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As Moore’s law and Dennard scaling come to an end, new devices and computing architectures are being explored. The development of computing hardware designed to address the rapidly growing need for computational power to accelerate artificial intelligence applications has prompted investigations into both. While silicon photonics is typically viewed as a communications platform, we discuss its application to artificial intelligence and some outstanding challenges to be addressed.

Following a qualitative research design, this study aims to explore the differences between international and American graduate students in terms of their class participation. The data for the study were collected from a graduate-level class at a university in upstate New York. There were seven participants in the study, three of the participants were American students, and the other four participants were international students, two from China, one from Iran, and one from Sudan. Main source of data was classroom observations. Three classroom sessions were observed and field notes were taken during observations. There were two phases of the data analysis process. During the first phase, field notes were reviewed after observations and five general categories of classroom participation were identified. During the second phase, the data were further analyzed in order to see the differences between American and international students in terms of their class participation according to these five categories. Results of data analysis revealed three main differences between American and international students' class participation in the observed graduate classroom setting. The first main difference involved the short answer/example and explanation categories, the second main difference involved the questions to the instructor/classmates for clarification/repetition and the questions to raise discussion categories, and the last main difference involved the answer/explanation assigned by the instructor category.

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