Schedule
This event will offer a hybrid delivery (in-person with online attendees) spread across two days. Each day will feature different sessions and different panel presentations.
9:00 – 10:00 am
Faculty sessions (4 x 15 minutes)
Presenter: Robin Bhattacharya
Description: The FNCE 3170 Fixed Income Study Buddy is a custom GPT-based teaching assistant designed to strengthen student learning in Fixed Income and Alternative Investments. It addresses a core instructional challenge: students often struggle with the multi-step logic and formula discipline required for pricing bonds, swaps, and other fixed income instruments. These difficulties frequently lead to repetitive procedural questions that are difficult to support within limited class and office-hour time. To improve learning while maintaining academic integrity, the Study Buddy follows a strict framework-first design. Every response is structured using a standardized template—Given, Find, Assumptions, Formula, Steps, and Next Step—to guide students through methodical problem solving. Full numeric solutions are only provided for verbatim matches from two approved sources: the FNCE 3170 Classroom Problems file and the Fixed Income Analysis Workbook (CFA Institute, 5th Ed.). All other questions receive conceptual scaffolding rather than completed calculations. Studies of large-language-model (LLM) tutors—particularly those emphasizing conceptual guidance rather than direct answers—have shown gains in problem-solving quality, reduced cognitive load during complex tasks, and higher retention of procedural knowledge. The FNCE 3170 Fixed Income Study Buddy therefore exposes the students to a valuable and appropriate AI use in the university setting.
Presenter: Glenn Borthistle
Description: Students in a graduate-level course on Educational Management were asked to incorporate a role for Artificial Intelligence (AI) in evaluating the strength of their strategic plan. Using Chat GPT, students wrote three questions about the topic of their strategic plan and evaluated the responses received. Outcomes are to evaluate the content of the response from Chat GPT about their topic, determine how this could be incorporated into their strategic plan, and discuss the ethics of the use of artificial intelligence as a source of information. The talk will include responses from students about the use of AI in this assignment and the use of proper citation format. This assignment is consistent with the First Peoples Principle of Learning that learning is “holistic, reflexive, experiential, and relational.” Current sources will be cited, presenting arguments for being proactive in teaching students how to use AI, recommendations for taking an experimental approach to teaching AI, and having a clear policy about boundaries for the use of AI in the classroom. Further, developing individual competencies such as prompt-writing, and evaluating the accuracy of AI responses will be discussed. Student feedback on the strengths and weaknesses of responses, accuracy, and ethics will also be shared.
Presenter: Jason Hogue
Description: Modern AI has complicated the traditional educational environment by granting learners assignment-completing super powers. This presentation flips the script on that adversarial dynamic, proposing a conceptual framework to reposition AI as a collaborative assistant in the learning process. This proposal advocates for a shift away from task-oriented outcomes, which are an easy target for AI, toward a focus on the learning journey itself. This framework reframes the learner’s role, from “knowledge keeper and tasker” to “visionary and architect” of their own educational journey. Through the implementation of open-ended goals in this learner-centered model, AI can be leveraged as a personal assistant to facilitate discovery, nurture curiosity, and inspire the creative process. This framework relies heavily on the integration of reflective practices, such as journaling, which encourages students to engage with their learning process. By focusing on process over product, we can create a sustainable educational environment where AI serves to enhance, rather than replace, human ingenuity and critical thought.
Presenters: Quan Nguyen, Nisha Puthiyedth
Description: In this presentation, we report and reflect on the adoption of TRU-Think, a pedagogy-driven AI system designed to promote productive struggle through guided hints and scaffolding rather than direct solutions. Drawing from classroom implementations, we illustrate how intentional design of technology and assessment can foster responsible, reflective, and ethical engagement with AI tools. While grounded in computing education, the approach and findings have broader implications across disciplines—highlighting how pedagogy-aligned AI systems can cultivate deeper learning, critical thinking, and self-regulated problem-solving in diverse educational contexts.
10:00 – 10:10 am
Break
10:10 – 10:50 am
Student Panel Discussion
Panelists: Gaurav Gharat (Business), Ruth Stead (Health Science), Nathan Wilson (Tourism Management), Deepansh Sharma (Computer Science)
10:50 -11:00am
Break
11:00am – 12:00pm
Faculty sessions (4 x 15 minutes)
Presenter: Jason Hogue
Description: Prompts are not enough. In this presentation I will demonstration that we can move beyond the chat interface and build a complete and custom “Learning Space”. Using a bare-bones tech stack of standard web technologies, this demo will showcase an environment that allows the learner and an AI assistant, to co-create dynamic content in real-time. A small Local AI model, integrated into the app, acts as the assistant, empowering the learner on their journey. This environment focuses on dynamic document creation, containing those documents in “Topic” folders, quizes, and some basic pedagogical discussion styles – all within an easy to use interface. It is hoped that this demo inspires others to leverage AI to build their own “Learning Spaces” without the dependance on third-parties and without the time and cost of software development.
Presenter: Mohammed Danial Shaikh
Description:
Presenters: Gursahib Singh, Anusha Venkataraman
Description: In this work, we present an AI-driven educational platform that converts lecture materials into interactive simulations through a multi-agent LLM pipeline. A large language model (LLM) first analyzes uploaded PDFs or lecture slides, extracting key concepts and identifying suitable simulation candidates. Each simulation is then autonomously constructed in parallel by specialized coding models using HTML, CSS, and JavaScript. The resulting simulations enable real-time interactivity, allowing students to adjust parameters and visualize concepts. This platform is currently being developed for first-year physics students, but it could be extended in the future to support other disciplines. Examples of generated interactive simulations will be shared during the presentation, illustrating how AI reasoning, automated code synthesis, and interactive visualization can enhance the teaching and learning of complex academic content.
Presenters: Ghazanfar Latif
Description: This presentation introduces an innovative AI-driven learning platform designed to transform teaching, assessment, and student engagement through the power of Large Language Models (LLMs) and Generative AI. The system integrates a suite of intelligent tools, including a Dual Mode Exam Creator for online and in-class assessments, a Grading Assistant, and a RAG-based knowledge retrieval system that enhances instructional efficiency. For researchers and educators, the platform offers a Research Proposal Writing Assistant, Automated Report Assessment, and Proposal Review capabilities , all while ensuring privacy preservation. Students benefit from personalized Exam Preparation, an AI-powered Student Advisor, and a Wellness Hub supporting mental and academic wellbeing. Administrators and staff gain access to AI-guided training modules and data-driven analytics for informed decision-making. This holistic ecosystem redefines online education through adaptive learning, inclusive accessibility, and smart automation, marking a significant leap toward the future of intelligent academic environments.
9:00 – 10:00 am
Faculty sessions (4 x 15 minutes)
Presenter: Joseph Alexander Brown
Description: The use of generative techniques has lead to an increase in the amount of cases in the realm of Fabrication from TRU’s ED 5-0 process. As a member of the Senate Academic Integrity Council, Dr. Brown is part of the adjudication process of cases all over the campus. This presentation will examine the issue of Fabrication from the notices in course outlines and assignments, the manner in which we submit ED 5-0 cases on Fabrication, how to put forward a convincing case to the committee, and what to do in the aftermath.
Presenters: Deeparsh Singh Dang, Jessica Allingham
Description: The application of artificial intelligence in drug discovery is revolutionizing the pharmaceutical industry. However, introducing these advanced concepts to undergraduate science students presents significant technical hurdles. Traditional methods that require complex coding and software management often detract from the core scientific learning objectives. This presentation introduces the development of an AI-powered web application designed to remove these barriers. The platform will guide students through a complete, simulated drug discovery workflow, translating complex chemical data into clear, interpretable results on a single dashboard. It integrates multiple AI models to provide five key insights: predicted bioactivity against a disease target, solubility and lipophilicity forecasts, drug-likeness using Lipinski’s Rule of Five, multi-pathway toxicity screening and 2D visualization. The true innovation lies in its pedagogical design. Each predication will include the model’s confidence level and test accuracy, encouraging students to critically evaluate AI-generated results as probabilistic rather than definitive. This scaffolding supports authentic AI literacy and helps students build intuition around model limitations and interpretability. As AI becomes increasingly prevalent in medicinal chemistry and across scientific disciplines, educators have a responsibility to prepare students for this changing technological landscape. By developing tools that foreground accessibility and thoughtful engagement, we aim to empower students to explore AI in a scientifically rigorous, intuitive way. The platform is being designed with adaptability in mind, and we envision future applications across biology, environmental sciences and data literacy education.
Presenter: Jeff McLaughlin
Description: As a professor of philosophy and author and editor, I have tried to use AI in a few different ways. However, all of them failed at achieving the task I set out for it resulting in excessive frustrations and a significant waste of time when time was of the essence. This doesn’t necessarily defeat the purpose of using AI but rather provides justification for getting users to become better critical thinkers before using AI. I will discuss how it failed miserably at helping me index a forthcoming book, continually lied to me when I refuted its historical claims, and easily, without any guilt, switched back and forth between accurate and inaccurate descriptions of a movie scene that I showed in my Philosophy and Pop Culture course. My conclusion is tentative but leaning towards perceiving these applications of AI in the same way we treat Wikipedia. Namely, using AI to learn about something you don’t know is dangerous. Instead, you need to already know the answer to your question before you ask it of AI.
Presenters: Yasin Mamatjan
Description: Integrating artificial intelligence (AI) into engineering education presents both opportunities and challenges as educators seek to apply cutting-edge AI-driven tools and online platforms to enhance learning outcomes. The field is beginning to explore how AI-driven personalized tutoring, on-demand adaptive learning, and innovative assessment strategies can reshape traditional pedagogical models, though broad adaptation and seamless integration remain ongoing challenges. However, this integration brings challenges such as ensuring academic integrity, preventing over-reliance on AI for solution generation, and balancing personalized assistance with deep, hands-on learning experiences. This paper presents a framework for incorporating AI, online platforms, project-based methods, and new assessment strategies to improve engineering education. It addresses academic integrity challenges, promotes deeper engagement through real-world competitions, and aligns projects with industry objectives via guest lectures and research-driven collaborative work. Creative case studies on real-world Health Monitoring and collaborative Smart Assistant projects equip students with practical skillsets that illustrate the effectiveness of this framework, while a SWOT analysis (Strengths, Weaknesses, Opportunities and Threats) supports student research to be more practical and creative. Finally, the successful student competitions demonstrate how these pedagogical methods can lead to award-winning and real-world relevant results that eventually equip them with practical problem-solving skills.
10:00 – 10:10 am
Break
10:10 – 10:50 am
Stakeholder Panel Discussion
Panelists: TBA
10:50 -11:00am
Break
11:00am – 12:00pm
Faculty sessions (4 x 15 minutes)
Presenter: Yan Song
Description: Artificial intelligence is reshaping how software design and development are taught, prompting educators to reconsider how students can use AI tools to enhance, but not replace, learning. In the course Object-Oriented Design and Programming in C++ (COMP 3140), I designed a structured, multi-phase project to examine how guided AI interaction can support design reasoning, coding implementation, and reflective practice. The course emphasizes conceptual understanding of object-oriented principles such as abstraction, encapsulation, and class collaboration, alongside coding proficiency. The guiding research question is: How does optional and transparent AI involvement during structured course project phases influence students’ learning behaviors, critical thinking, and the quality of their work in an upper-year software design and programming course? The project includes two major stages, design and implementation, each divided into iterative phases. During the design stage, students create UML class and sequence diagrams and may choose to seek AI feedback on their models or proceed independently. They then write structured reflections describing their use of AI, including the prompts, the feedback they received, and their reasoning for accepting or rejecting AI suggestions. Students who do not use AI provide self-explanations describing how they improved their designs independently. In the implementation stage, students again have the option to consult AI in up to three ways for targeted feedback, followed by reflection on the usefulness of the feedback. This design allows multiple pathways for meaningful AI integration without compromising individual thinking. Data collection is ongoing and includes students’ reflections, AI prompts, and feedback logs. Ethical review for collecting survey responses is in progress and expected to be completed by the end of November. The expected outcomes include gaining a deeper understanding of students’ preferences and decision-making processes regarding AI use, identifying how AI support influences their reasoning and engagement, and developing pedagogical models that integrate ethical, reflective, and transparent AI practices into computing education.
Presenters: Mohammed Danial Shaikh
Description: Artificial Intelligence (AI) has rapidly transformed numerous domains worldwide, integrating seamlessly into almost every field. However, its adoption in education remains limited despite extensive research, primarily due to ethical concerns, the foundational role of education in society, and the need for human oversight. One of the persistent challenges in academia is assisting students in selecting appropriate courses that align with their academic history, eligibility, and long-term career objectives. Academic advisors also face significant difficulties in managing large cohorts and providing individualized guidance. To address these challenges, this study proposes a multi-agent framework designed to deliver personalized course recommendations based on students’ historical academic data. The proposed system integrates multiple agents that analyze students’ grades, skills, and professional aspirations to recommend optimal learning paths—whether focused on academic excellence or career-oriented skill development. The framework ensures a human-in-the-loop approach, wherein academic advisors retain decision-making authority while benefiting from AI-driven insights. The model is implemented using the CrewAI framework for intelligent agent collaboration, and a Django-based web application serves as the user interface for student interaction. This agentic pipeline aims to enhance academic planning, streamline advisory processes, and better prepare students for success in both academia and the job market.
Presenters: Nisha Puthiyedth, Musfiq Rahman
Description: Curriculum mapping is a cornerstone of academic quality. It ensures that courses align with program goals, support accreditation, and promote progressive skill development. Yet at TRU, as in most institutions, curriculum mapping is still a manual, time-consuming, and labor-intensive process. Because it relies on faculty input and spreadsheets rather than automation, maps are rarely updated when courses change, meaning that valuable connections between learning outcomes can quickly become outdated or inconsistent. Early consultations with TRU’s Computing Science faculty and curriculum committee have revealed the impact of this gap: repeated coverage of basic coding concepts across multiple courses, uneven scaffolding of critical skills like ethical reasoning and problem-solving, and a lack of a clear roadmap to show students how their learning progresses across the program. These challenges are not unique to computing; similar issues arise in disciplines such as business, nursing, arts, and sciences, where outcome alignment, competency progression, and accreditation documentation require structured mapping and regular review. To address these challenges, our team is developing an LLM-assisted curriculum mapping framework for the Bachelor of Computing Science program. Using Large Language Models (LLMs), the system can automatically analyze course outlines, learning outcomes, and assessments to suggest program-level mappings, identify redundancies, classify outcomes by cognitive level, and flag gaps. A conversational interface will allow faculty to interact with the AI, verify and refine its suggestions, and maintain academic decision-making while streamlining the workflow. By introducing automation, the framework can keep curriculum maps continuously updated, making it easier to test changes, adapt improvements, and visualize how courses contribute to student learning. Once validated in Computing Science, this approach can be adapted across TRU to support cross-disciplinary curriculum review and accreditation readiness. Ultimately, this innovation will enable more agile, data-informed, and student-centred program design.