The transition to quantum-resistant cryptography is becoming increasingly urgent as advances in quantum computing threaten widely deployed public-key systems such as RSA and elliptic-curve cryptography. While the National Institute of Standards and Technology (NIST) has recently standardized several post-quantum cryptographic (PQC) algorithms, there remains limited empirical data on their practical performance and energy characteristics on commodity hardware.

Distributed ledger technologies (DLTs) form critical infrastructure for decentralized applications, yet their security relies heavily on classical asymmetric cryptographic primitives that are vulnerable to quantum attacks. Post-quantum cryptography (PQC) provides candidate algorithms designed to resist such threats, but integrating these schemes into operational blockchain systems introduces significant architectural and performance trade-offs.

Driven by a cybercrime industry worth over $6 trillion, the demand for cybersecurity professionals is surging with a projected increase of over 35% in jobs over the next decade according to the US Department of Labor's BLS. Simultaneously, advancements in Artificial Intelligence (AI), Data Science (DS), and Machine Learning (ML) are reshaping industries and cre-ating challenges, notably job displacement for the under-skilled. This research-to-practice full paper addresses the critical need to integrate AI/DS/ML into cybersecurity education to meet these evolving demands. We envision a marriage of AI/DS/ML and cybersecurity (ADM4CYB) through a research-to-education paradigm. Despite extensive research, there remains a significant lack of publicly accessible labs that apply AI, DS, and ML to real-world cyber-security challenges. As such, readily available, easy to adopt, modular, and hands-on labs applying AI/DS/ML solutions to cybersecurity problems are highly desirable. At this juncture, we are developing a methodology that enables instructors and researchers to transform their work into practical, safe, and interactive teaching labs, enhancing student learning across various cybersecurity topics. Our initiative aims to integrate AI, DS, and ML techniques into interactive labs to effectively address current cybersecurity challenges. These modular labs provide students with both theoretical knowledge and hands-on experience, essential for adapting to the rapidly evolving field. By creating accessible labs, our methodology empowers students to develop skills across various cybersecurity topics. We also report results of our preliminary evaluation after using the first lab from this methodology in two classes that show increased learning, heightened levels of interest and motivation in the fields of networking, cybersecurity, AI/DS/ML, as well as coding.

This innovative practice full paper introduces an automated assessment system, Michigan System of Automated Assessment for Accreditation (MSA3), designed for computing and engineering programs to meet both external accreditation and internal university requirements while minimally increasing faculty and staff workload. The system integrates with Canvas, enabling instructors to input assessment data directly during grading, streamlining the process by eliminating the need for separate data entry and report dissemination. Key features include automated data export and the generation of visual analytics, securely hosted on university servers. Additionally, the paper presents a preliminary study on how this system affects faculty attitudes towards the assessment workload required by ABET. Our preliminary survey results indicate a favorable perception by faculty, which should increase their buy-in and engagement with assessment, supporting assertions made in the literature. Our findings also suggest that the system may help align faculty efforts with accreditation demands more seam-lessly. Its code will be made available on GitHub, allowing for easy adaptation by other institutions using Canvas. Our future work includes increasing the level of automation, extending our methodology to other learning management systems, and conducting a larger evaluation to gauge MSA3's efficacy on the perception of the accreditation and faculty/staff buy-in.