Experimental Humanities Distinguished Lecture Series: Teach Anything, Open-Access AI for Educators, a Workshop with Dr. Alexa Alice Joubin

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The Second Annual Experimental Humanities Distinguished Lecture Series will feature Dr. Alexa Alice Joubin, Director of the Digital Humanities Institute and Professor of English, Women's, Gender and Sexuality Studies, Theatre, International Affairs, and East Asian Languages and Literatures at George Washington University.

About the Workshop: Teach Anything, Open-Access AI for Educators
In the liberal arts, AI is happening to us rather than with us and for us, leading to marginalization and participatory inequalities. We need an inclusive environment to share untold stories and to tell new stories about society and civic technology. Faculty do not have opportunities to design AI. In higher education, bigger is not always better, as large foundation models are giving way to smaller, more task-specific models.
This interactive workshop introduces faculty participants to Professor Joubin’s open-access platform Teach Anything, https://www.teachanything.ai, that enables professors to create their own open-access AI applications. They will choose from several open-source LLMs, upload course files, fine-tune system prompts, and adjust the AI’s “temperature” of creativity. Faculty’s AI can easily be shared via permalinks that do not require login.
Teach Anything supports the creation of custom-trained, multilingual AI chatbots by faculty partners. These chatbots, operating in 58 languages, can offer adaptive responses to suit the learner's needs.

Students do not need to login, and their full privacy is guaranteed. This open-access and open-source platform models ethical practices in human-AI collaboration and uses inclusive design principles. It uses both the embedding and retrieval augmented generation (RAG) methods to ground responses in pre-defined, curated content.

Teach Anything’s humanistic methods and community-oriented principles move away from a binaristic model of humans versus machines (or consumers versus corporations) to hypothesize an ecology of ideas in the liberal arts. The training data for these faculty-created “narrow” AI is transparent and pre-defined. In contrast, commercial AI scrapes data from the web. Commercial LLMs are trained on datasets with inherent biases baked into them. Moreover, all AI applications currently in use in education are commercial and were not designed specifically for the purposes of teaching or learning.

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