Event Information
1. The Problem (intro panel): Most AI in ed-tech optimizes for the average learner. What does it look like when AI is designed for every learner?
2. The Framework: Wayground's four-stage instructional journey — Create, Adapt, Engage, Analyze — and where AI intervenes at each stage
3. Live Demo Stations (core of poster): One visual panel per stage showing real product examples, with emphasis on accommodations (text-to-speech, extended time, simplified language, IEP alignment)
4. Responsible Innovation: How guardrails, teacher control, and transparency are built into AI features — not bolted on
5. Takeaway: A one-page evaluation framework attendees can use to assess any AI tool against equity and instructional criteria
1. Identify where AI can be embedded across the instructional journey — from content creation to real-time adaptation, engagement, and data analysis
2. Evaluate AI-powered tools against an equity lens, specifically for students with IEPs, 504 plans, and diverse learning needs
3. Apply a replicable framework for responsible AI adoption that balances innovation with inclusion
4. Articulate the difference between AI as a productivity shortcut vs. AI as an instructional equity lever
Universal Design for Learning — CAST, 2018: Framework for flexible learning experiences that meet the needs of all students; Wayground's AI accommodations operationalize UDL principles at scale
AI and Personalized Learning — Zawacki-Richter et al., 2019: Systematic review finding AI's strongest educational impact is in adaptive learning and formative feedback
IDEA (Individuals with Disabilities Education Act): Federal mandate requiring appropriate accommodations — AI can reduce teacher burden while improving consistency
Formative Assessment and Achievement — Black & Wiliam, 1998: Landmark research showing timely, specific feedback dramatically improves outcomes — the evidence base for AI-powered real-time analytics
Equity in Ed-Tech — Reich & Ito, 2017: Documents how technology amplifies existing inequities unless designed explicitly for marginalized learners