Qusay H. Mahmoud

Qusay H. Mahmoud

Generative AI systems such as ChatGPT challenge traditional assumptions about academic assessment by enabling students to generate explanations, code, and solutions in real time. Rather than attempting to restrict AI use, this study investigates how students actually interact with such systems during formal evaluation. Engineering students were permitted to use ChatGPT during take-home open-book exams and were required to submit interaction transcripts alongside exam solutions. This provided direct observational evidence of reasoning processes rather than relying on self-reported behavior. Qualitative analysis revealed three progressive patterns of use: answer retrieval, guided collaboration, and critical verification. While some students initially copied questions verbatim and received generic responses, many refined prompts iteratively and tested outputs. Some of the strongest evidence of reasoning appeared when students evaluated incorrect or incomplete AI responses, revealing evaluative reasoning through debugging, comparison, and justification. The presence of generative AI shifted the cognitive task of assessment from producing solutions to assessing solution validity. The findings suggest that, in AI-mediated assessment environments, correctness of final answers alone may no longer provide sufficient evidence of comprehension. Instead, competencies such as prompt formulation, verification, and judgment become visible indicators of learning. Transparent integration of AI appeared to reduce focus on rule avoidance and promote self-regulation. Assessments should evolve to evaluate reasoning about solutions rather than independent solution production. Generative AI therefore does not invalidate assessment but has the potential to expose deeper forms of understanding aligned with professional practice.

Qusay H. Mahmoud, Kimberly Davis, Paula Duru et al.

Persistent inequities in STEM education continue to limit the participation of Black youth in science and technology fields across Canada. Structural barriers, underrepresentation, and limited access to culturally affirming learning spaces can restrict both opportunity and confidence in pursuing STEM pathways. This paper examines Ontario Tech University's Engineering Outreach Black Youth Program as an exploratory, practice-based case study of culturally responsive STEM outreach. The program creates inclusive environments where Black youth engage in hands-on, culturally grounded STEM experiences supported by mentorship, representation, and community connection. Its recent integration of artificial intelligence (AI) literacy reflects a growing recognition that early engagement with emerging technologies may expand access to future STEM learning opportunities. The paper discusses how AI-focused activities were introduced within this outreach model and examines short-term outcomes related to AI knowledge, confidence, and critical awareness. Findings suggest gains across these areas, while highlighting the need for future research to examine longer-term outcomes related to STEM belonging, identity, and persistence.

Nathaniel Aldred, Luke Baal, Graeham Broda et al.

A blockchain is a distributed ledger forming a distributed consensus on a history of transactions. It is the underlying technology for the Bitcoin cryptocurrency, but there are many applications beyond the financial sector. With built-in security and removal of the need for third party trust, blockchain has started to see some use within contract applications among other things. In this paper, we present the design and implementation of a permissioned-based blockchain third party consent management system, whose policy can be decided by a government agency. We have constructed a proof of concept implementation using Hyperledger Fabric to provide a service that allows end-users to control and consent to who manages their private information. We believe our solution meets the guiding principles of EU General Data Protection Regulation or GDPR. While our performance and usability evaluation are limited, our solution design and its implementation meet the 7 foundational principles of privacy by design.

Rahma Mahmood, Qusay H. Mahmoud

It is quite common for security testing to be delayed until after the software has been developed, but vulnerabilities may get noticed throughout the implementation phase and the earlier they are discovered, the easier and cheaper it will be to fix them. Software development processes such as the secure software development lifecycle incorporates security at every stage of the design and development process. Static code scanning tools find vulnerabilities in code by highlighting potential security flaws and offer examples on how to resolve them, and some may even modify the code to remove the susceptibility. This paper compares static analysis tools for Java and C/C++ source code, and explores their pros and cons.