Students enter CS1 courses with widely varying programming experience, mathematical maturity, and learning preferences. We propose a framework called PathMX that addresses this challenge through a ``curriculum as code'' methodology that treats curriculum materials as structured, version-controlled repositories editable by both instructors and LLM-based coding agents. When curriculum is authored as code following clear conventions, agents can generate personalized content on demand, enabling scalable differentiated instruction that was previously impractical. Our CS1 case study demonstrates that this approach makes true differentiated instruction achievable within the constraints of real classroom teaching.

In today’s digital world, understanding how websites function is

essential, yet the inner workings of web applications remain unclear to many

learners. While web technologies are widely used, practical opportunities to

explore client-side scripting, dynamic content, and user interface design in a

cohesive project are often limited. There is a need for interactive tools that

both engage users and illustrate the integration of core web development

concepts. This paper presents the proposed tool, Apache Sudoku, a webbased game designed to demonstrate and teach web development principles.

The application integrates multiple programming languages, including

HTML, CSS, and JavaScript, to create an interactive and engaging user

experience. Apache Sudoku illustrates fundamental concepts such as DOM

manipulation, event handling, and dynamic content updates, while

providing a complete, playable game. Beyond its entertainment value, the

tool serves as a practical platform for learning how different web

technologies interact to produce a functional application. By combining

educational purpose with interactive design, Apache Sudoku provides a

hands-on example for students to explore and understand modern web

development techniques.

This paper presents a digital learning framework, coLearn-AI (hereafter referred to as the system), that combines guided inquiry pedagogy with intelligent, adaptive feedback mechanisms to support collaborative learning in introductory programming courses. The system enables instructors to author structured inquiry activities, manage student groups, and provide embedded AI guidance that scaffolds learning in real time. Students work in groups and engage with progressively complex problems through exploration, collaboration, and reflection. The system integrates synchronous collaborative workspaces, turn-taking mechanisms, and analytics dashboards that help instructors monitor student progress at scale. A key innovation is the integration of AI-driven formative feedback, where instructors embed AI guidance directives within activity files. These directives, visible only to instructors, enable the AI module to provide context-aware feedback that aligns with pedagogical goals. By bridging structured human facilitation with adaptive, intelligent tutoring capabilities, the system promotes engagement, accountability, and deeper conceptual understanding. This paper describes the pedagogical foundations, system architecture, activity model, AI feedback pipeline, and group-learning mechanisms and outlines directions for formal efficiency evaluation and refinement.

As artificial intelligence(AI) becomes increasingly visible in everyday life, many students are experiencing growing anxiety about what AI is and how it affects them. This signals the urgent need for meaningful AI literacy education that helps learners understand how AI systems work and empowers them to use these tools confidently and effectively. Recognizing the importance of ensuring that all learners can develop this understanding, this paper introduces a learner-centered AI literacy lesson designed for 6-8 grade students. The lesson envisions AI literacy through a learner-centered lens, prioritizing accessibility, agency, and inclusivity. Grounded in the AI4K12’s “Big Five Ideas in AI” and informed by learning science frameworks such as Universal Design for Learning (UDL), Funds of Knowledge, and Depth of Knowledge, the proposed lesson supports students to learn AI in ways that connect to their experiences, questions, and capabilities.

Foundational STEM areas—including artificial intelligence, cybersecurity, and inclusive software development—are often missing from 9–12 computing curricula. A major reason for this lack of inclusion is the shortage of high-quality, easy-to-use teaching materials. This gap is especially challenging for smaller or under-resourced schools that disproportionately serve underrepresented students. As a result, many 9-12 classrooms are unable to cover these essential topics, contributing to a broader ``diversity crisis'' in the U.S. STEM pipeline.

The [hidden] are a collection of 15 experiential, computing-focused educational modules originally developed and validated for undergraduate learners. Designed to promote hands-on engagement with computing-focused topics such as machine learning, artificial intelligence, inclusive software development and ethics-focused topics, these modules have demonstrated strong effectiveness in building foundational technical skills and increasing student confidence. This paper presents the adaptation of the [hidden] modules for high school students in grades 9–12, highlighting modifications to content, scaffolding, and delivery to ensure developmental appropriateness and curricular alignment with secondary education standards. We describe the pedagogical framework guiding the redesign, pilot implementations strategies, and provide recommendations for integrating the modules into existing computer science pathways. By offering flexible, accessible, and engaging learning experiences, the adapted [hidden] modules aim to expand early exposure to inclusive computing practices and support a more diverse pipeline of future technologists. Complete project materials are openly available on our website: [hidden]

This experience report describes a six-week Collaborative Online In-

ternational Learning (COIL) module integrated into an undergraduate

Software Engineering course, connecting students from the United States

and Panama. The primary aim was to enhance student engagement and

motivation through cross-cultural, project-based collaboration.

Students collaborated to complete software projects, including a stand-

out game featuring campus-native animals symbolizing the partnership.

Survey data from U.S. participants indicate increased motivation, im-

proved project completion rates, and heightened interest in global col-

laboration.

Challenges such as time zone coordination and language barriers

emerged but were effectively mitigated through the universal language

of programming. The experience also fostered the development of soft

skills, including enhanced communication and intercultural competence.

This report offers insights into the pedagogical benefits, technological

tools employed, and lessons learned.