Guest Editorial: Introduction to special issue of AI magazine on AI literacy and AI education

Abstract

Artificial intelligence has evolved into a readily accessible tool, increasing its impact on our daily lives, society, and the economy. This accessibility necessitates a critical reassessment of existing AI educational programs and curricula. There is a pressing need to develop strategies that increase the capacity for AI education, establish diverse pathways for individuals to enter and contribute to AI, and foster a greater awareness of the multifaceted implications of AI-driven technologies. The proliferation of AI has triggered concerns about potential consequences, including the over-reliance on AI leading to worker deskilling, the automation of various job functions, and the resulting uncertainty surrounding the future of work.

It is important to note that this special issue specifically focuses on AI education, not the application of AI in education, often referred to as “AI edtech,” where AI tools are utilized to facilitate teaching and learning across various subjects. While the latter is a rapidly evolving and significant domain, it constitutes a separate area of inquiry from the topics addressed here.

Several key factors contribute to scalable AI education in K-12. Central to success is comprehensive and ongoing teacher professional development (PD), which should include initial intensive workshops, sustained support during the school year, co-teaching with experienced instructors, and time for teachers to plan and adapt lessons. Developing teacher leaders who can provide PD and mentorship is essential for long-term sustainability and wider dissemination. A co-design process that actively involves teachers in curriculum development ensures the materials are relevant and adaptable to diverse learners and classroom environments.

Research-practitioner partnerships (RPPs) leverage expertise from both universities and educational settings, bridging content knowledge with practical realities. A diverse and inclusive approach, considering varying student demographics and learning needs, is critical for broad accessibility. Effective curriculum design that aligns with established AI frameworks, incorporates active learning strategies, and focuses on fundamental understandings is crucial. Ongoing implementation support, online resources, community building, flexibility, adaptability, and continuous evaluation and improvement all contribute to a robust and scalable AI education program in K-12.

Touretzky et al. address AI education in middle school (grades 6–8) in the “AI for Georgia” project (AI4GA.org). Capacity building in K-12 AI education requires extensive teacher support, as most K-12 teachers, including computing teachers, start with little or no AI knowledge. The paper describes a teacher professional development program that begins with instruction in the basics of AI and also brings in teachers as co-designers to help shape the curriculum and tailor it to their classrooms. With continued support, some of these teachers have gone on to become teacher leaders and have begun training other teachers. Another notable result of AI4GA is that, with appropriate scaffolding, middle school students can engage with fairly deep technical concepts such as multi-dimensional feature spaces, linear threshold neurons, and the characteristics and limitations of various types of robot sensors. The AI4GA curriculum does more than foster AI literacy: it empowers students to view themselves as creators of AI-powered technology and to think about future career options that involve the use of AI. The project is now expanding to schools in Texas and Florida.

AI literacy as an area of study is evolving, with a focus on defining the concept, addressing ethical and societal considerations, developing assessment tools, and integrating it into existing educational programs. There are numerous arguments for including AI literacy as a core component of education. As AI technologies, especially generative AI, are becoming increasingly integrated into society and education, AI literacy equips individuals with the understanding and skills needed to navigate this AI-driven world. AI literacy helps build capacity for both AI-related careers and an AI-enabled workforce. It empowers individuals to critically evaluate and engage with AI technologies they encounter daily. This includes understanding the strengths, limitations, and potential biases of AI systems to promote responsible use of AI. The expansion of AI literacy at all levels of formal and informal education enables informed participation in discussions and decisions about AI policy and regulation. Such an outcome fosters a citizenry that can understand and contribute to shaping the ethical future of AI.

Tadimalla and Maher present a curriculum for AI literacy, arguing that a socio-technical introduction to AI should become a core component of computing education. Although technical proficiency remains fundamental and a broad priority, the recognition of the societal, ethical, and future implications of AI is leading to its integration in AI education. This paper presents an AI literacy sociotechnical course framework to ensure that all students not only acquire relevant AI technical skills but also develop an understanding of the ethical, societal, and future implications of AI, preparing them for responsible and informed participation in an AI-enabled workforce. The paper argues for a four-pillar approach that includes: “understanding scope and technical dimensions of AI, learning how to interact with (generative) AI technologies, applying principles of critical, ethical, and responsible AI usage, and analyzing implications of AI on society.” For each pillar, the paper presents the scope of content for course modules, along with frameworks for assessing AI literacy and learning experiences. The adoption of AI literacy as a core component of computing education aims to broaden participation in AI by providing a pathway for all to be part of the AI workforce and/or the AI-enabled workforce.

The paper, “Attracting Artificial Intelligence Talent in the Time of Generative AI” by Wollowski, addresses concerns about attracting talent to the AI field amid public statements suggesting AI could soon automate many jobs, including software engineering. The paper argues that these statements, while stimulating, may discourage potential students from pursuing AI careers despite a tremendous and growing need for AI talent. The paper presents evidence of this need across various sectors, including research, AI infrastructure engineering, application development, and industry modernization. It highlights that AI work extends beyond programming and could attract individuals from diverse backgrounds.

The paper also examines perceptions about AI, noting that while many graduates feel threatened by AI's potential to replace jobs, they also express a strong interest in gaining AI skills. It discusses the prospects of AI work, emphasizing that while automation is a concern, many complex problems still require human expertise. Furthermore, the paper discusses the challenges of automating software engineering jobs, noting that coding represents only a portion of a software engineer's responsibilities. It also touches upon the broader impacts of AI, including its role in productivity, automation, and the development of high-level machine intelligence.

Finally, the paper advocates for developing clear pathways to AI education and skills development, especially for those not pursuing traditional CS or AI degrees. It cites various initiatives and programs aimed at broadening AI literacy across different age groups and backgrounds, including K-12 education and workforce development. Wollowski concludes by urging the AI community to present a reasoned and measured message about the future of AI and the continued need for human talent, addressing concerns and attracting more individuals to the field.

Scharff et al. introduce the equity-aware design thinking for AI (EquiThink4AI) framework, designed to address the pervasive issue of bias in AI systems, which can disproportionately affect marginalized communities. The paper takes some controversial positions with regard to the meaning of “equity” and the preference for “equity” over “fairness” that not all readers will agree with. However, even if they hold a different view on these issues, they can find value in the framework.

The authors argue that traditional AI development education often overlooks the systematic integration of equity, leading to systems that perpetuate societal inequalities. EquiThink4AI aims to rectify this by extending the established design thinking (DT) methodology with principles from EquityXDesign (EXD) and liberatory design (LD). This dual-component model not only incorporates equity principles into each phase of DT—empathize, define, ideate, prototype, and test—but also enhances the framework with pedagogical strategies like problem-based learning (PBL) to foster ethical reasoning and real-world problem-solving.

EquiThink4AI's first component focuses on embedding equity throughout the AI development lifecycle, ensuring that considerations of fairness, inclusion, and power dynamics are actively addressed from the outset. By leveraging EXD's emphasis on marginalized perspectives and LD's focus on self-awareness and situational context, the framework encourages designers to identify and challenge their own biases while recognizing systemic factors that perpetuate inequity. The second component integrates pedagogical methods like PBL, which promotes student engagement through real-world case studies, role-playing scenarios, and iterative projects. These strategies facilitate a deeper understanding of ethical dilemmas in AI and provide students with practical experience in developing equity-centered solutions.

Utilizing a design science research (DSR) approach, the paper develops and validates the EquiThink4AI framework, demonstrating its relevance and rigor through examples from AI Ethics and Global Innovation Practice courses. The framework provides a structured methodology for teaching and applying equity-centered AI development, bridging the gap between design justice, AI ethics, and educational practices. By advocating for a fundamental shift in AI education, the authors aim to equip future professionals with the ability to systematically integrate equity into AI design, fostering the creation of more ethical, inclusive, and socially responsible AI systems.

In conclusion, this special issue of AI magazine underscores the critical need for a paradigm shift in AI education. By focusing on developing robust AI education programs, we can equip individuals with the knowledge, skills, and ethical awareness necessary to navigate an increasingly AI-driven world. The articles highlight the importance of scalable pathways for K-12 education, the integration of AI literacy as a core competency in higher education and addressing equity and bias in AI system design. Initiatives like AI4GA demonstrate the feasibility of introducing complex AI concepts at the middle school level, while the proposed AI Literacy curriculum ensures all students understand both the technical and sociotechnical dimensions of AI. Furthermore, by acknowledging the challenges and opportunities in the AI workforce and advocating for frameworks like EquiThink4AI, this special issue calls for a more inclusive, responsible, and future-oriented approach to AI education. As AI continues to evolve and impact our lives, fostering a well-informed and ethically conscious AI-literate society is paramount, and the insights provided here offer a valuable foundation for AI educational initiatives to achieve this goal.

The authors declare that there is no conflict.