AI Assistance for Discretionary Work: Increasing Feedback Provision in Higher Education
Romina Mahinpei, Victoria Dean, Ruth Fong, Lydia T. Liu, Manoel Horta Ribeiro (2026) — arXiv. 📄 Full text (arXiv) This field experiment shows that AI-generated feedback drafts can measurably increase the rate and length of feedback that teaching assistants actually deliver to stu...
Generative AI without guardrails can harm learning: Evidence from high school mathematics
Hamsa Bastani, Osbert Bastani, Alp Sungu, Haosen Ge, Özge Kurucu, Rehema Mushi (2025) — Proceedings of the National Academy of Sciences (PNAS) , 122(26). doi:10.1073/pnas.2422633122. 📄 Full text (PNAS) — RCT preregistered with the University of Pennsylvania IRB; anonymized data a...
How AI Is Changing Teaching Workflows
Lin Ler (2026) — Edtech Insiders. Part 2 of 7 in the AI & Efficacy Editorial Research Series, drawing from Stanford's AI Hub for Education Research Repository (SCALE Initiative). 📄 Full article Core Thesis AI saves teachers roughly 30% of lesson preparation time with no measurabl...
Randomized Controlled Trials in AI Education Research
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Transforming GenAI Policy to Prompting Instruction (RCT)
Transforming GenAI Policy to Prompting Instruction: RCT Synthesis This RCT investigates the impact of transforming institutional GenAI policies into actionable prompting instruction for K-12 educators. Key findings: Policy-to-Practice Gap: Most GenAI policies lack implementation...
A meta-analysis of the effect of generative AI on productivity and learning in programming
Generative AI Meta-Analysis: Programming Productivity vs. Learning Core Contribution Maier, Gunzenhäuser & Schweisthal (2026) conduct a meta-analysis synthesizing evidence on how generative AI tools affect both programming productivity and learning outcomes. This is a confidence:...
AI in K-12 Evidence Base
> 📄 Full text: Stanford SCALE · local AI in K-12 Evidence Base > As of October 2025, only 20 of 818 papers in the AI Hub Research Repository meet standards for strong causal inference (RCTs or QEDs) on AI in education — and zero examine U.S. K-12 student settings.^ stanford-evide...
Analysis and Prediction of At-Risk Students Using Machine Learning Algorithms
Soheila Gheisari, Hamid Salarian (2026) — arXiv:2606.20617 (cs.CY; cs.LG) 📄 Full text (arXiv) Gheisari and Salarian (2026) apply supervised machine learning classification to identify at-risk students before they withdraw from higher education programs. The study evaluates Logist...
Do Gains from Generative AI-Enabled Adaptive Pretesting Persist? Evidence from a Retention Study
Mahir Akgun, Sacip Toker (2026) — 27th International Conference on AI in Education 📄 Full text (arXiv) Akgun and Toker (2026) examine whether learning gains from GenAI-enabled adaptive pretesting persist over a seven-week retention period. Undergraduate participants completed ada...