AI & Education Policy
Report: AI R&D Should Align with ED Recommendations and Focus on Context, Partnership, and Public Policy
- By Kate Lucariello
is sometimes presented as a race to be the first to advance new
techniques or scale new applications — innovation is sometimes
portrayed as rapidly going to scale with a minimally viable product,
failing fast, and only after failure, dealing with context," according to a new report, "Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations," by the Office of Educational Technology (OET) of the U.S. Department of Education (ED).
far back as 2010, the National Education Technology Plan (NETP) set a
research and development (R&D) challenge for ed tech developers
to "create personalized learning systems that continuously improve
as they are used."
new AI report suggests further R&D
goals and makes recommendations for AI ed tech developers, keeping in
mind a focus on "context sensitivity" for success in educational
look forward to new meanings of 'adaptive' that broaden outward
from what the term has meant in the past decade. For example, 'adaptive' should not always be a synonym of 'individualized'
because people are social learners. Researchers therefore are
broadening 'adaptivity' to include support for what students do
as they learn in groups," the report notes.
R&D focus on context must be prioritized early and habitually in
R&D; we don't want to win a race to the wrong finish line,"
recommendations are made from these perspectives:
Attention to the "long tail of learner variability," that is,
the multiple ways in which people engage in teaching and learning
according to their "strengths and needs." This replaces the "teaching to the middle" philosophy.
Partnership in design-based research, the shift toward co-design
from multiple stakeholders — teachers, students, parents, and
others. A commitment to this can foster digital inclusion and
generate discussions about the need for AI explainability,
transparency, and responsibility.
Teacher professional development and the expectation that teachers
should adopt and embrace emerging ed tech, especially AI, but have
too little training. Focus should be placed on how to increase
teacher literacy about AI.
Alignment with public policy efforts, including funding, to keep AI
algorithmically unbiased, ethical, inclusive, private, and secure.
on these considerations, the report concludes with several
recommendations moving forward with regard to the use of AI in ed
Keep "humans in the loop" so that a technology-enhanced future
is "more like an electric bike and less like robot vacuums";
Promote AI models that conform to "a shared vision for education,"
i.e., humans determining goals and evaluating such models, with
heavy involvement from local, state, and federal policymakers
keeping an eye on and holding developers accountable for overblown
promises and unsupported claims;
Design AI ed tech based on modern learning pedagogy;
Strengthen public trust in AI by demonstrating its "safety,
usability, and efficacy";
Keep educators informed and involved in AI ed tech at every step and
foster respect for their skills and value to society;
Focus R&D on enhancing context, trust, and safety;
Develop "guidelines and guardrails" for the use of AI ed tech.
the report, reference is made to the "Blueprint
for an AI Bill of Rights," released by the White
House in fall 2022. Five basic rights are outlined and elaborated: 1.
Safe and effective systems; 2. Algorithmic discrimination
protections; 3. Data privacy; 4. Notice and explanation; 5. Human
alternatives, consideration, and fallback.
this page for a summary handout of the report's main
points. A webinar going into more depth on this report will be held
Tuesday, June 13, 2023, at 2:30 p.m. ET. Signup is available by QR
code on the handout page.
full report can be downloaded
from this page.
Kate Lucariello is a former newspaper editor, EAST Lab high school teacher and college English teacher.