The software industry is clamoring for computer science undergraduates to know how to ''Use AI'' for software development. But it isn't clear what ''Using AI'' entails, especially as the technology rapidly evolves. While industry has created this narrative, it is educators that must drive it to its conclusion by identifying specific skills graduates need to effectively use present-day AI tools and foundational principles that will help graduates use them in the future. There are many ideas proposed about what the future of programming looks like, from prompt engineering to agentic/cybernetic programming, but little consensus as to what students ought to learn. Because the landscape is constantly evolving, continued discussion is necessary for educators to keep up or stay ahead of these changes.

Although there is widespread agreement that computer science curricula should include content that encourages students to think deeply about the ethical and societal impacts of their work, a majority of institutions of higher education do not require Computer Science majors to take a course on ethics in order to graduate. While adding a required course to the curriculum may be difficult, students can still be required to consider issues of ethics, societal impact, and cultural competency when those issues are explicitly incorporated into the curricula of other courses.

Generative Artificial Intelligence (GenAI), especially in the form of Large Language Models (LLMs), has been the subject of many papers both positive and negative. No matter the stance, it is clear that its existence has huge implications for education, and people are looking for advice on how to use it appropriately, if that is even possible. As Computer Science educators we have particular interests in GenAI, especially the ways in which CS students use and understand GenAI.

A key to personalized online learning is presenting content at an appropriate difficulty level; content that is too difficult can cause frustration and content that is too easy may result in boredom. Appropriate content can improve students' engagement and learning outcome. In this research, we propose a computer vision enhanced problem selector (COVES), a deep learning model to select a personalized difficulty level for each student. A combination of visual information and traditional log data is used to predict student-problem interactions, which are then used to guide problem difficulty selection in real time. COVES was trained on a dataset of fifty-one sixth-grade students interacting with the online math tutor MathSpring. Once COVES was integrated into the tutor, its effectiveness was tested with twenty-two seventh-grade students in controlled experiments. Students who received problems at an appropriate difficulty level, based on real-time predictions of their performance, demonstrated improved engagement with the math tutor. Results indicate that COVES leads to higher mastery of math concepts, better timing, and higher scores, thus providing a positive learning experience for the participants.