Although the calls for ethics in Computer Science (CS) education and efforts made to answer the calls have been longstanding, there exists fear and urgency particularly now in regards to the increasing threats made possible with AI. Simulations have been shown to be effective for learning in a number of higher education disciplines and we present Rehearsals, a digital simulation tool for learning about the ACM Code of Ethics in CS education. As there is also evidence of personal transformation being required for the development of critical consciousness, Rehearsals immerses students in realistic scenarios where intelligent coaching agents guide their learning and support them to develop the ethical decision-making and reflective thinking skills required on this journey.

Group work in high school classes often faces challenges like unequal participation and poor team dynamics, but these issues are particularly significant in coding classes. Collaboration is a core component of CS and CSed, where students must work together to solve problems, debug, and manage projects. To address these challenges, this talk introduces an AI-driven human centered tool, CollabCode, specifically designed to monitor and enhance group work in high school coding classes. CollabCode uses machine learning algorithms to track individual student participation, task distribution, and communication patterns in real time. Based on this data, the system provides personalized feedback to students and generates actionable insights for teachers. The tool can suggest appropriate roles or task assignments based on real-tie data, helping students demonstrate and enhance their skills in different capacities. By identifying patterns of teamwork such as disengagement or dominance, CollabCode can recommend equitable group structures. Teachers receive detailed collaboration analytics that suggest how tasks can be distributed to maximize each student's contribution and foster more balanced cooperation. Through visual integrations of recommendations and real-time monitoring of CollabCode recommendations, teachers can stay in control and make holistic decisions for student groups. Training data for CollabCode is context aware and continuously updated through a feedback loop. With dynamic task and role management, CollabCode ensures that group work is more productive, allowing students to develop both their technical and collaboration skills, which are critical in and outside of computer science classrooms.

As large language models (LLMs) increasingly permeate educational applications, concerns about the perpetuation of bias persist. We present our preliminary work on developing prompt-engineering strategies to mitigate bias in content generated by LLMs in computer science (CS) education. This work investigates both empirical insights into fairness-aware prompt formulation and actionable takeaways for educators. We focus on an initial list of prompting strategies for mitigating bias and explore their impact on educational content generation. Recent research has shown the efficacy of prompt-base debiasing [1] as well as the potential disadvantages of using prompts that have not been mitigated for bias, from user dissatisfaction [2] to unsafe outputs [5, 6]. Additionally, a growing body of empirical work points to the idea that certain properties of in-context examples such as flow [7], illustration [3], and order [4] could either improve or derail LLM performance. Our study leverages these findings in the context of generating educational content. The goal is to promote fairness-aware approaches which can be applied to the automated generation of learning materials and the development of LLM-based educational tools. This work also contributes practical insights on prompt-engineering to the evolving curriculum of Ethics in Artificial Intelligence (AI).

The need for and challenges of including ethics in Computer Science (CS) education are well documented. At our university we will embed ethics in CS courses through a web-based simulation tool (Rehearsals) where students can practice scenarios based on the ACM Code of Ethics with the support of personalized AI coaching. We hypothesize that this focus on ethics will attract more historically marginalized groups of students to CS, recognizing how embodying ethical behavior such that it is part and parcel of how they show up in the world as computer scientists will contribute to flourishing for all [1].

This study explores interdisciplinary interactions between learning through the Arts and Computer Science in an embodied classroom. Improving Computing Literacy at a young age has incredible benefits, especially with the increasing adoption of Artificial intelligence. This research study explores Music as a pathway to Computer Science (CS) by engaging middle and high school students in an after-school program with a musical robot. Learning modules introduce CS concepts such as algorithm design, problem decomposition and debugging. A preliminary study in our lab showed that Music and CS can intersect when discussing concepts like looping and debugging. Results from the study showed that interest in CS and getting feedback in the form of music had a significant impact in learning.