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Browsing Digital Promise Reports and Publications by Subject "artificial intelligence"
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Item AI and the Future of Learning: Expert Panel Report(Digital Promise, 2020-11) Roschelle, Jeremy; Lester, James; Fusco, JudiThis report is based on the discussion that emerged from a convening of a panel of 22 experts in artificial intelligence (AI) and in learning. It introduces three layers that can frame the meaning of AI for educators. First, AI can be seen as “computational intelligence” and capability can be brought to bear on educational challenges as an additional resource to an educator’s abilities and strengths. Second, AI brings specific, exciting new capabilities to computing, including sensing, recognizing patterns, representing knowledge, making and acting on plans, and supporting naturalistic interactions with people. Third, AI can be used as a toolkit to enable us to imagine, study, and discuss futures for learning that don’t exist today. Experts voiced the opinion that the most impactful uses of AI in education have not yet been invented. The report enumerates important strengths and weaknesses of AI, as well as the respective opportunities and barriers to applying AI to learning. Through discussions among experts about these layers, we observed new design concepts for using AI in learning. The panel also made seven recommendations for future research priorities.Item Ambitious Mashups: Reflections on a Decade of Cyberlearning Research(Digital Promise, 2020-09) Center for Innovative Research in CyberlearningThis report reflects on progress from over eight years of research projects in the cyberlearning community. The community involved computer scientists and learning scientists who received NSF awards to investigate the design of more equitable learning experiences with emerging technology—focusing on developing the learning theories and technologies that are likely to become important within 5-10 years. In early 2020, the Center for Innovative Research in Cyberlearning's team analyzed the portfolio of past and current projects in this community, and convened a panel of experts to reflect on important trends and issues, including artificial intelligence and learning; learning theories; research methods; out-of-school-time learning; and trends at NSF and beyond.Item Artificial Intelligence and the Future of Teaching and Learning(Digital Promise, 2024-03) Jeremy Roschelle; Judi Fusco; Pati RuizThis talk, entitled "Artificial Intelligence and the Future of Teaching and Learning" focuses on AI Literacy, giving background and definitions to give educators a foundation as they bring AI into their practice. Originally presented at the AI K12 Deeper Learning Summit, it frames the discussion of AI in Education in the context of how people learn and considers how AI may change the process. It further considers equity, ethics, bias in AI, and some hidden costs of AI to humans and the environment. Links to resources are given.Item Driving Interdisciplinary Collaboration through Adapted Conjecture Mapping: A Case Study with the PECAS Mediator(Digital Promise, 2022-05) Chang, Michael Alan; Magana, Alejandra; Benes, Bedrich; Kao, Dominic; Fusco, JudithIn this report, we demonstrate how an interdisciplinary team of computer science and learning sciences researchers utilize an adapted conjecture mapping tool during a collaborative problem-solving session. The session is documented through an edited “Dialogue” format, which captures the process of conjecture map construction and subsequent reflection. We find that creating the conjecture map collaboratively surfaces a key tension: while learning sciences theory often highlights the nuanced and complex relational nature of learning, even the most cutting-edge computing techniques struggle to discern these nuances. Articulating this tension proved to be highly generative, enabling the researchers to discuss how considering impacted community members as a critical “part of the solution” may lead to a socio-technical tool which supports desired learning outcomes, despite limitations in learning theory and technical capability. Ultimately, the process of developing the conjecture map directed researchers towards a precise discussion about how they would need to engage impacted community members (e.g., teachers) in a co-design process.Item Ethics, Artificial Intelligence, and Digital Equity: Making Informed Decisions about the Integration of AI(Digital Promise, 2024-03) Jeremy Roschelle; Judi Fusco; Pati RuizThis talk, entitled "Ethics, Artificial Intelligence (AI), and Digital Equity: Making Informed Decisions about the Integration of AI" explores the intersection of digital equity, ethics, and artificial intelligence. Originally presented at the AI K12 Deeper Learning Summit, it aims to provide participants with knowledge and resources to make informed decisions regarding the integration of AI systems and tools in their learning context. In an interactive portion, attendees will consider scenarios and learn strategies for ensuring and impact of technology while fostering, responsible and inclusive uses of AI.Item Executive Summary: A Look at AI Literacy, and AI and Digital Equity(Digital Promise, 2024-04) Pati Ruiz; Jeremy RoschelleThis one pager summarizes Digital Promise's vision for Artificial Intelligence in education. Digital Promise is focused on AI in education to foster a future where every person engages in sustained and impactful experiences of powerful learning that lead to a life of well-being, fulfillment, and economic mobility.Item Review of Guidance from Seven States on AI in Education(Digital Promise, 2024-02) Jeremy Roschelle, Judi Fusco, Pati RuizAs Artificial Intelligence within education becomes increasingly important, Digital Promise reviewed the guidance documents released by seven states—California, North Carolina, Ohio, Oregon, Virginia, Washington state, and West Virginia—on how to approach artificial intelligence (AI) in education. Throughout this report we summarize the overall themes and considerations that each guidance document covers.Item School Policies for Integrating AI in Classroom Practices(CIRCLS, 2021-09) Jackson, Tanner; Pakhira, Deblina; Narayanan, Arun Balajiee Lekshmi; Ruiz, Pati; Fusco, Judi; Glazer, Kip; Eaglin, Phillip; Eguchi, AmyRecent shifts in the delivery of learning experiences have accelerated the importance of emerging technologies in schools and classrooms. The urgency for educators to become familiar with emerging technologies such as Artificial Intelligence (AI) necessitates additional policies including specific safeguards for all. Educators are in a position of responsibility and should be fully aware of the latest technologies, such as AI, so they can make good decisions and educate students as informed citizens and future workforce. Educators must be considered and supported as full partners in developing and implementing these tools in all learning environments. The goal of this policy brief is to delineate areas that require educator attention around AI so they are empowered to develop recommendations that support literacy on AI that work within their contexts. In this context, we define literacy as general competency around how AI works, the types of data it collects, and how that data can be used. By doing so, we aim to provide useful guidance to build additional knowledge and skills, including the ethical and unbiased decisions by educators in selecting and using AI systems and technologies in classroom environments.