"Are you an instructional designer frustrated by low learner engagement? Do your courses feel generic and miss the mark?
This free research report explores the power of learner personas – detailed profiles that go beyond demographics to understand your learners' motivations, challenges, and learning preferences. "
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"FALCON is an informal group founded in 2024 to connect higher-education faculty and staff interested in the intersection of artificial intelligence and higher education. It has no membership fees and anyone can join."
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"The emergence of AI will provide “a real renaissance for the humanities,” Reed said. But higher ed institutions, including liberal arts colleges, are often not great at explaining how critical thinking skills that students learn can be applied to technology like AI, Reed said. "
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"utoring systems based on generative AI (GenAI) now make large-scale personalized tutoring not only possible but also accessible to all. However, the important social aspects of learning will be missing if all students study independently. Does AI offer the possibility of combining the effectiveness of tutoring with the beneficial social aspects of learning? I believe the answer is yes: the chasm between these approaches can be bridged through the thoughtful use of GenAI tutors in both classroom and online settings. The key lies in intentionally designing GenAI tutors and structuring learning activities to take advantage of social interaction with peers."
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"Our Unique AI Learning Solution
Conversational AI Q&A
3D Objects Integrated
Teaching & Demonstratives
Regular Assessment
Build-Your-Own Lessons
Deployed to Chrome Browsers (and VXRLabs 2.0)"
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"How it works: Morehouse professors will collaborate with technology partner VictoryXR to create virtual 3D spatial avatars. The avatars use OpenAI to have two-way oral conversations with students."
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"Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems."
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