This paper asks the following question: what was the effect of surging immigration on average and individual wages of U.S.-born workers during the period 1990-2004? We emphasize the need for a general equilibrium approach to analyze this problem. The impact of immigrants on wages of U.S.-born workers can be evaluated only by accounting carefully for labor market and capital market interactions in production. Using such a general equilibrium approach we estimate that immigrants are imperfect substitutes for U.S.- born workers within the same education-experience-gender group (because they choose different occupations and have different skills). Moreover, accounting for a reasonable speed of adjustment of physical capital we show that most of the wage effects of immigration accrue to native workers within a decade. These two facts imply a positive and significant effect of the 1990-2004 immigration on the average wage of U.S.-born workers overall, both in the short run and in the long run. This positive effect results from averaging a positive effect on wages of U.S.-born workers with at least a high school degree and a small negative effect on wages of U.S.-born workers with no high school degree.
We analyze the relationship between inequality and economic growth from two directions. The first part of the survey examines the effect of inequality on growth, showing that when capital markets are imperfect, there is not necessarily a trade-off between equity and efficiency. It therefore provides an explanation for two recent empirical findings, namely, the negative impact of inequality and the positive effect of redistribution upon growth. The second part analyzes several mechanisms whereby growth may increase wage inequality, both across and within education cohorts. Technical change, and in particular the implementation of "General Purpose Technologies," stands as a crucial factor in explaining the recent upsurge in wage inequality.
We focus on reflections and suggestions of five college quantum educators from four different institutions (two from same institution) regarding what can be done to diversify the second quantum revolution. They are leading QIST researchers, and very passionate about improving quantum education. The educators were asked about their thoughts on whether the interdisciplinary nature of the field, in which nobody can claim to be an expert in all aspects of QIST, may make it easier to create a better culture from the beginning, supportive of equitable participation of diverse groups unlike physics. This is because disciplines such as physics have an ingrained inequitable culture based on brilliance attribution that is a major impediment to diversity, equity and inclusion. Educators were interviewed on Zoom using a semi-structured think-aloud protocol about various issues related to QIST education including those pertaining to how to diversify the second quantum revolution. Their suggestions can be invaluable and can help other educators adapt and implement strategies to diversify QIST.
Quantum Information Science and Engineering (QISE) is rapidly gaining interest from those within many disciplines and higher education needs to adapt to the changing landscape. Although QISE education still has a strong presence and roots in physics, the field is becoming increasingly interdisciplinary. There is a need to understand the presence of QISE instruction and quantum-related instruction across all disciplines in order to figure out where QISE education is already happening and where it could be expanded. Although there is recent work that characterizes introductory QISE courses, there is no holistic picture of the landscape of QISE and quantum-related education in the United States. We analyzed course catalogs from 1,456 U.S. institutions. We found 61 institutions offering QISE degree programs, mostly at PhD-granting schools, with physics, electrical and computer engineering (ECE), and computer science(CS) as their primary contributors . Across all institutions, we identified over 8,000 courses mentioning 'quantum,' but about one-third of institutions in our study had none. We also found over 500 dedicated QISE courses, concentrated in PhD-granting institutions, primarily in physics, ECE, and CS. Physics leads in offering both general quantum-related ($\sim$4,700) and QISE-specific ($\sim$200) courses. Across multiple disciplines, we see that QISE topics are being introduced in courses not fully dedicated to QISE, which may be a productive strategy for increasing access to QISE education. Our dataset and analysis provide the most comprehensive overview to date of quantum education across US higher education. To ensure broad access, all data are publicly available and downloadable at quantumlandscape.streamlit.app. We hope these findings will support and guide future efforts in curriculum design, workforce development, and education policy across the quantum ecosystem.
Veronika Hackl, Alexandra Mueller, Maximilian Sailer
The integrative literature review addresses the conceptualization and implementation of AI Literacy (AIL) in Higher Education (HE) by examining recent research literature. Through an analysis of publications (2021-2024), we explore (1) how AIL is defined and conceptualized in current research, particularly in HE, and how it can be delineated from related concepts such as Data Literacy, Media Literacy, and Computational Literacy; (2) how various definitions can be synthesized into a comprehensive working definition, and (3) how scientific insights can be effectively translated into educational practice. Our analysis identifies seven central dimensions of AIL: technical, applicational, critical thinking, ethical, social, integrational, and legal. These are synthesized in the AI Literacy Heptagon, deepening conceptual understanding and supporting the structured development of AIL in HE. The study aims to bridge the gap between theoretical AIL conceptualizations and the practical implementation in academic curricula.
Limited infrastructure, scarce educational resources, and unreliable internet access often hinder physics and photonics education in underdeveloped regions. These barriers create deep inequities in Science, Technology, Engineering, and Mathematics (STEM) education. This article explores how Small Language Models (SLMs)-compact, AI-powered tools that can run offline on low-power devices, offering a scalable solution. By acting as virtual tutors, enabling native-language instruction, and supporting interactive learning, SLMs can help address the shortage of trained educators and laboratory access. By narrowing the digital divide through targeted investment in AI technologies, SLMs present a scalable and inclusive solution to advance STEM education and foster scientific empowerment in marginalized communities.
FREESS is a free, interactive simulator that illustrates instruction-level parallelism in a RISC-V-inspired superscalar processor. Based on an extended version of Tomasulo's algorithm, FREESS is intended as a hands-on educational tool for Advanced Computer Architecture courses. It enables students to explore dynamic, out-of-order instruction execution, emphasizing how instructions are issued as soon as their operands become available. The simulator models key microarchitectural components, including the Instruction Window (IW), Reorder Buffer (ROB), Register Map (RM), Free Pool (FP), and Load/Store Queues. FREESS allows users to dynamically configure runtime parameters, such as the superscalar issue width, functional unit types and latencies, and the sizes of architectural buffers and queues. To simplify learning, the simulator uses a minimal instruction set inspired by RISC-V (ADD, ADDI, BEQ, BNE, LW, MUL, SW), which is sufficient to demonstrate key pipeline stages: fetch, register renaming, out-of-order dispatch, execution, completion, commit, speculative branching, and memory access. FREESS includes three step-by-step, illustrated examples that visually demonstrate how multiple instructions can be issued and executed in parallel within a single cycle. Being open source, FREESS encourages students and educators to experiment freely by writing and analyzing their own instruction-level programs and superscalar architectures.
The article provides a brief description of the MathPartner service. This freely available cloud-based Mathematics is a universal system for symbolic-numeric calculations. Its Mathpar language is a subset of the LaTeX language, but allows you to create mathematical texts that contain "computable" mathematical operators. This opens up completely new opportunities for improving the educational process for all natural science disciplines, for the use of mathematics in scientific and engineering calculations. To save and freely exchange educational and other texts in the Mathpar language, a GitHub repository has been created. It is concluded that cloud mathematics MathPartner is a new breakthrough technology for school and university natural science education, for scientific and engineering applications.
Quantum Information Science and Engineering (QISE) is rapidly gaining interest across a wide range of disciplines. As QISE continues to evolve, engineering will play an increasingly critical role in advancing quantum technologies. While efforts to characterize introductory QISE courses are underway, a comprehensive understanding of QISE education across the United States remains lacking. Developing a broad understanding of the QISE education landscape is crucial for addressing the needs of the growing quantum industry and ensuring equitable access for a diverse range of participants. This paper presents part of an ongoing effort to characterize the current landscape of QISE courses and degree programs in higher education in the US. To achieve this, we used publicly available information from university and college websites to capture information on over 8000 courses that address quantum in some way and nearly 90 QISE specific programs (e.g., degrees, minors, certificates). The majority of these programs are interdisciplinary and include engineering; 14 of them are housed exclusively in engineering departments. We find most programs are offered at research intensive institutions. Our results showcase an opportunity for program developers at non-research intensive institutions to justify the creation of QISE programs, which would also address calls from different stakeholders in QISE education for a more diverse QISE workforce. We suggest strategies based on the findings of this study such as integrating QISE into existing engineering courses, investing in the development of QISE courses and programs at non-PhD-granting institutions, and making courses with QISE content accessible to students from a variety of majors.
This is not a study, but a research note on academic writing practices in our field, whose purpose it is to serve as a foundation for discussion. It provides a brief introduction into researcher reflexivity, my own positioning towards the topic, and a numerical thematic overview of authorial presence (pronouns, third-person terms, and their semantic functions) in data-based research articles published in the Scenario journal over the last ten years. I do not draw conclusions, but from the angle of researcher reflexivity, I submit questions with respect to clarity of premises and ethics, for possible consideration by future authors in our field.
The purpose of this study is to explore the direction of general education in the era of generative AI. Although generative AI, first introduced at the end of 2022, is only two years old, it has already had a profound impact not only on various fields such as academic research and education but also on broader aspects of human life. To investigate the concerns of university education regarding generative AI over the past two years, a keyword analysis was conducted to identify relevant research and guidelines. This analysis highlighted the critical need for education in areas such as plagiarism and ethics. Additionally, by examining recent studies on generative AI literacy, this study identifies a new direction for general education. General education, which serves as foundational preparation for both students' academic majors and their lives, should not merely aim to increase acceptance of technology. Instead, it should help learners comprehend a society changing due to generative AI. This approach encourages students to design their subjective, critical, and ethical lives. At this juncture, where there is concern about general education shifting to focus on technical education, this discussion holds significant meaning as it makes us think about the nature of humanities education and critical thinking.
Bayode Ogunleye, Kudirat Ibilola Zakariyyah, Oluwaseun Ajao
et al.
The higher education (HE) sector benefits every nation's economy and society at large. However, their contributions are challenged by advanced technologies like generative artificial intelligence (GenAI) tools. In this paper, we provide a comprehensive assessment of GenAI tools towards assessment and pedagogic practice and, subsequently, discuss the potential impacts. This study experimented using three assessment instruments from data science, data analytics, and construction management disciplines. Our findings are two-fold: first, the findings revealed that GenAI tools exhibit subject knowledge, problem-solving, analytical, critical thinking, and presentation skills and thus can limit learning when used unethically. Secondly, the design of the assessment of certain disciplines revealed the limitations of the GenAI tools. Based on our findings, we made recommendations on how AI tools can be utilised for teaching and learning in HE.
Edwin Barnes, Michael B. Bennett, Alexandra Boltasseva
et al.
In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science.
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.