Odds are we've all used (or tried!) quite a few tools to facilitate efficiency inside and outside of the classroom and empower students to learn more effectively, whether on campus or off. Some of those tools are perhaps homegrown and unique to one's own institution, but freely available educational technologies abound as well, some in the cloud, some for Macs and PCs, some open-source. And quite a few commercial tools offer free or discounted educational plans as well. In this BoF, we'll begin with a whirlwind tour of the tools we ourselves use, including artificial intelligence (AI), identifying the problems they solve and how well, then quickly open the floor to everyone to share their favorites as well. Along the way, we'll jot down every tool mentioned and share the results. With educational technology an evergreen landscape, this year's list will surely be different from last! Attendees should exit this session with a better understanding of the current landscape, familiarized with innovations they can bring back to their own classes (whether high school, undergraduate, or graduate), without reinventing wheels themselves.

In our introductory computer science course, we face the challenge of supporting a sizable team of undergraduate teaching assistants while fostering both consistency and community. To address this challenge, we implement a cohort-based model for organizing staff, in which Head Teaching Assistants (TAs) take on additional leadership and course logistics responsibilities and lead a small group of four or five other TAs. This model decentralizes communication, granting Head TAs more agency in how they guide their groups while creating smaller communities where staff feel comfortable raising concerns, asking questions, and sharing strategies. In this lightning talk, we will share concrete examples of how cohort meetings have improved information flow, strengthened staff development, and cultivated a greater sense of belonging among teaching staff. Additionally, we will highlight challenges such as uneven dissemination of information across groups and discuss strategies we have implemented to mitigate these issues. Lastly, we will reflect on why we believe this structure offers a scalable and transferable model for other large courses seeking to balance efficiency with community while developing leadership pathways for experienced staff.

Teaching computer science at scale can be challenging. From our experience in CS50, Harvard University's introductory course, we've seen firsthand the impactful role that generative artificial intelligence can play in education. Recognizing its potential and stakes, we integrated OpenAI's GPT into our own teaching methodology. The goal was to emulate a 1:1 teacher-to-student ratio, incorporating ''pedagogical guardrails'' to maintain instructional integrity. The result was a personalized, AI-powered bot in the form of a friendly rubber duck aimed at delivering instructional responses and troubleshooting without giving outright solutions. In this tutorial, we share our journey and offer insights into responsibly harnessing AI in educational settings. Participants will gain hands-on experience working with GPT through OpenAI's latest APIs, understanding and crafting prompts, answering questions using embedding-based search, and, finally, collaboratively building their own AI chatbot. We will also explore the growing landscape of agentic AI tools such as Claude Code, GitHub CoPilot, OpenAI Codex, discussing their applications in educational contexts. Prior knowledge of Python is beneficial but not required, as all demo source code will be provided. Ultimately, we'll not only share lessons learned from our own approach but also equip educators hands-on with the knowledge and tools with which they, too, can implement these technologies in their unique teaching environments.

Learner-directed creative programming can broaden participation in computing by enabling students to bring their own interests, backgrounds, and identities into the learning experience. However, learner-directed work is challenging for students, especially as they encounter frustration and obstacles in their learning. Collaboration is an effective strategy, but organizing effective collaborative learning experiences is difficult, particularly when addressing the diverse needs, backgrounds, and learning styles of students. We sought to understand how students collaborate on learner-directed Scratch projects and how to organize this type of collaboration. To explore this question, we designed a collaborative programming Scratch curriculum consisting of 22 activities, 13 of which were tested in a 5-day workshop with six students aged 11-14. Through participant observation, interviews, and analysis of student work and survey data, we found that students had many different preferences around collaborative structures, each of which had different affordances and challenges. Some pairs preferred traditional pair programming, while others worked with a partner but focused on individual projects. Student success and satisfaction with collaboration depended on their goals and experiences. Regardless of approach, students benefited from opportunities to learn from one another and share their work.