MusicLOGO 2.0 (ML2) is a free online programming environment designed to engage learners in foundational music concepts through composing music with computer code. The platform introduces a novel interval-based representation in which learners compose by specifying the relationships between notes rather than encoding fixed pitch values. This design reflects research in music cognition suggesting that listeners naturally organize pitch relationally. In our research with fifth-grade students and undergraduates working with the platform, we have found that the interval-based representation draws learners’ attention to musical patterns—such as scales, chords, and musical form—and that learners leverage computational abstractions to capture and transform those patterns. Our interactive demo will share music projects created by primary school students who have worked with ML2 and will offer attendees a short activity introducing them to the platform.

As K–12 computer science expands in the United States, students encounter a growing array of programming tools. Many introductory experiences use block-based environments, where programs are assembled by snapping together visual blocks instead of typing code. While these tools can support learning, high school students often perceive them negatively, even when they support the same underlying logic as text-based coding. Using a constructivist grounded theory approach, we interviewed 17 high school students to trace how early experiences, tool design, peer discourse, and cultural framings shape these views. We find that students develop informal folk theories: that computer science is about accumulating languages, that block-based programming is for young children, and that limitations in programming activities stem from the block modality itself—beliefs that can shift when students encounter counterexamples. Our findings call for more deliberate design and sequencing of tools that are attentive to the meanings students construct as they progress, and that promote more expansive notions of programming beyond modality.

Due to large enrollments, undergraduate computer science (CS) courses often incorporate lectures that can scale to many students. However, there is strong evidence that students learn best through active meaning-making, particularly in collaboration with others. In this paper, we explore how students seek out opportunities to learn collaboratively during class time in a large introductory CS (CS1) course and how pedagogical decisions can create opportunities for such collaboration. We use an ethnographic approach to observe natural student interactions in a CS1 class, contributing to limited research exploring CS classroom activity through ethnographic observation. We find that students engage in frequent siding (i.e., side-talk and other backchanneling during class) to address their in-the-moment learning needs for clarification, tutoring, and support with debugging, as well as to co-construct new understandings and connect with others. We also find that students can meet some of these needs by siding with digital tools. From this, we introduce the concept of digital siding, in which a student turns to the Internet or AI to achieve a goal rather than a peer, and discuss benefits and drawbacks of peer and digital siding. Our data shows that siding happens often and serves important learning needs, providing a way for students to actively engage in learning despite the large scale of CS1. Therefore, we argue that instructors should not view siding purely as a distraction and provide design recommendations to help instructors promote siding in ways that support learning.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of thirteen AI assignments from the 2025 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu