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Though the 28 laureates in attendance this year gave and listened to each other’s talks with optimistic titles such as “Computing for Social Good,” Inside Higher Ed took the opportunity to ask them questions about computerscience’s challenges in higher education. Missing Seats at Important Tables.
Students usually tackle on-campus issues, advancing ideas like “It shouldn’t be so hard to get into computerscience classes” or “Student fees should be lower” or “Campus housing should be more affordable.”
Video-based learning is a powerful tool for many reasons; one study even notes that “well selected online educational video lectures advance students’ communication on the discourse level, critical thinking, deeplearning, and future employability” (Elgeddawy, 2018, 726). Education Sciences 12, no.
Video-based learning is a powerful tool for many reasons; one study even notes that “well selected online educational video lectures advance students’ communication on the discourse level, critical thinking, deeplearning, and future employability” (Elgeddawy, 2018, 726). Education Sciences 12, no.
The following reflects these conversations, and I seek to align them with my thoughts envisioning how Gen AI, machine learning, and deeplearning can tackle these hurdles. With Gen AI, we enter an entirely new era where machines can interact with humans to understand and process natural language.
My prediction is that 50 years from now, human-centered AI (HCAI) systems will become incredibly prevalent in our everyday lives. Today, we see the emergence of dedicated interdisciplinary teams who are working to identify and solve these problems from multiple angles: Ethics, psychology, computerscience, law, and more.
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