Ai Magazine最新文献

{"title":"Guest Editorial: Introduction to special issue of AI magazine on AI literacy and AI education","authors":"Mary Lou Maher, David S. Touretzky, Michael Wollowski","doi":"10.1002/aaai.70006","DOIUrl":"https://doi.org/10.1002/aaai.70006","url":null,"abstract":"<p>Artificial intelligence has evolved into a readily accessible tool, increasing its impact on our daily lives, society, and the economy. This accessibility necessitates a critical reassessment of existing AI educational programs and curricula. There is a pressing need to develop strategies that increase the capacity for AI education, establish diverse pathways for individuals to enter and contribute to AI, and foster a greater awareness of the multifaceted implications of AI-driven technologies. The proliferation of AI has triggered concerns about potential consequences, including the over-reliance on AI leading to worker deskilling, the automation of various job functions, and the resulting uncertainty surrounding the future of work.</p><p>It is important to note that this special issue specifically focuses on AI education, not the application of AI in education, often referred to as “AI edtech,” where AI tools are utilized to facilitate teaching and learning across various subjects. While the latter is a rapidly evolving and significant domain, it constitutes a separate area of inquiry from the topics addressed here.</p><p>Several key factors contribute to scalable AI education in K-12. Central to success is comprehensive and ongoing teacher professional development (PD), which should include initial intensive workshops, sustained support during the school year, co-teaching with experienced instructors, and time for teachers to plan and adapt lessons. Developing teacher leaders who can provide PD and mentorship is essential for long-term sustainability and wider dissemination. A co-design process that actively involves teachers in curriculum development ensures the materials are relevant and adaptable to diverse learners and classroom environments.</p><p>Research-practitioner partnerships (RPPs) leverage expertise from both universities and educational settings, bridging content knowledge with practical realities. A diverse and inclusive approach, considering varying student demographics and learning needs, is critical for broad accessibility. Effective curriculum design that aligns with established AI frameworks, incorporates active learning strategies, and focuses on fundamental understandings is crucial. Ongoing implementation support, online resources, community building, flexibility, adaptability, and continuous evaluation and improvement all contribute to a robust and scalable AI education program in K-12.</p><p>Touretzky et al. address AI education in middle school (grades 6–8) in the “AI for Georgia” project (AI4GA.org). Capacity building in K-12 AI education requires extensive teacher support, as most K-12 teachers, including computing teachers, start with little or no AI knowledge. The paper describes a teacher professional development program that begins with instruction in the basics of AI and also brings in teachers as co-designers to help shape the curriculum and tailor it to their classrooms. With continued support, some of the","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence education in Georgia middle schools","authors":"David S. Touretzky,&nbsp;Christina Gardner-McCune,&nbsp;Bryan Cox,&nbsp;Judith Uchidiuno,&nbsp;Xueru Yu,&nbsp;William Gelder,&nbsp;Tom McKlin,&nbsp;Taneisha Lee Brown,&nbsp;Bejanae Kareem,&nbsp;Woojin Chung,&nbsp;Amber Jones,&nbsp;Janet Kolodner","doi":"10.1002/aaai.70013","DOIUrl":"https://doi.org/10.1002/aaai.70013","url":null,"abstract":"<p>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.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From abstraction to reality: DARPA's vision for robust sim-to-real autonomy","authors":"Erfaun Noorani,&nbsp;Zachary Serlin,&nbsp;Ben Price,&nbsp;Alvaro Velasquez","doi":"10.1002/aaai.70015","DOIUrl":"https://doi.org/10.1002/aaai.70015","url":null,"abstract":"<p>The DARPA Transfer from Imprecise and Abstract Models to Autonomous Technologies (TIAMAT) program aims to address rapid and robust transfer of autonomy technologies across dynamic and complex environments, goals, and platforms. Existing methods for simulation-to-reality (sim-to-real) transfer often rely on high-fidelity simulations and struggle with broad adaptation, particularly in time-sensitive scenarios. Although many approaches have shown incredible performance at specific tasks, most techniques fall short when posed with unforeseen, complex, and dynamic real-world scenarios due to the inherent limitations of simulation. In contrast to current research that aims to bridge the gap between simulation environments and the real world through increasingly sophisticated simulations and a combination of methods typically assuming a small sim-to-real gap—such as domain randomization, domain adaptation, imitation learning, meta-learning, policy distillation, and dynamic optimization—TIAMAT takes a different approach by instead emphasizing transfer and adaptation of the autonomy stack directly to real-world environments by utilizing a breadth of low(er)-fidelity simulations to create broadly effective sim-to-real transfers. By abstractly learning from multiple simulation environments in reference to their shared semantics, TIAMAT's approaches aim to achieve abstract-to-real transfer for effective and rapid real-world adaptation. Furthermore, this program endeavors to improve the overall autonomy pipeline by addressing the inherent challenges in translating simulated behaviors into effective real-world performance.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligent disobedience: Rethinking the agency of our artificial teammates","authors":"Reuth Mirsky","doi":"10.1002/aaai.70011","DOIUrl":"https://doi.org/10.1002/aaai.70011","url":null,"abstract":"<p>The impressive advancements of AI in recent years are indisputable, and AI algorithms have already reached superhuman performance in many tasks. On the other hand, in most existing work on cooperative AI, artificial agents are bound to follow the instructions they are given by their human teammates and to comply with their users' expectations. This paper advocates for expanding the agency capabilities of AI teammates, enabling them to make genuine and unique contributions in human-AI teams. It presents a scale for AI agency levels and discusses the importance and inevitability of researching AI autonomy as an independent capability in cooperative AI, using a set of representative examples. The paper then presents how intelligent disobedience of artificial agents might look at each autonomy level. Finally, it outlines some initial boundaries that should be set when researching AI agency and its disobedience capabilities.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144582283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating the new frontier: The role of AI-driven virtual influencers in consumer engagement","authors":"Bo-Chiuan Su","doi":"10.1002/aaai.70012","DOIUrl":"https://doi.org/10.1002/aaai.70012","url":null,"abstract":"<p>This paper examines the evolving role of artificial intelligence (AI)-driven virtual influencers (VIs) in enhancing consumer engagement within the digital marketing landscape. Using qualitative case studies of notable VIs such as Lil Miquela and Ayayi, this research highlights critical factors influencing their effectiveness, including advanced technology, cultural significance, and shifts in consumer expectations. Findings indicate that VIs create authentic connections with younger demographics, particularly Millennials and Generation Z, by offering tailored content and reinforcing emotional ties. The study emphasizes the significance of authenticity and transparency for building consumer trust, alongside addressing ethical concerns such as representation and manipulation in marketing practices. It explores how VIs operate as cultural influencers, reshaping consumer identities within the digital realm. This research underscores the need for brands to adopt responsible practices that prioritize ethical engagement and inclusivity, enabling them to navigate the complexities of VI marketing while fostering meaningful consumer relationships.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144582314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When science fiction collides with reality: The future of learning and the one after that","authors":"Steve Joordens","doi":"10.1002/aaai.70009","DOIUrl":"https://doi.org/10.1002/aaai.70009","url":null,"abstract":"<p>This article provides a somewhat whimsical discussion of the impact that AI, or “robots”, will have on the future of education. Interwoven with numerous references to science fiction, and at least one to Alice Cooper, is a very serious consideration of the manner in which AI may completely redefine the way we learn and grow as humans. From Holistic Assessment of Learning (HAL) to a Yoda on Yer Shoulda, a future of life-embedding learning is described.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144551058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Governance in the age of artificial intelligence: A comparative analysis of policy framework in BRICS nations","authors":"Animesh Kumar Sharma,&nbsp;Rahul Sharma","doi":"10.1002/aaai.70010","DOIUrl":"https://doi.org/10.1002/aaai.70010","url":null,"abstract":"<p>This study investigates the dynamic landscape of governance frameworks for emerging technologies, particularly artificial intelligence (AI), within the context of public policy in expanded BRICS nations (Brazil, Russia, India, China, South Africa, Egypt, Ethiopia, Iran, and the United Arab Emirates). Understanding the ethical implications and crafting policy tools to guide the development and deployment of AI is crucial. Analyzing findings from AI policy initiatives, this research delves into managing new technologies, emphasizing the evolving discourse on AI ethics. It stresses the importance of embedding ethical considerations into governance frameworks to address societal concerns and foster responsible AI advancement. Additionally, strong legal frameworks are essential, striking a balance between fostering innovation and ensuring accountability, thereby enhancing confidence and transparency in AI systems. This study underscores the significance of public policy in shaping AI governance, advocating for inclusive, participatory approaches involving stakeholders from diverse sectors. Adaptive governance frameworks capable of navigating the evolving AI landscape and its societal ramifications are emphasized. A holistic governance strategy based on insights from AI policy is recommended, aiming to reconcile innovation with ethical, legal, and societal considerations. Policymakers are urged to foster stakeholder engagement, ensuring that AI advancements benefit society while upholding ethical, just, and accountable standards.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144551018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building trust: Foundations of security, safety, and transparency in AI","authors":"Huzaifa Sidhpurwala,&nbsp;Garth Mollett,&nbsp;Emily Fox,&nbsp;Mark Bestavros,&nbsp;Huamin Chen","doi":"10.1002/aaai.70005","DOIUrl":"https://doi.org/10.1002/aaai.70005","url":null,"abstract":"<p>This paper explores the rapidly evolving ecosystem of publicly available AI models and their potential implications on the security and safety landscape. Understanding their potential risks and vulnerabilities is crucial as AI models become increasingly prevalent. We review the current security and safety scenarios while highlighting challenges such as tracking issues, remediation, and the absence of AI model lifecycle and ownership processes. Comprehensive strategies to enhance security and safety for both model developers and end-users are proposed. This paper provides several foundational pieces for more standardized security, safety, and transparency in developing and operating generative AI models and the larger open ecosystems and communities forming around them.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What is reproducibility in artificial intelligence and machine learning research?","authors":"Abhyuday Desai,&nbsp;Mohamed Abdelhamid,&nbsp;Nakul R. Padalkar","doi":"10.1002/aaai.70004","DOIUrl":"https://doi.org/10.1002/aaai.70004","url":null,"abstract":"<p>In the rapidly evolving fields of artificial intelligence (AI) and machine learning (ML), the reproducibility crisis underscores the urgent need for clear validation methodologies to maintain scientific integrity and encourage advancement. The crisis is compounded by the prevalent confusion over validation terminology. In response to this challenge, we introduce a framework that clarifies the roles and definitions of key validation efforts: repeatability, dependent and independent reproducibility, and direct and conceptual replicability. This structured framework aims to provide AI/ML researchers with the necessary clarity on these essential concepts, facilitating the appropriate design, conduct, and interpretation of validation studies. By articulating the nuances and specific roles of each type of validation study, we aim to enhance the reliability and trustworthiness of research findings and support the community's efforts to address reproducibility challenges effectively.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Open issues in open world learning","authors":"Steve Cruz,&nbsp;Katarina Doctor,&nbsp;Christopher Funk,&nbsp;Walter Scheirer","doi":"10.1002/aaai.70001","DOIUrl":"https://doi.org/10.1002/aaai.70001","url":null,"abstract":"<p>Meaningful progress has been made in open world learning (OWL), enhancing the ability of agents to detect, characterize, and incrementally learn novelty in dynamic environments. However, novelty remains a persistent challenge for agents relying on state-of-the-art learning algorithms. This article considers the current state of OWL, drawing on insights from a recent DARPA research program on this topic. We identify open issues that impede further advancements spanning theory, design, and evaluation. In particular, we emphasize the challenges posed by dynamic scenarios that are crucial to understand for ensuring the viability of agents designed for real-world environments. The article provides suggestions for setting a new research agenda that effectively addresses these open issues.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}