This study shares lessons learned from a culturally responsive program designed to broaden computer science (CS) participation and build foundational AI literacy among minority high school students from low-income communities. The program emphasized collaborative, project-based app design as a strategy to foster CS interest and develop AI literacy skills. The program included two phases: a one-month summer academy and a semester-long fall project. In the summer academy, 42 students engaged in MIT App Inventor programming, Agile software development, and AI lessons covering machine learning concepts and AI ethics. In the fall, 26 students continued in bi-weekly online Zoom meetings, collaboratively designing personally meaningful mobile app prototypes, such as a hair-care recommendation app for Black women and a mental-health support app for teenagers. Evaluation methods included surveys, focus group interviews, and app design presentations. Results indicated participants' growth in problem-solving, time management, and collaboration, as well as an increased interest in CS career pathways previously not envisioned. However, students encountered several challenges, particularly in using and integrating AI. These included fear or hesitation in using AI, over-reliance on AI for research or solutions, and a tendency to focus on coding without understanding how AI works. This work highlights both the promise and the complexities of promoting AI literacy through mobile app design in pre-college settings. We emphasize the need for scaffolded instruction that supports students in understanding the fundamentals and applications of AI through concrete, youth-relevant activities, while reinforcing the critical role of human judgment when working with AI.

This innovative practice work-in-progress paper describes using H5P-based activities to support student learning and engagement in face-to-face courses.

This innovative practice paper explores the varied perspectives of a computing faculty member and a group of instructional designers, who partnered to revise courses to increase active learning practices and integrate cross-disciplinary skills and dispositions into two face-to-face computing courses (CS 1 and CS 2), as part of a larger, grant-funded systemic change effort. The instructional design team members varied in terms of level of formal educational experience in engineering, computing, and instructional design

The primary goal of the authentic learning approach is to engage and motivate students in learning real world problem solving. We report our experience in developing k-nearest neighbor (KNN) classification for anomaly user behavior detection, one of the authentic machine learning for cybersecurity (ML4Cybr) learning modules based on 10 cybersecurity (CybrS) cases with machine learning (ML) solutions. All portable labs are made available on Google CoLab. So students can access and practice these hands-on labs anywhere and anytime without software installation and configuration which will engage students in learning concepts immediately and getting more experience for hands-on problem solving skills.

New cyberattacks grow in an exponential rate. However, the state-of-the-art in cyber defense efforts cannot keep pace with the sophisticated cybersecurity threats and attacks. The industry relies excessively on anti-virus software which is effective for known malware signatures, but not sustainable for the massive amount of malware samples released daily, and its inefficiency in handling cyberattacks such as zero-day or polymorphic/metamorphic malware. Machine learning (ML) has been extensively studied with a good performance in identifying and extracting knowledge from a big dataset. This can be an alternative to the signature-based approach. Currently, many schools offer courses on topics in ML and cybersecurity (MLC), individually. It is believed that cybersecurity professionals require a deep understanding on ML in order to cope with the growing cyberattacks. Research has shown that authentic learning will create an engaging and motivating learning environment in learning emerging technologies. We propose to develop cyber workforce using authentic learning on machine learning in cybersecurity through a set of real-world cybersecurity learning modules, including a Pre/Lab/Post model focusing on real-world problem solving skills. Our goal is to integrate developed resources into existing curriculum, disseminate the resources through webinars and faculty workshops, and assess the effectiveness of authentic learning of MLC for fostering cybersecurity workforce.

Machine Learning (ML) analyzes, and processes data and develop patterns. In the case of cybersecurity, it helps to better analyze previous cyber attacks and develop proactive strategy to detect and prevent the security threats. Both ML and cybersecurity are important subjects in computing curriculum, but ML for cybersecurity is not well presented there. We design and develop case-study based portable labware on Google CoLab for ML to cybersecurity so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will help students more focus on learning of concepts and getting more experience for hands-on problem solving skills.

Machine Learning (ML) analyzes, and processes data and discover patterns. In cybersecurity, it effectively analyzes big data from existing cybersecurity attacks and develop proactive strategies to detect current and future cybersecurity attacks. Both ML and cybersecurity are important subjects in computing curriculum, but using ML for cybersecurity is not commonly explored. This paper designs and presents a case study-based portable labware experience built on Google's CoLaboratory (CoLab) for a ML cybersecurity application to provide students with hands-on labs accessing from anywhere and anytime, reducing or eliminating tedious installations and configurations. This approach allows students to focus on learning essential concepts and gaining valuable experience through hands-on problem solving skills. Our preliminary results and student evaluations are reported for a case-based hands-on regression labware in cyber fraud prediction using credit card fraud as an example.

In this poster we present an innovative authentic learning approach for Secure Mobile Software Development(SMSD) through real-world-scenario case studies. The primary goal of this learning approach is to create an engagement and motivating learning environment that encourages all students in learning emerging SMSD technologies and enhances their secure software development concepts. This approach provides students with hands-on laboratory practices on real-world SMSD and mobile security. The laboratory consists of multiple modules covering input validation, output encoding, secure inter-process communication, secure data protection, secure mobile database. Each topic consists of a series of progressive sub-labs: a pre-lab, lab activities, and a student add-on post-lab. The preliminary feedback from students is positive. Students have gained hands-on real world experiences on Android software security with Android mobile devices, which also greatly promoted students' self-efficacy and confidences in their mobile security learning.