Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations
Artificial Intelligence and the Future of Teaching and Learning Insights and Recommendations
This report from the U.S. Department of Education addresses the rise of artificial intelligence in education, exploring opportunities and risks while providing recommendations for policy development. It highlights the need to center humans in the loop, promote equity and safety, and guide the integration of AI tools in teaching and learning. The document emphasizes the importance of stakeholder collaboration to harness benefits while mitigating risks such as bias and data privacy concerns.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Teachers, educational leaders, policymakers, researchers, and educational technology developers should read this to understand how to safely and effectively navigate AI integration in education.
What the document contains
The document addresses the integration of artificial intelligence in educational settings, examining its impacts on student learning, teaching practices, formative assessment, and research and development. It provides specific policy recommendations and guiding questions for building ethical, equitable AI policies in education.
The report does not have the force and effect of law, does not provide legal advice or procurement approvals, and does not evaluate specific commercial AI products or tools.
- Artificial intelligence presents significant opportunities to enhance educational equity, personalization, and teaching effectiveness when properly governed.
- Human-in-the-loop systems are essential to ensure that automated decisions do not replace human judgment and accountability in education.
- Educational institutions must prioritize data privacy, safety, and the mitigation of algorithmic bias.
- AI tools in education should be inspectable, explainable, and overridable by educators and administrators.
- Research and development in educational AI must focus on addressing context and learner variability.
· Introduction · Building Ethical, Equitable Policies Together · What is AI? · Learning · Teaching · Formative Assessment · Research and Development · Recommendations
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
2/5Clear responsibility for AI outcomes
Everyone in education has a responsibility to harness the good to serve educational priorities while also protecting against the dangers that may arise
Rising Interest in AI in Education
screening score · 2026-10-23
Fairness
2/5Non-discrimination and equitable treatment
The process of developing an AI system may lead to bias in how patterns are detected and unfairness in how decisions are automated.
Introduction
screening score · 2026-10-23
Human Dignity
1/5Respect for the inherent worth of persons
Foundation 1: Center People (Parents, Educators, and Students)
Table of Contents
screening score · 2026-10-23
Human Oversight
2/5Humans remain in control of consequential decisions
Key Recommendation: Human in the Loop AI
Table of Contents
screening score · 2026-10-23
Privacy
2/5Protection of personal data and private life
Useful, powerful functionality can also be accompanied with new data privacy and security risks.
Rising Interest in AI in Education
screening score · 2026-10-23
Transparency
1/5Openness about how AI systems work and are used
Foundation 4: Promote Transparency
Table of Contents
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
Introduction
screening score · 2026-10-23
Impact Assessment
2/5Required assessment of an AI system's risks/impacts before or during use
Thus, educational systems must govern their use of AI systems. This report describes opportunities for using AI to improve education, recognizes challenges that will arise
Introduction
screening score · 2026-10-23