The rapid expansion of artificial intelligence (AI) necessitates promoting AI education at the K-12 level. However, educating young learners to become AI literate citizens poses several challenges. The components of AI literacy are ill-defined and it is unclear to what extent middle school students can engage in learning about AI as a sociotechnical system with socio-political implications. In this paper we posit that students must learn three core domains of AI: technical concepts and processes, ethical and societal implications, and career futures in the AI era. This paper describes the design and implementation of the Developing AI Literacy (DAILy) workshop that aimed to integrate middle school students’ learning of the three domains. We found that after the workshop, most students developed a general understanding of AI concepts and processes (e.g., supervised learning and logic systems). More importantly, they were able to identify bias, describe ways to mitigate bias in machine learning, and start to consider how AI may impact their future lives and careers. At exit, nearly half of the students explained AI as not just a technical subject, but one that has personal, career, and societal implications. Overall, this finding suggests that the approach of incorporating ethics and career futures into AI education is age appropriate and effective for developing AI literacy among middle school students. This study contributes to the field of AI Education by presenting a model of integrating ethics into the teaching of AI that is appropriate for middle school students.

The rapid expansion of Artificial Intelligence (AI) in our society makes it urgent and necessary to develop young students’ AI literacy so that they can become informed citizens and critical consumers of AI technology. Over the past decade many efforts have focused on developing curricular materials that make AI concepts accessible and engaging to young learners; and yet, limited research investigated how to assess learners’ AI literacy, which is critically important to inform the teaching and learning of AI. This paper addresses this issue by reporting the development and validation findings of the AI Literacy Concept Inventory Assessment (AI-CI), a set of multiple-choice questions designed to assess understanding of AI literacy concepts among middle school students. The AI-CI consists of 20 multiple choice questions examining student understanding of four topics: AI general concepts, logic systems, machine learning general concepts, and supervised learning. The content validity of AI-CI was established through multiple rounds of expert panel reviews with AI educators and experts, observations of student learning of AI, and cognitive validation interviews. The validity of the AI-CI was established with a sample of 981 students and the pre-posttest reliability was established with a sample of 108 middle school students who learned AI through experiencing the Developing AI literacy (DAILy) curriculum. The findings show that the AI-CI is a valid and reliable tool to assess AI literacy at the middle school level.

Artificial intelligence (AI) has rapidly pervaded and reshaped almost all walks of life, but efforts to promote AI literacy in K-12 schools remain limited. There is a knowledge gap in how to prepare teachers to teach AI literacy in inclusive classrooms and how teacher-led classroom implementations can impact students. This paper reports a comparison study to investigate the effectiveness of an AI literacy curriculum when taught by classroom teachers. The experimental group included 89 middle school students who learned an AI literacy curriculum during regular school hours. The comparison group consisted of 69 students who did not learn the curriculum. Both groups completed the same pre and post-test. The results show that students in the experimental group developed a deeper understanding of AI concepts and more positive attitudes toward AI and its impact on future careers after the curriculum than those in the comparison group. This shows that the teacher-led classroom implementation successfully equipped students with a conceptual understanding of AI. Students achieved significant gains in recognizing how AI is relevant to their lives and felt empowered to thrive in the age of AI. Overall this study confirms the potential of preparing K-12 classroom teachers to offer AI education in classrooms in order to reach learners of diverse backgrounds and broaden participation in AI literacy education among young learners.

The call to increase student interest in science and related careers continues to receive national attention in the United States. While many current efforts have focused on infrastructure support and innovative pedagogies to better reach and engage students, research suggests that having a career role model can influence adolescents’ interest in and choices of science courses and career pathways. This exploratory study investigated the impact of a week-long online biotechnology program called Biotech in Action (BIA) that featured career role modeling and authentic science learning. Students were engaged in career modeling sessions with multiple professionals working in related STEM fields while learning life sciences within authentic biotechnology research contexts. In total, over 400 high school students participated in BIA. The results showed that students felt they developed a better understanding of the biotechnology field and became more cognizant about steps to achieve their future career goals after BIA. Many students reported that the interactions with career role models and learning about their educational and professional pathways helped demystify science career fields. Overall, this research provides new insights to curriculum designers and researchers on integrating career role modeling and authentic learning to spark and sustain student interest in science.

AI curricula are being developed and tested in classrooms, but wider adoption is premised by teacher professional development and buy-in. When engaging in professional development, curricula are treated as set in stone, static and educators are prepared to offer the curriculum as written instead of empowered to be leaders in efforts to spread and sustain AI education. This limits the degree to which teachers tailor new curricula to student needs and interests, ultimately distancing students from new and potentially relevant content. This paper describes an AI Educator Make-a-Thon, a two-day gathering of 34 educators from across the United States that centered co-design of AI literacy materials as the culminating experience of a year-long professional development program called Everyday AI (EdAI) in which educators studied and practiced implementing an innovative curriculum for Developing AI Literacy (DAILy) in their classrooms. Inspired by the energizing and empowering experiences of Hack-a-Thons, the Make-a-Thon was designed to increase the depth and longevity of the educators' investment in AI education by positively impacting their sense of belonging to the AI community, AI content knowledge, and their self confidence as AI curriculum designers. In this paper we describe the Make-a-Thon design, findings, and recommendations for future educator-centered Make-a-Thons.

The need for sustainability-oriented K–12 engineering education that expands beyond the classroom and the increased accessibility of educational technologies create an opportunity for examining the affordances of educational technologies in low-stakes informal engineering education settings. In this paper, we share our experiences of using novel technologies to develop sustainability-oriented mental models in K–12 informal engineering education. Through the use of technologies including Augmented Reality (AR), Virtual Reality (VR), Minecraft video games, Tinkercad (browser-based application for computer-aided design (CAD)), and physical computing, we have designed and tested approaches to introduce students to engineering design and engineering habits of mind with an overarching theme of developing sustainability-oriented mental models among K–12 youth in informal engineering education spaces. In this paper, we share our approaches, and lessons learned, and outline directions for future research.