Conference Agenda

The rise of generative AI necessitates faculty development to equip diverse student populations with essential AI skills and knowledge. This paper details the findings of an AI Across the Curriculum (AIAC) initiative at an urban community college, a Hispanic Serving Institution (HSI). The project trained faculty in both STEM and non-STEM disciplines on designing and implementing formal AI learning activities. Qualitative data was collected from 13 focus groups, involving 157 students in Spring 2025 courses taught by AIAC faculty. Students consistently reported the activities were highly instructive, moving them beyond superficial tool use to a nuanced understanding of generative AI, particularly concerning AI ethics and societal implications. These learning experiences fostered increased comfort and confidence with AI technology, enhancing their ability to evaluate information reliability and apply AI in future studies and careers. The results demonstrate the potential of this interdisciplinary approach to increase AI literacy and reduce intimidation barriers across a diverse student body. The AIAC model serves as a practical, adaptable template for Computer Science faculty to collaborate with colleagues, successfully expanding AI learning experiences for all students.

Algorithmic Bias is the result of algorithms that have systematic errors that produce biased (unfair or discriminatory) results. Understanding Algorithmic Bias is of vital importance to both Computer Science majors and non-majors. Computer Science majors are poised to become the developers of systems that can be susceptible to bias, while non-majors (as well as majors) live in a world where many of their day to day interactions are mediated by machine learning applications that can exhibit bias. Aspects of our lives that are impacted by these algorithms include social media, marketing campaigns, government interactions, credit ratings, college admissions, and many more. Understanding the causes of algorithmic bias and the preventative measures necessary to produce systems that are equitable and fair, is an important ethical issue for future programming professionals.