UMBC faculty at Teaching and Learning with AI Conference

This week three UMBC faculty had papers in the Second Teaching and Learning with AI Conference held in Orlando and organized by the University of Central Florida.

Timothy Nohe, University of Maryland, Baltimore County

AI has grabbed headlines and sparked fervid debates among visual artists and faculty. This presentation will open an opportunity to engage on the topic of natural language AI generative image engines like Midjourney and the controversies that attend them, such as the illicit use of artists’ works to train the systems and the efforts to “poison” these generators. Timothy Nohe has investigated these tools, and exhibited works that allow him to speculate on engineered biomaterials and plants. As a graduate assistant to the late AI pioneering artist Harold Cohen, he brings over 30 years of experience to this conversation.

Muhammad Ali Yousuf & M. Nicole Belfiore, University of Maryland, Baltimore County

Akbar Ali, University of Virginia

We present a variety of innovative AI tools that show significant potential in enhancing the experience of students with learning challenges. These tools, some of which are already available and others actively in development, leverage the power of AI to address a range of challenges. These include the creation of art for the visually impaired, support for students with Dyslexia/Dyscalculia, writing tools for students with Dysgraphia, etc. We have developed activities that consider different learning styles, abilities, and limitations of the students with the help of AI. A brief presentation will follow a group discussion on the limitless possibilities.

Muhammad Ali Yousuf & M. Nicole Belfiore, University of Maryland, Baltimore County

Akbar Ali, University of Virginia

Grading of assignments created with the help of Generative AI tools poses a major challenge to instructors who were trained on rubrics developed decades ago. Such rubrics are incapable of handling situations where the work is clearly a violation of honor agreements. We propose a set of metrics that may be useful for grading student work that is at least partially generated with the help of AI. The audience is encouraged to bring their own ideas as explicit feedback will be sought and will be part of the discussion.