arXiv subjects

Qusay H. Mahmoud

Publications and source records attributed to Qusay H. Mahmoud.

6 recordsLinked to original sources

arXiv2026

Early AI Literacy in Culturally Responsive STEM Outreach for Black Youth

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.

cs.CY↗

arXiv2026

Reimagining Assessment in the Age of Generative AI: Lessons from Open-Book Exams with ChatGPT

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.

cs.CY↗

arXiv2026

Generative AI in Software Testing: Current Trends and Future Directions

This paper investigates current software testing systems and explores how artificial intelligence, specifically Generative AI, can be integrated to enhance these systems. It begins by examining different types of AI systems and focuses on the potential of Generative AI to transform software testing processes by improving test coverage, increasing efficiency, and reducing costs. The study provides a com-prehensive overview of the current applications of AI in software testing, emphasizing its significant contributions in areas such as test case generation and validation. Through an extensive literature re-view, it highlights how Generative AI can streamline these processes, resulting in more robust and thorough testing outcomes. The paper also examines methods to improve the efficiency of Generative AI systems, such as prompt engineering and fine-tuning. Additionally, it explores the use of AI in specific tasks, including input generation, oracle generation, data generation, test data creation, and test case prioritization. By analyzing the current landscape and identifying both the opportunities and challenges in integrating Generative AI, this paper provides valuable insights and recommendations for practitioners and researchers. It underscores the need for ongoing advancements and targeted development efforts to overcome existing hurdles and fully leverage AI's capabilities. The findings further show that with continued innovation and careful implementation, Generative AI has the potential to significantly enhance the efficiency, effectiveness, and reliability of software testing, particularly in the rapidly evolving field of IoT testing.

cs.SE↗

arXiv2019

Design and Implementation of a Blockchain-based Consent Management System

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.

cs.CR↗

arXiv2018

Evaluation of Static Analysis Tools for Finding Vulnerabilities in Java and C/C++ Source Code

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.

cs.SE↗

arXiv2017

A Cloud-based Service for Real-Time Performance Evaluation of NoSQL Databases

We have created a cloud-based service that allows the end users to run tests on multiple different databases to find which databases are most suitable for their project. From our research, we could not find another application that enables the user to test several databases to gauge the difference between them. This application allows the user to choose which type of test to perform and which databases to target. The application also displays the results of different tests that were run by other users previously. There is also a map to show the location where all the tests are run to give the user an estimate of the location. Unlike the orthodox static tests and reports conducted to evaluate NoSQL databases, we have created a web application to run and analyze these tests in real time. This web application evaluates the performance of several NoSQL databases. The databases covered are MongoDB, DynamoDB, CouchDB, and Firebase. The web service is accessible from: nosqldb.nextproject.ca.

cs.DB↗