evidence-based criteria and third-party verification — they are micro-credentials, not stickers.
- evidence: [T06-S008, T06-S012, T06-S032]

**Ed-Fi Data Standard**
- Version: Ed-Fi Data Standard v4.0 (K-12 US focus). Published by Ed-Fi Alliance (sponsored by Michael & Susan Dell Foundation).
- Status: Active, widely adopted in US K-12 for SIS/LMS data interoperability.
- Insider usage: Defines a common data model and REST API for exchanging K-12 education data (students, schools, assessments, attendance, grades). Unlike xAPI/Caliper (learning activity events), Ed-Fi focuses on administrative and demographic data. Ed-Fi ODS (Operational Data Store) serves as the central data warehouse for district analytics.
- Outsider tell: Confusing Ed-Fi with learning analytics standards. Ed-Fi is a school data infrastructure standard, not a learning experience tracking standard.
- evidence: [T06-S024]

**中国教育信息化标准 / CELTS**
- Version: CELTS (China E-Learning Technology Standards) series, published by 教育部教育信息化技术标准委员会.
- Status: Active. China maintains its own education technology standards framework parallel to international standards.
- Insider usage: Includes CELTS-2 (Learning Object Metadata, aligned with IEEE LOM), CELTS-3 (Learner Model), CELTS-11 (Content Packaging), and others. The 教育管理信息 standards (JY/T 1001-1010 series) define administrative data schemas for Chinese schools. In practice, most Chinese EdTech companies follow a mix of CELTS for government compliance and international standards (SCORM/xAPI) for product development.
- Outsider tell: Assuming Chinese EdTech follows the same standards ecosystem as the US/EU. China has parallel standards bodies and government compliance requirements that create a distinct technical landscape.
- evidence: [T06-S017, T06-S018]

---

## 3. Regulations and Compliance

**FERPA (Family Educational Rights and Privacy Act)**
- Jurisdiction: United States. Year: 1974 (20 U.S.C. section 1232g).
- Insider usage: Protects "education records" — any record directly related to a student maintained by an educational agency/institution receiving federal funding. Key provisions: parents (or eligible students 18+) have rights to inspect, amend, and control disclosure of records. "Directory information" (name, address, enrollment status) may be disclosed without consent unless the family opts out. The "school official exception" allows disclosure to third parties with "legitimate educational interest" under a written agreement — this is how EdTech vendors access student data without individual consent. FERPA does NOT provide a private right of action; enforcement is through potential loss of federal funding.
- Outsider tell: Assuming FERPA covers all student data. FERPA only applies to institutions receiving federal funds and only protects "education records" maintained by the institution. Data collected directly by an EdTech app (not through the school) may fall outside FERPA's scope.
- evidence: [T06-S013, T06-S014, T06-S015]

**COPPA (Children's Online Privacy Protection Act)**
- Jurisdiction: United States. Year: 1998, updated Rule 2013 (15 U.S.C. sections 6501-6506).
- Insider usage: Requires verifiable parental consent before collecting personal information from children under 13. Applies to commercial websites and online services "directed to children" or with "actual knowledge" of child users. The FTC enforces COPPA with fines that can exceed $50,000 per violation. In EdTech, the "school authorization" exception allows schools to consent on behalf of parents when the data is used solely for educational purposes — but this exception is narrow and heavily scrutinized.
- Outsider tell: Believing COPPA means "no data collection from children." COPPA allows collection WITH proper consent. The violation is collecting without consent, not collecting per se. Also, COPPA's age threshold is 13, not 18 — teens 13-17 are NOT covered by COPPA.
- evidence: [T06-S013, T06-S014, T06-S031]

**GDPR Article 8 (EU — Children's Data)**
- Jurisdiction: European Union. Year: 2018 (Regulation 2016/679).
- Insider usage: Article 8 requires parental consent for processing children's data by "information society services" offered directly to a child. The age threshold varies by member state: 16 (default), but states can lower it to 13. Unlike COPPA, GDPR applies to ALL personal data processing (not just collection), requires Data Protection Impact Assessments for large-scale processing of children's data, and grants extensive individual rights (erasure, portability, objection to automated decisions including profiling). Penalties up to EUR 20 million or 4% of global annual turnover.
- Outsider tell: Applying GDPR uniformly without checking member state age thresholds. France, UK, and Ireland set the threshold at 16; Germany at 16; Spain at 14; UK (post-Brexit) at 13 under UK GDPR / Age Appropriate Design Code.
- evidence: [T06-S014, T06-S015, T06-S031]

**个人信息保护法 (PIPL, Personal Information Protection Law)**
- Jurisdiction: China. Year: 2021 (effective November 1, 2021).
- Insider usage: China's comprehensive data protection law. Article 28 classifies data of minors under 14 as "sensitive personal information" requiring separate consent from parents/guardians. Requires: purpose limitation, data minimization, storage limitation, and mandatory DPIA for sensitive data processing. Cross-border data transfer requires security assessment by CAC, standard contractual clauses, or certification. Penalties: up to RMB 50 million or 5% of prior year revenue for severe violations.
- Outsider tell: Treating PIPL as "China's GDPR copy." While influenced by GDPR, PIPL has unique features: government access provisions, data localization requirements for Critical Information Infrastructure Operators, and integration with the broader Cybersecurity Law / Data Security Law framework.
- evidence: [T06-S016]

**未成年人保护法 / 未成年人网络保护条例**
- Jurisdiction: China. Year: 未成年人保护法 revised 2024; 未成年人网络保护条例 2023 (effective January 1, 2024).
- Insider usage: The 网络保护条例 adds specific rules for minors online: mandatory annual compliance audits for processors of minors' personal information, anti-addiction measures (time management, permission management, consumption management), default privacy settings for minors, prohibition of content that induces addiction. Internet platforms must provide "minor mode" with restricted content and time limits. Penalties enforced by 网信办 (CAC) in coordination with education and public security departments.
- Outsider tell: Focusing only on gaming restrictions. The 网络保护条例 covers all online services used by minors, including educational platforms. An adaptive learning app that collects learning behavior data from minors must comply with the same audit and consent requirements.
- evidence: [T06-S018]

**双减政策 (Double Reduction Policy)**
- Jurisdiction: China. Year: July 2021.
- Insider usage: "关于进一步减轻义务教育阶段学生作业负担和校外培训负担的意见" — jointly issued by CPC Central Committee and State Council. Bans approval of new K-9 academic subject tutoring institutions, requires existing ones to register as non-profits, prohibits training during holidays/weekends/summer, caps fees under government guidance pricing, and bans EdTech companies from raising capital or going public for K-9 academic tutoring. Decimated China's $100B+ private tutoring industry in 2021-2022. Adaptive learning platforms serving K-9 academic subjects must operate within public school systems or as non-profit entities.
- Outsider tell: Assuming 双减 killed all Chinese EdTech. Non-academic subjects (arts, sports, STEM enrichment), adult education, and technology/infrastructure companies were not directly targeted. Companies like Squirrel AI pivoted from K-9 academic tutoring to STEM and adaptive learning technology licensing.
- evidence: [T06-S017, T06-S033]

**Section 508 / WCAG (Accessibility)**
- Jurisdiction: US (Section 508 of Rehabilitation Act, 1998 refresh); Global (WCAG by W3C).
- Insider usage: Section 508 requires federal agencies and their vendors to make ICT accessible to people with disabilities. WCAG 2.1 (2018) / 2.2 (2023) provides the technical criteria at three levels: A (minimum), AA (standard target for compliance), AAA (enhanced). For adaptive learning systems, key challenges include: screen reader compatibility with dynamic content updates, keyboard navigation for interactive assessments, alternative text for visual/graphical items, sufficient color contrast, and captions/transcripts for multimedia. WCAG AA is the de facto legal standard in US (DOJ ADA guidance) and EU (European Accessibility Act 2025).
- Outsider tell: Treating accessibility as a checkbox ("we ran an automated scanner"). Automated tools catch roughly 30% of WCAG issues; manual testing with assistive technologies (screen readers, switch access) is required for meaningful compliance.
- evidence: [T06-S015, T06-S031]

**ESSA (Every Student Succeeds Act)**
- Jurisdiction: United States. Year: 2015 (replaced NCLB).
- Insider usage: Requires states to use "evidence-based interventions" (Tiers I-IV based on strength of evidence) for school improvement. Tier I (strong evidence) requires at least one well-designed randomized controlled trial. Adaptive learning platforms marketing to US K-12 must classify their evidence tier — claims of "proven effective" require Tier I or II evidence. ESSA also mandates state assessment systems that adaptive testing programs (e.g., SBAC interim assessments) must align with.
- Outsider tell: Not knowing ESSA's evidence tiers when evaluating EdTech claims. A vendor claiming "evidence-based" with only a case study or testimonial does not meet any ESSA evidence tier.
- evidence: [T06-S013]

**State-Level Student Privacy Laws (US)**
- Jurisdiction: United States (varies by state). Year: 2014-present (wave of legislation post-inBloom controversy).
- Insider usage: Over 40 US states have enacted student data privacy laws beyond FERPA. Key examples: California SOPIPA (2014, bans targeted advertising using student data), New York Ed Law 2-d (requires data security plans and parent bill of rights), Colorado Student Data Transparency and Security Act (2016). The Student Data Privacy Consortium (SDPC) manages the National Data Privacy Agreement (NDPA) — a standardized contract template adopted by 28+ states for school-vendor data sharing agreements.
- Outsider tell: Assuming FERPA is sufficient for US compliance. State laws often impose stricter requirements: bans on selling student data, mandatory breach notification, specific data retention/deletion timelines, and prohibition of behavioral advertising using student data.
- evidence: [T06-S015]

---

## 4. Certifications and Qualifications

**ISTE Certification for Educators**
- Issuer: International Society for Technology in Education (ISTE).
- Insider usage: A competency-based certification demonstrating an educator's ability to integrate technology for learning, including adaptive and personalized learning tools. Aligned with the ISTE Standards for Educators (7 standards: Learner, Leader, Citizen, Collaborator, Designer, Facilitator, Analyst). The "Analyst" standard specifically covers using data to inform instruction — directly relevant to adaptive learning platforms. Validity: 3 years, renewable with professional learning evidence.
- Outsider tell: Confusing ISTE standards with a product certification. ISTE certifies educators and their pedagogical technology competence, not software products.
- evidence: [T06-S019]

**1EdTech Product Certification (LTI / QTI / Open Badges / CLR / OneRoster)**
- Issuer: 1EdTech Consortium (formerly IMS Global).
- Insider usage: Certifies that a product's implementation of a given specification conforms to the standard. Products must pass the official conformance test suite. Certified products are listed in the TrustEd Apps directory. LTI Advantage certification has three components: Core (launch), Assignment and Grade Services, Names and Role Provisioning Services. For adaptive learning vendors, LTI certification is table-stakes for selling into higher education; QTI certification matters for assessment interoperability. Membership in 1EdTech is required.
- Outsider tell: Claiming "LTI compatible" without certification. Self-reported compatibility without passing the conformance suite often means partial or buggy integration. Institutional buyers should verify certification in the TrustEd Apps directory.
- evidence: [T06-S008, T06-S009, T06-S032]

**Quality Matters (QM) Course Certification**
- Issuer: Quality Matters (non-profit).
- Insider usage: A peer-review process certifying that an online or blended course meets QM Standards (currently the Higher Education Rubric, 7th Edition, with 8 General Standards and 42 Specific Review Standards). Courses must score 85% or above on the rubric. Certification valid for 5 years (recertification: 3 years). QM also offers Program-level certifications (Online Program, Online Teaching, Online Learner Support). Over 700 institutions use QM. For adaptive learning, QM Standard 3 (Assessment and Measurement) and Standard 5 (Learning Activities and Learner Interaction) are most relevant — they require that assessments align with objectives and that activities promote active engagement.
- Outsider tell: Treating QM certification as a quality guarantee for learning outcomes. QM certifies course design quality (alignment, accessibility, usability), not learning effectiveness. A QM-certified course is well-designed but not necessarily proven effective.
- evidence: [T06-S019]

**WCAG Conformance Levels (A / AA / AAA)**
- Issuer: W3C (World Wide Web Consortium).
- Insider usage: Three levels of conformance to Web Content Accessibility Guidelines. Level A: minimum accessibility (e.g., alt text for images, keyboard operability). Level AA: standard target for legal compliance (e.g., sufficient color contrast 4.5:1, text resize to 200%, focus visible). Level AAA: enhanced accessibility (e.g., sign language for video, reading level assessment, no timing). Adaptive learning platforms should target WCAG 2.2 Level AA as the baseline. Dynamic content updates (common in adaptive systems) require ARIA live regions to communicate changes to screen readers.
- Outsider tell: Claiming "WCAG compliant" without specifying the level or version. "WCAG compliant" without qualification is meaningless — always specify version (2.1 or 2.2) and level (A, AA, or AAA). Also, WCAG conformance applies to specific web pages/states, not to an entire product generically.
- evidence: [T06-S015, T06-S031]

**Learning Engineering Certification Programs**
- Issuer: Various (IEEE IC ICICLE, Carnegie Mellon, Georgia Tech, University of Pennsylvania).
- Insider usage: Learning Engineering is an emerging discipline applying engineering methods (measurement, iteration, evidence-based design) to learning system design. IEEE's Industry Consortium on Learning Engineering (ICICLE) promotes the field. CMU's Simon Initiative and LearnLab pioneered the practice. Graduate certificates and master's programs are available but there is no single universally recognized certification yet. The field emphasizes: data-informed design, rapid experimentation (A/B testing in courseware), and measurable learning outcomes.
- Outsider tell: Confusing "learning engineering" with "instructional design." Learning engineering specifically emphasizes data-driven measurement and iterative improvement — not just designing instruction but empirically testing and optimizing it at scale.
- evidence: [T06-S011, T06-S030]

**STAR Assessments / MAP Growth (Vendor-Specific Adaptive Assessment Certifications)**
- Issuer: Renaissance (STAR) / NWEA (MAP Growth).
- Insider usage: The two dominant IRT-based adaptive assessment platforms in US K-12. Both provide vertically scaled scores enabling growth measurement across grades. MAP Growth uses the RIT scale (Rasch Unit); STAR uses Scaled Scores. Both require alignment studies to state standards and undergo independent technical review. Peer-reviewed technical manuals document reliability, validity, and fairness evidence. These are not open certifications but proprietary assessment systems that serve as de facto standards for adaptive formative assessment in K-12.
- Outsider tell: Treating STAR/MAP scores as interchangeable with state assessment scores. Vertically scaled adaptive assessments measure growth trajectories; state summative assessments measure grade-level proficiency. Different constructs, different scales, different purposes.
- evidence: [T06-S021, T06-S027]

---

## 5. Outsider Tells

**"Adaptive" vs. truly adaptive**
- The tell: Marketing materials claim "adaptive learning" for systems that are merely branching (if score < 70%, show remediation; else advance). True adaptive systems continuously update a student model (BKT, DKT, IRT-based) and make content selection decisions based on that model in real-time. The diagnostic question: "Does your system maintain a per-student probabilistic knowledge state that updates after every interaction?" If the answer is "we have three difficulty levels," it's branching, not adaptive.
- Why it matters: Branching produces 3-5 learning paths; true adaptation produces a unique path for every student. The difference in learning outcomes is measurable — Baker et al. (2014) showed that model-based adaptive systems outperform branching systems by 0.3-0.5 standard deviations on post-tests.
- evidence: [T06-S025, T06-S004]

**"AI-powered" marketing vs. actual ML models**
- The tell: A vendor claims "AI-powered personalization" but cannot describe: (1) what model architecture they use, (2) what training data it requires, (3) what predictions it makes, and (4) how those predictions change the learner experience. Many "AI-powered" EdTech products use rule-based decision trees or simple threshold logic, which are programmed heuristics, not learned models.
- Why it matters: True ML-based adaptive systems require ongoing data collection, model training infrastructure, and psychometric validation. The overhead is substantial. A platform with 50 learners cannot train a meaningful DKT model — it needs population-scale data. If the vendor has no data science team and no published research, the "AI" is likely a marketing label.
- evidence: [T06-S004, T06-S003]

**Confusing knowledge tracing with simple quiz scoring**
- The tell: "We track student mastery by averaging their quiz scores." Knowledge tracing infers a latent state (mastered or not) from observable responses using probabilistic inference. It accounts for guessing, slipping, forgetting, and learning rate. Simple score averaging treats a 70% as "70% mastery" regardless of item difficulty, recency, or response pattern.
- Why it matters: A student who gets 3 hard items right and 7 easy items wrong has a very different knowledge state than one who gets 7 easy items right and 3 hard items wrong — both score 70%. Knowledge tracing distinguishes these; score averaging does not.
- evidence: [T06-S001, T06-S002, T06-S004]

**Misusing "personalized learning" as synonym for "self-paced"**
- The tell: "Our platform provides personalized learning — students can work at their own pace." Self-pacing is necessary but far from sufficient for personalization. Personalized learning also includes: adaptive content selection (what to study), adaptive sequencing (what order), adaptive feedback (how to remediate), and adaptive assessment (how to measure). A self-paced platform with a fixed content sequence is giving students control over clock time, not over the learning experience.
- Why it matters: The distinction matters for procurement decisions. Self-paced platforms are simpler, cheaper, and appropriate for motivated self-directed learners. True personalized/adaptive platforms add value for struggling learners who need the system to make pedagogical decisions they cannot make themselves.
- evidence: [T06-S025]

**Confusing mastery-based learning with grade inflation**
- The tell: "If everyone gets an A, standards must be low." In Bloom's mastery learning, the standard for each unit is high (80-90%) and fixed; what varies is the time and support each student receives. Students who don't reach mastery on the first attempt receive corrective instruction and reassess. The outcome distribution shifts right (more students achieve mastery) not because the bar is lower but because the support is better.
- Why it matters: Adaptive systems implementing mastery-based progression must communicate this distinction to administrators and parents. The perception of grade inflation can undermine adoption of evidence-based mastery approaches.
- evidence: [T06-S026]

**Using "IRT" without understanding model assumptions**
- The tell: Applying IRT to a test without checking: (1) unidimensionality (does the test measure one construct?), (2) local independence (are item responses independent conditional on ability?), (3) model fit (does the chosen model — 1PL, 2PL, 3PL — actually fit the data?). Applying IRT to a multidimensional assessment (e.g., a mixed math-and-reading test) violates the unidimensionality assumption and produces meaningless ability estimates.
- Why it matters: IRT's power comes from its assumptions — when assumptions are met, it provides invariant measurement. When violated, the estimates are biased and the claimed properties (item-invariant scoring, efficient CAT) do not hold.
- evidence: [T06-S007, T06-S020, T06-S022]

**Conflating CAT with any online test**
- The tell: "We offer computerized adaptive testing" for a system that selects items from difficulty bins (easy/medium/hard) based on cumulative score. A genuine CAT requires: (1) an IRT-calibrated item bank with item parameters, (2) real-time maximum-information item selection, (3) Bayesian or MLE ability estimation updated after each response, and (4) formal stopping rules (SE threshold or item count).
- Why it matters: The precision and efficiency guarantees of CAT (50% fewer items for equal or better measurement precision) depend on the IRT machinery. Without it, difficulty-binned tests can produce worse measurement than a well-constructed fixed-form test.
- evidence: [T06-S027, T06-S020]

**Assuming spaced repetition = flashcards**
- The tell: "We use spaced repetition — our flashcard app spaces out reviews." Spaced repetition is a scheduling algorithm applicable to any practice activity. The algorithm can schedule problem sets, essay prompts, lab exercises, or motor skill drills — not just vocabulary flashcards. Furthermore, the scheduling algorithm itself matters: Anki's SM-2 is heuristic-based; modern approaches (DASH, HLR, FSRS) use machine learning to optimize review scheduling per learner-item pair.
- Why it matters: Reducing spaced repetition to flashcards limits its application and ignores advances in algorithm design. The most impactful applications of spaced repetition in adaptive learning are for complex procedural skills and conceptual understanding, not vocabulary memorization.
- evidence: [T06-S023]

**Ignoring the ethical dimension of student data**
- The tell: Building adaptive learning systems that collect granular behavioral data (keystroke patterns, time-on-task, eye tracking, emotional state detection) without a clear data governance framework. The insider knows that just because you CAN collect data doesn't mean you SHOULD — especially with minors. Key ethical questions: Does the data collection serve the learner's interest or the platform's? Is the student/parent aware of what's being collected? How long is data retained? Can a student request deletion?
- Why it matters: FERPA, COPPA, GDPR, and PIPL all impose legal constraints, but ethical practice often requires going beyond legal minimums. The EdTech industry's social license depends on responsible data stewardship, particularly after the inBloom controversy (2014) where a student data aggregation project was shut down due to parent backlash despite being technically legal.
- evidence: [T06-S013, T06-S014, T06-S016]

**Believing generative AI solves the content problem**
- The tell: "We use ChatGPT to generate unlimited practice questions, so we don't need an item bank." Generated items without psychometric calibration have unknown difficulty, unknown discrimination, and unknown bias properties. An uncalibrated item in a CAT system can degrade measurement precision. Furthermore, LLM-generated distractors may be implausible to domain experts, reveal the correct answer through linguistic patterns, or contain factual errors.
- Why it matters: The item bank is the most expensive and valuable asset in an adaptive assessment system. LLMs can accelerate item authoring (50-70% time savings on first drafts) but cannot replace the psychometric calibration pipeline (field testing, IRT parameter estimation, DIF analysis, content expert review).
- evidence: [T06-S004, T06-S028]

---

## Phase 2 提炼提示

**Outsider tells → 表达 DNA 候选**:
- "adaptive" vs truly adaptive (branching ≠ model-based): 遇到"自适应"声称时自动追问"你的系统是否维护逐学生概率知识状态?"
- "AI-powered" 空标签检测: 追问模型架构 / 训练数据 / 预测内容 / 体验变化四要素
- knowledge tracing ≠ quiz scoring: 正确区分潜在状态推断 vs 简单分数平均
- personalized ≠ self-paced: 自适应内容选择 + 自适应序列 + 自适应反馈 + 自适应评估四维度
- mastery-based ≠ grade inflation: 固定高标准 + 可变时间/支持, 非降标
- spaced repetition ≠ flashcards: 调度算法适用于任何练习形式, SM-2 vs DASH/HLR/FSRS 算法差异
- CAT ≠ 在线测试: 必须有 IRT 校准题库 + 实时能力估计 + 信息最大化选题 + 形式化终止规则
- GenAI ≠ 无限题库: 未经心理测量校准的 AI 生成题目无法用于 CAT 或高风险评估

**标准/法规 → 工作流触发点**:
- 产品集成 LMS: 检查 LTI 1.3 Advantage 认证 (TrustEd Apps 目录验证)
- 学习数据追踪: xAPI + LRS 架构, 非 SCORM (遗留系统除外)
- 评估互操作: QTI 3.0 格式保证题库不被厂商锁定
- 中国 K-9 学科辅导: 双减政策合规检查 (非营利注册 / 假期禁令 / 费用政府指导定价)
- 面向 <14 岁中国用户: 个保法第28条敏感个人信息 + 未保条例年度合规审计
- 面向 <13 岁美国用户: COPPA 可验证家长同意 + 学校授权例外范围
- 面向欧盟未成年人: GDPR 第8条成员国年龄门槛差异 (13-16 不等)
- 无障碍: WCAG 2.2 Level AA 基线 + ARIA live regions (动态内容更新)
- 美国 K-12 采购: ESSA 证据分级 (Tier I-IV) + 州级学生隐私法 + NDPA 合同模板
- 数据互操作: Ed-Fi (行政数据) vs xAPI/Caliper (学习行为数据) 区分使用场景

**术语 → 候选心智模型**:
- BKT 四参数模型 (P(L0)/P(T)/P(S)/P(G)) → "每一次答题都是对潜在掌握状态的概率推断, 不是分数累加"
- IRT 不变性原理 → "题目参数不随样本变, 能力估计不随题目变 — 这是自适应测试数学上成立的根基"
- exploration-exploitation 张力 → "短期最优 ≠ 长期最优; 有时让学生做看似不必要的练习是在积累模型信息"
- ZPD + 认知负荷 → "太容易无效, 太难有害, 最佳挑战区是动态的、因人而异的"
- desirable difficulties → "学习时正确率高不等于学得好; 适度困难反而增强长期保持"
- forgetting factor → "不建模遗忘的自适应系统假设掌握永久, 这在现实中不成立"
- cold start → "新用户冷启动是根本限制, 不是工程缺陷; '第一天就个性化' 的承诺需要追问实现机制"
- item bank ≠ question database → "心理测量校准是题库的核心价值, 题目内容本身只是载体"
