In October, the skills development subcommittee of
SREB’s Commission on AI in Education will publish a framework of
the skills students need, K-12 and beyond, to be competitive in a
workplace that integrates artificial intelligence. Here is an
early look at the introduction to the report.
In today’s rapidly evolving technological landscape, it is
crucial to equip students with foundational skills to prepare
them for a workforce that increasingly integrates AI
technologies. These skills enhance adaptability, problem-solving,
efficiency and interdisciplinary knowledge while emphasizing
the ethical and responsible use of AI.
By mastering these foundational skills, students will gain a
competitive advantage in the job market and be better positioned
to drive innovation. The skills development subcommittee of
SREB’s Commission on AI in Education recommends focusing on the
following foundational skill sets to prepare all
students ─ those who will develop technology
solutions as well as those who will use them.
Foundational Skill Areas
This graphic serves as a roadmap for integrating AI skills and
concepts throughout across age levels. The progression of
learning begins with foundational concepts in the early grades,
focuses on effective use and application in middle grades, and
supports students to evaluate or create with AI in high school
and beyond.
Summary of Skill Recommendations
Research on the skills needed for an AI-prepared workforce shows
trends across three primary areas: success, industry
baseline and technical skills.
Success skills, often called employability
skills, include critical thinking, creativity,
problem-solving and effective communication. These skills
are essential for adaptability and lifelong learning.
Industry baseline skills encompass AI ethics,
responsible use of AI, cybersecurity and data privacy, and domain
knowledge, ensuring that students understand the broader
implications and applications of AI across all career fields.
Technical skills focus on the specific knowledge
and abilities needed to develop and utilize AI technologies, such
as coding, data analysis, machine learning, deep learning,
natural language processing and computer vision.
To develop these skills, states, districts and schools will
need to revise existing computer science and digital learning
frameworks, as well as graduate profiles, to include these three
foundational areas.
For more information about this report, contact Ivy Coburn.