LEF Award Summary 2026

Student veterinarians engage in peer coaching and provide feedback to their peers throughout their clinical communication education. The goals of peer coaching are to enhance students’ communication skills knowledge and encourage them to take their coaching and feedback skills with them when they enter clinical practice to support their colleagues’ communication development. The purpose of this project is to implement a new, skills-based peer coaching module in the communication program for third-year student veterinarians. The impact of this module on students’ communication self-confidence, coaching self-confidence, skills knowledge, feedback skills, and intention to apply communication coaching skills in veterinary practice will be assessed through questionnaires and observation of simulated client communication laboratories. Evaluation of the module will help determine how student veterinarians’ coaching skills can be developed in the communication curriculum moving forward.  

The Simulation Learning Lab (Sim Lab) functions as a bridge between classroom theory and professional practice, allowing students in the Social Services and Well-Being Studies (SSWS) program to synthesize skills developed in our core counseling and groupwork courses. Sim Lab provides a structured, authentic teaching environment utilizing hired actors and nuanced practice scenarios to challenge students to navigate complex interpersonal dynamics in a safe yet rigorous setting. The project seeks to answer how immersive simulations can more effectively refine transferable skills, such as emotion regulation and self-awareness, compared to traditional pedagogical methods. Through immediate, tailored feedback from faculty and industry professionals, students are empowered to build on their foundational knowledge, refining their professional identity and preparedness for the workforce.  

This project explores how postsecondary instructors practice kindness and compassion in their teaching while maintaining clear boundaries and academic standards. Through interviews with instructors across disciplines, the research will examine how educators balance flexibility, care, and student support with accountability, fairness, and academic rigour. The project aims to explore a growing challenge in higher education: fostering student well-being without creating unsustainable expectations for instructors or compromising learning outcomes. Findings aim to identify practical, evidence-informed strategies that help instructors create supportive learning environments while maintaining consistency and ethical teaching practices.

This project explores how postsecondary instructors practice kindness and compassion in their teaching while maintaining clear boundaries and academic standards. Through interviews with instructors across disciplines, the research will examine how educators balance flexibility, care, and student support with accountability, fairness, and academic rigour. The project aims to explore a growing challenge in higher education: fostering student well-being without creating unsustainable expectations for instructors or compromising learning outcomes. Findings aim to identify practical, evidence-informed strategies that help instructors create supportive learning environments while maintaining consistency and ethical teaching practices.

Generative artificial intelligence (genAI) is rapidly transforming how students approach academic work, creating both opportunities and challenges for teaching and learning. This project explores how assessment design can respond to this evolving landscape by moving beyond approaches focused primarily on detecting or restricting AI use toward models that encourage transparency, critical engagement, and meaningful learning. Through the design, implementation, and evaluation of AI-integrated assessments, we will examine how students can engage with genAI tools as part of a guided and reflective learning process. The project will redesign written assignments to assess not only the final product, but also the processes behind it – including students’ decision-making, reflections on AI use, and ability to critically evaluate AI-generated content. Piloted in upper-year courses and expanded, through collaboration with colleagues, into first- and second-year courses, this initiative will generate an evidence-informed assessment toolkit, including assignment guidelines, reflection activities, and evaluation criteria that can be adapted across disciplines. By making AI use visible, intentional, and structured, the project aims to support student learning, and enhance assessment transparency and fairness, while providing instructors with sustainable approaches for integrating emerging technologies into their teaching practices.

The goal of this project is to investigate the impact of implementing a scaffolded multi-year anti-bias education (ABE) training within a post-secondary program’s curriculum. To study this, we will develop, implement, and evaluate an intentional ABE training program within the Department of Family Relations and Applied Nutrition’s Bachelor of Applied Science in Child Studies (CSTU). The ABE training program will help prepare CSTU students who are working with children and families, to recognize, interrupt and respond to bias and systemic inequities. The training modules will be embedded across several years and responsively designed in collaboration with several community partners and individuals with lived experiences. We will assess students’ self-reflexivity, the ways they demonstrate application of anti-bias and decolonial principles within experiential learning contexts, and the most effective pedagogies and teaching practices to build student capacity to identify, interrupt and prevent bias. 

This project will redesign the seminar component of BIOL*1070: Discovering Biodiversity to provide over 2,200 first-year students each year with an embedded course-based undergraduate research experience. Students will develop testable questions, design experiments, analyze data, and evaluate evidence that investigates how generative AI-supported study strategies affect learning within the context of the course. The broader goal is for the experience to inform how students approach their own decisions regarding use of Generative AI and encourage them to use these tools in evidence-based ways that aim to support, rather than undermine, their learning. As part of the development of these course components, the project will create training resources for graduate teaching assistants and transmissible instructional materials and assessments, creating a scalable model for teaching science process and reasoning skills and supporting evidence-informed GenAI use in large-enrolment STEM courses.