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 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.

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. 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.

The scalability challenges of code review and pair-programming assessments in large computer science courses, such as CS50 at Harvard University, have opened up opportunities for the application of Generative AI. Leveraging large language models (LLMs), CS50.ai offers a suite of AI-based tools that assist both students and instructors in mastering course material while overcoming the limitations posed by human resource constraints. This demo highlights how generative AI can be employed to conduct code reviews and pair-programming simulations, providing real-time feedback, code explanations, and collaborative programming insights. By integrating these AI tools into students' learning journeys, we aim to mimic the 1:1 interaction between instructor and student, improving both formative and summative assessments. We will showcase how these tools are implemented to scale personalized feedback, ensure academic integrity, and maintain pedagogical efficacy. Our presentation will also reflect on lessons learned from deploying these AI-driven tools in recent course offerings.

In 2023, we developed and deployed AI-based tools in CS50 at Harvard University to provide students with 24/7 interactive assistance, approximating a 1:1 teacher-to-student ratio. These tools offer code explanations, style suggestions, and responses to course-related inquiries, emulating human educators to foster critical thinking. However, maintaining alignment with instructional goals is challenging, especially with frequent updates to the underlying large language models (LLMs). We thus propose a continuous improvement process for LLM-based systems using a collaborative human-in-the-loop approach. We introduce a systematic evaluation framework for assessing and refining the performance of AI-based tutors, combining human-graded and model-graded evaluations. Using few-shot prompting and fine-tuning, we aim to ensure our AI tools adopt pedagogically sound teaching styles. Fine-tuning with a small, high-quality dataset has shown significant improvements in aligning with teaching goals, as confirmed through multi-turn conversation evaluations. Additionally, our framework includes a model-evaluation backend that teaching assistants periodically review, ensuring the AI system remains effective and aligned with instructional objectives. This paper offers insights into our methods and the impact of these AI tools on CS50 and contributes to the discourse on AI in education, showcasing scalable, personalized learning enhancements.

As CS enrollments have grown and professors have increased their reliance upon undergraduate teaching assistants (TAs), it has become all the more important to identify strategies that equip TAs to assist students with coursework while providing the best learning experience during teaching sessions. In this lightning talk, we present a strategy consisting of clarifying and probing questions that highlight the importance of equipping students with tools to solve computer science problems on their own, instead of seeking help from the teaching staff every time they face a challenge. Toward this end, we will share a guide that we adopted during TA training for Harvard University's introductory course, CS50. Additionally, we will share scenarios in which such strategies might be most useful, as during group tutorials or one-on-one office hours. Lastly, we will share preliminary findings on how the strategies can not only benefit a student during the time when a TA is helping them but also equip them with skills to solve such problems on their own.