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Dramatic changes in the security environment in the past two decades have led to a greatly increased demand for U.S. Air Force intelligence, surveillance, and reconnaissance (ISR) capabilities. Today, the Air Force must provide ISR to support a growing set of missions while remaining postured to support major combat operations (MCOs) should the need arise. To meet these requirements, the Air Force is currently undertaking unprecedented measures to expand and enhance its ISR capabilities. Particular urgency has been attached to cultivating its fleet of sophisticated remotely piloted aircraft (RPA) to support current operations in Afghanistan and Iraq. Equally critical to these efforts, however, is the Air Force's extensive processing, exploitation, and dissemination (PED) force, which is essential to convert the raw data collected into usable intelligence and deliver it to the warfighter. It is therefore imperative to assess the size and mix of the Air Force's PED force to ensure that the ability to conduct all necessary PED within required timelines keeps pace with the increases in the amount and type of information collected. This study addressed the particular challenges associated with the exploitation of motion imagery within the Air Force Distributed Common Ground System (DCGS). Motion imagery collections from full-motion video (FMV) sensors on RPAs have risen rapidly to the point at which they now consume the largest share of Air Force DCGS resources, and new wide-area motion imagery (WAMI) sensors now being deployed have the potential to vastly increase the amount of raw data collected. The information explosion resulting from these vast amounts of motion imagery threatens to leave Air Force intelligence analysts drowning in data. One approach to meeting these challenges was inspired by an examination of related practices in the commercial world. It consists of implementing certain process changes and adopting a new organizational construct to improve the effectiveness of Air Force intelligence analysts while confronting the reality of limited resources.

Aerial reconnaissance --- Military intelligence --- United States.

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English learners (ELs) are 10 percent of U.S. students; in some states, they comprise as much as 20 percent of the student body. Despite continued growth in the EL population, schools nationwide have struggled to support ELs, and researchers consistently find wide, persistent academic achievement disparities between ELs and non-ELs. Equitable access to academic content is critical in addressing persistent achievement gaps between ELs and native English speakers, but schools and teachers face several challenges in enabling this access. Teachers report feeling inadequately prepared to work effectively with ELs and might lack of core subject-area materials and tools that are accessible to ELs or that provide language scaffolding. In this Data Note, researchers draw on data from the spring 2020 American Instructional Resources Survey to examine both teachers' perceptions of whether their main English language arts, mathematics, and science materials meet the needs of ELs and the modifications that teachers make to make those materials more appropriate for this population.

Artificial intelligence --- Military intelligence --- Military applications --- Evaluation. --- United States. --- Intelligence specialists.

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The U.S. Air Force's electronic warfare integrated reprogramming (EWIR) enterprise examines intelligence on adversary threats that emit in the electromagnetic spectrum (EMS) (in particular, radars and jammers) and configures electronic warfare software and hardware to enable aircraft or other resources to react to and/or respond to adverse changes in the EMS environment. With the growing advancements in U.S. adversaries' electronic warfare assets that enable complex and diverse EMS capabilities, identifying, tracking, and responding to these threats requires much faster updates than the existing EWIR enterprise was designed for. The research team conducted four interrelated technology case studies that together comprise the fundamental elements necessary for creating a near-real-time, autonomous, inflight software reprogramming capability and, more specifically, artificial intelligence–enabled cognitive electronic warfare capabilities—the use of machine learning algorithms that enable platforms to learn, reprogram, adapt, and effectively counter threats in flight. The research team also highlighted important continuing roles for the existing EWIR enterprise even as the U.S. Air Force moves toward a cognitive future. This executive summary encapsulates the findings in Outsmarting Agile Adversaries in the Electromagnetic Spectrum (RR-A981-1) 2023.

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The geographic diversity of many military enterprises, along with that of their partners and customers, has made virtual collaboration indispensable for conducting daily operations. Virtual collaboration tools can enable intrasite and intersite collaborative analyses, allow for sites to provide more effective surge capacity, and allow the regional expertise developed at each site to be applied wherever necessary across the enterprise. But communication between non-colocated (virtual) teams poses important challenges, including potential difficulty building cohesiveness and trust among team members and difficulty establishing a common understanding of information or situations. This report addresses these challenges through an assessment of three modes of virtual collaboration, computer-mediated communication, audioconferencing, and videoconferencing, and recommends several ways for intelligence enterprises to tackle them using virtual collaboration tools. These recommendations include: (1) determine which virtual collaboration tools and features are most beneficial using experimental research involving simulated tasks and constraints that closely mirror the military enterprise's operational environment; (2) standardize the lexicon and communications practices associated with virtual collaboration-chat, in particular-and train personnel in these practices; and (3) explore the use of videoconferencing in real-time communications between personnel, their partners, and their customers at different sites. In particular, we recommend that Air Force intelligence enterprises consider the use of personal or webcam-based videoconferencing between intelligence personnel located at different sites, as well as between these personnel and remotely piloted aircraft flight crews.

Communications, Military --- Virtual work teams --- Military intelligence --- Teleconferencing --- Military & Naval Science --- Law, Politics & Government --- Armies --- Eteams (Virtual work teams) --- Virtual teams (Work teams) --- VTeams (Virtual work teams) --- Communications, Naval --- Military communications --- Naval communications --- Telecommunication --- Teams in the workplace --- Communication and traffic --- United States --- Air Force --- Communication systems. --- ABŞ --- ABSh --- Ameerika Ühendriigid --- America (Republic) --- Amerika Birlăshmish Shtatlary --- Amerika Birlăşmi Ştatları --- Amerika Birlăşmiş Ştatları --- Amerika ka Kelenyalen Jamanaw --- Amerika Qūrama Shtattary --- Amerika Qŭshma Shtatlari --- Amerika Qushma Shtattary --- Amerika (Republic) --- Amerikai Egyesült Államok --- Amerikanʹ Veĭtʹsėndi︠a︡vks Shtattnė --- Amerikări Pĕrleshu̇llĕ Shtatsem --- Amerikas Forenede Stater --- Amerikayi Miatsʻyal Nahangner --- Ameriketako Estatu Batuak --- Amirika Carékat --- AQSh --- Ar. ha-B. --- Arhab --- Artsot ha-Berit --- Artzois Ha'bris --- Bí-kok --- Ē.P.A. --- EE.UU. --- Egyesült Államok --- ĒPA --- Estados Unidos --- Estados Unidos da América do Norte --- Estados Unidos de América --- Estaos Xuníos --- Estaos Xuníos d'América --- Estatos Unitos --- Estatos Unitos d'America --- Estats Units d'Amèrica --- États-Unis --- Ètats-Unis d'Amèrica --- États-Unis d'Amérique --- Fareyniḳṭe Shṭaṭn --- Feriene Steaten --- Feriene Steaten fan Amearika --- Forente stater --- FS --- Hēnomenai Politeiai Amerikēs --- Hēnōmenes Politeies tēs Amerikēs --- Hiwsisayin Amerikayi Miatsʻeal Tērutʻiwnkʻ --- Istadus Unidus --- Jungtinės Amerikos valstybės --- Mei guo --- Mei-kuo --- Meiguo --- Mî-koet --- Miatsʻyal Nahangner --- Miguk --- Na Stàitean Aonaichte --- NSA --- S.U.A. --- SAD --- Saharat ʻAmērikā --- SASht --- Severo-Amerikanskie Shtaty --- Severo-Amerikanskie Soedinennye Shtaty --- Si︠e︡vero-Amerikanskīe Soedinennye Shtaty --- Sjedinjene Američke Države --- Soedinennye Shtaty Ameriki --- Soedinennye Shtaty Severnoĭ Ameriki --- Soedinennye Shtaty Si︠e︡vernoĭ Ameriki --- Spojené obce severoamerické --- Spojené staty americké --- SShA --- Stadoù-Unanet Amerika --- Stáit Aontaithe Mheiriceá --- Stany Zjednoczone --- Stati Uniti --- Stati Uniti d'America --- Stâts Unîts --- Stâts Unîts di Americhe --- Steatyn Unnaneysit --- Steatyn Unnaneysit America --- SUA (Stati Uniti d'America) --- Sŭedineni amerikanski shtati --- Sŭedinenite shtati --- Tetã peteĩ reko Amérikagua --- U.S. --- U.S.A. --- United States of America --- Unol Daleithiau --- Unol Daleithiau America --- Unuiĝintaj Ŝtatoj de Ameriko --- US --- USA --- Usono --- Vaeinigte Staatn --- Vaeinigte Staatn vo Amerika --- Vereinigte Staaten --- Vereinigte Staaten von Amerika --- Verenigde State van Amerika --- Verenigde Staten --- VS --- VSA --- Wááshindoon Bikéyah Ałhidadiidzooígíí --- Wilāyāt al-Muttaḥidah --- Wilāyāt al-Muttaḥidah al-Amirīkīyah --- Wilāyāt al-Muttaḥidah al-Amrīkīyah --- Yhdysvallat --- Yunaeted Stet --- Yunaeted Stet blong Amerika --- ZDA --- Združene države Amerike --- Zʹi︠e︡dnani Derz︠h︡avy Ameryky --- Zjadnośone staty Ameriki --- Zluchanyi︠a︡ Shtaty Ameryki --- Zlucheni Derz︠h︡avy --- ZSA --- Η.Π.Α. --- Ηνωμένες Πολιτείες της Αμερικής --- Америка (Republic) --- Американь Вейтьсэндявкс Штаттнэ --- Америкӑри Пӗрлешӳллӗ Штатсем --- САЩ --- Съединените щати --- Злучаныя Штаты Амерыкі --- ولايات المتحدة --- ولايات المتّحدة الأمريكيّة --- ولايات المتحدة الامريكية --- 미국 --- É.-U. --- ÉU

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This report concerns the potential for artificial intelligence (AI) systems to assist in Air Force command and control (C2) from a technical perspective. The authors present an analytical framework for assessing the suitability of a given AI system for a given C2 problem. The purpose of the framework is to identify AI systems that address the distinct needs of different C2 problems and to identify the technical gaps that remain. Although the authors focus on C2, the analytical framework applies to other warfighting functions and services as well. The goal of C2 is to enable what is operationally possible by planning, synchronizing, and integrating forces in time and purpose. The authors first present a taxonomy of problem characteristics and apply them to numerous games and C2 processes. Recent commercial applications of AI systems underscore that AI offers real-world value and can function successfully as components of larger human-machine teams. The authors outline a taxonomy of solution capabilities and apply them to numerous AI systems. While primarily focusing on determining alignment between AI systems and C2 processes, the report's analysis of C2 processes is also informative with respect to pervasive technological capabilities that will be required of Department of Defense (DoD) AI systems. Finally, the authors develop metrics — based on measures of performance, effectiveness, and suitability — that can be used to evaluate AI systems, once implemented, and to demonstrate and socialize their utility.