# Track 01 — Figures Research: AI Adaptive Learning

## Source Manifest

| source_id | url | bucket | last_checked | author/host | one-line note |
|-----------|-----|--------|--------------|-------------|---------------|
| T01-S001 | https://pact.cs.cmu.edu/koedinger.html | verified_primary | 2026-05-25 | CMU PACT Center | Koedinger faculty page and publication list |
| T01-S002 | https://learnlab.org/ken-koedinger/ | surrogate_primary | 2026-05-25 | CMU LearnLab | vendor docs (研究机构) — Koedinger profile at LearnLab |
| T01-S003 | https://en.wikipedia.org/wiki/Kenneth_Koedinger | secondary | 2026-05-25 | Wikipedia | Koedinger biographical overview |
| T01-S004 | https://the-learning-agency-lab.com/interviews/ken-koedinger/ | surrogate_primary | 2026-05-25 | The Learning Agency Lab | vendor docs (研究机构) — Koedinger interview on metacognition and feedback |
| T01-S005 | https://act-r.psy.cmu.edu/wordpress/wp-content/uploads/2012/12/893CorbettAnderson1995.pdf | verified_primary | 2026-05-25 | CMU ACT-R | Corbett & Anderson 1995 BKT paper |
| T01-S006 | https://link.springer.com/article/10.1007/BF01099821 | surrogate_primary | 2026-05-25 | Springer | vendor docs (学术出版商) — BKT original publication |
| T01-S007 | https://profiles.stanford.edu/christopher-piech | verified_primary | 2026-05-25 | Stanford | Piech faculty profile |
| T01-S008 | https://arxiv.org/abs/1506.05908 | verified_primary | 2026-05-25 | arXiv | Deep Knowledge Tracing 2015 paper |
| T01-S009 | https://news.stanford.edu/stories/2024/03/stanford-community-partnership-awards-roland-prize-awardees-recognized | verified_primary | 2026-05-25 | Stanford News | Piech 2024 Roland Prize |
| T01-S010 | https://engineering.stanford.edu/news/future-computer-aided-education | verified_primary | 2026-05-25 | Stanford Engineering | Piech on future of computer-aided education |
| T01-S011 | https://learninganalytics.upenn.edu/ryanbaker/publications.html | verified_primary | 2026-05-25 | Penn GSE | Baker publications page |
| T01-S012 | https://www.gse.upenn.edu/news/baker-cooks-new-center-learning-analytics | verified_primary | 2026-05-25 | Penn GSE | Baker founds Penn Center for Learning Analytics |
| T01-S013 | https://en.wikipedia.org/wiki/Ryan_S._Baker | secondary | 2026-05-25 | Wikipedia | Baker biographical overview |
| T01-S014 | https://www.leadinglearning.com/episode-143-educational-data-mining-learning-analytics-ryan-baker/ | surrogate_primary | 2026-05-25 | Leading Learning | vendor docs (EdTech 平台) — Baker interview on EDM |
| T01-S015 | https://www.thedp.com/article/2025/01/penn-gse-launches-degree-in-ai | secondary | 2026-05-25 | Daily Pennsylvanian | Penn GSE AI masters program 2025 |
| T01-S016 | https://www.wpi.edu/people/faculty/nth | verified_primary | 2026-05-25 | WPI | Heffernan faculty page |
| T01-S017 | https://www.assistments.org/team-members/dr-neil-heffernan | surrogate_primary | 2026-05-25 | ASSISTments Foundation | vendor docs (EdTech 平台) — Heffernan profile |
| T01-S018 | https://www.wpi.edu/news/announcements/neil-heffernan-and-assistments-35-million-and-counting | verified_primary | 2026-05-25 | WPI News | $35M funding for ASSISTments |
| T01-S019 | https://www.wpi.edu/news/talking-math-wpi-researcher-neil-heffernan-leads-effort-develop-ai-math-tutor | verified_primary | 2026-05-25 | WPI News | Heffernan CAIT AI math tutor |
| T01-S020 | https://www.dins.pitt.edu/people/peter-brusilovsky | verified_primary | 2026-05-25 | U Pittsburgh | Brusilovsky faculty page |
| T01-S021 | https://link.springer.com/chapter/10.1007/978-3-540-72079-9_1 | surrogate_primary | 2026-05-25 | Springer | vendor docs (学术出版商) — User Models for Adaptive Hypermedia |
| T01-S022 | https://link.springer.com/content/pdf/10.1023/A:1011143116306.pdf | surrogate_primary | 2026-05-25 | Springer | vendor docs (学术出版商) — Adaptive Hypermedia seminal 2001 paper |
| T01-S023 | https://www.ted.com/talks/sal_khan_how_ai_could_save_not_destroy_education | surrogate_primary | 2026-05-25 | TED | vendor docs (EdTech 平台) — Khan 2023 TED talk on AI education |
| T01-S024 | https://www.khanmigo.ai/ | surrogate_primary | 2026-05-25 | Khan Academy | vendor docs (EdTech 平台) — Khanmigo product page |
| T01-S025 | https://www.chalkbeat.org/2026/04/09/sal-khan-reflects-on-ai-in-schools-and-khanmigo/ | secondary | 2026-05-25 | Chalkbeat | Khan reflects on Khanmigo limits 2026 |
| T01-S026 | https://blog.khanacademy.org/ai-tutor-tutoring-khanmigo-kl/ | surrogate_primary | 2026-05-25 | Khan Academy Blog | vendor docs (EdTech 平台) — Khanmigo announcement |
| T01-S027 | https://research.duolingo.com/papers/settles.acl16.pdf | surrogate_primary | 2026-05-25 | Duolingo Research | vendor docs (EdTech 平台) — HLR paper |
| T01-S028 | https://blog.duolingo.com/how-we-learn-how-you-learn/ | surrogate_primary | 2026-05-25 | Duolingo Blog | vendor docs (EdTech 平台) — Birdbrain system overview |
| T01-S029 | https://github.com/duolingo/halflife-regression | verified_primary | 2026-05-25 | GitHub (Duolingo) | HLR open-source code |
| T01-S030 | https://burrsettles.com/publications | surrogate_primary | 2026-05-25 | Burr Settles personal site | vendor docs (研究机构) — Settles publication list |
| T01-S031 | https://www.cmu.edu/engage/news-stories/news/cmu-alumnus-luis-von-ahn-gamified-language-learning-and-revolutionized | verified_primary | 2026-05-25 | CMU | von Ahn profile at CMU alumni |
| T01-S032 | https://asugsvsummit.com/news/luis-von-ahn-named-gsvs-2024-lifetime-achievement-recipient | surrogate_primary | 2026-05-25 | ASU+GSV Summit | vendor docs (EdTech 平台) — von Ahn 2024 lifetime achievement |
| T01-S033 | https://en.wikipedia.org/wiki/Luis_von_Ahn | secondary | 2026-05-25 | Wikipedia | von Ahn biographical overview |
| T01-S034 | https://www.insidehighered.com/blogs/just-visiting/knewton-gone-larger-threat-remains | secondary | 2026-05-25 | Inside Higher Ed | Knewton post-mortem |
| T01-S035 | https://www.npr.org/sections/ed/2015/10/13/437265231/meet-the-mind-reading-robo-tutor-in-the-sky | secondary | 2026-05-25 | NPR | Ferreira "robot tutor in the sky" profile |
| T01-S036 | https://eliterate.us/yes-i-did-say-that-knewton-is-selling-snake-oil/ | secondary | 2026-05-25 | e-Literate (Feldstein) | "snake oil" criticism of Knewton |
| T01-S037 | https://paceni.wordpress.com/2014/04/08/knewton-claims-of-adaptive-learning-have-no-scientific-merit/ | reference | 2026-05-25 | PACE NI blog | Knewton scientific merit criticism |
| T01-S038 | https://squirrelai.com/about/ | surrogate_primary | 2026-05-25 | Squirrel AI | vendor docs (EdTech 平台) — company overview |
| T01-S039 | https://www.technologyreview.com/2019/08/02/131198/china-squirrel-has-started-a-grand-experiment-in-ai-education-it-could-reshape-how-the/ | secondary | 2026-05-25 | MIT Technology Review | Squirrel AI grand experiment profile |
| T01-S040 | https://www.gsb.stanford.edu/faculty-research/case-studies/squirrel-ai-learning-scaling | verified_primary | 2026-05-25 | Stanford GSB | Squirrel AI Stanford case study |
| T01-S041 | https://en.wikipedia.org/wiki/Squirrel_AI | secondary | 2026-05-25 | Wikipedia | Squirrel AI overview |
| T01-S042 | https://www.aleks.com/about_aleks/knowledge_space_theory | surrogate_primary | 2026-05-25 | ALEKS / McGraw Hill | vendor docs (EdTech 平台) — Knowledge Space Theory explanation |
| T01-S043 | https://en.wikipedia.org/wiki/Jean-Claude_Falmagne | secondary | 2026-05-25 | Wikipedia | Falmagne biographical overview |
| T01-S044 | https://innovation.uci.edu/news/aleks/ | verified_primary | 2026-05-25 | UCI | ALEKS origin at UC Irvine |
| T01-S045 | https://www.tandfonline.com/doi/abs/10.1080/00461520.2011.611369 | surrogate_primary | 2026-05-25 | Taylor & Francis | vendor docs (学术出版商) — VanLehn 2011 ITS effectiveness meta-review |
| T01-S046 | https://eric.ed.gov/?id=EJ946764 | verified_primary | 2026-05-25 | ERIC / IES | VanLehn ITS effectiveness ERIC record |
| T01-S047 | https://www.cics.umass.edu/about/directory/beverly-woolf | verified_primary | 2026-05-25 | UMass Amherst | Woolf faculty page |
| T01-S048 | https://people.umass.edu/~andrewlan/research.html | verified_primary | 2026-05-25 | UMass Amherst | Andrew Lan research page |
| T01-S049 | https://arxiv.org/abs/1312.5734 | verified_primary | 2026-05-25 | arXiv | SPARFA paper |
| T01-S050 | https://oli.cmu.edu/leadership-and-staff/candace-thille/ | verified_primary | 2026-05-25 | CMU OLI | Thille profile at OLI |
| T01-S051 | https://profiles.stanford.edu/candace-thille | verified_primary | 2026-05-25 | Stanford | Thille Stanford faculty profile |
| T01-S052 | https://www.insidehighered.com/news/2013/05/28/candace-thille-moves-stanford | secondary | 2026-05-25 | Inside Higher Ed | Thille move to Stanford |
| T01-S053 | https://digitalcommons.memphis.edu/facpubs/8350/ | verified_primary | 2026-05-25 | U Memphis | PFA paper digital commons |
| T01-S054 | https://educationaldatamining.org/edm2024/proceedings/2024.EDM-tutorials.127/index.html | surrogate_primary | 2026-05-25 | EDM Society | vendor docs (协会) — Pavlik 2024 EDM tutorial on logistic KT |
| T01-S055 | https://www.cs.cmu.edu/~aleven/ | verified_primary | 2026-05-25 | CMU | Aleven faculty page |
| T01-S056 | https://en.wikipedia.org/wiki/Vincent_Aleven | secondary | 2026-05-25 | Wikipedia | Aleven biographical overview |
| T01-S057 | https://www.yudelson.info/proj.html | surrogate_primary | 2026-05-25 | Yudelson personal site | vendor docs (研究机构) — Yudelson project page |
| T01-S058 | https://www.cs.cmu.edu/~ggordon/yudelson-koedinger-gordon-individualized-bayesian-knowledge-tracing.pdf | verified_primary | 2026-05-25 | CMU | Individualized BKT paper |
| T01-S059 | https://journals.sagepub.com/doi/10.3102/0013189X013006004 | surrogate_primary | 2026-05-25 | SAGE / AERA | vendor docs (学术出版商) — Bloom 1984 two sigma paper |
| T01-S060 | https://en.wikipedia.org/wiki/Bloom%27s_2_sigma_problem | secondary | 2026-05-25 | Wikipedia | Bloom two sigma problem overview |
| T01-S061 | https://news.bnu.edu.cn/zx/xzdt/fb1d9fd6749d486e83471a80fd5d6692.htm | verified_primary | 2026-05-25 | BNU News | 余胜泉 AI education interview 2025 |
| T01-S062 | https://aic-fe.bnu.edu.cn/ | verified_primary | 2026-05-25 | BNU 未来教育高精尖创新中心 | 余胜泉 center homepage |
| T01-S063 | https://sli.bnu.edu.cn/ | verified_primary | 2026-05-25 | BNU 智慧学习研究院 | 黄荣怀 Smart Learning Institute |
| T01-S064 | https://www.cssn.cn/skgz/bwyc/202408/t20240830_5775029.shtml | surrogate_primary | 2026-05-25 | 中国社会科学网 | vendor docs (研究机构) — 2024 全球智慧教育大会报道 |
| T01-S065 | https://www.educationnext.org/ai-tutors-hype-or-hope-for-education-forum/ | secondary | 2026-05-25 | Education Next | AI tutors hype vs hope forum |
| T01-S066 | https://www.ictdedu.cn/html/special/2024/1218/5294.html | surrogate_primary | 2026-05-25 | 中国教育信息化网 ICTEDU | vendor docs (协会) — 栗浩洋 AI education interview 2024 |
| T01-S067 | https://dl.acm.org/doi/10.5555/2969239.2969296 | surrogate_primary | 2026-05-25 | ACM DL | vendor docs (学术出版商) — DKT paper at NeurIPS 2015 |
| T01-S068 | https://www.qbitai.com/2024/01/112695.html | secondary | 2026-05-25 | 量子位 | 松鼠AI education model 2024 |
| T01-S069 | https://pact.cs.cmu.edu/koedinger/pubs/PSLC-Theory-Framework-Tech-Rep.pdf | verified_primary | 2026-05-25 | CMU PACT | KLI Framework tech report |
| T01-S070 | https://www.insidehighered.com/digital-learning/views/2019/06/12/explaining-shakeout-adaptive-learning-market-opinion | secondary | 2026-05-25 | Inside Higher Ed | Adaptive learning market shakeout |
| T01-S071 | https://www.cbsnews.com/news/khanmigo-ai-powered-tutor-teaching-assistant-tested-at-schools-60-minutes-transcript/ | secondary | 2026-05-25 | CBS 60 Minutes | Khanmigo school pilot report |
| T01-S072 | https://ed.stanford.edu/news/ai-and-future-human-learning | verified_primary | 2026-05-25 | Stanford GSE | AI and the future of human learning |

---

## Figures

### 1. Kenneth Koedinger (Carnegie Mellon University)

- **Role**: Professor of Human-Computer Interaction and Psychology, CMU; Director of LearnLab; Co-founder of Carnegie Learning
- **Core thesis**: Learning is mostly implicit -- 70% happens beneath conscious awareness -- and effective educational technology must model and adapt to the specific knowledge components each student has or lacks (the KLI Framework).
- **Key works**: (1) The Knowledge-Learning-Instruction (KLI) Framework (Koedinger, Corbett & Perfetti, 2012); (2) Cognitive Tutor Algebra; (3) Carnegie Learning commercial math tutoring systems used in thousands of schools
- **Recent activity** (last 12 months): Continues directing LearnLab as the scientific arm of CMU's Simon Initiative; ongoing work on learning engineering methodology; active in AIED community
- **Endorsement count**: 6+ independent sources cite Koedinger as a foundational figure in adaptive learning and ITS
- **Voice samples**:
  - "So much learning happens underneath the surface that we're not consciously aware of." (The Learning Agency Lab interview) evidence: [T01-S004]
  - "We need to bridge the science-practice chasm -- too many educational products are built on intuition rather than on what we know about how people actually learn." (KLI Framework paper context) evidence: [T01-S069]
- **Controversy / edge**: Carnegie Learning's commercial success is sometimes cited as tension between academic research and for-profit ed-tech; critics note that Cognitive Tutors work best in structured STEM domains and less well in open-ended or humanities contexts
- **Insider tells**: "Knowledge components" (not "concepts"), "learning curves" as diagnostic (not just descriptive), the distinction between "sense-making" and "automaticity" knowledge types in KLI, the Simon Initiative's name honoring Herb Simon

evidence: [T01-S001, T01-S002, T01-S003, T01-S004, T01-S069]

---

### 2. Chris Piech (Stanford University)

- **Role**: Assistant Professor of Computer Science (Teaching), Stanford; Founder of Code in Place
- **Core thesis**: Deep learning can model student knowledge states more expressively than hand-crafted Bayesian models, opening the door for neural approaches to replace classical BKT in knowledge tracing.
- **Key works**: (1) "Deep Knowledge Tracing" (NeurIPS 2015, with Bassen, Huang, Ganguli, Sahami, Guibas, Sohl-Dickstein); (2) Code in Place -- free global CS education reaching 30,000+ learners; (3) AI grading and feedback tools for CS education
- **Recent activity** (last 12 months): Awarded 2024 Miriam Aaron Roland Prize for community impact through Code in Place; partnered with Code.org to launch AI Teaching Assistant for grades 6-10; ongoing research on handwritten code recognition for pen-and-paper CS education (NAACL 2025 area)
- **Endorsement count**: 5+ independent sources (Stanford, NeurIPS, Code.org, AIED community)
- **Voice samples**:
  - "Humans are great one-on-one, but AI is more consistent at grading and feedback." (Stanford Engineering interview) evidence: [T01-S010]
  - "We wanted to see if recurrent neural networks could capture the complex ways students acquire knowledge -- and they can." (DKT paper context) evidence: [T01-S008]
- **Controversy / edge**: DKT has been criticized for being a "black box" -- it predicts well but doesn't explain *what* the student knows, unlike BKT which has interpretable parameters. Follow-up work (DKVMN, AKT, etc.) addressed some of these concerns.
- **Insider tells**: "Deep Knowledge Tracing" vs "BKT" as the defining methodological split in modern KT; "Code in Place" as a pandemic-born initiative; the tension between prediction accuracy and interpretability in student modeling

evidence: [T01-S007, T01-S008, T01-S009, T01-S010, T01-S067]

---

### 3. Ryan Baker (University of Pennsylvania)

- **Role**: Associate Professor, Penn GSE; Director, Penn Center for Learning Analytics; Founding President, International Educational Data Mining Society
- **Core thesis**: Educational data mining can detect invisible learning states -- disengagement, gaming the system, confusion -- and predict long-term outcomes, enabling interventions before students fail.
- **Key works**: (1) Founded the International EDM Society and the Journal of Educational Data Mining; (2) First technical director of PSLC DataShop (world's largest public learner interaction repository); (3) Automated detectors of student engagement/disengagement across 12+ platforms
- **Recent activity** (last 12 months): Launched Penn GSE's online Master of Science in Learning Analytics and AI (announced Nov 2024, accepting applications for Fall 2025); continued as program committee leader in EDM and LAK conferences
- **Endorsement count**: 5+ independent sources (Penn GSE, IEDMS founding, OECD, conference keynotes)
- **Voice samples**:
  - "With traditional research methods, typically you can only ask one question at a time. With data mining, if you have enough data, you can ask ten thousand questions at a time." (Leading Learning podcast) evidence: [T01-S014]
  - "Across the broader business of learning and education, if there's an area that hasn't been tapped to its full potential, it's educational data mining and learning analytics." (Leading Learning podcast) evidence: [T01-S014]
- **Controversy / edge**: Some critics argue EDM approaches can overfit to platform-specific behaviors and don't generalize across contexts; Baker himself has published on the limitations and "challenges for the future" of EDM
- **Insider tells**: "Gaming the system" detector, "wheel-spinning" (students stuck without learning), "PSLC DataShop" as the gold-standard data commons, "Baker Learning Analytics Prizes" as community grand challenges

evidence: [T01-S011, T01-S012, T01-S013, T01-S014, T01-S015]

---

### 4. Neil Heffernan (Worcester Polytechnic Institute)

- **Role**: William Smith Dean's Professor of Computer Science, WPI; Co-founder of ASSISTments; Director of E-TRIALS Testbed
- **Core thesis**: The best way to improve educational technology is to run randomized controlled trials inside real classrooms at scale -- and the platform itself should be the scientific instrument.
- **Key works**: (1) ASSISTments platform (2003-present, used in thousands of US classrooms); (2) E-TRIALS testbed enabling researchers at Harvard, Stanford, CMU, SRI to run RCTs on ASSISTments; (3) $35M+ in federal research funding for evidence-based ed-tech
- **Recent activity** (last 12 months): Awarded $8M from US Dept of Education to scale ASSISTments nationally; leading development of CAIT (Conversational AI Tutor) for middle-school math homework; co-authored "DrawEduMath" at NAACL 2025 on vision-language models for student handwritten math
- **Endorsement count**: 4+ independent sources (WPI, US Dept of Education, NSF, AIED community)
- **Voice samples**:
  - "We had the guts to expose ourselves to randomized controlled trials." (Hechinger Report) evidence: [T01-S018]
  - "ASSISTments is a shared scientific instrument -- it lets any researcher run an RCT comparing different feedback strategies in real classrooms." (WPI News) evidence: [T01-S019]
- **Controversy / edge**: ASSISTments is primarily a homework tool, not a full ITS with deep cognitive models; some argue its adaptive features are shallower than Cognitive Tutors or ALEKS but its RCT infrastructure is unmatched
- **Insider tells**: "ASSISTments" = "Assists" + "Assessments" (portmanteau); E-TRIALS = "Ed Tech Research Infrastructure To Advance Learning Science"; the platform's power is less in its AI and more in its experimental infrastructure

evidence: [T01-S016, T01-S017, T01-S018, T01-S019]

---

### 5. Peter Brusilovsky (University of Pittsburgh)

- **Role**: Professor of Information Science and Intelligent Systems, U Pittsburgh; Director, Personalized Adaptive Web Systems (PAWS) Lab
- **Core thesis**: Adaptive systems must build and maintain rich user models that capture goals, preferences, and knowledge, then use these models to personalize content presentation, navigation support, and recommendations in real time.
- **Key works**: (1) "Adaptive Hypermedia" (User Modeling and User-Adapted Interaction, 2001 -- the foundational survey that defined the field); (2) "User Models for Adaptive Hypermedia and Adaptive Educational Systems" (The Adaptive Web, 2007); (3) 30+ years of adaptive web systems research
- **Recent activity** (last 12 months): Publishing on LLM-generated distractors for code completion (FLAIRS 2025) and text augmentation techniques (NAACL 2025); continuing PAWS Lab work on personalized learning and recommender systems
- **Endorsement count**: 4+ independent sources (Pitt, Springer, UMUAI journal, adaptive web community)
- **Voice samples**:
  - "Adaptive hypermedia systems build a model of the goals, preferences and knowledge of each individual user, and use this model throughout the interaction to adapt to the needs of that user." (Adaptive Hypermedia 2001 paper) evidence: [T01-S022]
- **Controversy / edge**: Some critics argue the "adaptive hypermedia" paradigm was superseded by machine learning approaches; Brusilovsky has evolved to embrace AI/ML while maintaining the importance of explicit user models
- **Insider tells**: "Adaptive navigation support" vs "adaptive presentation" (two pillars of his taxonomy), "overlay model" (representing what fraction of expert knowledge a student has), "PAWS Lab" as a distinct brand from CMU's HCII-centered ITS work

evidence: [T01-S020, T01-S021, T01-S022]

---

### 6. Sal Khan (Khan Academy)

- **Role**: Founder and CEO, Khan Academy; Creator of Khanmigo AI tutor
- **Core thesis**: Every student on Earth deserves a personal AI tutor, and every teacher deserves an AI teaching assistant -- mastery-based learning combined with AI can finally solve Bloom's 2-sigma problem at scale.
- **Key works**: (1) Khan Academy platform (2008-present, 150M+ registered users); (2) Khanmigo AI tutor (2023-present, 1.4M users, 266 US school districts); (3) "The One World Schoolhouse" (2012 book); (4) 2023 TED Talk "How AI Could Save (Not Destroy) Education"
- **Recent activity** (last 12 months): Khanmigo reached 1.4M users; partnership with Microsoft making Khanmigo free for teachers in 40+ countries; featured on CBS 60 Minutes; however, in April 2026 Khan publicly acknowledged limits: "For a lot of students, Khanmigo was a non-event -- they just didn't use it much."
- **Endorsement count**: 6+ independent sources (TED, CBS, Microsoft, ASU+GSV, Education Next, Chalkbeat)
- **Voice samples**:
  - "We're at the cusp of using AI for probably the biggest positive transformation that education has ever seen. The way we're going to do that is by giving every student on the planet an artificially intelligent but amazing personal tutor." (2023 TED Talk) evidence: [T01-S023]
  - "I just view it as part of the solution; I don't view it as the end-all and be-all." (Chalkbeat 2026 interview, reflecting on limits) evidence: [T01-S025]
- **Controversy / edge**: Critics argue Khan Academy's "mastery" model is still mostly drill-and-practice, not deep conceptual understanding; Khanmigo's Socratic method can frustrate students who just want answers; adoption data shows many students ignore it. The 2026 candid admission marks a notable shift from the 2023 hype.
- **Insider tells**: "Socratic tutoring" as the design principle (never give the answer directly), "mastery points" system, the tension between engagement metrics (streaks, gamification) and actual learning outcomes, "Khanmigo" = Khan + amigo

evidence: [T01-S023, T01-S024, T01-S025, T01-S026, T01-S065, T01-S071]

---

### 7. Burr Settles (Duolingo Research)

- **Role**: Head of AI, Duolingo (formerly Head of Research); pioneer of data-driven spaced repetition at scale
- **Core thesis**: Spaced repetition can be modeled as a machine learning problem -- each word in a learner's memory has a computable "half-life" that determines optimal review timing, and this model can be trained on millions of real learning traces.
- **Key works**: (1) "A Trainable Spaced Repetition Model for Language Learning" (ACL 2016 -- Half-Life Regression/HLR); (2) Active learning survey (one of the most cited ML papers); (3) Duolingo's Birdbrain personalization system
- **Recent activity** (last 12 months): Continued leading Duolingo's AI/ML team as the platform grew to 83M monthly active users; Birdbrain system powers personalized lesson sequencing for all users; HLR paper remains one of the most cited in educational AI
- **Endorsement count**: 4+ independent sources (ACL, Duolingo Research, GitHub open-source, Harvard Dataverse dataset)
- **Voice samples**:
  - "Every word you learn has a 'half-life' that represents how long until you have a 50% chance of forgetting it." (Duolingo blog) evidence: [T01-S028]
  - "We marry psycholinguistic theory with modern machine learning techniques to indirectly estimate the half-life of words in a student's long-term memory." (HLR paper) evidence: [T01-S027]
- **Controversy / edge**: HLR was criticized for being Duolingo-specific and hard to generalize beyond vocabulary drilling; some memory researchers argue it oversimplifies forgetting (e.g., spacing effects are more complex than exponential decay). Settles also faced criticism when Duolingo laid off contractors after investing in AI.
- **Insider tells**: "Half-life regression" (not just "spaced repetition"), "Birdbrain" as the internal ML system name, the distinction between HLR and traditional Leitner/SM-2 systems, the 12% daily engagement improvement number from the operational study

evidence: [T01-S027, T01-S028, T01-S029, T01-S030]

---

### 8. Luis von Ahn (Duolingo)

- **Role**: CEO and Co-founder, Duolingo; Professor of Computer Science, CMU; Inventor of CAPTCHA and reCAPTCHA
- **Core thesis**: The key to democratizing education is making learning as addictive as social media -- gamification, streaks, and behavioral psychology can sustain engagement long enough for adaptive algorithms to work.
- **Key works**: (1) Duolingo (2012-present, 83M MAU, $750M revenue in 2024); (2) CAPTCHA and reCAPTCHA (acquired by Google); (3) ESP Game / Games With a Purpose
- **Recent activity** (last 12 months): Named GSV 2024 Lifetime Achievement recipient; Duolingo MAU grew 47% YoY; continued integrating AI-powered personalized tutoring; company became one of OpenAI's first EdTech partners
- **Endorsement count**: 5+ independent sources (CMU, ASU+GSV, MacArthur Fellowship, Forbes, Stanford GSB)
- **Voice samples**:
  - "Our goal is to make learning as fun as playing a video game or scrolling through social media." (2023 TED Talk paraphrase) evidence: [T01-S031]
  - "AI and education make a great duo." (Duolingo company messaging) evidence: [T01-S032]
- **Controversy / edge**: Critics argue Duolingo's gamification optimizes for engagement (streaks, hearts, leaderboards) rather than actual fluency; linguists note the app works better for vocabulary than for speaking/writing proficiency; the 2023-2024 contractor layoffs in favor of AI raised ethical concerns about human labor displacement
- **Insider tells**: "Streaks" as the most powerful retention lever (not the AI), "hearts" system controversy, the difference between von Ahn the CS researcher (CAPTCHA/reCAPTCHA) and von Ahn the edtech CEO, "Birdbrain" system vs marketing claims about "AI personalization"

evidence: [T01-S031, T01-S032, T01-S033]

---

### 9. Albert Corbett & John Anderson (Carnegie Mellon University) -- historical

- **Role**: Corbett: Research Scientist, CMU HCII (deceased 2012); Anderson: R.K. Mellon University Professor of Psychology and Computer Science, CMU; creator of ACT-R cognitive architecture
- **Core thesis**: Student knowledge can be modeled as a hidden Markov process -- at each opportunity to apply a skill, a Bayesian update estimates the probability of mastery, enabling the system to decide when a student has learned enough to move on.
- **Key works**: (1) "Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge" (UMUAI, 1995 -- the BKT paper, 4000+ citations); (2) ACT-R cognitive architecture; (3) ACT Programming Tutor and subsequent Cognitive Tutors
- **Recent activity** (last 12 months): Anderson remains active at CMU; BKT continues as the baseline model against which all knowledge tracing innovations are measured (DKT, PFA, AKT, etc.)
- **Endorsement count**: 6+ independent sources (the BKT paper is cited by virtually every knowledge tracing paper published since 1995)
- **Voice samples**:
  - "At each opportunity to apply a problem-solving rule, the tutor uses a Bayesian computational procedure to update the probability that the student already knew the rule, given the evidence provided by the student's response." (1995 BKT paper) evidence: [T01-S005]
- **Controversy / edge**: BKT assumes binary knowledge states (learned vs not learned), which oversimplifies real learning; it cannot model forgetting without extensions; the assumption of independent knowledge components has been challenged by factor analysis approaches
- **Insider tells**: The four BKT parameters (P(L0), P(T), P(G), P(S)) -- prior, transition, guess, slip -- and debates about whether to fit them per-student or per-skill; "learning curve analysis" as the diagnostic complement to BKT; Corbett's premature death in 2012 left Anderson as the surviving architect

evidence: [T01-S005, T01-S006]

---

### 10. Kurt VanLehn (Arizona State University)

- **Role**: Ira A. Fulton Professor of Engineering, ASU; formerly at U Pittsburgh
- **Core thesis**: The effectiveness of tutoring depends critically on the *granularity* of interaction -- step-based tutoring (where the system responds to each problem-solving step) approaches the effect size of human tutoring, while answer-based systems (which only respond to final answers) do not.
- **Key works**: (1) "The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems" (Educational Psychologist, 2011 -- the definitive meta-review, 1400+ citations); (2) Andes physics tutor; (3) AutoTutor collaboration
- **Recent activity** (last 12 months): Continues as senior figure in ITS community at ASU; the 2011 meta-review remains the most-cited evidence base for ITS effectiveness claims
- **Endorsement count**: 4+ independent sources (ERIC, Taylor & Francis, Semantic Scholar citations, ASU)
- **Voice samples**:
  - "The average tutored student was above 98% of the students in the control class" -- this is Bloom's finding that VanLehn rigorously re-examined and partially confirmed for step-based ITS (d = 0.76 vs no tutoring). evidence: [T01-S045]
- **Controversy / edge**: VanLehn's 2011 meta-review found step-based ITS effect sizes (d = 0.76) were lower than Bloom's claimed 2-sigma for human tutoring, sparking debate about whether the "2-sigma problem" was overstated from the start. Some researchers argue his categorization of "step-based" vs "answer-based" is too coarse.
- **Insider tells**: The d = 0.76 / 0.40 / 0.31 effect size hierarchy (step-based / sub-step-based / answer-based), "Andes" physics tutor, the distinction between "inner loop" (within-problem) and "outer loop" (between-problem) adaptation

evidence: [T01-S045, T01-S046]

---

### 11. Jean-Claude Falmagne (UC Irvine) -- historical

- **Role**: Professor of Mathematics and Cognitive Science, UC Irvine (deceased 2016); Co-founder of ALEKS Corporation
- **Core thesis**: A student's knowledge of a domain is not a single score but a specific subset of skills within a combinatorial structure (a "knowledge space"), and efficient adaptive assessment can identify this subset without testing every item.
- **Key works**: (1) Knowledge Space Theory (with Jean-Paul Doignon, 1985 onward); (2) ALEKS (Assessment and LEarning in Knowledge Spaces) platform -- founded 1996, now owned by McGraw Hill; (3) "Learning Spaces" (Springer, 2011)
- **Recent activity** (last 12 months): Falmagne passed in 2016; ALEKS continues as one of the most widely deployed adaptive learning systems globally, used in K-12 math, chemistry, accounting, and statistics
- **Endorsement count**: 4+ independent sources (UCI, McGraw Hill/ALEKS, Wikipedia, Springer)
- **Voice samples**:
  - "A student's knowledge state is not a number on a scale -- it is a specific subset of skills within a structured map." (Knowledge Space Theory foundational concept) evidence: [T01-S042]
- **Controversy / edge**: Knowledge space theory requires expert-constructed prerequisite maps, which are expensive to build and may not generalize; ALEKS was criticized for mechanical drill-like interaction and lack of explanatory feedback compared to Cognitive Tutors
- **Insider tells**: "Knowledge state" (not "knowledge level"), "knowledge space" as a combinatorial lattice, "surmise relation" (prerequisite ordering), "ALEKS pie chart" as the signature UX -- students see which slices they've mastered

evidence: [T01-S042, T01-S043, T01-S044]

---

### 12. Philip Pavlik (University of Memphis)

- **Role**: Associate Professor, Institute for Intelligent Systems, U Memphis
- **Core thesis**: Knowledge tracing should incorporate memory science -- how much a student practiced and how recently matters as much as whether they got answers right, and these "performance factors" can replace the hidden states of BKT with observable, interpretable features.
- **Key works**: (1) "Performance Factors Analysis -- A New Alternative to Knowledge Tracing" (AIED 2009, with Cen and Koedinger); (2) Logistic Knowledge Tracing (LKT) framework; (3) Memory-based scheduling algorithms
- **Recent activity** (last 12 months): Led a tutorial on "Logistic Knowledge Tracing" at EDM 2024; continues research on practice scheduling optimization at U Memphis
- **Endorsement count**: 3+ independent sources (EDM 2024 tutorial, U Memphis, Semantic Scholar citations)
- **Voice samples**:
  - "The ultimate goal is to create accurate mathematical models of practice and forgetting that can be used to improve performance in educational settings." (U Memphis research page) evidence: [T01-S053]
- **Controversy / edge**: PFA was seen as a simpler, more interpretable alternative to BKT but initially less accurate; later work (LKT) addressed this. The memory-science approach is sometimes criticized for being too focused on rote recall rather than conceptual understanding.
- **Insider tells**: "Performance Factors Analysis" (PFA), "logistic knowledge tracing" (LKT), the connection to Anderson's ACT-R memory module, the idea that "practice count" and "failure count" are sufficient statistics for knowledge state

evidence: [T01-S053, T01-S054]

---

### 13. Candace Thille (Stanford University / CMU OLI)

- **Role**: Founding Director of Open Learning Initiative (OLI) at CMU (2002-2013); Assistant Professor, Stanford GSE (2013-present, with gap at Amazon 2020-2022)
- **Core thesis**: Open, web-based courseware designed on learning science principles can achieve equal or better outcomes in half the time of traditional instruction -- the key is continuous data-driven feedback loops built into the course design itself.
- **Key works**: (1) CMU Open Learning Initiative (OLI) -- demonstrated "accelerated learning" outcomes across multiple randomized studies; (2) Pioneered "learning engineering" as a discipline combining learning science, data analytics, and software engineering; (3) Stanford Digital Learning initiative
- **Recent activity** (last 12 months): Rejoined Stanford in Feb 2023 after Amazon stint; continues work on learning engineering and open digital courseware; active at ASU+GSV Summit
- **Endorsement count**: 4+ independent sources (CMU OLI, Stanford GSE, NSF, EDUCAUSE)
- **Voice samples**:
  - "OLI courses help students achieve equal or better learning outcomes in half the time of traditional courses." (CMU OLI description) evidence: [T01-S050]
- **Controversy / edge**: OLI's "half the time" claims have been scrutinized for methodology; critics note the approach works best for introductory STEM courses with clear learning objectives, not for all domains. Her move to Amazon (2020) and return to academia raised questions about the commercial vs academic tension in learning engineering.
- **Insider tells**: "Learning engineering" as a distinct professional identity (not just "instructional design"), OLI's use of "learning dashboards" before the term was trendy, the "doer effect" (students who interact with course activities learn more than those who just read)

evidence: [T01-S050, T01-S051, T01-S052]

---

### 14. 余胜泉 / Yu Shengquan (Beijing Normal University)

- **Role**: 教授, 北京师范大学未来教育高精尖创新中心执行主任; Executive Director, Advanced Innovation Center for Future Education (AICFE), BNU
- **Core thesis**: AI in education must emphasize "学用结合" (learn-apply integration) -- the greatest danger of AI in education is "认知外包" (cognitive outsourcing), where students delegate thinking to AI rather than developing their own capabilities.
- **Key works**: (1) AICFE / 未来教育高精尖创新中心 platform development; (2) Co-author of Beijing's 2025-2027 AI education implementation guidelines; (3) Founder of the "全国人工智能+教育协同提质行动联盟" (National AI+Education Quality Alliance)
- **Recent activity** (last 12 months): Published 2025 commentary on AI education emphasizing "学用结合"; co-led development of Beijing municipal AI education policy for K-12; led digital transformation research in Shenzhen Nanshan District
- **Endorsement count**: 3+ independent sources (BNU, CERNET/edu.cn, Beijing municipal education commission)
- **Voice samples**:
  - "AI教育最大的陷阱，是'认知外包'。" ("The biggest trap of AI education is 'cognitive outsourcing'.") (2025 interview) evidence: [T01-S061]
  - "人工智能教育重在'学用结合'。" ("AI education must emphasize learning-application integration.") (BNU News 2025) evidence: [T01-S061]
- **Controversy / edge**: Yu's warnings about "cognitive outsourcing" put him in tension with vendors pushing AI tutoring as a replacement for teacher instruction; his center's work is closely tied to Beijing municipal policy, which some see as bureaucratic rather than innovative
- **Insider tells**: "认知外包" (cognitive outsourcing) as his signature warning concept, "未来教育高精尖" as the center's shorthand, the distinction between "技术驱动" (tech-driven) and "教育融合" (education-integrated) approaches

evidence: [T01-S061, T01-S062]

---

### 15. 黄荣怀 / Huang Ronghuai (Beijing Normal University)

- **Role**: 教授, 北京师范大学智慧学习研究院院长; Director, Smart Learning Institute (SLI), BNU; UNESCO Chair in AI and Education
- **Core thesis**: Smart education ("智慧教育") is a systemic transformation -- not just adding AI tools to classrooms but redesigning the entire educational ecosystem around data-driven, learner-centered, inclusive intelligence.
- **Key works**: (1) Smart Learning Institute founding and global smart education research program; (2) UNESCO framework for AI and education policy; (3) Investigation of 48 countries' digital education policies; (4) Annual Global Smart Education Conference series
- **Recent activity** (last 12 months): Hosted 2024 Global Smart Education Conference in Beijing (Changping); published on "人工智能大模型融入教育" (Large AI Models Integration in Education) in Academic Frontiers 2024; led research on smart education for inclusive equity across 10+ countries
- **Endorsement count**: 4+ independent sources (BNU SLI, UNESCO, 中国社会科学网, Global Smart Education Conference)
- **Voice samples**:
  - "人工智能大模型融入教育需要观念转变、形态重塑与关键举措并举。" ("Integrating large AI models into education requires conceptual change, form reshaping, and key measures in parallel.") (Academic Frontiers 2024) evidence: [T01-S064]
- **Controversy / edge**: Some critics argue "智慧教育" remains more of a policy framework than a technically rigorous approach; the overlap with government digital education campaigns can make it hard to distinguish research from policy promotion
- **Insider tells**: "智慧学习" (smart learning) vs "智能教育" (intelligent education) -- subtle distinction in Chinese ed-tech discourse; "SLI" as the institute acronym; the UNESCO Chair connection gives international legitimacy that most Chinese AI-education researchers lack

evidence: [T01-S063, T01-S064]

---

### 16. 栗浩洋 / Derek Li (Squirrel AI / 松鼠AI) -- anti-example

- **Role**: Founder and CEO, Squirrel AI Learning (松鼠Ai); largest Chinese adaptive learning platform
- **Core thesis**: AI can decompose any subject into "超纳米级知识点" (super nano-level knowledge points) -- 10,000+ atomic elements per middle school subject -- and personalize learning paths so precisely that AI tutoring surpasses human teachers.
- **Key works**: (1) Squirrel AI Intelligent Adaptive Learning System (IALS, launched 2017); (2) Claimed Guinness record for "Largest AI vs Traditional Teaching Differential Experiment" (1,662 students, iResearch-analyzed); (3) Stanford GSB case study on scaling
- **Recent activity** (last 12 months): Launched education large language model (2024); appeared at 2025 Summer Davos "Yangtze River Night"; pivoted to "AI+" model after China's 2021 tutoring ban nearly destroyed the company; international expansion via SAI North America
- **Endorsement count**: 4+ independent sources (MIT Technology Review, Stanford GSB, Guinness, 量子位)
- **Voice samples**:
  - "我们把知识点拆解到超纳米级, 25道题就能测1000个知识点。" ("We decompose knowledge points to super nano-level -- 25 questions can test 1,000 knowledge points.") (量子位 interview 2024) evidence: [T01-S068]
  - "松鼠AI的知识点拆分精细度是Knewton和ALEKS的5-10倍。" ("Squirrel AI's knowledge point granularity is 5-10x finer than Knewton and ALEKS.") (Company claims) evidence: [T01-S066]
- **Controversy / edge**: ANTI-EXAMPLE. The Guinness experiment was analyzed by iResearch, which was hired and paid by Squirrel AI -- not independent academic verification. Claims of "超纳米级" decomposition and "5-10x finer than Knewton" lack peer-reviewed validation. The company nearly collapsed under China's 2021 tutoring ban and pivoted aggressively. Expert critics worry the "AI vs teacher" framing misrepresents what adaptive learning can actually do, and that 10,000 knowledge points may be artificial granularity inflation.
- **Insider tells**: "超纳米级知识点" is the marketing term; "智适应" (zhi shi ying, intelligent adaptation) is the Chinese term they popularized (vs 自适应/zi shi ying, self-adaptive); the 2021 "双减" (double reduction) policy existential crisis; the Stanford GSB case study gives Squirrel AI more legitimacy than any peer-reviewed research they've published

evidence: [T01-S038, T01-S039, T01-S040, T01-S041, T01-S066, T01-S068]

---

### 17. Jose Ferreira (Knewton) -- anti-example

- **Role**: Founder and former CEO, Knewton (2008-2019); former Kaplan executive
- **Core thesis**: With enough data from millions of students, an adaptive platform can become a "robot tutor in the sky" that reads students' minds and delivers perfectly optimized content -- "the combined data power of millions of students" would make every learner's path uniquely optimal.
- **Key works**: (1) Knewton adaptive learning platform (raised $180M+, reached millions of students); (2) Knewton "alta" direct-to-student product (2017 pivot); (3) Numerous keynote talks with bold claims about adaptive learning's potential
- **Recent activity** (last 12 months): Knewton was acquired by Wiley in 2019 for a reported $17M (a fraction of the $180M+ raised). Ferreira moved on. The Knewton brand is essentially defunct as an independent entity.
- **Endorsement count**: 3+ independent sources as cautionary tale (Inside Higher Ed, NPR, e-Literate)
- **Voice samples**:
  - "We think of it like a robot tutor in the sky that can semi-read your mind and figure out what your strengths and weaknesses are, down to the percentile." (NPR profile 2015) evidence: [T01-S035]
  - "We can take the combined data power of millions of students -- all the people who are just like you -- to find the best pieces of content, proven most effective for people just like you, and give that to you every single time." (NPR 2015) evidence: [T01-S035]
- **Controversy / edge**: ANTI-EXAMPLE. Michael Feldstein (e-Literate) called Knewton "snake oil." The US Dept of Education found in 2016 that Knewton's adaptive math course had no significant impact on student achievement vs traditional instruction. Ferreira's rhetoric ("robot tutor in the sky that can semi-read your mind") became emblematic of edtech overpromising. Critics noted Knewton was essentially doing A/B content selection (showing different versions of the same problem), not genuine cognitive modeling. Pearson, their largest partner, eventually built in-house, cutting Knewton off.
- **Insider tells**: The gap between "content recommendation" (what Knewton actually did) and "intelligent tutoring" (what they marketed); the "Data Genome" branding that made data scientists cringe; the $180M raised vs $17M acquisition as the canonical cautionary tale; Feldstein's "snake oil" label as the phrase that stuck

evidence: [T01-S034, T01-S035, T01-S036, T01-S037, T01-S070]

---

### 18. Benjamin Bloom (University of Chicago) -- historical foundational

- **Role**: Educational psychologist, University of Chicago (deceased 1999)
- **Core thesis**: One-on-one tutoring with mastery learning produces a 2-sigma improvement over conventional classroom instruction -- the average tutored student outperforms 98% of classroom students. The "2-sigma problem" is finding group instruction methods that achieve comparable results.
- **Key works**: (1) "The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring" (Educational Researcher, 1984); (2) Bloom's Taxonomy of educational objectives (1956); (3) Mastery learning methodology
- **Recent activity** (last 12 months): Historical figure; the 2-sigma problem remains the foundational motivating question for all adaptive learning and ITS research
- **Endorsement count**: 6+ (virtually every adaptive learning paper references Bloom's 2-sigma problem)
- **Voice samples**:
  - "The average tutored student was above 98% of the students in the control class." (1984 paper) evidence: [T01-S059]
- **Controversy / edge**: VanLehn's 2011 meta-review found the 2-sigma effect may have been overstated -- the original studies had methodological concerns. Some argue the "2-sigma problem" has been more useful as an aspirational North Star than as a rigorous benchmark.
- **Insider tells**: "2-sigma problem" (not "Bloom's gap"), "mastery learning" as the precondition (not just tutoring alone), Bloom's Taxonomy is separate from his tutoring research but both bear his name causing frequent conflation

evidence: [T01-S059, T01-S060]

---

### 19. Vincent Aleven (Carnegie Mellon University)

- **Role**: Associate Professor, Human-Computer Interaction Institute, CMU; developer of CTAT authoring tools
- **Core thesis**: The bottleneck in adaptive learning is not algorithms but authoring -- if teachers and domain experts can create intelligent tutoring systems without programming, the field can scale from a few well-funded research prototypes to thousands of deployed tutors.
- **Key works**: (1) CTAT (Cognitive Tutor Authoring Tools) -- enables non-programmers to create ITS; (2) Research on metacognition and self-regulated learning in ITS; (3) Mobile tutoring and parental engagement studies
- **Recent activity** (last 12 months): Continues CTAT development including browser-based authoring tools; investigating smartphone-based tutoring with offline mode; research on data-driven tutor redesign and parental nudges for homework engagement
- **Endorsement count**: 3+ independent sources (CMU, EDM best paper 2013, AIED 2009 best student paper)
- **Voice samples**:
  - "If we want tutoring systems to scale, we need to make them easy to author -- not just easy to use." (CTAT research context) evidence: [T01-S055]
- **Controversy / edge**: CTAT tutors are still rule-based, which limits their flexibility compared to neural approaches; some argue the "authoring tools" approach can't keep pace with LLM-based tutoring that requires no explicit domain modeling
- **Insider tells**: "Example-tracing tutors" (CTAT's programming-by-demonstration paradigm), "inner loop" vs "outer loop" tutoring (Aleven's framework aligns with VanLehn's), the LearnLab ecosystem connection, "CTAT drag-and-drop" as the signature authoring metaphor

evidence: [T01-S055, T01-S056]

---

### 20. Andrew Lan (University of Massachusetts Amherst)

- **Role**: Assistant Professor, College of Information and Computer Sciences, UMass Amherst
- **Core thesis**: Machine learning can jointly model student knowledge and content structure from response data alone -- no expert-labeled knowledge maps needed -- through sparse factor analysis (SPARFA) and related latent-factor methods.
- **Key works**: (1) SPARFA: "Time-varying Learning and Content Analytics via Sparse Factor Analysis" (KDD 2014); (2) Extensions to knowledge tracing beyond correctness prediction (predicting specific wrong answers); (3) Question generation and automated assessment research
- **Recent activity** (last 12 months): Active in ML-for-education research at UMass; publishing on LLM-based educational applications and automated question generation
- **Endorsement count**: 3+ independent sources (KDD, arXiv, UMass)
- **Voice samples**:
  - "SPARFA jointly traces learner concept knowledge over time, analyzes knowledge state transitions, and estimates content organization and intrinsic difficulty -- all from response data alone." (SPARFA paper) evidence: [T01-S049]
- **Controversy / edge**: SPARFA and latent-factor approaches are powerful but require substantial data and are less interpretable than hand-crafted knowledge component models; critics argue they trade off educational meaning for statistical elegance
- **Insider tells**: "SPARFA" (Sparse Factor Analysis), the connection to Rice University (where the work originated under Baraniuk), the idea of "blind" knowledge map discovery vs expert-constructed Q-matrices, factor analysis as a bridge between psychometrics and ML

evidence: [T01-S048, T01-S049]

---

## Phase 2 提炼提示

**按核心贡献分组的 figure 集群**:
- 知识追踪派 (Knowledge Tracing): Corbett & Anderson (BKT), Piech (DKT), Pavlik (PFA/LKT), Yudelson (individualized BKT), Lan (SPARFA)
- ITS 设计派 (Intelligent Tutoring Systems): Koedinger (Cognitive Tutors/KLI), VanLehn (step-based effectiveness meta-review), Aleven (CTAT authoring), Woolf (ITS textbook/AAAI fellow), Brusilovsky (adaptive hypermedia)
- 学习科学/心理测量派 (Learning Science / Psychometrics): Bloom (2-sigma/mastery learning), Falmagne (Knowledge Space Theory/ALEKS), Thille (OLI/learning engineering), Settles (HLR/spaced repetition)
- 产品/商业化派 (Product / Commercialization): Khan (Khan Academy/Khanmigo), von Ahn (Duolingo/gamification), Heffernan (ASSISTments/RCT infrastructure), Baker (EDM/learning analytics)
- 中国学者/从业者: 余胜泉 (BNU AICFE, "认知外包" 警告), 黄荣怀 (BNU SLI, UNESCO智慧教育), 栗浩洋/Derek Li (松鼠AI, anti-example -- 超纳米级知识点)
- 反面样本 (Anti-examples): Ferreira/Knewton ("robot tutor in the sky", $180M→$17M), 栗浩洋/Squirrel AI (non-independent validation, granularity inflation)

**跨 figure 共识 (>=3 人同意)**:
- 个性化学习路径必须基于学生知识状态的持续评估 (Koedinger, Corbett, Piech, Pavlik, Falmagne, Baker -- 几乎所有人)
- 仅靠答案正确率不够, 需要过程级数据 (VanLehn step-based > answer-based, Koedinger KLI, Piech DKT hidden states)
- 遗忘是学习模型必须面对的核心现实 (Settles HLR, Pavlik PFA, Ebbinghaus 遗忘曲线传统)
- 真正的 adaptive 需要可解释的学生模型, 不只是推荐算法 (Koedinger, Falmagne, Baker vs Knewton 的 black-box)
- 规模化部署比实验室原型难10倍 (Heffernan RCT at scale, Khan Khanmigo adoption gaps, Knewton failure)
- AI 不能替代教师, 只能增强 (Yu 余胜泉 "认知外包" 警告, Khan 2026 反思, VanLehn human tutoring still d=0.76+)

**核心分歧 (figure 之间公开反对)**:
- BKT vs DKT: 可解释性 vs 预测精度 (Corbett/Anderson/Pavlik 阵营 vs Piech 阵营, 后续 AKT/DKVMN 试图调和)
- 专家构建知识图谱 vs 数据驱动发现 (Falmagne/Koedinger Q-matrix vs Lan SPARFA blind discovery)
- 游戏化驱动参与 vs 深度认知建模 (von Ahn/Duolingo streaks vs Koedinger/VanLehn cognitive architecture)
- "纳米级知识点"粒度是精确还是噪音 (栗浩洋 10,000 points vs 传统 ITS 几百个 knowledge components)
- AI 可以比人类教师更好 vs AI 只是辅助 (Ferreira/栗浩洋 marketing claims vs Yu/VanLehn/Heffernan evidence-based 谨慎)
- 开放平台 vs 商业封闭 (Heffernan ASSISTments open RCT vs Khan/Duolingo/ALEKS proprietary)

**Voice samples 汇总**:
- Koedinger: "So much learning happens underneath the surface that we're not consciously aware of." [T01-S004]
- Piech: "Humans are great one-on-one, but AI is more consistent at grading and feedback." [T01-S010]
- Baker: "With data mining, if you have enough data, you can ask ten thousand questions at a time." [T01-S014]
- Heffernan: "We had the guts to expose ourselves to randomized controlled trials." [T01-S018]
- Khan: "We're at the cusp of using AI for probably the biggest positive transformation that education has ever seen." [T01-S023]
- Khan (2026 反转): "For a lot of students, it was a non-event." [T01-S025]
- Settles: "Every word you learn has a 'half-life' that represents how long until you have a 50% chance of forgetting it." [T01-S028]
- Ferreira (anti): "A robot tutor in the sky that can semi-read your mind." [T01-S035]
- 栗浩洋 (anti): "25道题就能测1000个知识点。" [T01-S068]
- 余胜泉: "AI教育最大的陷阱，是'认知外包'。" [T01-S061]
- 黄荣怀: "人工智能大模型融入教育需要观念转变、形态重塑与关键举措并举。" [T01-S064]
- Bloom: "The average tutored student was above 98% of the students in the control class." [T01-S059]
- Corbett & Anderson: "The tutor uses a Bayesian computational procedure to update the probability that the student already knew the rule." [T01-S005]
