This fully open access and peer-reviewed collection features 34 undergraduate-level assignments to support students' AI literacy, rhetorical and ethical engagements, creative exploration, and professional writing text gen technology, along with an Introduction to guide instructors' understanding and their selection of what to emphasize in their courses. TextGenEd enables teachers to integrate text generation technologies into their courses and respond to this crucial moment.
Problem formulation: Students define the problem or challenge they want to solve. AI tool selection: Students choose the best generative AI tools to help them with their problem. They learn how to explore, compare, and evaluate different generative AI tools and their features. Interaction: Students use the generative AI tools to solve their problem. They experiment with different inputs and outputs and see how the generative AI tools affect their problem-solving process and outcome. Reflection: Students assess and report their experiences with the generative AI tools.
Guides to AI powered educational technology and tips for using AI tools in the classroom. Educator's Technology /
Educational Technology and Mobile Learning (ETML) is a blog owned and operated by Med Kharbach. Dr. Kharbach holds a PhD in educational studies from Mount Saint Vincent University in Halifax, Canada, and was a K-12 teacher with over 12 years of classroom teaching experience.
BigLAM started as a datasets hackathon focused on making data from Libraries, Archives, and Museums (LAMS) with potential machine-learning applications accessible via the Hugging Face Hub. We are continuing to work on making more datasets available via the Hugging Face hub to help make these datasets more discoverable, open them up to new audiences, and help ensure that machine-learning datasets more closely reflect the richness of human culture.