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The transfer of patients between two aircraft using an underway watercraft increases medical evacuation reach and flexibility in maritime environments. The selection of any one of multiple underway watercraft for patient exchange is complicated by participating aircraft utilization histories and participating watercraft positions and velocities. The selection problem is modeled as a semi-Markov decision process with an action space, including both fixed land and moving watercraft exchange points. Monte Carlo tree search with root parallelization is used to select optimal exchange points and determine aircraft dispatch times. Model parameters are varied in simulation to identify representative scenarios where watercraft exchange points reduce incident response times. We find that an optimal policy with watercraft exchange points outperforms an optimal policy without watercraft exchange points and a greedy policy by 35% and 40%, respectively. In partnership with the United States Army, we deploy for the first time the watercraft exchange point by executing a mock patient transfer with a manikin between two HH-60M medical evacuation helicopters and an underway Army Logistic Support Vessel south of the Hawaiian island of Oahu. Both helicopters were dispatched in accordance with our optimized decision strategy.

The advent of generative AI, particularly large language models like ChatGPT, has precipitated a seismic shift in academia. Far from a gradual evolution, its sudden emergence has jolted educational institutions, leaving many academics grappling with a perceived encroachment upon their intellectual domain. This upheaval has sparked intense debates, with concerns ranging from the erosion of academic integrity to the devaluation of scholarly labor. This essay contends that such apprehensions, while understandable, may overlook the transformative potential of AI as a collaborative tool. Drawing parallels to historical disruptions—such as the advent of photography challenging traditional art forms—we explore how AI can augment human creativity rather than supplant it. By examining the dynamics of authorship, originality, and accountability, we argue for a redefinition of these concepts in the context of AI-assisted work. Emphasizing the importance of human oversight in guiding AI outputs, we advocate for a framework that recognizes the symbiotic relationship between human intellect and machine efficiency. Such a perspective not only preserves the essence of academic rigor but also embraces the democratization of knowledge production. Ultimately, this essay calls for a balanced approach that mitigates risks while harnessing the innovative capacities of generative AI in academia.

The rapid advancement of generative artificial intelligence (AI) poses significant challenges to traditional copyright frameworks, intensifying debates over the copyrightability of AI-generated outputs. By comparing judicial practices in China and the United States, it has been observed that the United States maintains a conservative stance of adhering to substantive control, while China demonstrates an inclusive approach through the criterion of creative contribution. Building upon this, this article transcends the traditional binary judgment model and constructs a tiered copyright determination model. Based on the level of human control and contribution in the AI generation process, it introduces dimensions such as technological controllability and density of human intent, classifying generative AI into three tiers: strong protection, weak protection, and non-protection. Regarding the copyrightability of content generated by generative AI, this article argues that the issue should be addressed within the framework of copyright law itself. When human participation is involved and the substantial contribution of the direct user is reflected in the AI-generated content, meeting the requirements for copyrightable works under copyright law, corresponding protective measures should be granted.

In this Comment, we critique the growing “AI welfare” movement and propose a novel guideline, the Precarity Guideline, to determine care entitlement. In contrast to approaches that emphasize potential for suffering, the Precarity Guideline is grounded in empirically identifiable features. The severity of ongoing humanitarian crises, biodiversity loss, and climate change provides additional reasons to prioritize the needs of living beings over machine learning algorithms as candidates for care.

Public statements by leading AI researchers and recognizable people in the computer world are suggesting that AI may soon replace many jobs, including software engineers. Some even state that soon, AI will be smarter than us. We believe such statements are unhelpful when it comes to attracting talent to our field. We document several such statements. We believe that the future need for AI talent is tremendous and that we should take extreme efforts to attract students to our field. We present a sample of the expected opportunities and needs. Some of these opportunities may be attractive to students who in the past may not have considered AI as a career option. We argue that even with the anticipated automation of AI work, there nevertheless will be a prodigious need for talent to develop good AI. We summarize work that argues that AI is going to be a fundamental skill and as such should be introduced to learners across many age groups and many backgrounds. We suggest that a well-reasoned statement of the anticipated needs be developed by experts in our field and communicated to future talent. We suggest that, as part of this message, pathways forward toward developing AI talent across a wide range of backgrounds be developed and communicated.

As generative artificial intelligence (AI) becomes increasingly integrated into society and education, more institutions are implementing AI usage policies and offering introductory AI courses. These courses, however, should not replicate the technical focus typically found in introductory computer science (CS) courses like CS1 and CS2. In this paper, we use an adjustable, interdisciplinary socio-technical AI literacy framework to design and present an introductory AI literacy course. We present a refined version of this framework informed by the teaching of a 1-credit general education AI literacy course (primarily for freshmen and first-year students from various majors), a 3-credit course for CS majors at all levels, and a summer camp for high school students. Drawing from these teaching experiences and the evolving research landscape, we propose an introductory AI literacy course design framework structured around four cross-cutting pillars. These pillars encompass (1) understanding the scope and technical dimensions of AI technologies, (2) learning how to interact with (generative) AI technologies, (3) applying principles of critical, ethical, and responsible AI usage, and (4) analyzing implications of AI on society. We posit that achieving AI literacy is essential for all students, those pursuing AI-related careers, and those following other educational or professional paths. This introductory course, positioned at the beginning of a program, creates a foundation for ongoing and advanced AI education. The course design approach is presented as a series of modules and subtopics under each pillar. We emphasize the importance of thoughtful instructional design, including pedagogy, expected learning outcomes, and assessment strategies. This approach not only integrates social and technical learning but also democratizes AI education across diverse student populations and equips all learners with the socio-technical, multidisciplinary perspectives necessary to navigate and shape the ethical future of AI.

AI-powered systems increasingly influence critical aspects of daily life, yet these systems often embed and reinforce biases, disproportionately disadvantaging marginalized communities. Addressing these challenges requires a fundamental shift in how we teach the development of these systems, ensuring that future professionals develop not only technical expertise but also are equipped with the skills needed for ethical AI design. This paper adopts a design science research (DSR) approach to develop the equity-aware design thinking for AI (EquiThink4AI) framework, a dual-component model that systematically embeds equity principles into AI education. EquiThink4AI's first component extends design thinking (DT) by incorporating principles from EquityXDesign (EXD) and liberatory design (LD), ensuring that equity concerns are proactively addressed throughout AI system development. The second component enhances the framework with pedagogical strategies, including problem-based learning (PBL), experiential learning, and interdisciplinary collaboration, fostering student engagement, real-world problem-solving, and ethical reasoning. EquityThink4AI provides educators and students with a structured methodology for teaching and applying equity-centered AI development. This study is explorative in nature, yet it presents concrete strategies for integrating EquiThink4AI into AI curricula, bridging the gap between design, AI ethics, and educational practices.

In a partnership between four universities, the Georgia Department of Education, and nine Georgia school districts, we developed a 9-week middle school elective called “Living and Working with Artificial Intelligence,” and a professional development (PD) program for prospective middle school AI teachers. To ensure that our curriculum could meet the needs of all learners, we recruited a diverse set of districts that included rural districts serving mainly White students, urban districts that were majority African American, and suburban districts serving a mix of Hispanic and African American students. Now in its fourth year, our “AI for Georgia” project (AI4GA) has provided PD to 20 teachers and AI education to over 1600 students. 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. In this article, we review the history of the project, discuss our co-design process with our teachers, and present results from studies of teacher PD and student learning.