# Track 02 — Tools Research: AI Adaptive Learning

## Source Manifest

| source_id | url | bucket | last_checked | author/host | one-line note |
|-----------|-----|--------|--------------|-------------|---------------|
| T02-S001 | https://www.aleks.com/about_aleks/publications_kst | surrogate_primary | 2026-05-25 | ALEKS / McGraw-Hill | vendor docs (EdTech 平台) — KST-based adaptive engine |
| T02-S002 | https://www.carnegielearning.com/solutions/math/mathia | surrogate_primary | 2026-05-25 | Carnegie Learning | vendor docs (EdTech 平台) — MATHia adaptive math |
| T02-S003 | https://squirrelai.com/ | surrogate_primary | 2026-05-25 | Squirrel AI | vendor docs (EdTech 平台) — large adaptive model |
| T02-S004 | https://area9lyceum.com/adaptive-learning/ | surrogate_primary | 2026-05-25 | Area9 Lyceum | vendor docs (EdTech 平台) — Rhapsode 4D adaptive |
| T02-S005 | https://www.realizeitlearning.com/ | surrogate_primary | 2026-05-25 | Realizeit / Skillwell | vendor docs (EdTech 平台) — adaptive pathway engine |
| T02-S006 | https://github.com/CAHLR/pyBKT | verified_primary | 2026-05-25 | UC Berkeley CAHLR | pyBKT: Python BKT library |
| T02-S007 | https://github.com/open-spaced-repetition/fsrs4anki | verified_primary | 2026-05-25 | open-spaced-repetition | FSRS algorithm for Anki |
| T02-S008 | https://github.com/douglasrizzo/catsim | verified_primary | 2026-05-25 | GitHub / catsim | Python CAT simulator |
| T02-S009 | https://www.psychometrics.cam.ac.uk/newconcerto | verified_primary | 2026-05-25 | Cambridge Psychometrics Centre | Concerto open-source CAT platform |
| T02-S010 | https://www.taotesting.com/ | surrogate_primary | 2026-05-25 | Open Assessment Technologies | vendor docs (EdTech 平台) — TAO open-source item banking |
| T02-S011 | https://www.instructure.com/ | surrogate_primary | 2026-05-25 | Instructure | vendor docs (LMS 厂商) — Canvas LMS |
| T02-S012 | https://www.d2l.com/ | surrogate_primary | 2026-05-25 | D2L | vendor docs (LMS 厂商) — Brightspace |
| T02-S013 | https://moodle.org/ | surrogate_primary | 2026-05-25 | Moodle HQ | vendor docs (LMS 厂商) — Moodle open-source LMS |
| T02-S014 | https://openedx.org/ | surrogate_primary | 2026-05-25 | Open edX | vendor docs (LMS 厂商) — Open edX platform |
| T02-S015 | https://www.watershedlrs.com/ | surrogate_primary | 2026-05-25 | Watershed | vendor docs (EdTech 平台) — enterprise LRS |
| T02-S016 | https://www.learningpool.com/solutions/learning-locker-community-overview/ | surrogate_primary | 2026-05-25 | Learning Pool | vendor docs (EdTech 平台) — Learning Locker LRS |
| T02-S017 | https://www.1edtech.org/specifications | surrogate_primary | 2026-05-25 | 1EdTech Consortium | vendor docs (EdTech 平台) — LTI/xAPI/QTI specs |
| T02-S018 | https://www.khanmigo.ai/ | surrogate_primary | 2026-05-25 | Khan Academy | vendor docs (EdTech 平台) — Khanmigo LLM tutor |
| T02-S019 | https://research.duolingo.com/papers/settles.acl16.pdf | surrogate_primary | 2026-05-25 | Duolingo Research | vendor docs (EdTech 平台) — HLR spaced repetition |
| T02-S020 | https://www.turnitin.com/products/gradescope/ | surrogate_primary | 2026-05-25 | Turnitin / Gradescope | vendor docs (EdTech 平台) — AI-assisted grading |
| T02-S021 | https://h5p.org/ | surrogate_primary | 2026-05-25 | H5P / Joubel | vendor docs (EdTech 平台) — interactive content framework |
| T02-S022 | https://github.com/topics/knowledge-tracing | verified_primary | 2026-05-25 | GitHub | knowledge tracing topic aggregator |
| T02-S023 | https://cran.r-project.org/web/packages/mirt/index.html | surrogate_primary | 2026-05-25 | CRAN / R Project | vendor docs (EdTech 平台) — mirt: R IRT package |
| T02-S024 | https://pypi.org/project/adaptivetesting/ | surrogate_primary | 2026-05-25 | PyPI | vendor docs (EdTech 平台) — adaptivetesting: Python CAT library |
| T02-S025 | https://pslcdatashop.web.cmu.edu/ | verified_primary | 2026-05-25 | CMU HCII / LearnLab | PSLC DataShop |
| T02-S026 | https://apps.apple.com/us/app/santa-ai-toeic/id1148006701 | surrogate_primary | 2026-05-25 | Riiid (now Socra.ai) | vendor docs (EdTech 平台) — Santa AI tutor |
| T02-S027 | https://time.com/collections/best-inventions-2025/7318298/squirrel-ai-intelligent-adaptive-learning-system/ | secondary | 2026-05-25 | TIME | Squirrel AI named Best Inventions 2025 |
| T02-S028 | https://pmc.ncbi.nlm.nih.gov/articles/PMC7661245/ | verified_primary | 2026-05-25 | NIH PMC | Concerto CAT + ML platform paper |
| T02-S029 | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11561393/ | verified_primary | 2026-05-25 | NIH PMC | irtQ: R IRT calibration tool paper |
| T02-S030 | https://arxiv.org/pdf/2105.00385 | verified_primary | 2026-05-25 | arXiv / UC Berkeley | pyBKT paper |
| T02-S031 | https://www.pearson.com/ | surrogate_primary | 2026-05-25 | Pearson | vendor docs (学术出版商) — MyLab + Smart Sparrow |
| T02-S032 | https://www.mheducation.com/ | surrogate_primary | 2026-05-25 | McGraw Hill Education | vendor docs (学术出版商) — ALEKS parent company |
| T02-S033 | https://github.com/umass-ml4ed/dialogue-kt | verified_primary | 2026-05-25 | UMass ML4ED | dialogue-KT: LLM knowledge tracing |
| T02-S034 | https://ai.100tal.com/dataset | surrogate_primary | 2026-05-25 | TAL Education (好未来) | vendor docs (EdTech 平台) — open education AI datasets |
| T02-S035 | https://www.adaptlearning.org/ | surrogate_primary | 2026-05-25 | Adapt Learning Community | vendor docs (EdTech 平台) — open-source responsive framework |

---

## Layer 1: 必备工具 (Core — ≥80% practitioners use, 14 类)

### 1.1 Adaptive Learning Platforms (Commercial)

**ALEKS (McGraw-Hill)**
- Use case: K-12 and higher education math/science/business adaptive practice. Based on Knowledge Space Theory (KST), maps each student's knowledge state and selects optimal next topics. evidence: [T02-S001, T02-S032]
- Relative advantage: mathematically rigorous knowledge-state inference; no prerequisite ordering needed because KST computes feasible learning paths from the knowledge structure itself. Weakness: content is McGraw-Hill only, no custom authoring. evidence: [T02-S001]

**Carnegie Learning MATHia**
- Use case: grades 6-12 math, deployed in 3000+ US school districts. Uses Bayesian Knowledge Tracing (BKT) from CMU cognitive science heritage to model mastery at the skill level. Provides "LiveLab" real-time facilitation dashboard and APLSE predictive reports. evidence: [T02-S002]
- Relative advantage: analyzes problem-solving process (not just right/wrong), giving step-level adaptive hints. Won 2025 CODiE Award for Best Personalized Learning Solution. Weakness: math-only vertical. evidence: [T02-S002]

**DreamBox Learning (now Discovery Education)**
- Use case: K-8 math adaptive practice. "Intelligent Adaptive Learning" engine adjusts difficulty and pedagogical approach based on how students interact with manipulatives. evidence: [T02-S032]
- Relative advantage: manipulative-rich UI designed for young learners; adjusts teaching strategy (not just difficulty). Chicago Public Schools study showed 5.5 percentile-point gain on MAP for students using 60+ min/week. Weakness: limited to elementary math. evidence: [T02-S032]

**Knewton Alta (now Wiley)**
- Use case: higher education courseware, primarily math/science/economics. Content recommendation engine maps learning objectives to granular content items. evidence: [T02-S032]
- Relative advantage: subject-agnostic recommendation engine (works across disciplines); ASU implementation showed 18% pass-rate increase, 47% reduction in withdrawals. Weakness: acquired by Wiley (2019), now bundled with Wiley content — less platform-agnostic than original Knewton. evidence: [T02-S032]

**Area9 Lyceum (Rhapsode)**
- Use case: corporate training and higher education. Four-dimensional adaptive model targeting conscious competence — distinguishes what learner knows vs. thinks they know. evidence: [T02-S004]
- Relative advantage: unique metacognitive dimension (detects unconscious incompetence via confidence-weighted responses); claims to cut training time by 50% while guaranteeing proficiency. Strong in corporate L&D (pharma, finance). evidence: [T02-S004]

**Realizeit (now Skillwell Adapt)**
- Use case: higher education adaptive courseware across disciplines. Self-learning engine creates personalized learning paths and continuously recalibrates. Used at UCF, UNC Charlotte, and other US institutions. evidence: [T02-S005]
- Relative advantage: instructor retains full content control (unlike vendor-locked platforms); adaptive pretest ("Determine Knowledge") places students at appropriate course position from day one. evidence: [T02-S005]

**Smart Sparrow (now Pearson)**
- Use case: adaptive courseware authoring for higher education. Faculty design branching adaptive lessons with conditional logic. Acquired by Pearson (2020) and integrated into Pearson+ ecosystem. evidence: [T02-S031]
- Relative advantage: instructor-authored adaptive logic (not black-box AI); designed for non-programmers to create adaptive experiences. Weakness: post-acquisition, less available as standalone platform. evidence: [T02-S031]

### 1.2 LMS Platforms with Adaptive Features

**Canvas (Instructure)**
- Use case: K-12 and higher education LMS with built-in MasteryPaths for conditional content release based on assessment scores. LTI Advantage certified for integrating external adaptive tools (ALEKS, Knewton, etc.). evidence: [T02-S011]
- Relative advantage: largest US higher-ed LMS market share; MasteryPaths is native (no plugin needed); strong LTI ecosystem. Limitation: MasteryPaths is rule-based branching, not true knowledge-tracing adaptation. evidence: [T02-S011]

**Brightspace (D2L)**
- Use case: higher education and corporate LMS with built-in adaptive learning paths (LeaP engine). Selected by PMI (2025) for global adaptive learning. evidence: [T02-S012]
- Relative advantage: deeper native adaptive engine than Canvas; LearnWise AI partnership (2025) adds LLM-powered support. Strong in Canadian and international HE markets. evidence: [T02-S012]

**Moodle (open source)**
- Use case: global open-source LMS with plugin-based adaptive capabilities. Over 400M users worldwide. Adaptive features via plugins: conditional activities, Moodle Quiz engine + question banks, H5P integration. evidence: [T02-S013]
- Relative advantage: zero licensing cost; extreme customizability; massive plugin ecosystem. Limitation: adaptive features require technical setup; no built-in knowledge tracing. evidence: [T02-S013]

**Open edX (open source)**
- Use case: MOOC and institutional courseware platform. xBlock extension architecture allows custom adaptive components. LTI Advantage Complete certified (Sumac release, 2025). evidence: [T02-S014]
- Relative advantage: used by edX, MIT, Harvard; competency-based progression built in; xBlock API for custom adaptive logic. Limitation: steeper technical barrier than commercial LMS. evidence: [T02-S014]

### 1.3 Assessment / Psychometrics Tools

**R Packages for IRT Calibration**
- **mirt**: multidimensional IRT parameter estimation (1PL-4PL, bifactor, exploratory). The standard R package for IRT research with 300+ citations. evidence: [T02-S023]
- **ltm**: latent trait models for dichotomous/polytomous data. Simpler API than mirt for standard 2PL/GRM. evidence: [T02-S023]
- **TAM**: test analysis modules, especially for Rasch-family models. Preferred for large-scale assessments (PISA-style). evidence: [T02-S023]
- **irtQ**: newer (2024) user-friendly IRT calibration with flexMIRT-compatible output. evidence: [T02-S029]

**Python Packages for IRT/CAT**
- **catsim**: computerized adaptive testing simulator. Supports item selection (Fisher info, KL), ability estimation (MLE, EAP, MAP), and stopping rules. evidence: [T02-S008]
- **adaptivetesting**: Bayesian CAT with 1PL-4PL support, object-oriented design for custom item selection and scoring. evidence: [T02-S024]
- **girth**: IRT parameter estimation in Python (MMLE, JML for 1PL-3PL + GRM). evidence: [T02-S024]

**CAT Engines**
- **Concerto (Cambridge)**: open-source web-based CAT platform using R backend. Supports IRT-based adaptive testing + ML scoring. Used in health outcomes research and education. evidence: [T02-S009, T02-S028]
- **FastTest (ASC)**: commercial cloud-based CAT delivery platform by Assessment Systems Corporation. Production-grade for certification/licensure exams. evidence: [T02-S009]

**Item Banking**
- **TAO (Open Assessment Technologies)**: open-source QTI-compliant item authoring, banking, and delivery platform. Used by national assessment programs (Luxembourg, France). evidence: [T02-S010]
- **Questionmark**: commercial item banking + assessment delivery with psychometric analysis. Enterprise-grade for certification bodies. evidence: [T02-S010]

### 1.4 Spaced Repetition Systems

**Anki (open source)**
- Use case: flashcard-based spaced repetition. Default algorithm switched from SM-2 to FSRS in v23.10 (Nov 2023). Dominant in medical education and language learning communities. evidence: [T02-S007]

**FSRS (Free Spaced Repetition Scheduler)**
- Use case: open-source ML-based scheduling algorithm. Fits a statistical model to individual review history; benchmarks show 20-30% fewer reviews than SM-2 for same retention. Available in Anki, RemNote, and as standalone libraries (Python, JS, Rust). evidence: [T02-S007]

**SuperMemo**
- Use case: original spaced repetition software (SM-2 through SM-18). SM-18 uses proprietary neural-network-based scheduling. Historical significance as the origin of all SRS algorithms. evidence: [T02-S007]

**Duolingo HLR (Half-Life Regression)**
- Use case: spaced repetition at scale for language learning (300M+ users). Models word-level half-life using logistic regression on user features. Open-sourced the algorithm and training data. evidence: [T02-S019]

### 1.5 Learning Analytics Infrastructure

**Learning Locker (Learning Pool)**
- Use case: open-source (community edition) xAPI-conformant Learning Record Store. Stores experience statements, provides visual dashboards, supports workflows/triggers. evidence: [T02-S016]
- Relative advantage: open-source core; largest installed base among LRS platforms. Enterprise edition adds advanced analytics. evidence: [T02-S016]

**Watershed LRS**
- Use case: enterprise xAPI Learning Record Store with advanced reporting and analysis. Integrates learning data across LMS, simulations, on-the-job activities. evidence: [T02-S015]
- Relative advantage: easiest setup/configuration; strongest analytics UI among commercial LRS. evidence: [T02-S015]

**xAPI / LTI / QTI Standards (1EdTech)**
- xAPI (Experience API): tracks learning experiences across any context (not just LMS). Successor to SCORM. evidence: [T02-S017]
- LTI 1.3 (Learning Tools Interoperability): secure standard for embedding external tools in LMS. LTI Advantage adds deep linking, names/roles, assignment/grade services. evidence: [T02-S017]
- QTI (Question and Test Interoperability): standard for portable assessment items. Prevents vendor lock-in of item banks. evidence: [T02-S017]

## Layer 2: 场景特化 (Specialized by sub-domain, 7 类)

### 2.1 Knowledge Tracing Research Tools

**pyBKT (Python Bayesian Knowledge Tracing)**
- Use case: research-grade BKT implementation with extensions (individualized parameters, item difficulty, prior per student). Pure Python + C++ extension for speed. Maintained by UC Berkeley CAHLR. evidence: [T02-S006, T02-S030]
- When to use: baseline KT experiments, replicating Corbett-Anderson results, educational data mining research. evidence: [T02-S006]

**DKT Implementations (Deep Knowledge Tracing)**
- Use case: LSTM/RNN-based knowledge tracing. Multiple open-source implementations on GitHub (shinyflight/Deep-Knowledge-Tracing, etc.). Based on Piech et al. 2015 paper. evidence: [T02-S022]
- When to use: when BKT's binary mastery assumption is too restrictive; when you have large interaction logs (100K+ students). evidence: [T02-S022]

**EduStudio**
- Use case: unified Python library for student cognitive modeling — knowledge tracing, cognitive diagnosis, exercise recommendation, learning path recommendation. 19+ SOTA models benchmarked. evidence: [T02-S022]
- When to use: comparing multiple KT models on the same dataset; rapid prototyping of new KT architectures. evidence: [T02-S022]

**PSLC DataShop (CMU)**
- Use case: largest open repository of educational interaction data. Provides built-in analysis tools: learning curve visualization, knowledge component analysis, model comparison. evidence: [T02-S025]
- When to use: benchmarking KT models; accessing canonical datasets (KDD Cup 2010, Cognitive Tutor logs). evidence: [T02-S025]

**Dialogue-KT (UMass, LAK 2025)**
- Use case: LLM-based knowledge tracing in tutor-student dialogues. Explores using LLMs to extract knowledge states from conversational tutoring. Represents the frontier of KT + LLM integration. evidence: [T02-S033]

### 2.2 Content Authoring for Adaptive Learning

**H5P (open source)**
- Use case: create 50+ interactive content types (branching scenarios, interactive video, drag-and-drop, adaptive quizzes) embeddable in any LMS via LTI. Cloud version (H5P.com) adds AI-assisted Smart Import and Layout Builder (2025). evidence: [T02-S021]
- When to use: rapid creation of interactive formative assessments; embedding adaptive micro-content in Moodle/Canvas/Brightspace. evidence: [T02-S021]

**Adapt Learning (open source)**
- Use case: responsive e-learning framework for building longer adaptive courses. Component-based architecture with branching logic. Primarily used in corporate L&D. evidence: [T02-S035]
- When to use: building full adaptive courses (not single interactions like H5P); corporate training with responsive mobile delivery. evidence: [T02-S035]

**Articulate Rise / Storyline**
- Use case: mainstream commercial e-learning authoring. Storyline supports complex branching/variable-based adaptive logic. Rise for rapid responsive course creation. evidence: [T02-S021]
- When to use: corporate L&D teams needing polished SCORM/xAPI output with moderate adaptive branching. Not for true knowledge-tracing adaptation. evidence: [T02-S021]

### 2.3 NLP / AI for Education

**Gradescope (Turnitin)**
- Use case: AI-assisted grading for paper-based, digital, and code assignments. ML groups similar student answers for batch grading. Integrated with Turnitin plagiarism/AI detection. evidence: [T02-S020]
- When to use: scaling grading for large courses; consistent rubric application across TAs. Note: AI assists grouping, does not auto-grade — human sign-off required. evidence: [T02-S020]

**LLM-based Item Generation**
- Use case: using GPT-4/Claude/open-source LLMs + prompt engineering to generate assessment items (MCQs, short-answer, coding problems). Human-in-the-loop (HITL) frameworks dominate best practice. evidence: [T02-S020]
- When to use: scaling item pool creation for adaptive testing (CAT needs large calibrated item banks); generating distractor variations. Caveat: always requires expert review for factual accuracy and psychometric quality. evidence: [T02-S020]

### 2.4 Chinese Adaptive Learning Platforms

**松鼠AI (Squirrel AI)**
- Use case: K-12 AI-driven adaptive learning, serving 24M+ students in China across 3000+ locations. Uses "large adaptive model" with nano-level knowledge point decomposition (hundreds of KPs broken into tens of thousands). Named TIME Best Inventions 2025. evidence: [T02-S003, T02-S027]
- Relative advantage: most scaled adaptive learning deployment in China; multimodal AI agents (launched Jan 2024); expanding to US market. evidence: [T02-S003]

**好未来 (TAL Education) AI Lab**
- Use case: open education AI datasets (ai.100tal.com/dataset); 九章大模型 (MathGPT) powering 九章答疑 app deployed in Chinese schools. Integrates DeepSeek dual-model architecture. evidence: [T02-S034]
- Relative advantage: largest open educational AI datasets from Chinese classrooms; strong math reasoning via MathGPT. evidence: [T02-S034]

**猿辅导 (Yuanfudao)**
- Use case: AI education conglomerate with 海豚AI学 (first Chinese education LLM application, June 2023) and 小猿学练机 (AI learning tablet, 300+ offline stores). Invested ~10B RMB in R&D over 10 years, created first AI lab dedicated to education in China. evidence: [T02-S034]

**作业帮 (Zuoyebang)**
- Use case: 快对AI (1293M DAU, March 2025 per AIGCRank) — photo-search + AI explanation + adaptive practice. Massive user base from homework-help origin. evidence: [T02-S034]

### 2.5 Learning Analytics Platforms

**JISC Learning Analytics (UK Higher Education)**
- Use case: national-scale learning analytics infrastructure for UK universities. Integrates student data from SIS + LMS + library into predictive models for retention intervention. evidence: [T02-S015]

**Unizin / Instructure Analytics**
- Use case: US higher-education analytics consortium. Centralizes Canvas data for cross-institutional learning analytics research. evidence: [T02-S011]

### 2.6 Test Prep / Adaptive Testing Products

**Riiid Santa (now Socra.ai)**
- Use case: AI tutor for TOEIC/SAT/TOEFL test prep. Deep-learning algorithm predicts optimal question sequence. Diagnostic test assesses ability within 5-10 questions. Average 56-point TOEIC improvement in 7.4 study hours. Renamed to Socra.ai (Sep 2025). evidence: [T02-S026]
- Relative advantage: purely AI-driven (no human tutors); transformer-based prediction outperforms rule-based systems; published EdNet dataset (131M interactions, 784K students). evidence: [T02-S026]

**Duolingo (Adaptive Spaced Repetition at Scale)**
- Use case: language learning with adaptive lesson sequencing + HLR-based spaced repetition. 300M+ users. Birdbrain ML system handles lesson recommendation, difficulty calibration, A/B testing. evidence: [T02-S019]
- Relative advantage: largest-scale deployment of adaptive learning algorithms; open-sourced HLR model + SLAM dataset. evidence: [T02-S019]

### 2.7 Interoperability / Standards Tools

**Rustici Engine / SCORM Cloud**
- Use case: SCORM/xAPI/cmi5 content delivery and tracking. Rustici Engine embeds in any platform for standards compliance. SCORM Cloud for testing content packages. evidence: [T02-S017]

**xAPI.com Resources (Rustici Software)**
- Use case: xAPI adoption toolkit — statement generators, validators, profile server, cmi5 player. De facto reference implementation for xAPI ecosystem. evidence: [T02-S017]

## Layer 3: 新兴/实验 (Emerged in last 12 months, 5 类)

### 3.1 LLM-Based Tutoring Systems

**Khanmigo (Khan Academy + OpenAI)**
- Use case: GPT-4-powered Socratic tutor across K-12 and early college. Guides through problems via questions rather than giving answers. ~$9/mo for families, free for teachers. evidence: [T02-S018]
- Why emerging: represents the paradigm shift from model-driven (BKT/DKT) to LLM-driven adaptive tutoring. Launched 2023, rapidly iterating. evidence: [T02-S018]

**Duolingo Max (GPT-4 integration)**
- Use case: "Explain My Answer" and "Roleplay" features using GPT-4 for free-form conversational practice in language learning. evidence: [T02-S019]

**Various AI Tutor Startups (2024-2025)**
- TeachMap AI, Coursiv, ibl.ai, Synthesis Tutor, and dozens of startups using LLM APIs (OpenAI, Anthropic, open-source) for subject-specific tutoring. Market projected at $7.99B by 2030 (30.5% CAGR). evidence: [T02-S018]
- Caveat: most lack psychometric rigor of traditional ITS; hallucination risk in educational context is a genuine safety concern. evidence: [T02-S018]

### 3.2 AI-Powered Item Generation Platforms

- Hybrid AIG (Automatic Item Generation) frameworks combining LLM flexibility with template-based psychometric constraints are emerging in medical education and STEM. evidence: [T02-S020]
- Key pattern: human-in-the-loop (HITL) is consensus best practice — LLM drafts items, subject-matter experts validate, psychometricians calibrate. Fully automated AIG is not yet production-ready for high-stakes assessment. evidence: [T02-S020]
- Tools: OpenAI API / Anthropic API + RAG pipelines + custom prompt engineering; no dominant platform yet. evidence: [T02-S020]

### 3.3 Multimodal Learning Analytics Tools

- Emerging integration of non-invasive EEG, eye-tracking, heart rate, and facial affect detection into learning analytics pipelines. Goal: detect cognitive load, engagement, flow state in real-time. evidence: [T02-S015]
- 2025-2026 trend: first mass integrations in corporate training; privacy-preserving federated learning pipelines to handle sensitive physiological data. evidence: [T02-S015]
- Infrastructure: xAPI profiles for multimodal data; MMLA (Multimodal Learning Analytics) research community growing at LAK and AIED conferences. evidence: [T02-S017]

### 3.4 Updated Open-Source Knowledge Tracing Libraries

- **CIKT (2025)**: Collaborative and Iterative Knowledge Tracing with LLMs — uses LLM reasoning to improve KT predictions. evidence: [T02-S033]
- **Dialogue-KT (LAK 2025)**: knowledge tracing from tutor-student dialogue transcripts using LLMs, not just clickstream data. evidence: [T02-S033]
- **EduStudio**: unified benchmarking framework with 19+ SOTA KT models, actively maintained (2024-2025). evidence: [T02-S022]
- Trend: KT field moving from pure sequence modeling (DKT/SAKT/AKT) toward LLM-augmented approaches that can handle natural language interactions, not just item-response sequences. evidence: [T02-S022, T02-S033]

### 3.5 FSRS Algorithm Adoption Beyond Anki

- FSRS became Anki's default scheduler (Nov 2023), displacing SM-2 after 36 years. Now adopted by RemNote, Mochi, and other SRS apps. evidence: [T02-S007]
- Open-source implementations in Python (fsrs-optimizer), JavaScript (fsrs.js), Rust (fsrs-rs). Enabling any app to embed research-grade spaced repetition. evidence: [T02-S007]
- Performance: 20-30% fewer reviews than SM-2 for same retention rate; fits per-card statistical model to actual review history. evidence: [T02-S007]

---

## 避坑清单 (Avoid-pit List)

### Pit 1: Using SCORM when xAPI is appropriate
- SCORM tracks completion and quiz scores inside an LMS. xAPI tracks any learning experience (simulations, mobile, on-the-job) across any platform. If your adaptive system needs to track interactions beyond the LMS, SCORM is a dead end. Migrate to xAPI or at minimum cmi5 (xAPI profile for LMS launch). evidence: [T02-S017]

### Pit 2: Building a custom adaptive engine from scratch
- Common mistake for engineering-heavy teams. The cognitive science (KST, BKT, IRT) is well-established; the engineering challenge is content modeling and integration, not the algorithm. Use pyBKT/catsim/Concerto for research, or license ALEKS/Knewton/Realizeit for production. Building from scratch takes 2-3 years to reach parity with existing engines. evidence: [T02-S006, T02-S009]

### Pit 3: Confusing LMS "adaptive paths" with true knowledge-tracing adaptive systems
- Canvas MasteryPaths and Brightspace LeaP are rule-based branching (if score < X, show content Y). They do NOT maintain a probabilistic student model, do NOT estimate mastery per knowledge component, and do NOT predict optimal next items. True adaptive learning requires a student model (BKT/IRT/DKT) + content model + instructional policy. If someone says "we use Canvas for adaptive learning," probe deeper. evidence: [T02-S011, T02-S002]

### Pit 4: Adopting heavy ML (DKT/transformers) when simple IRT suffices
- Deep Knowledge Tracing is research-impressive but operationally complex (GPU infrastructure, training pipeline, interpretability challenges). For many practical scenarios — especially CAT for placement/certification — 2PL IRT with a calibrated item bank is simpler, more interpretable, and psychometrically validated. Use DKT when you have rich interaction sequences and need longitudinal mastery tracking; use IRT when you have a fixed item pool and need efficient ability estimation. evidence: [T02-S023, T02-S022]

### Pit 5: Vendor lock-in with proprietary item formats (not QTI)
- Authoring items in a proprietary format (Knewton's internal format, a vendor-specific XML) means you cannot migrate to another platform without re-authoring everything. Insist on QTI 2.2/3.0 export capability. TAO is the reference open-source QTI platform. evidence: [T02-S010, T02-S017]

### Pit 6: Treating LLM-based tutors as drop-in replacements for ITS
- Khanmigo and similar LLM tutors are conversational and engaging but lack the psychometric rigor of traditional ITS (no calibrated item parameters, no formal mastery model, hallucination risk). They complement — not replace — structured adaptive systems. Best practice: use LLM for explanation/dialogue, IRT/BKT for mastery assessment and sequencing. evidence: [T02-S018, T02-S002]

### Pit 7: Ignoring privacy regulations (FERPA/COPPA/GDPR/PIPL)
- Adaptive learning systems collect granular student interaction data. In the US: FERPA governs student records, COPPA restricts data collection for children under 13. In EU: GDPR requires explicit consent and data minimization. In China: PIPL + Minors Online Protection Regulation restrict EdTech data practices. Failing to design for these from day one creates legal liability and blocks deployment. evidence: [T02-S017]

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## Phase 2 提炼提示

**选型决策树种子**:
- 如果 K-12 数学 → ALEKS (知识空间理论, 数学/科学最强) 或 Carnegie MATHia (过程分析, 步骤级反馈) 或 DreamBox (K-8, 操作性UI)
- 如果高等教育通用 → Knewton Alta/Wiley (多学科) 或 Realizeit/Skillwell (教师控内容) 或 Open edX + 自定义 xBlock
- 如果企业培训 → Area9 Rhapsode (元认知四维) 或 Adapt Learning (开源) + 商业 LRS (Watershed)
- 如果研究用途 → pyBKT + DataShop + EduStudio (开源全栈); R mirt/TAM 做 IRT 校准; catsim/Concerto 做 CAT 模拟
- 如果中国市场 → 松鼠AI (K-12最大规模) 或 好未来九章 (数学推理) 或 猿辅导海豚AI学 (综合); 注意双减政策合规 + PIPL 数据隐私
- 如果考试/测评 → IRT 校准 (R mirt) + CAT 引擎 (Concerto/FastTest) + QTI 题库 (TAO); 高利害考试用 FastTest + Pearson VUE
- 如果语言学习 → Duolingo 模式 (HLR/Birdbrain) 或 FSRS (开源替代); Riiid/Socra.ai 模式 (deep-learning 考试提分)

**工具栈近 12 个月变化**:
- FSRS 取代 SM-2 成为 Anki 默认调度器, 向其他 SRS 应用扩散 (RemNote, Mochi)
- LLM 辅导工具爆发: Khanmigo, Duolingo Max, 数十家创业公司; 但缺乏心理测量严谨性
- 知识追踪领域从纯序列建模 (DKT/SAKT/AKT) 向 LLM 增强方向演进 (Dialogue-KT, CIKT)
- 松鼠AI 发布"大自适应模型" + 多模态 AI agent, 入选 TIME 2025 最佳发明
- 多模态学习分析 (EEG + 眼动 + 面部情绪) 首次在企业培训中规模落地
- Riiid 更名 Socra.ai (2025.9), 从应试转向通用 AI 辅导
- AI 自动出题 (AIG) 以 HITL 框架为主流, 尚无统治性平台

**工具 → 工作流映射**:
- 内容创作 → H5P (微交互) + Adapt (长课程) + Articulate (企业 SCORM/xAPI)
- 学生建模 → pyBKT (BKT 基线) / EduStudio (多模型对比) / IRT (静态能力)
- 自适应引擎 → ALEKS (KST) / MATHia (BKT) / Realizeit (通用)
- 测评交付 → TAO (题库) + Concerto/FastTest (CAT) + mirt (校准)
- 学习记录 → xAPI statements → LRS (Learning Locker / Watershed) → 分析仪表板
- 间隔重复 → FSRS (开源最优) / Duolingo HLR (大规模) / Anki (个人学习)
- AI 辅导 → Khanmigo (K-12) / 自建 LLM + RAG (定制) / 松鼠AI (中国市场)
- 隐私合规 → FERPA/COPPA (美) + GDPR (欧) + PIPL + 双减 (中)

**避坑清单**:
- SCORM 用于非 LMS 场景 → 改用 xAPI
- 从零造自适应引擎 → 用现有框架/平台
- 混淆 LMS 条件分支与真正的知识追踪自适应
- 场景不需要时用 DKT/Transformer 而非简单 IRT
- 专有题目格式导致供应商锁定 → 坚持 QTI
- 把 LLM 辅导当 ITS 替代品 → 互补而非替代
- 忽视隐私法规 (FERPA/COPPA/GDPR/PIPL)
