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.

This paper presents a digital learning framework, coLearn-AI (hereafter referred to as the system), that combines guided inquiry pedagogy with intelligent, adaptive feedback mechanisms to support collaborative learning in introductory programming courses. The system enables instructors to author structured inquiry activities, manage student groups, and provide embedded AI guidance that scaffolds learning in real time. Students work in groups and engage with progressively complex problems through exploration, collaboration, and reflection. The system integrates synchronous collaborative workspaces, turn-taking mechanisms, and analytics dashboards that help instructors monitor student progress at scale. A key innovation is the integration of AI-driven formative feedback, where instructors embed AI guidance directives within activity files. These directives, visible only to instructors, enable the AI module to provide context-aware feedback that aligns with pedagogical goals. By bridging structured human facilitation with adaptive, intelligent tutoring capabilities, the system promotes engagement, accountability, and deeper conceptual understanding. This paper describes the pedagogical foundations, system architecture, activity model, AI feedback pipeline, and group-learning mechanisms and outlines directions for formal efficiency evaluation and refinement.

As artificial intelligence(AI) becomes increasingly visible in everyday life, many students are experiencing growing anxiety about what AI is and how it affects them. This signals the urgent need for meaningful AI literacy education that helps learners understand how AI systems work and empowers them to use these tools confidently and effectively. Recognizing the importance of ensuring that all learners can develop this understanding, this paper introduces a learner-centered AI literacy lesson designed for 6-8 grade students. The lesson envisions AI literacy through a learner-centered lens, prioritizing accessibility, agency, and inclusivity. Grounded in the AI4K12’s “Big Five Ideas in AI” and informed by learning science frameworks such as Universal Design for Learning (UDL), Funds of Knowledge, and Depth of Knowledge, the proposed lesson supports students to learn AI in ways that connect to their experiences, questions, and capabilities.

Our [hidden] initiative helps participants learn how to design ethical software while introducing them to essential concepts in Artificial Intelligence and Machine Learning (AI/ML). Several of these developed labs focus on foundational ethics-focused topics. These experiential interactive modules highlight why ethics in AI/ML matters and provide practical experiences that reveal the diverse ways ethics-focused topics can influence modern systems. The tutorial is suitable for a broad audience within the software engineering community—from students to seasoned professionals—who wish to better understand ethical implications across domains and ensure that the software they develop is ethical and fair. Complete project materials are openly available on our website: [hidden]

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.

Students frequently choose correct answers for incorrect reasons, leaving traditional formative assessments unable to surface the misconceptions that matter most for learning. We introduce Structured Post-Evaluation Interviews and Remediation (SPEIR), a workflow that extends two-tier Justified Multiple-Choice Questions (JMCQs) with guided discussions and targeted recovery opportunities to surface and address those hidden misunderstandings. Implemented across ten course sections and compared with a traditional MCQ control, SPEIR showed that correctness alone substantially overestimates understanding, while per-question analyses revealed higher rates of fully correct reasoning in SPEIR sections. Students who completed recovery quizzes demonstrated notable gains, and instructors reported that SPEIR enabled efficient, focused feedback. These results suggest that SPEIR is a scalable approach for integrating diagnostic assessment with timely remediation.

This paper is a case-study on utilizing learning analytics to evaluate the success of integrating Smart Learning Content (SLC) into a project-based undergraduate Python programming course delivered through an online learning platform. We showcase how integration of the logging capabilities of SLC with the platform enabled us to study students' engagement with the SLC activities, and examine connection between the SLC usage and student learning outcomes. There are large publicly available repositories of SLC materials, yet their integration into a specific course and evaluation of its success are often not straightforward. By providing access to several SLC types, such as program construction examples, code animations, and parsons puzzles, we aimed to bridge the gap between static conceptual reading and programming projects already present in the course. To evaluate the integration of SLC, we released the augmented course in the Spring 2025 semester to 252 students at 20 community colleges across the US and analyzed collected interaction logs.

Foundational STEM areas—including artificial intelligence, cybersecurity, and inclusive software development—are often missing from 9–12 computing curricula. A major reason for this lack of inclusion is the shortage of high-quality, easy-to-use teaching materials. This gap is especially challenging for smaller or under-resourced schools that disproportionately serve underrepresented students. As a result, many 9-12 classrooms are unable to cover these essential topics, contributing to a broader ``diversity crisis'' in the U.S. STEM pipeline.

The [hidden] are a collection of 15 experiential, computing-focused educational modules originally developed and validated for undergraduate learners. Designed to promote hands-on engagement with computing-focused topics such as machine learning, artificial intelligence, inclusive software development and ethics-focused topics, these modules have demonstrated strong effectiveness in building foundational technical skills and increasing student confidence. This paper presents the adaptation of the [hidden] modules for high school students in grades 9–12, highlighting modifications to content, scaffolding, and delivery to ensure developmental appropriateness and curricular alignment with secondary education standards. We describe the pedagogical framework guiding the redesign, pilot implementations strategies, and provide recommendations for integrating the modules into existing computer science pathways. By offering flexible, accessible, and engaging learning experiences, the adapted [hidden] modules aim to expand early exposure to inclusive computing practices and support a more diverse pipeline of future technologists. Complete project materials are openly available on our website: [hidden]

This experience report describes a six-week Collaborative Online In-

ternational Learning (COIL) module integrated into an undergraduate

Software Engineering course, connecting students from the United States

and Panama. The primary aim was to enhance student engagement and

motivation through cross-cultural, project-based collaboration.

Students collaborated to complete software projects, including a stand-

out game featuring campus-native animals symbolizing the partnership.

Survey data from U.S. participants indicate increased motivation, im-

proved project completion rates, and heightened interest in global col-

laboration.

Challenges such as time zone coordination and language barriers

emerged but were effectively mitigated through the universal language

of programming. The experience also fostered the development of soft

skills, including enhanced communication and intercultural competence.

This report offers insights into the pedagogical benefits, technological

tools employed, and lessons learned.