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.

This paper examines how Generative AI coding tools impact computer science education and describes instructional experiments conducted across several courses that inform the design of a new Special Topics course, Software Development in the GenAI Era. The newly designed course introduces students to the evolving landscape of AI-assisted development through five modules: The Stochastic Engine, Prompt & Context Engineering, Vibe Coding, Agentic Coding, and Specification-Driven Development. Drawing from classroom experiences, the paper highlights how students respond to AI tools, where they benefit most, and where they struggle with the verification and documentation practices required for responsible AI use. The goal is to offer a practical, evidence-based framework for preparing students to become architects and supervisors of AI-driven software systems, equipped with the foundational CS knowledge and modern AI-native skills needed in today’s rapidly evolving development environment.

The era of GenAI began with the release of ChatGPT 3.5 in November 2022. GenAI systems that can generate passable code for undergraduate-level programming-focused courses require a shift in faculty mindset in what is taught in programming-focused courses and in how student learning is assessed. Rather than trying to create assignments that the current GenAI systems cannot easily generate code for, this paper suggests approaching the GenAI era by updating assessments of student learning and incorporating GenAI into the coursework. Small class sizes at small colleges provide an excellent environment for experimenting with new pedagogical approaches.

Eye-Assist is an AI visual navigation system that converts camera input into concise, context-aware audio feedback for people with visual impairments. It fills gaps in current tools by combining real-time object detection, distance measurement, and an advanced priority algorithm so users can move safely, read text, and understand their surroundings. Eye-Assist uses a YOLOv8-based TensorFlow Lite model trained on custom day and night datasets, an Intel RealSense D435i depth camera for distance and motion cues, and a scoring algorithm that ranks nearby objects before generating spoken guidance. The demo will showcase real-time navigation alerts, a Read Mode for signs and documents using on-device OCR, and an Explain Surroundings feature triggered by voice commands, running on Android and in a Raspberry Pi 5 hybrid setup.

In classroom and online learning environments, identifying which students need help at any moment is challenging. Students often enter a state of ``spinning,'' continuing to work without making progress, and would benefit from timely intervention. We are developing a real-time system to detect spinning using behavioral patterns from students' programming editors. We collected fine-grained, often keystroke-level data from a Massively Open Online Course (MOOC) programming environment. In the first phase, we focus on assignments with known correct solutions, developing tools to measure students' distance from the goal using Levenshtein and AST edit distances, revealing proximity to or struggle toward the correct answer. By segmenting work into active sessions, we map progress over time across 28,000 students and 70 exercises, revealing improvement, divergence, and sustained effort without progress. We find that spinning often involves ups and downs rather than stagnation. Behavioral features extracted from these episodes will train a machine learning model in Phase II to detect spinning when solutions are unknown, enabling smarter, more responsive learning tools for online and classroom orchestration. We present our analytical approach, findings on student behavior patterns, and hypotheses for future work.