2,807 research outputs found
Innovating Language Education: An NMC Horizon Project Strategic Brief
The NMC is a leading educational technology organization. A main outcome of the collaboration between the Language Flagship Technology Innovation Center and the NMC was this publication, which highlights recommendations that emerged from discussions of major trends, challenges, and technology developments by experts and practitioners in language technologies in higher education. Innovating Language Education identifies main trends and areas of interest and constitutes a rich resource that includes key definitions and proofs of concept
THE USE OF AI IN LANGUAGE LEARNING: WHAT YOU NEED TO KNOW
Kecerdasan Buatan adalah kekuatan transformasional dalam pendidikan, terutama dalam pembelajaran bahasa. Studi ini membahas berbagai alat AI yang digunakan untuk pembelajaran bahasa, seperti terjemahan mesin, teknologi ucapan, chatbot, dan konten yang dihasilkan oleh kecerdasan buatan. Studi ini secara komprehensif menjelajahi potensi dan tantangan yang terkait dengan peran kecerdasan buatan dalam pendidikan bahasa. Di satu sisi, kecerdasan buatan menawarkan manfaat seperti panduan personal, keterlibatan interaktif, dan pelacakan kemajuan. Namun, juga menimbulkan kekhawatiran tentang interaksi manusia yang berkurang, dampak potensial pada otonomi pembelajar, dan peran yang berkembang dari guru bahasa. Oleh karena itu, studi ini menekankan pentingnya menggabungkan prinsip-prinsip etika, transparansi, dan inklusivitas untuk memandu integrasi kecerdasan buatan dalam pendidikan secara bertanggung jawab. Penelitian ini menggunakan metodologi penelitian perpustakaan untuk membangun landasan teoritis yang kuat, menekankan peran penting integrasi kecerdasan buatan yang bertanggung jawab dalam meningkatkan pendidikan bahasa sambil menjaga standar etika yang tinggi
The Word Made Digital: Leveraging Artificial Intelligence to Increase Bible Engagement
The purpose of this descriptive study was to understand whether a relationship exists between an individual\u27s behavioral intention to use a Bible-based chatbot that leverages AI to create human-like engagement with Scripture and the constructs of performance expectancy, effort expectancy, perceived enjoyment, and perceived risk, controlling for gender, age, and experience among registered users of the Inductive Bible Study App. Data was collected through an online survey and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), multi-group analysis (MGA), and homogeneity-of-slopes analysis of covariance (ANCOVA). While this quantitative descriptive study validated the correlation between each of the four reflective constructs and the formative construct (behavioral intent), the data suggests that perceived enjoyment maintains the strongest link to behavioral intent. In addition, the moderators appear to indicate that the strongest correlation to behavioral intent is found in communities of younger males with no prior exposure to chatbots. The results of this study provide useful insights into how individuals perceive and make decisions about using technology for religious or spiritual purposes, and how these perceptions may differ based on demographic factors. Additionally, the results inform the development and implementation of similar AI-based tools in religious or spiritual contexts and provide insights into how leaders in these contexts can effectively utilize technology to engage with their communities
Anticipating Information Needs Based on Check-in Activity
In this work we address the development of a smart personal assistant that is
capable of anticipating a user's information needs based on a novel type of
context: the person's activity inferred from her check-in records on a
location-based social network. Our main contribution is a method that
translates a check-in activity into an information need, which is in turn
addressed with an appropriate information card. This task is challenging
because of the large number of possible activities and related information
needs, which need to be addressed in a mobile dashboard that is limited in
size. Our approach considers each possible activity that might follow after the
last (and already finished) activity, and selects the top information cards
such that they maximize the likelihood of satisfying the user's information
needs for all possible future scenarios. The proposed models also incorporate
knowledge about the temporal dynamics of information needs. Using a combination
of historical check-in data and manual assessments collected via crowdsourcing,
we show experimentally the effectiveness of our approach.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM '17), 201
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