Southern Alberta AI Conference 2025

Abstract: As artificial intelligence continues to reshape industries, automation tools like n8n provide a powerful way to integrate AI into workflows seamlessly. This presentation will explore how n8n enables users to connect AI models, automate decision-making processes, and create intelligent workflows that optimize efficiency. From AI-powered data processing to smart task automation, we will demonstrate practical applications where AI and n8n work together to revolutionize business operations. Whether you're an AI enthusiast, developer, or business leader, this session will offer strategies and real-world examples on leveraging AI-driven automation with n8n.

Abstract: We're proposing a session on FairGrantAI, a concept-stage Generative AI system designed to support equitable grant selection in the nonprofit sector. In the face of increased pressure to apply AI ethically, our project explores how tools like GPT-4 with RAG can be used not just for automation — but to promote fairness, transparency, and human accountability in decision-making. This is especially relevant as more public and nonprofit institutions seek AI-driven efficiencies, yet risk unintentionally replicating systemic bias. Our solution integrates human-in-the-loop design, intersectional fairness metrics, and real-world remediation planning — offering a blueprint for how AI can scale equity, not just speed.

Abstract: BlastSoft AI is a practical application of artificial intelligence developed for the mining industry to analyze and evaluate blast patterns at open-pit operations such as Côté Gold. Given a problem statement with videos, CSV files, and blast data, we designed a solution to classify blasts as good or bad by breaking down key factors that define blast quality. Using machine learning models on post-blast data—including fragmentation analysis, deflagration, vibration metrics, and cost-efficiency—BlastSoft AI identifies effective and ineffective blast events. Our platform leverages pattern recognition and unsupervised learning to detect anomalies, optimize blast outcomes, and minimize environmental impacts such as overbreak and excessive vibration. This poster/presentation details our technical approach and explores the potential for extending these methods toward environmentally sustainable mining, enhanced safety, and improved human-AI collaboration in engineering teams.

Abstract: Join Certified Management Consultant and Organizational Wellness Thought Leader Wesley Paterson, CMC, for an insightful session focused on equipping Business Professionals with the tools they need to thrive in the Sustainable Digital Age. This presentation will explore practical tips, cutting-edge techniques, and best practices designed to enhance productivity and performance. Learn from real-world success stories and discover strategies to maximize impact and achieve exceptional results. Tailored specifically for Business Professionals, this presentation promises to provide valuable insights into navigating the complexities and opportunities of today’s technologically advanced and sustainability-driven business environment.

Abstract: This discussion explores the impact of Artificial Intelligence (AI) on the English language within the postcolonial context. It has three main concerns: the impact of Artificial Intelligence on the English language, the dominance of Artificial Intelligence compared to the dominance of colonialism, and Balancing the dominance of Artificial Intelligence. People develop AI models from different regional English varieties, but the output is mostly American English, and these models often eliminate regional variations, such as Bangladeshi English. In contrast to the bi-directional cultural exchange during colonialism, the influence of AI is one-way, endangering local languages and cultures. This discussion presents the problems, its nature and also recommends some policy measures to stop the hegemony, such as public awareness initiatives and "dialect fairness" in AI development, to help protect linguistic diversity and push back against AI's homogenization. It is essential to address these issues as AI continues to advance exponentially, and its capabilities further exacerbate the marginalization of non-American varieties of English.

Abstract: The ubiquity and dependence on software systems by people, businesses and organizations in the 21st century has resulted in the upsurge in cyber-attacks in recent times. These attacks are generally characterized by different levels of sophistication, occurrence and complexity that makes it difficult for conventional cybersecurity approaches to adequately mitigate them. Although, cybercriminals including hackers are usually blamed for most cyberattacks, the remote causes are however, typically associated with the inherent weaknesses or bugs in the software themselves called vulnerabilities. They are the loopholes in the software source codes through which hackers exploit systems for various nefarious activities that constitutes cybercrimes. Hence, in recent years, various AI based approaches have been proposed or explored in studies to address the challenges. These innovative methods are aimed at complementing the conventional approaches (including awareness training, malware scanning, manual code inspection) that have been adopted over the years. Hence, in my research, I experimented with the use of emerging AI models called Large Language Models (LLMs) in the detection of vulnerabilities in a software system. As a case study, the most popular mobile software – Android was selected in this work. This choice of Android is ideal because current statistics reveals that over 71 percent of all mobile phones across the world are based on this software. This underscores the reasons for more innovative research studies aimed at protecting end users of this critical software. In my experiment, I utilized LVDAndro - a recently released open source Android vulnerabilities-dataset for training my selected LLMs - CodeBERT and GraphCodeBERT in detecting vulnerabilities in Android code bases. Overall, my approach achieved better performance (0.97 Accuracy, 0.97 F1) in Android vulnerability detection compared to the classical Machine Learning (ML) (0.94 Accuracy, 0.94 F1) model used in previous study.

Abstract: Though access to elite academic institutions remains limited to a small percentage of learners globally, recent advances in artificial intelligence have introduced new ways to simulate high-level academic thinking without formal enrollment. This study introduces Prompt-Based Cognitive Equivalence Theory (PB-CET), which argues that with well-structured and recursive prompt design, students from non-elite institutions can develop thinking patterns and scholarly output comparable to that of Ivy League–trained scholars. The aim of this study is to explore how dialogic prompting using generative AI tools like ChatGPT can support such cognitive transformation. To investigate this, I will show how prompts can replicate the mentoring, synthesis, and critical feedback processes often associated with elite academic training. A series of original prompts were developed and matched to a theoretical framework to show how learners can initiate inquiry, emulate expert thinking, simulate intellectual dialogue, internalize advanced epistemic habits, and independently generate original insights. Using the prompt-based simulation method, I argue that prompt-based learning can support independent reasoning and can serve as a bridge into deep academic engagement even for students with limited institutional support. My submission is that improved prompt literacy can help reduce the cognitive access gap between the Global North and South, and that prompting is a promising alternative to traditional institutional methods for fostering knowledge excellence.

Abstract: In the evolving landscape of artificial intelligence, the conversation is shifting from automation to collaboration. This presentation explores Cooperative AI, a paradigm where AI systems are designed to work with humans, enhancing decision-making rather than replacing it. By aligning with human values, adapting to contextual needs, and fostering transparency, cooperative AI enables more trust-centered, agile, and human-centric organizations. Drawing from real-world business applications and research-informed principles, we will examine how cooperative AI supports strategic alignment, amplifies employee agency, and overcomes resistance often seen with traditional automation. Attendees will gain a practical understanding of how businesses can harness AI not just as a tool, but as a thinking partner—unlocking new forms of shared intelligence between people and machines.