There is an increasing imperative to integrate programming platforms within AI frameworks to enhance educational tasks for both teachers and students. However, commonly used platforms such as Code.org, Scratch, and Snap fall short of providing the desired AI features and lack adaptability for interdisciplinary applications. This study explores how educational platforms can be improved by incorporating AI and analytics features to create more effective learning environments across various subjects and domains. We interviewed 8 K-12 teachers and asked their practices and needs while using any block-based programming (BBP) platform in their classes. We asked for their approaches in assessment, course development and expansion of resources, and student monitoring in their classes. Thematic analysis of the interview transcripts revealed both commonalities and differences in the AI tools needed between the STEM and non-STEM groups. Our results indicated advanced AI features that could promote BBP platforms. Both groups stressed the need for integrity and plagiarism checks, AI adaptability, customized rubrics, and detailed feedback in assessments. Non-STEM teachers also emphasized the importance of creative assignments and qualitative assessments. Regarding resource development, both AI tools desired for updating curricula, tutoring libraries, and generative AI features. Non-STEM teachers were particularly interested in supporting creative endeavors, such as art simulations. For student monitoring, both groups prioritized desktop control, daily tracking, behavior monitoring, and distraction prevention tools. Our findings identify specific AI-enhanced features needed by K-12 teachers across various disciplines and lay the foundation for creating more efficient, personalized, and engaging educational experiences.

Additional Feedback: The main concern with this work is its relevance to NeurIPS. The application is clearly relevant, as it combines program synthesis, constraint satisfaction, and intelligent tutoring, all well-established in the AI literature. However, the solution is almost entirely symbolic – it combines SMT solving, symbolic execution, and delta debugging over programs. The only probabilistic component is MCTS, and even there (as far as I understand) it is vanilla MCTS without learned policies. The paper has many interesting contributions, but none are related to machine learning.

Overall, reviewers liked how the paper combined constraint solving, symbolic execution and MCTS to solve the problem. The main reservation was some concern about relevance to the venue given the lack of any learning in the approach. However, reviewers believed that both the problem and the techniques are of interest to people in the community, for example working on ML-guided program synthesis.

Block-based visual programming environments play a critical role in introducing computing concepts to K-12 students. One of the key pedagogical challenges in these environments is in designing new practice tasks for a student that match a desired level of difficulty and exercise specific programming concepts. Our methodology is based on the realization that the mapping from the space of visual tasks to their solution codes is highly discontinuous; hence, directly mutating reference task \task {in} to generate new tasks is futile. Then, the algorithm performs symbolic execution over a code \code {out} to obtain a visual task \task {out}; this step uses the Monte Carlo Tree Search (MCTS) procedure to guide the search in the symbolic tree. We demonstrate the effectiveness of our algorithm through an extensive empirical evaluation and user study on reference tasks taken from the Hour of Code: Classic Maze challenge by Code.org and the Intro to Programming with Karel course by CodeHS.com.

Block-based programming is increasingly the way that learners are being introduced to the practice of programming and the field of computer science more broadly. Led by the success of environments like Scratch (see the figure appearing later in this column) and initiatives like Code.org's Hour of Code, block-based programming is now an established part of the computer science education landscape. While not a recent innovation (for example, LogoBlocks has been around since the mid-1990s), the last decade has seen a blossoming of new toys, games, programming environments, and curricula that incorporate block-based programming features. Given this growing presence, it is important that we as a community look critically at the block-based programming modality to understand its affordances, drawbacks, and identify how best to use it as a means to welcome people into the discipline of computer science and support them as they grow and learn.