🧠 AI Ed Wiki

Robinson, Gormley, Ribeiro & Loeb (2026) ran two RCTs showing that AI tutoring's binding constraint is take-up, not capability: despite dedicated session time, nearly half of students never used the platform and users averaged only 2–5 minutes per week. An in-person engagement tutor (not direct instruction) raised usage by 1–4 minutes/week and engagement by 71–80% β€” but dosage stayed far below the level needed for reading gains, and achievement did not improve.

The two RCTs

  • Districts A and B, elementary students, AI literacy (reading) platform; treatment = in-person tutor focused on engagement (10-min check-in + 15 min platform + 2–5 min reflection), control = platform alone with the full ~30 min
  • Pre-registered outcomes: minutes/week (usage) and stories read/week (engagement); spring ELA as achievement
  • Interventions lasted 14–31 weeks
  • Take-up is the story

  • Only 60.7% / 53.3% of control students ever used the platform
  • Average weekly usage: 2.18 / 5.23 minutes; even users-only averages were 13.2 / 25.8 minutes
  • Students used the platform in only 4–5 of 14–31 weeks
  • Platform users skewed higher-achieving and less likely to receive special-education services β€” an equity flag: the students who need adaptive support most are least likely to engage
  • What human support did

  • Usage: +1 min/week (A), +4.4 min/week (B) β€” significant, but a drop against the ~30 min/week the provider recommends for measurable reading gains
  • Engagement: +0.20 stories/week (A, +71%) and +0.92 stories/week (B, +80%)
  • Total added dosage: ~22 minutes (A) and ~98 minutes (B) across the entire intervention
  • No reading achievement gains in either district (negative, non-significant) β€” consistent with the achieved dosage
  • Strong site-level variation: some sites saw little effect, others meaningful gains β€” local implementation conditions matter
  • Connected Concepts

  • AI Literacy
  • K 12
  • Student Experience
  • RAG
  • Connected Articles

  • Beyond Detection Authentic Assessment AI 2025 β€” Beyond Detection: redesigning authentic assessment in an AI-mediated world
  • Care Full Feedback GenAI β€” The care-full craft of feedback in an age of generative AI
  • Learner AI Interaction Patterns Oop β€” Patterns of Learner-AI Interaction and Academic Performance in an Object-Oriented Programming Course
  • Aaai2026 Prompting Literacy K12 β€” Learning to Use AI for Learning: Teaching Responsible Use of AI Chatbot to K-12 Students Through an AI Literacy Module
  • Academiclaw Student Agent Benchmark β€” AcademiClaw: When Students Set Challenges for AI Agents
  • Adapt Adaptive Lesson Plan Transformer β€” AdaPT: Adaptive Lesson Plan Transformer for Cross-Regional and Differentiated Instruction
  • Adaptive Pretesting Retention β€” Do Gains from Generative AI-Enabled Adaptive Pretesting Persist? Evidence from a Retention Study
  • Affective Text Wearable Student Health β€” A Formative Study of Brief Affective Text as a Complement to Wearable Sensing for Longitudinal Student Health Monitoring
  • Agency Gap AI Writing β€” The agency gap in AI-supported writing: how reactive and proactive agent designs shape multimodal reasoning
  • Agent Voice Accents K12 Group Learning β€” Exploring How Agent Voice Accents Shape Human-AI Collaboration in K-12 Group Learning
  • Agentic AI Education Scoping Review β€” Agentic AI in Education: A Scoping Review of Research Landscape, Capabilities, and the Frontier Agent Paradigm
  • Agentic Literacy Debt β€” Agentic Literacy Debt: A Structural Problem the AI Literacy Field Has Not Yet Named
  • Agentic Workflows Education β€” Agentic Workflows in Education
  • Agreement Not Quality LLM Coding Verification β€” Agreement Is Not Quality: Blind Expert Verification of Human and LLM Qualitative Coding When Human Consensus Is Not G...
  • AI Adoption Training Public Sector β€” The Main Barrier to AI Adoption in the Public Sector is Lack of Training
  • AI Agents Peer Learning Discourse β€” When AI Agents Teach Each Other: Discourse Patterns Resembling Peer Learning in the Moltbook Community
  • AI Assessment Human Tutors β€” AI-Driven Assessment of Human Tutors: Linking Training Performance to Real-Life Practice
  • AI Assessment Scale Reform β€” A bit of chaos and madness": The AI Assessment Scale and the work of assessment reform
  • AI Assistance Discretionary Feedback β€” AI Assistance for Discretionary Work: Increasing Feedback Provision in Higher Education
  • AI Assisted Learning Modes Eeg β€” An exploratory behavioral and electroencephalographic study of artificial intelligence-assisted learning modes in hig...
  • AI Availability Student Motivation β€” Why Put in This Much Effort?": How AI Availability Shapes Students’ Motivation in Introductory Programming
  • AI Campus Wellbeing Tools β€” AI-Driven Tools for Enhancing Campus Well-being: Prevention and Intervention
  • AI Changing Teaching Workflows β€” How AI Is Changing Teaching Workflows
  • AI Coaching RL Skill Development β€” AI Coaching for Accelerating Human Skill Development with Reinforcement Learning
  • AI Education Global Capacity β€” What AI in Education Needs Next: Lessons from Youth Leaders Across Five Countries
  • Citation

    Robinson, C. D., Gormley, D., Trindade Ribeiro, A., & Loeb, S. (2026). Access is Not Enough: Human Support Improves Engagement with AI Tutoring. EdWorkingPaper No. 26-1451, Annenberg Institute at Brown University. DOI