skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Attention:

The NSF Public Access Repository (PAR) system and access will be unavailable from 10:00 PM ET on Friday, February 6 until 10:00 AM ET on Saturday, February 7 due to maintenance. We apologize for the inconvenience.


Title: Chapter 7: Education
AI has entered the public consciousness through generative AI’s impact on work—enhancing efficiency and automating tasks—but it has also driven innovation in education and personalized learning. Still, while AI promises benefits, it also poses risks—from hallucinating false outputs to reinforcing biases and diminishing critical thinking. With the AI education market expected to grow substantially, ethical concerns about the technology’s misuse—AI tools have already falsely accused marginalized students of cheating—are mounting, highlighting the need for responsible creation and deployment. Addressing these challenges requires both technical literacy and critical engagement with AI’s societal impact. Expanding AI expertise must begin in K–12 and higher education in order to ensure that students are prepared to be responsible users and developers. AI education cannot exist in isolation—it must align with broader computer science (CS) education efforts. This chapter examines the global state of AI and CS education, access disparities, and policies shaping AI’s role in learning. This chapter was a collaboration prepared by the Kapor Foundation, CSTA, PIT-UN and the AI Index. The Kapor Foundation works at the intersection of racial equity and technology to build equitable and inclusive computing education pathways, advance tech policies that mitigate harms and promote equitable opportunity, and deploy capital to support responsible, ethical, and equitable tech solutions. The CSTA is a global membership organization that unites, supports, and empowers educators to enhance the quality, accessibility, and inclusivity of computer science education. The Public Interest Technology University Network (PIT-UN) fosters collaboration between universities and colleges to build the PIT field and nurture a new generation of civic-minded technologists.  more » « less
Award ID(s):
2311746 2444214
PAR ID:
10603946
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
Corporate Creator(s):
;
Editor(s):
Maslej, Nestor; Fattorini, Loredana; Perrault, Raymond; Gil, Yolanda; Parli, Vanessa; Kariuki, Njenga; Capstick, Emily; Reuel, Anka; Brynjolfsson, Erik; Etchemendy, John; Ligett, Katrina; Lyons, Terah; Manyika, James; Niebles, Juan Carlos; Shoham, Yoav; Wald, Russell; Walsh, Tobi; Hamrah, Armin; Santarlasci, Lapo; Betts_Lotufo, Juliamore »; Rome, Alexandra; Shi, Andrew; Oak, Sukrut« less
Publisher / Repository:
The AI Index 2025 Annual Report
Date Published:
Edition / Version:
8
Subject(s) / Keyword(s):
artificial intelligence research report education
Format(s):
Medium: X Size: 3MB Other: per
Size(s):
3MB
Sponsoring Org:
National Science Foundation
More Like this
  1. The rapid expansion of K-12 computer science education highlights the urgent need for well-prepared teachers. The Computer Science Teachers Association (CSTA) facilitates the development of local teacher professional learning communities (PLCs) through CSTA chapters. This study investigated the types of support CSTA chapters provide, how teacher leaders establish local PLCs and engage teachers of computer science, and the challenges encountered in this process. The investigation included multi-year focus group interviews with chapter leaders and teacher member surveys. The findings reveal that CSTA chapters serve as vital resources of professional support, amplify teachers’ voices, and nurture their professional identities in teaching computer science. This study provides a nuanced understanding of local PLCs for computer science educators, informing future endeavors in teacher preparation and development. 
    more » « less
  2. As generative AI technologies proliferate across higher education, many U.S. universities are still developing institutional policies to address their ethical, pedagogical, and accessibility implications. This posIT column critically examines AI policies and resources at 50 four year universities—one from each U.S. state—to assess alignment with the Association of Research Libraries’ (ARL) Guiding Principles for Artificial Intelligence. Through content analysis of LibGuides, AI taskforce membership, campus events, and public-facing policies, the study reveals widespread adoption of AI resources but a significant lack of clarity, consistency, and librarian involvement in policy development. While most institutions meet baseline criteria related to privacy, plagiarism, and algorithmic transparency, fewer address AI’s potential harms to marginalized communities or its impact on accessibility for students with disabilities. Notably, fewer than half of the AI taskforces surveyed included library staff, despite librarians’ expertise in digital literacy and ethical information use. This column urges academic librarians to actively seek leadership roles in institutional AI governance to help shape inclusive, responsible, and human-centered AI policy frameworks. 
    more » « less
  3. Abstract As generative artificial intelligence (AI) becomes increasingly integrated into society and education, more institutions are implementing AI usage policies and offering introductory AI courses. These courses, however, should not replicate the technical focus typically found in introductory computer science (CS) courses like CS1 and CS2. In this paper, we use an adjustable, interdisciplinary socio‐technical AI literacy framework to design and present an introductory AI literacy course. We present a refined version of this framework informed by the teaching of a 1‐credit general education AI literacy course (primarily for freshmen and first‐year students from various majors), a 3‐credit course for CS majors at all levels, and a summer camp for high school students. Drawing from these teaching experiences and the evolving research landscape, we propose an introductory AI literacy course design framework structured around four cross‐cutting pillars. These pillars encompass (1) understanding the scope and technical dimensions of AI technologies, (2) learning how to interact with (generative) AI technologies, (3) applying principles of critical, ethical, and responsible AI usage, and (4) analyzing implications of AI on society. We posit that achieving AI literacy is essential for all students, those pursuing AI‐related careers, and those following other educational or professional paths. This introductory course, positioned at the beginning of a program, creates a foundation for ongoing and advanced AI education. The course design approach is presented as a series of modules and subtopics under each pillar. We emphasize the importance of thoughtful instructional design, including pedagogy, expected learning outcomes, and assessment strategies. This approach not only integrates social and technical learning but also democratizes AI education across diverse student populations and equips all learners with the socio‐technical, multidisciplinary perspectives necessary to navigate and shape the ethical future of AI. 
    more » « less
  4. AI is rapidly emerging as a tool that can be used by everyone, increasing its impact on our lives, society, and the economy. There is a need to develop educational programs and curricula that can increase capacity and diversity in AI as well as awareness of the implications of using AI-driven technologies. This paper reports on a workshop whose goals include developing guidelines for ensuring that we expand the diversity of people engaged in AI while expanding the capacity for AI curricula with a scope of content that will reflectthe competencies and needs of the workforce. The scope for AI education included K-Gray and considered AI knowledge and competencies as well as AI literacy (including responsible use and ethical issues). Participants discussed recommendations for metrics measuring capacity and diversity as well as strategies for increasing capacity and diversity at different level of education: K-12, undergraduate and graduate Computer Science (CS) majors and non-CS majors, the workforce, and the public. 
    more » « less
  5. This paper reports a study of CSTA chapter leaders' perceptions of their chapter's roles in supporting computer science (CS) teachers. Intent on understanding the impact of member-ship in a professional organization on the development of teacher professional identity, our research revealed that in the chapter leaders' perceptions local CSTA chapters had an important role in supporting the development of their members' professional identity as a CS teacher. 
    more » « less