Developing problem-solving competency is central to Science, Technology, Engineering, and Mathematics (STEM) education, yet translating this priority into effective approaches to problem-solving instruction and assessment remain a significant challenge. The recent proliferation of generative artificial intelligence (genAI) tools like ChatGPT in higher education introduces new considerations about how these tools can help or hinder students' development of STEM problem-solving competency. Our research examines these considerations by studying how and why college students use genAI tools in their STEM coursework, focusing on their problem-solving support. We surveyed 40 STEM college students from diverse U.S. institutions and 28 STEM faculty to understand instructor perspectives on effective genAI tool use and guidance in STEM courses. Our findings reveal high adoption rates and diverse applications of genAI tools among STEM students. The most common use cases include finding explanations, exploring related topics, summarizing readings, and helping with problem-set questions. The primary motivation for using genAI tools was to save time. Moreover, over half of student participants reported simply inputting problems for AI to generate solutions, potentially bypassing their own problem-solving processes. These findings indicate that despite high adoption rates, students' current approaches to utilizing genAI tools often fall short in enhancing their own STEM problem-solving competencies. The study also explored students' and STEM instructors' perceptions of the benefits and risks associated with using genAI tools in STEM education. Our findings provide insights into how to guide students on appropriate genAI use in STEM courses and how to design genAI-based tools to foster students' problem-solving competency.

The term "K-12" stands for "Kindergarten through 12th grade" and represents the full range of primary and secondary education. Within this system, a strong emphasis has been placed on STEM (Science, Technology, Engineering, and Mathematics) education as a means to prepare students for a technology-driven future. STEM education at the K-12 level focuses on building foundational knowledge in scientific inquiry, technological literacy, engineering principles, and mathematical reasoning [10, 29, 64]. The K-12 STEM education emphasizes interdisciplinary learning, where students apply concepts from multiple domains to solve real-world challenges, such as integrating mathematics with science to tackle engineering problems [29]. The importance of K-12 STEM education lies in its ability to prepare students for a rapidly evolving, technology-driven world by fostering critical thinking, creativity, and problem-solving skills from an early age [10]. Students who engage in well-structured STEM curricula are more likely to pursue further education and careers in high-demand fields like information technology and engineering which are essential for technological innovation. Additionally, K-12 STEM education equips students with competencies such as analytical thinking, which prepare them for a wide range of career paths while enabling them to tackle complex problems [64]. Recognizing the importance of STEM education at the K-12 level, it is essential to deliver K-12 STEM education at scale to ensure equitable access to individual students.

Incremental learning (IL) is essential to realize the human-level intelligence in the neural network. However, existing IL scenarios and datasets are unqualified for assessing forgetting in PLMs, giving an illusion that PLMs do not suffer from catastrophic forgetting. To this end, we propose a challenging IL scenario called instance-incremental learning (IIL) and a novel dataset called Concept-1K, which supports an order of magnitude larger IL steps. Based on the experiments on Concept-1K, we reveal that billion-parameter PLMs still suffer from catastrophic forgetting, and the forgetting is affected by both model scale, pretraining, and buffer size. Furthermore, existing IL methods and a popular finetuning technique, LoRA, fail to achieve satisfactory performance. Our study provides a novel scenario for future studies to explore the catastrophic forgetting of PLMs and encourage more powerful techniques to be designed for alleviating the forgetting in PLMs. The data, code and scripts are publicly available at https://github.com/zzz47zzz/pretrained-lm-for-incremental-learning.

Yiwen Lin, University of California, Irvine Lauren Snow, University of California, Irvine Acknowledgments: This work was partially supported by the National Science Foundation (Grant Number 1535300), and National Institutes of Health (Grant Number 5UC2NS128361-02). Abstract Collaboration is key to STEM, where multidisciplinary team research can solve complex problems. However, inequality in STEM fields hinders their full potential, due to persistent psychological barriers in underrepresented students' experience. This paper documents teamwork in STEM and explores the transformative potential of computational modeling and generative AI in promoting STEM-team diversity and inclusion. Leveraging generative AI, this paper outlines two primary areas for advancing diversity, equity, and inclusion. First, formalizing collaboration assessment with inclusive analytics can capture fine-grained learner behavior. Second, adaptive, personalized AI systems can support diversity and inclusion in STEM teams. Four policy recommendations highlight AI's capacity: formalized collaborative skill assessment, inclusive analytics, funding for socio-cognitive research, human-AI teaming for inclusion training.

Vice President Kamala Harris' Office released a new framework for US space policy on Wednesday, showing how the Biden Administration plans approach to civil, commercial and national security-related use of space amid growing commercial interests and concerns about Chinese and Russian competition. The seven-page document, titled the'United States Space Priorities Framework,' includes many of the same space priorities from the Trump administration, such as the Artemis program, but cites using space to fight climate change and the importance of investing in STEM education. Harris, set to convene the inaugural meeting of the National Space Council at 1:30pm ET, plans to ask members of the government body'to accelerate, expand, and develop rules and norms for responsible behavior in space,' the White House said. 'We are on the cusp of historic changes in access to and use of space - changes that have the potential to bring the benefits of space to more people and communities than ever before,' the administration said in a report outlining its space priorities. Vice President Kamala Harris' Office released a new framework for US space policy on Wednesday, showing how the Biden Administration plans approach to civil, commercial and national security-related use of space amid growing commercial interests and concerns about Chinese and Russian competition This will mark the first meeting of the National Space Council during the Biden Administration.

There's so much opportunity for students to learn AI -- and it doesn't have to be hard. Learning AI sounds daunting and until recently, it's been challenging for students to find enough guidance and support to build AI projects. There are great resources to help students learn coding languages, but how do students go from learning the basics of Python and Linux to building an autonomous robot (especially on a budget)? Guidelines to build with AI should be accessible to all. After all -- creativity and innovation happen when barriers are taken down, not added.

Over the last few years, artificial intelligence (AI) has been delivering competitive advantage to businesses across a wide spectrum of industries. By Deloitte's most recent count, 37 percent of organizations have deployed AI solutions (up 270 percent from 2016) and a majority predict it will "substantially transform" their companies by 2023. The shift may also mean transforming their workforce. "As AI drives these transformations, it is changing how work gets done in organizations by making operations more efficient, supporting better decision-making, and freeing up workers from certain tasks," Deloitte reports. "The nature of job roles and the skills that are most needed are evolving."

Jasmine Lawrence works with the Everyday Robots project from Alphabet's X moonshot factory. She thinks there's a lot of unanswered ethical questions about how to use robots and how to think of them: Are they slaves or tools? Do they replace or complement people? As a product manager, she said, confronting some of those questions can be frightening, and it brings up the question of bias and the responsibility of the creator. Lawrence said she wants to be held accountable for the good and bad things she builds.

Of late, few things make me cringe more than the mischaracterization of the word "data," particularly with regards to its variants in Data Science, Data Analytics, Big Data, and the like. However, I don't fault the perpetrators: data's influence on your life is increasing at a rate much faster than that at which efforts are being made to educate the majority of people on what it is and how it is translated into actionable insights. At this point, most of us realize that properly-leveraged data has many lucrative use-cases. In turn, businesses and organizations are seeking to tap into this movement without always knowing what its tangible benefits will be for their specific operations. I don't mean to echo any Revere-esque rhetoric to the tune of "The Data are coming!

Ivanka Trump laid out her vision for the future of work and technology's role in reshaping the American economy and workforce in the coming decades during a controversial'fireside chat' at the Consumer Electronic Show (CES) on Tuesday. Speaking the Venetian's Pallazo Ballroom - which just a night prior had been used to launch a slew of new robots and artificial intelligence by Samsung - Trump chose to focus on the business implications of technology. Trump said she had'no sympathy' for companies that complain about unskilled workforces, don't invest in're-skilling them,' and still lay off employees. While the president's daughter's talking points centered mainly on business, she said she believes that innovations like'robotic arms' would create jobs that aren't yet possible. Prior to her keynote, tech industry insiders hit out at the conference, questioning whether the White House advisor was a relevant speaker, but CES President and CEO, Gary Shapiro defended her presence and moderated her talk, entitled'The Path to the Future of Work.' Dressed smartly in pinstripes (left), Trump said she has'no sympathy' for employers who don't're-skill' their workers as she laid out her'vision' for the future workforce of the US and hit back at companies over layoffs Trump plugged the government panel she co-chairs, the American Workforce Policy Advisory Board - which includes members from Apple, and IBM - and took the private sector to task for their role in helping to train current members of the workforce.