There are several types of school–university partnerships (SUPs) situated within various educational structures with varying missions. However, these SUPs may have many general visions that are more similar than disparate. Regardless of the specific type of SUP, areas of funding, policy, and politics may affect the development and maintenance of these SUPs. The recent and current external funding opportunities that relate to SUPs are discussed. Educational polices of both K-12 and universities related to SUP development are examined. Also, national, state, and local political shifts may have an impact on the SUP development cycle. These issues of funding, policy, and politics also may intersect within the day-to-day implementation of a SUP. Suggestions for aggregating the influence of different types of SUPs to inform policy and dealing with barriers are provided. Understanding the cyclical development of SUPs in response to funding, policy, and political changes in the K-12 or university is discussed

While generative AI enables the creation of diverse content, including images, videos, text, and music, it also raises significant ethical and societal concerns, such as bias, transparency, accountability, and privacy. Therefore, it is crucial to ensure that AI systems are both trustworthy and fair, optimising their benefits while minimising potential harm. To explore the importance of fostering trustworthiness in the development of generative AI, this chapter delves into the ethical implications of AI-generated content, the challenges posed by bias and discrimination, and the importance of transparency and accountability in AI development. It proposes six guiding principles for creating ethical, safe, and trustworthy AI systems. Furthermore, legal perspectives are examined to highlight how regulations can shape responsible generative AI development. Ultimately, the chapter underscores the need for responsible innovation that balances technological advancement with societal values, preparing us to navigate future challenges in the evolving AI landscape.

Pre-service teachers (PSTs), particularly those learning to teach in urban contexts unfamiliar to them, can learn a great deal about their students and the issues they face by connecting with the communities where their students and their families reside (Koerner & Abdul-Tawwab, 2006; Zeichner, 2010). Research suggests that working alongside community members in service-oriented organizations can provide opportunities for PSTs to learn about the community’s cultural wealth (Yosso, 2005) and develop a beginning critical consciousness (Zygmunt et al., 2018). Engaging with the community can help PSTs understand the systemic issues their students and their families face and learn how to build relationships with students’ families as well as support PSTs’ attentiveness to the role of context in their students’ learning (Bryk & Schneider, 2002; McDonald et al., 2011).

As evidenced in the four chapters in Part IV: Leadership in School–University Partnerships, leadership in a multifaceted school–university partnership (SUP) is complicated, complex, and nuanced. As Snow observed in her chapter, SUP leadership is made extremely complex because it is connected to teaching and learning – and humans who “are not logical creatures, but association making creatures who are capable of logic” (Davis & Sumara, 2006, p. 35, as cited in Snow). The chapters illustrate that there are a variety of thoughtful ways to explain, delineate, and describe the nature of SUP leadership. In one chapter, Henning applied design theory to leadership processes during SUP startups while Snow utilized complexity theory frameworks to contextualize ongoing collaborative efforts. Provinzano and Mayger explored the roles of principals who guide collaboration in community school partnerships and Roselle and colleagues analyzed the potential contributions of teacher leaders. Even though the authors come from different perspectives, commonalities – explicit and inferred – emerge from their analysis. This part provides a multitude of ideas that could be explored and unpacked, but three concepts – third spaces, boundary spanners, and brokers – offer important and meaningful ways to describe and understand SUP leadership practices.

In this part, seven individual authors and teams of authors explored inquiry and innovation in school–university partnership (SUP) research. Inquiry is central to professional development schools (PDSs), and has even been dubbed the “signature pedagogy” (Yendol-Hoppey & Franco, 2014) of PDS. Specifically, the authors in this part of the handbook explore the use of inquiry and action research within PDS and SUP research systematically through studying years of scholarly work. Several of them also explore the meaning of innovation in PDS and SUP research – however, as they demonstrate, sometimes this innovation is slow, or not particularly novel. These chapters were grouped together to connect research to innovation, and illustrate potential paths forward for scholars working in this field.

Universities have long collaborated with schools through various school–university partnerships (SUPs). Critiques of SUPs point to their inequitable power dynamics, with the university often prioritizing its own interests over the needs of the school. University-assisted community schools (UACS) seek to counter these critiques by centering the community, practicing deliberative democracy, and producing public scholarship. After briefly reviewing the current literature surrounding SUPs and UACS, this chapter examines the UACS model in the context of the UCLA Community School. Two examples illustrate how the UCLA Community School seeks to create more equitable relationships as a mutualistic school-university partnership. The chapter concludes with implications for policy and practice that support the development and expansion of university-assisted community schools, highlighting how they enhance equitable relationships between schools and universities and also bring together higher education community engagement reforms and the K-12 community schools movement.

In this chapter, our goal was to synthesize research from the last ten years on School–University partnerships that utilized theoretical frameworks. We open the chapter by operationalizing the term theoretical framework and distinguishing it from the term conceptual framework. We then describe our search process for the a priori systematic literature review that we conducted including our search terms. We provide a continuum of theory integration (from low to medium to high integration) that we found within the twenty-four articles we reviewed, and we also describe the various theoretical “families” represented in this review including context-specific teacher preparation and place-based learning, critical theories, post-colonial and decolonizing theories, and sociocultural theories. We conclude the chapter with an emphasis on hope for School–University partnerships.

This chapter advocates for schools and universities to work together to create a state of policy readiness for local-level partnerships. Here, policy is defined as the formalization of norms and structures that undergird the partnership and set the conditions for a thriving and sustainable collaboration. This chapter presents several policy readiness factors for school–university partnerships (SUPs), exemplified through a case study of the Indiana University Purdue University Indianapolis and Indianapolis Public Schools SUP. This chapter starts with a discussion of how Kingdon’s Multiple Streams Framework can help school and university leaders proactively engage in the policy readiness process. Next it offers a framework for policy readiness for all levels of local school/university partnerships. Finally, it provides evidence of a long-term sustainable partnership in practice.

Randi Weingarten, tireless advocate for community schools, states, “Improving student learning and educational equity require strong, consistent, and sustained collaboration among parents, teachers, school boards, superintendents and administrators, business leaders and the community” (Weingarten, 2013). The authors of the four chapters in this section argue that institutions of higher education (IHEs) are best suited to not only be partners in such collaboratives, but to actively pursue, develop and facilitate them, including educating their staff and evaluating these efforts for success. Since the mid-1960s, colleges and universities have taken on more central roles in a wide array of community partnerships, seeing their jobs as increasingly “mission driven” (Harkavy & Puckett, 1994, p. 313). Building on the early work of John Dewey and Jane Addams, among others, and with the leadership of the University of Pennsylvania’s Netter Center, many universities have found community schools to be among the best ways to focus this mission for a democratic, just society.

Leadership at all levels is pivotal as school–university partnerships (SUPs) seek to cultivate a culture of collaboration. Leaders across roles – be they school principals, university faculty and administrators, or teacher leaders – act as linchpins who not only facilitate the flow of knowledge and resources between institutions, but also engender a sense of shared vision and purpose. Leadership requires navigating the complexities of differing institutional norms, aligning diverse stakeholder interests, and fostering an environment conducive to collaborative innovation. The complex endeavor of developing dynamic leadership and robust partnerships between schools and universities underscores the pivotal work of partnerships seeking simultaneous renewal. This part of the handbook includes four compelling chapters that delineate both conceptual understanding of the work of leaders as well as the practical ramifications of leadership within SUPs.

The chapters in this section represent timely and relevant research related to justice in school–university partnerships (SUPs). Each chapter frames the effect of SUPs on the adults, as school-based and university-based educators, and their effect on the quality of teaching and learning in schools. In a broad review of the literature, D. Polly and E. Colonnese’s chapter reveals patterns linking SUPs and student learning outcomes. I value their call for more robust research about equity and student learning in SUPs. Simply, we need not be afraid to conduct more research closely examining student outcomes in SUPs. The authors beckon for research that draws on more alternative methodologies (beyond descriptive approaches) to show effects on a wide variety of student learning outcomes including but not limited to student’s grades, student self-reported data, attendance data, graduation data, student behavior data, researcher or teacher created assessments.

Generative AI promises to have a significant impact on intellectual property law and practice in the United States. Already several disputes have arisen that are likely to break new ground in determining what IP protects and what actions infringe. Generative AI is also likely to have a significant impact on the practice of searching for prior art, creating new materials, and policing rights. This chapter surveys the emerging law of generative AI and IP in the United States, sticking as close as possible to near-term developments and controversies. All of the major IP areas are covered, at least briefly, including copyrights, patents, trademarks, trade secrets, and rights of publicity. For each of these areas, the chapter evaluates the protectability of AI-generated materials under current law, the potential liability of AI providers for their use of existing materials, and likely changes to the practice of creation and enforcement.

It is well-known that, to be properly valued, high-quality products must be distinguishable from poor-quality ones. When they are not, indistinguishability creates an asymmetry in information that, in turn, leads to a lemons problem, defined as the market erosion of high-quality products. Although the valuation of generative artificial intelligence (GenAI) systems’ outputs is still largely unknown, preliminary studies show that, all other things being equal, human-made works are evaluated at significantly higher values than machine-enabled ones. Given that these works are often indistinguishable, all the conditions for a lemons problem are present. Against that background, this Chapter proposes a Darwinian reading to highlight how GenAI could potentially lead to “unnatural selection” in the art market—specifically, a competition between human-made and machine-enabled artworks that is not based on the merits but distorted by asymmetrical information. This Chapter proposes solutions ranging from top-down rules of origin to bottom-up signalling. It is argued that both approaches can be employed in copyright law to identify where the human author has exercised the free and creative choices required to meet the criterion of originality, and thus copyrightability.

This chapter will focus on how Chinese and Japanese copyright law balance content owner’s desire for copyright protection with the national policy goal of enabling and promoting technological advancement, in particular in the area of AI-related progress. In discussing this emerging area of law, we will focus mainly on the two most fundamental questions that the widespread adoption of generative AI pose to copyright regulators: (1) does the use and refinement of training data violate copyright law, and (2) who owns a copyright in content produced by or with the help of AI?

The education landscape is rich with partnerships between K-12 schools and colleges of education (Handscomb et al., 2014). The challenges that both institutions face are daunting. These partnerships arguably do an adequate job of facilitating a set of transactional activities that both schools and universities require to perform their objective functions. Policy recommendations need to lean into places where partnerships make sense; funding needs to follow and align; and while there will always be politics, we would hope for autonomy and deregulation so that ideas and people can flourish.

This chapter explores the intricate relationship between consumer protection and GenAI. Prominent tools like Bing Chat, ChatGPT4.0, Google’s Gemini (formerly known as Bard), OpenAI’s DALL·E, and Snapchat’s AI chatbot are widely recognized, and they dominate the generative AI landscape. However, numerous smaller, unbranded GenAI tools are embedded within major platforms, often going unrecognized by consumers as AI-driven technology. In particular, the focus of this chapter is the phenomenon of algorithmic consumers, whose interactions with digital tools, including GenAI, have become increasingly dynamic, engaging, and personalized. Indeed, the rise of algorithmic consumers marks a pivotal shift in consumer behaviour, which is now characterized by heightened levels of interactivity and customization.

Following on the heels of the publication A Nation at Risk (1983) and formation of the Holmes Group (1986), the author explores the development and evolution of school–university partnership as essential to quality teacher education. Select aspects of the empirical and conceptual work of John Goodlad and his colleagues are described as especially helpful for understanding partnership and addressing its considerable challenges. Among the most significant of these is the idea of “simultaneous renewal,” a reminder of the need to think ecologically about institutional change, and of “The Agenda for Education in a Democracy” as a response to the imperative need for clarity about the social purposes of education and attentiveness to the character and quality of human relationships, of how partners ought to treat one another. The author argues for focus on the “manners of democracy” as a way of life that include hospitality, attuned listening, voice, reflectivity and evidential discernment.

Nearly thirty years ago, the Holmes Partnership Group (1995) envisioned educators of color as essential to school–university partnerships (SUPs), to the transformation of teacher education, and to achieving equity in public schools. This chapter asserts that the Holmes Partnership Group linked together culture, pedagogy, and the proportional representation of educators of color as a core conceptual foundation of SUPs. Using their final report, Tomorrow’s Schools of Education, as a key SUP policy and governance document, the author provides a retrospective examination of literature on today’s racially and ethnically diverse PK-20 educator pipeline as connected to the goals of cultural pluralism within a democracy and equitable access and opportunity in student learning. The chapter concludes with implications for future research that connects SUPs, social justice teacher education, and the well-being and sustainability of educators of color.

Generative AI has catapulted into the legal debate through the popular applications ChatGPT, Bard, Dall-E, and others. While the predominant focus has hitherto centred on issues of copyright infringement and regulatory strategies, particularly within the ambit of the AI Act, it is imperative to acknowledge that generative AI also engenders substantial tension with data protection laws. The example of generative AI puts a finger on the sore spot of the contentious relationship between data protection law and machine learning built on the unresolved conflict between the protection of individuals, rooted in fundamental data protection rights and the massive amounts of data required for machine learning, which renders data processing nearly universal. In the case of LLMs, which scrape nearly the whole internet, this training inevitably relies on and possibly even creates personal data under the GDPR. This tension manifests across multiple dimensions, encompassing data subjects’ rights, the foundational principles of data protection, and the fundamental categories of data protection. Drawing on ongoing investigations by data protection authorities in Europe, this paper undertakes a comprehensive analysis of the intricate interplay between generative AI and data protection within the European legal framework.