AI in Education Reports

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.