The AI-Native Classroom
Applied AI in education — grounded in real classrooms, backed by emerging research.
Welcome to the first issue of The AI-Native Classroom — a monthly newsletter for the people building the future of teaching and learning with AI. Each issue: the three things you need to know, research worth your time, the best reads from across the field, and what's coming up. No hype, no fluff. Let's dig in.
The signal from the noise this month.
- 01
The largest US public university system just set a systemwide AI policy. SUNY will require all 64 campuses to adopt AI guidelines by December 31, 2026 — covering data privacy, AI literacy in general education, and procurement safeguards. One of the first coordinated, system-level AI governance frameworks in US higher ed.
Source: Inside Higher Ed · May 4, 2026
- 02
The federal government is now funding AI in higher ed — to the tune of $169M. The US Department of Education awarded $169 million through the FIPSE program, with a major focus on responsible AI to improve teaching and student success, drawing a historic number of applications.
Source: US Department of Education · Jan 5, 2026
- 03
Adoption is near-universal — but readiness isn't. Student AI use has hit 95%, yet a new global survey found only one in three universities have a clear AI strategy and fewer than one in five have governance structures. The gap between using AI and using it well is the story of 2026.
Source: IREX / Development Gateway · May 7, 2026
Two US classroom experiments that reframe the whole debate.
AI Tutoring Outperforms Active Learning in a Harvard RCT
Greg Kestin, Kelly Miller, and colleagues ran a randomized controlled trial with 194 undergraduates in Harvard's largest introductory physics course. Each week, students were randomly assigned to either in-class active learning or a custom AI tutor ("PS2 Pal") built with expert-authored scaffolds, step-by-step reasoning prompts, and guardrails against hallucination. Students using the AI tutor achieved roughly twice the learning gains of the active-learning group (effect size 0.73–1.3 SD), and reported higher engagement and motivation. The tutor revealed only one step at a time and prompted students to think before showing answers — mirroring the pedagogy of the in-person class.
Kestin et al. (2025), Scientific Reports 15, Article 17458. doi.org/10.1038/s41598-025-97652-6
This is the first rigorous RCT to show a custom AI tutor beating best-practice active learning — not a bad lecture, a good class. The key variable was pedagogical design: the AI scaffolded thinking rather than just answering.
Generative AI Without Guardrails Can Harm Learning
A University of Pennsylvania (Wharton) team led by Hamsa Bastani ran a field experiment with nearly 1,000 high school math students across three groups: "GPT Base" (standard ChatGPT-4), "GPT Tutor" (safeguarded to give hints, not answers), and a no-AI control. Both AI groups improved during practice (48% and 127%). But on a follow-up exam without AI, GPT Base users scored 17% worse than the control — the unguided tool had become a crutch. GPT Tutor users kept their gains. The guardrails preserved actual learning.
Bastani et al. (2025), PNAS 122(26), e2422633122. doi.org/10.1073/pnas.2422633122
Read together, these studies settle the framing: the question isn't "AI vs. no AI" — it's "designed AI vs. unguided AI." Same technology, opposite outcomes, depending entirely on pedagogical design.
Three frameworks you can put to work this month.
If AI means institutions can no longer assure the integrity of individual assessments, the focus must shift to assuring the integrity of overall awards. A practical framework for faculty rethinking how they evaluate learning.
Read more ›A five-level scale — from "No AI" to "AI Exploration" — that gives instructors a shared vocabulary for specifying exactly how much AI use each assignment allows. A practical tool for redesigning tasks and communicating expectations to students, rather than policing them.
Read more ›A curated map of the most useful AI-literacy frameworks (from EDUCAUSE, WCET, UNESCO, and others), organized by what each one helps students actually do. A practical starting point for choosing the framework that fits your course and discipline.
Read more ›From the ScholarStack team.
What does higher education need from AI that "off the shelf" genAI tools fail to provide?
In the first episode of Learning with AI, David Miller, Founder of ScholarStack and Professor at Carnegie Mellon University, shares the story behind the creation of ScholarStack and explains why he believes AI in higher education requires a fundamentally different approach.
The conversation explores student engagement, learning science, personalized learning, educational technology, and the opportunities AI creates for both educators and institutions.
Topics discussed
- AI in Higher Education
- Personalized Learning
- Student Engagement
- Educational Technology
- Learning Science
- The Future of Learning
Learning with AI is the ScholarStack podcast exploring how artificial intelligence is transforming teaching, learning, and higher education.
Teaching in the age of AI
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By the end of the course, participants will leave with:
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