Generative AI in Education
Key Concepts in Designing AI-Based Learning Strategies
- By Kate Lucariello
part of the recently released "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), OET hosted listening
sessions, in which marked concerns were raised in the area of
learning. Noting that ED's ed tech vision sees students as "active
learners" who "participate in discussions that advance their
understanding, use visualizations and simulations to explain
concepts,… and leverage helpful scaffolding and timely feedback as
they learn," OET heard that attendees "want technology to align
and build on these and other research-based understandings of how
people learn." An especially strong request was for students who
have been impacted by the pandemic to be strengthened and supported
in their learning.
key insight for AI-based learning is that it enables adaptivity, with
technology's "ability to meet students where they are, build on
their strengths, and grow their knowledge and skills." But the
caveat is that adaptivity can sometimes be too specific and limited,
and important parts of learning can be left out or insufficiently
developed. An important goal is to work "toward AI models that fit
the fullness of the visions for learning — and avoid limiting
learning to what AI can currently model well."
advances in "large language models" have limits, the report
noted, with experts in the listening sessions warning that AI models
are narrower, learning contexts can change; "common sense" human
judgments are lacking; and all of these can result in "unnatural or
incorrect" AI responses. New AI designs must account for that.
intelligent tutoring systems (ITS) have advanced significantly in
giving students feedback, it is still important for human teachers to
motivate students and help them self-regulate, the report said,
adding that "... any teacher knows there is more to supporting
learning than adjusting the difficulty and sequence of materials."
Human teachers have a better understanding of the totality of their
students than most ed tech does and can recognize a "teachable
moment" in a way that an AI cannot.
for expanding the core models of an AI system are to:
Design adaptivity that is "asset-oriented," building on student
competencies, rather than "deficit-based," or lack-focused;
Include social and other aspects of learning in addition to
individual cognition-based learning;
Include multiple learning strategies for neurodiverse learners and
those with disabilities;
Design models that include "active, open, and creative tasks"
and innovative approaches in addition to fixed tasks;
Expand beyond only "correct" or "incorrect" answers to teach
students how to keep working on problems and ask for help when
report notes that "two broad perspectives" arise around AI in
education: "AI in support of student learning" and "support for
learning about AI and related technologies." On the latter, the
report emphasizes the importance of students becoming educated about
AI, not only about what it can do but what risks it poses.
report applauds research and development attempts to address the
recommendations for AI learning strategies and reiterates that "our
key recommendation is to tease out the strengths and limitations of
AI models inside forthcoming ed tech products and to focus on AI
models that align closely to desired visions of learning. AI is now
advancing rapidly, and we should differentiate between products that
have simple AI-like features inside and products that have more
sophisticated AI models."
this page to read and download 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 at this link.
full report can be downloaded from this page.
Kate Lucariello is a former newspaper editor, EAST Lab high school teacher and college English teacher.