To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Artificial Intelligence (AI) appears to be advancing at an ever-accelerating pace and affecting much of human life. The power of AI has already been demonstrated in various areas – from smartphone personal assistants and customer support chatbots to medical diagnoses and driverless cars. At the same time, these applications bring multiple challenges and much hyperbole. Nonetheless, of particular importance here, AI systems have also entered the classroom. However, while promising to enhance education, the design and deployment of these tools again raise particular concerns and challenges.
We begin this chapter with a brief history and definition of AI outlining the evolution of AI techniques aiming to imitate or outperform human cognitive capacities. We continue by exploring what AI systems promise to deliver in educational contexts and their impact on learners, examining the interaction through the lens of three analytical categories: learning with AI, learning about AI and preparing for AI. We also explore the risks related to the introduction of AI into education and investigate transversal issues related to all three categories, noting that currently little attention has been paid to what is ethically acceptable for AI and education. Finally, we conclude by trying to answer two questions: how can we make better AI tools for education and how can education help address the challenges created by AI?
Artificial intelligence is constantly in the headlines. Almost every day, we read about another dramatic although often overhyped breakthrough, such as the use of AI to identify and counter COVID-19, software agents that appear capable of fluid conversations, or the creation of deep fake videos. However, we know less about how AI has infiltrated our daily lives. AI helps unlock your smartphone with face ID, provides personalized feeds in your social media, and monitors your whereabouts as you walk about town. Increasingly, while it rarely makes the headlines, AI is also being used in educational contexts, for example to automatically generate timetables, to adapt tutoring technologies to individual competencies, and to monitor whether students are concentrating in class. Advocates, such as developers and some researchers and policymakers, argue that the introduction of AI into classrooms enhances learning and thus de facto benefits students.
How do cognitive biases relevant to foreign policy decision making aggregate in groups? Many tendencies identified in the behavioral decision-making literature—such as reactive devaluation, the intentionality bias, and risk seeking in the domain of losses—have been linked to hawkishness in foreign policy choices, potentially increasing the risk of conflict, but how these “hawkish biases” operate in the small-group contexts in which foreign policy decisions are often made is unknown. We field three large-scale group experiments to test how these biases aggregate in groups. We find that groups are just as susceptible as individuals to these canonical biases, with neither hierarchical nor horizontal group decision-making structures significantly attenuating the magnitude of bias. Moreover, diverse groups perform similarly to more homogeneous ones, exhibiting similar degrees of bias and marginally increased risk of dissension. These results suggest that at least with these types of biases, the “aggregation problem” may be less problematic for psychological theories in international relations than some critics have argued. This has important implications for understanding foreign policy decision making, the role of group processes, and the behavioral revolution in international relations.
ABSTRACT IMPACT: This work will inform the ongoing development of adaptive capacity and preparedness of the CTSA Program and other clinical and translational research organizations in their quest of improving processes that drive outcomes and impacts, shaping effective programs and services, and strengthening their emergency readiness and sustainability. OBJECTIVES/GOALS: -Share the progress and preliminary findings of an ‘Adaptive Capacity and Preparedness of CTSA Hubs’ CTSA Working Group; -Improve our awareness and understanding of the efficient and effective changes helping CTSA hubs build robust capacity to address METHODS/STUDY POPULATION: A multi-case study including: - Triangulating multiple sources of information and mixed methods (survey/interviews of research administrators, researchers, evaluators, and other key stakeholders), literature review, document and M&E system information analysis, and expert review; - Describing CTSA hubs’ experiences as related to research implementation, translation, and support during the time of emergency; - Administering a comprehensive survey of the CTSAs addressing their challenges, lessons learned, and practices that work in various program components/areas. Data collection includes aggregate and cross-sectional data, with representation based on CTSA size, maturity, and population density. RESULTS/ANTICIPATED RESULTS: The described approach shows sound promise to investigate and share strategies and best practices for building adaptive capacity and preparedness of CTSAs -- across various scientific sectors, translational research spectrum, and the goals outlined by NCATS for the CTSA program. The anticipated results of this research will include the identified/shared innovative solutions and lessons learned for this rapidly emerging, high-priority clinical and translational science issue. ‘High-quality lessons learned’ are those that represent principles extrapolated from multiple sources and triangulated to increase transferability to new contexts and situations. DISCUSSION/SIGNIFICANCE OF FINDINGS: The project provides useful knowledge and tools to research organizations and stakeholders across multiple disciplines -- for mitigating the impact of the COVID-19 disaster via effective adjusting programs, practices, and processes, and building capacity for future successful, ‘emergency ready and responsive’ research and training.