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Free, publicly-accessible full text available April 1, 2025
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In the face of the rising prevalence of artificial intelligence (AI) in daily life, there is a need to integrate lessons on AI literacy into K12 settings to equitably engage young adolescents in critical and ethical thinking about AI technologies. This exploratory study reports findings from a teacher professional development project designed to advance teacher AI literacy in preparation for teaching an AI curriculum in their inclusive middle school classrooms. Analysis compares the learning experiences of 30 participating teachers (including Computer Science, Science, Math, English, and Social Studies teachers). Results suggest Science teachers’ understanding of AI concepts, particularly logic structures, is on average higher than their non-Science teacher counterparts. Teacher interviews reveal several thematic differences in Science teachers’ learning from the AI PD as compared to their counterparts, namely learning from reflective discourse with diverse groups. Findings offer insights on the depth and quality of Science teacher AI literacy after participating in an AI teacher PD, with implications for future research in the integration of AI education into Science teachers’ inclusive K12 classrooms.more » « lessFree, publicly-accessible full text available March 19, 2025
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In the face of the rising prevalence of artificial intelligence (AI) in daily life, there is a need to integrate lessons on AI literacy into K12 settings to equitably engage young adolescents in critical and ethical thinking about AI technologies. This exploratory study reports findings from a teacher professional development project designed to advance teacher AI literacy in preparation for teaching an AI curriculum in their inclusive middle school classrooms. Analysis compares the learning experiences of 30 participating teachers (including Computer Science, Science, Math, English, and Social Studies teachers). Results suggest Science teachers’ understanding of AI concepts, particularly logic structures, is on average higher than their non-Science teacher counterparts. Teacher interviews reveal several thematic differences in Science teachers’ learning from the AI PD as compared to their counterparts, namely learning from reflective discourse with diverse groups. Findings offer insights on the depth and quality of Science teacher AI literacy after participating in an AI teacher PD, with implications for future research in the integration of AI education into Science teachers’ inclusive K12 classrooms.more » « lessFree, publicly-accessible full text available March 19, 2025
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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.more » « lessFree, publicly-accessible full text available February 25, 2025
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Abstract One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model’s prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database.
Free, publicly-accessible full text available June 1, 2025 -
The rapid expansion of Artificial Intelligence (AI) necessitates educating all students about AI. This, however, poses great challenges because most K-12 teachers have limited prior knowledge or experience of teaching AI. This exploratory study reports the design of an online professional development program aimed at preparing teachers for teaching AI in classrooms. The program includes a book club where teachers read a book about AI and learned key activities of an AI curriculum developed for middle schoolers, and a 2-week practicum where teachers co-taught the curriculum in a summer camp. The participants were 17 teachers from three school districts across the United States. Analysis of their surveys revealed an increase in teachers’ content knowledge and self-efficacy in teaching AI. Teachers reported that the book club taught them AI concepts and the practicum sharpened their teaching practices. Our findings reveal valuable insights on teacher training for the AI education field.more » « less
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The rapid expansion of Artificial Intelligence (AI) necessitates a need for educating students to become knowledgeable of AI and aware of its interrelated technical, social, and human implications. The latter (ethics) is particularly important to K-12 students because they may have been interacting with AI through everyday technology without realizing it. They may be targeted by AI generated fake content on social media and may have been victims of algorithm bias in AI applications of facial recognition and predictive policing. To empower students to recognize ethics related issues of AI, this paper reports the design and implementation of a suite of ethics activities embedded in the Developing AI Literacy (DAILy) curriculum. These activities engage students in investigating bias of existing technologies, experimenting with ways to mitigate potential bias, and redesigning the YouTube recommendation system in order to understand different aspects of AI-related ethics issues. Our observations of implementing these lessons among adolescents and exit interviews show that students were highly engaged and became aware of potential harms and consequences of AI tools in everyday life after these ethics lessons.more » « less
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Teachers today have increasing access to professional learning communities (PLCs) through a rapidly expanding menu of online professional development offerings. While a valued opportunity for growth, online PLCs can limit opportunities for co-teaching, pedagogical practice, and experiential learning. This paper examines a teacher professional development program implemented in 2022, where 14 middle school teachers joined either an online or an in-person version of a summer practicum in which PLCs were fostered. In both versions of the PD, teachers worked in small teams of co-teachers to learn and practice teaching middle school students about Artificial Intelligence (AI), a topic in which teachers were non-experts. Findings from qualitative analysis of teacher interviews suggest affordances and barriers to teacher learning online as compared to in-person PLCs. The paper offers recommendations for online PLC structure and co-teaching to enhance teacher learning.more » « less
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Teachers today have increasing access to professional learning communities (PLCs) through a rapidly expanding menu of online professional development offerings. While a valued opportunity for growth, online PLCs can limit opportunities for co-teaching, pedagogical practice, and experiential learning. This paper examines a teacher professional development program implemented in 2022, where 14 middle school teachers joined either an online or an in-person version of a summer practicum in which PLCs were fostered. In both versions of the PD, teachers worked in small teams of co-teachers to learn and practice teaching middle school students about Artificial Intelligence (AI), a topic in which teachers were non-experts. Findings from qualitative analysis of teacher interviews suggest affordances and barriers to teacher learning online as compared to in-person PLCs. The paper offers recommendations for online PLC structure and co-teaching to enhance teacher learning.more » « less