Founded in 1995 by Caldwell, Canada's Top 40 Under 40 celebrates emerging visionaries, innovators and business leaders who have demonstrated exceptional achievement, leadership and community impact. Over the past three decades, the program has recognized many of Canada's most influential executives, entrepreneurs and changemakers at early stages of their careers.聽

The 2026 cohort includes Nick Frosst, Patrice Njoh and Jonathan Sherman, 91亚色 community members who have demonstrated leadership in AI, consulting and law.

Nick Frosst, former research assistant 

Frosst is a co-founder of Cohere, an AI company that creates large language models and tools specifically for businesses. Founded in 2019 and headquartered in Toronto, Cohere builds cutting-edge foundation models and end-to-end AI products that help businesses and governments solve real-world challenges with leading security, data privacy and deployment flexibility.  

Frosst plays a key role in the company鈥檚 research efforts with a focus on delivering top-tier user experiences for customers. Prior to founding Cohere, he was the first employee hired by Geoffrey Hinton at Google Brain鈥檚 Toronto lab, where his research focused on capsule networks, adversarial robustness and model interpretability.  

Frosst is a former research assistant at 91亚色's Tsotsos Computer Vision Lab at the where he worked on visual saliency prediction algorithms, focusing on benchmarking and optimization from 2013-15.

Patrice Njoh, alum (IMBA '13) 

Njoh is a partner at Deloitte Canada, where she advises senior executives on AI, technology, workforce transformation and enterprise change. She has partnered with some of Canada鈥檚 largest financial institutions and public sector organizations to adopt new technologies, redesign work and improve services used by millions of Canadians.  

A frequent speaker on AI, leadership and the future of work, the alum helps shape the global conversation on how organizations can harness emerging technologies responsibly. She champions practical, people-first adoption strategies that help organizations realize value from innovation while building trust and long-term resilience.

Beyond Deloitte, she serves on the board of The Kickback Foundation and mentors emerging leaders shaping the next generation of Canadian business. 

Jonathan Sherman, alum (JD '14) 

Sherman serves as executive Chair of Cassels Brock & Blackwell LLP, a partner in its Capital Markets and Public M&A Group and founder of its Cannabis Law Group. He is the youngest partner in the firm's history and a trusted advisor on some of the market鈥檚 most significant and complex transactions. 

His practice focuses on emerging high-growth industries, including technology, beverage alcohol, gaming, cryptocurrency and real estate.  

Sherman, an alum of , has advised on major capital markets and corporate transactions, and provides strategic and legal advice to a range of industry leaders, entrepreneurs and investment dealers. He also plays a central role in shaping Cassels鈥� strategic direction, and he is frequently invited to share his expertise and perspectives on business and law by journalists, conferences and academic institutions. 

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The Faculty of Graduate Studies (FGS) recently launched an AI-powered graduate funding virtual assistant that connects students and staff with information in seconds.

Navi provides students and staff with immediate answers to questions about graduate funding using verified information from FGS. The tool represents a new approach to student support, combining institutional expertise with emerging technology to make information easier to access and navigate.

The idea grew out of a persistent challenge facing the FGS graduate funding team: managing a high volume of inquires, many centred on routine questions.

For Dmitri Katsman, associate director, graduate funding and planning, the project began with a simple question: can a virtual assistant help address routine inquiries while creating more capacity for personalized support?

鈥淥ur graduate funding team was receiving a constant stream of questions, many of which were general inquiries,鈥� says Katsman 鈥淚 started thinking about whether a chatbot could handle some of those questions, giving our team more time to focus on other projects and the more complex needs of our students.鈥�

The path from idea to launch, however, was not straightforward.

An early attempt to build Navi using content from the FGS website produced inconsistent results and the project was nearly abandoned.

A turning point came when the team connected with 91亚色鈥檚 IT Innovation team and with Nick Valentino, a student digital experience strategist whose AI experience helped guide a new development approach.

The team reorganized information into focused question-and-answer training materials covering specific funding topics. Over several months, the content was refined and tested to improve accuracy, address gaps and correct inconsistencies.

After testing Navi with more than 100 questions, its responses were refined through repeated review, feedback and quality assurance exercises. FGS Graduate Finance & Funding and Communications & Digital Engagement team members also played key roles in final testing, launch planning and promotion.

Unlike general-purpose AI tools, says Katsman, Navi draws exclusively from trusted FGS resources. This distinction is particularly important because graduate funding information can be complex and may change from one academic year to the next.

Human oversight, says Katsman, was a central consideration during development and while exploring use cases.

Alice MacLachlan, vice-provost and dean of FGS, says the project is a real-world example of how AI-enabled tools can enhance student services while maintaining a commitment to expertise, accuracy and human oversight.

鈥淣avi demonstrates how we can thoughtfully use emerging technology to improve the student experience,鈥� she says. 鈥淎t the same time, our priority is always to keep our people, their expertise and our commitment to accurate information at the heart of that experience.鈥�

The tool provides quicker access to information while allowing faculty and staff to focus more on answering more complex questions and providing individualized support to students.

Katsman says the project reflects changing expectations around how information is delivered and accessed.

鈥淪tudents and staff increasingly expect information to be accessible and responsive to their individual questions. We need to keep adapting the way we provide support, and this is an important step in that direction.鈥�

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Across his teaching, Friedman has observed that as AI has made it possible to complete many cognitive tasks more quickly and easily, students have naturally integrated it into their learning experiences. They use it to brainstorm ideas, summarize information and assist with assignments. Friedman says this has become known as "cognitive offloading," a term used to describe shifting mental work to external tools. In the context of AI, it refers to relying on technology to do thinking that students could be doing themselves.

Friedman is not opposed to AI-enabled learning. In fact, he encourages transparency around its use in his courses and views it as a tool students will encounter in their professional lives.

To ensure cognitive offloading doesn't come at the expense of deeper learning, he began exploring how AI could be used to create richer learning experiences that require students to think critically, demonstrate their knowledge and take greater ownership of their work.

That idea led him to rework a presentation component of his MBA Change Management course. Student teams would still be asked to present, but rather than placing the emphasis on polished delivery, Friedman shifted the focus to learners鈥� ability to apply their understanding in the moment.

"What can I do to create the opportunity for just enough friction to get them to up their game?" he asked himself.

To do that, presentations would be followed by an extended question-and-answer session where students are expected to explain their research, defend their recommendations and answer questions about the sources and evidence in their project.

The presentations would also be transcribed in real time and fed to a custom AI agent Friedman created to generate live follow-up questions, requiring students to think on their feet to defend their ideas.

The technology also helped address a practical challenge. During presentations, Friedman often found himself trying to listen, assess participation, take notes and formulate meaningful follow-up questions all at once. By generating potential questions from the transcript, the AI tool allowed him to focus more closely on what students were saying while still supporting a richer discussion.

The result was an assessment that placed less emphasis on delivery and more on demonstrating understanding in the moment.

To build the system, Friedman "fed" the agent course readings, assignment instructions, grading rubrics and other materials. He then spent several weeks testing and refining the tool before the course began, experimenting with different instructions and examples to improve the quality of the questions it produced.

The results were encouraging. Friedman observed stronger preparation, greater command of project topics, greater confidence during discussions and more balanced participation among group members.

Students enjoyed the process, Friedman says, and he found they were challenged to think through their ideas in real time and demonstrate what they knew and how they arrived at their conclusions.

As for the impact on Friedman's role as an instructor, the AI tool didn't replaced him; instead, it supported his teaching while leaving key decisions in human hands.

Friedman reviewed the agent's AI-generated questions, refining or rejecting them before shareing with students. The result was an synergy that combined AI's ability to rapidly analyze information with the judgment, experience and flexibility of a human instructor.

"The question for me was never whether the AI was better than me or worse than me," he says. "It was: can I use it to make me better?"

There was another benefit, too 鈥� Friedman no longer needed to divide his attention between listening, observing, taking notes and preparing follow-ups. Instead, he could focus more fully on the students and their presentations.

"I could be really present for the students," he says.

That experience challenged a common assumption Friedman sees in conversations about AI in education. While many fear technology might reduce human interaction, he found the opposite to be true. Class members became more connected to their own thoughts and more engaged with their work, while the technology gave him more time to interact meaningfully with students.

The classroom exercise was not a one-off experiment. After using the approach in his MBA course for a second consecutive summer, Friedman is now adapting it for an undergraduate business course and continuing to refine the AI agent so it can be used in different teaching contexts.

Through his work at the Teaching Innovation Studio, he is also encouraging other faculty members to experiment with practical ways AI can support learning.

For Friedman, the technology's potential goes beyond improving the classroom experience. It can also help strengthen the connection between students and instructors.

"I want to use it in a way that enhances the classroom experience," he says. "Let's use technology to be more human."

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As AI becomes increasingly embedded in workplaces across Canada, 91亚色's introduces the Certificate in AI Governance, a five-month program designed to prepare professionals for emerging roles in AI oversight.

The new certificate arrives as AI adoption continues to accelerate across the country. Statistics Canada reports that 19.2 per cent of Canadian businesses used AI to produce goods or deliver services in the year leading up to the second quarter of 2026, up from 6.1 per cent two years earlier.

"Organizations are no longer asking whether they should adopt AI. Their challenge now is to use it responsibly," says Christine Brooks-Cappadocia, assistant vice-president of continuing studies at 91亚色. "There is an urgent need for professionals with the skills needed to oversee, manage risks and create effective governance practices related to AI in the workplace."

Developed with input from industry experts, the certificate is designed for professionals working in privacy, data protection, governance, cybersecurity, risk management, compliance and related fields, as well as business analysts, product managers and developers moving into AI policy or governance roles.

Through three project-based courses delivered online, learners will develop practical skills in AI risk assessment, governance frameworks, compliance, audit readiness and stakeholder communication. A comprehensive AI governance plan developed throughout the program will allow participants to apply their learning to a real-world organizational challenge or case study.

"At 91亚色 School of Continuing Studies, our role is to help efficiently close emerging skills gaps by preparing professionals to adapt to new technologies and the changing demands of industry," says Brooks-Cappadocia. "Our school鈥檚 approach of co-developing programs with industry ensures that graduates of the Certificate in AI Governance will have that unique combination of skills, knowledge and experience employers need to address this challenge along with the professional network to help them thrive."

The first cohort begins in January 2027. .

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This summer, YFile examines how 91亚色-led researchers move ideas into impact through the Connected Minds Prototyping Awards. This series shows innovation in practice 鈥� how socially responsible technologies are tested in real-world settings and how this work drives meaningful change.

As Canada's population ages and demand for care continues to grow, researchers are exploring how technology can help older adults live more independently while easing pressure on caregivers.  

An emerging area of focus is social robotics, a field dedicated to developing robots capable of interacting with people in natural, human-centred ways. Unlike industrial robots designed for manufacturing, social robots can communicate, respond to social cues and assist in settings such as homes, hospitals and long-term care facilities.  

At 91亚色, a team led by聽James Elder, professor聽at the 聽and the Faculty of聽Health聽and 91亚色 Research Chair in Human and Computer Vision,聽is聽exploring how聽social robots聽can better聽interact聽with the people they are designed to support.聽

The project centres on Psibot, a novel robotic vision system designed to help robots perceive and understand the people around them. By drawing inspiration from how humans see and direct their attention, the technology aims to equip robots with social awareness to assist, monitor and interact in more natural ways. 

"Our research explores the potential for robots and AI to work with human caregivers in providing continuous assessments, assistance and care to older adults," says Elder, who also serves the University as director of the Centre for AI & Society. "To fulfill this potential, robots must have a high degree of social intelligence."  

For social robots to be effective, they must be able to detect, recognize and interpret human behaviour and social cues. However, giving robots the visual capabilities to perform these tasks remains a significant challenge. 

Traditional robotic vision systems often face a trade-off: they can either scan a wide area at lower detail or focus closely on a specific person or object. Psibot tackles this challenge by combining broad awareness with the ability to focus on specific people and interactions when greater detail is needed. 

Much like people use peripheral vision to stay aware of their surroundings while focusing their attention on a person or activity,聽Psibot聽combines wide-angle cameras with a high-resolution sensor that can rapidly direct its gaze where it is needed most. This allows the robot to聽maintain聽awareness of its environment without losing the ability to closely聽observe聽important interactions.聽

The team has already developed two functioning prototypes, PSIBot 1.0 and PSIBot 2.0, and continues to refine the technology. 

The project received support through the Connected Minds Prototyping Award, which helps researchers move promising ideas closer to real-world testing and deployment. Connected Minds is a multi-million-dollar transdisciplinary research initiative led by 91亚色 and Queen鈥檚 universities that explores the bridge between emerging technology, public health and social justice.  

According to Elder, the funding has been instrumental in advancing this work toward field testing and commercialization opportunities. 

The prototype has significant potential in health care and senior care settings, where social robots could complement human caregivers by monitoring well-being, supporting daily activities and improving access to care. 

Elder also sees promise in their ability to support emotional and social health by identifying changes in social behaviour, such as an increase in isolation, and encouraging residents to participate in activities or connect with others. 

鈥淪ocial robots can serve as escorts, guiding an older adult from place to place within a potentially large facility,鈥� says Elder. 鈥淭his can preserve the mobility of the individual while keeping them safe.鈥� 

 Monitoring changes in mobility over time is another application, where caregivers could receive alerts about potential fall risks and offer earlier interventions when needed. 

As health care systems face growing demands, technologies that support independent living and aging in place may play an increasingly important role in helping older adults maintain their quality of life, says Elder. 

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This summer, YFile examines how 91亚色-led researchers move ideas into impact through the Connected Minds Prototyping Awards. This series shows innovation in practice 鈥� how socially responsible technologies are tested in real-world settings and how this work drives meaningful change.

From buttoning a shirt to turning a doorknob, people rely on their hands to perform countless tasks every day. Yet, despite the importance of the human hand, studying how people learn and perform these movements is challenging for researchers.

Traditional methods of tracking hand movements are often expensive, time-consuming and require participants to wear uncomfortable sensors or reflective markers. These techniques often interfere with the natural hand movements researchers attempt to study, rendering the results as ineffective.

At 91亚色, researchers are addressing this challenge through the development of ATHENA (Automatically Tracking Hands Expertly with No Annotations), an AI-powered motion-tracking system that captures natural hand movements without requiring markers, sensors or wearable devices.

The project is led by Jonathan Michaels, assistant professor at the School of Kinesiology and Health Science, alongside Daanish (Dan) Mulla, a postdoctoral fellow and lead researcher on the project.

"We wanted a way to measure natural hand movement as it actually happens, without attaching anything to the person," says Michaels.

Using synchronized video from multiple low-cost cameras and machine-learning algorithms, ATHENA captures footage of a person to map their hand, body and face movements in three dimensions. This open-source software removes the barriers associated with traditional motion capture systems by eliminating costs and tedious setup requirements while upholding accurate measurements.

In validation studies, ATHENA was tested against traditional marker-based technology commonly considered the gold standard in movement science. The results showed strong accuracy across a range of tasks, from simple reaching and grasping to handling objects such as mugs, books, pens and tools.

Ultimately, the team hopes ATHENA will become a tool for those seeking to better understand how people move, learn new skills and recover from injury.

Now fully functional and freely available to researchers, ATHENA鈥檚 success moving from concept to real-world application was driven in part by the Connected Minds Prototyping Award.

The award is designed to support prototype developments aligned with the mandate of Connected Minds, a multi-million-dollar transdisciplinary research initiative led by 91亚色 and Queen鈥檚 universities that explores the bridge between emerging technology, public health and social justice.

Funding for ATHENA enabled the team to develop portable versions of the system that can be transported and deployed outside the laboratory. These mobile setups have already been used in autism clinics in Israel and the GTA, expanding the tool鈥檚 reach beyond traditional research environments.

"The award was instrumental in moving ATHENA from a validated proof-of-concept into a tool that can actually be used out in the world," says Michaels.

Recipients of the award also receive support from other sources and partners. For ATHENA鈥檚 team, it fostered a collaboration with 91亚色's IP Innovation Clinic for guidance on developing a strategy to maximize adoption while keeping it freely available.

By remaining open source, affordable and developed in collaboration with the communities and clinics that use it, Michaels says ATHENA reflects Connected Minds' goal of creating accessible, socially responsible technology.

Through the prototyping process, the team learned that developing a useful technology requires more than proving it works.

"The hardest part of building a useful tool is often not the core idea, but making it robust, easy and trustworthy enough that other people will actually adopt it," says Michaels.

Navigating the challenges of moving from controlled laboratory environments to clinics and community settings, where lighting conditions vary, people move more freely and privacy considerations become essential, he says.

Because the tool operates entirely without bulky tracking equipment, it can capture movement in more natural settings and offer a more comfortable experience for participants. The comfort and ability to track naturalistic movements translates well with respect to ATHENA's most promising application in health care and clinical research.

An example of this is a collaboration with the Freud Lab at 91亚色 where researchers are using ATHENA to study how children move during activities such as building with LEGO. The goal is to identify patterns that could support earlier and more objective identification of autism and other neurodevelopmental conditions.

Michaels says plans are underway to expand ATHENA's capabilities, such as integrating eye tracking, object tracking and multi-person tracking. And, by working with additional clinical and educational partners, the team hopes to broaden adoption while maintaining the platform's open-source model.

"Our goal is for ATHENA to become a broadly used, trusted and freely available resource that helps the research and clinical community better understand human dexterity and improve lives," says Michaels.

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AI is increasingly influencing research, industry and everyday life, prompting new opportunities alongside important questions about its development, application and impact.

In recognition of AI Appreciation Day, this special edition of YFile highlights research and innovation in AI across the 91亚色 community.

The stories within reflect the diverse ways researchers and alumni are engaging with AI technologies to address complex challenges and advance knowledge. Topics range from efforts to make AI tools safer for youth mental health support and AI developments in cancer care, to emerging applications in geophysics and real estate technology.

These stories illustrate how members of the 91亚色 community are exploring AI across disciplines while considering the importance of human expertise, as well as critical thinking and oversight in shaping how these technologies are developed and used.

While this special issue explores advances in AI, all articles were researched, written and edited by people, reflecting the essential role of human insight in journalism.

Featured stories include:

How 91亚色 researchers are using AI to support cancer care
Can AI help clinicians spend less time reviewing scans and more time caring for patients? 91亚色 researcher Ali Sadeghi-Naini is developing tools designed to support faster, more informed decision-making in cancer care.

Can AI be trusted to assess climate risks? A 91亚色 study investigates
A 91亚色 study explores how AI can help communities prepare for climate-related hazards by investigating whether emerging tools are ready for real-world applications.

91亚色 prof explores AI safeguards for youth mental health
AI is becoming a trusted source of advice for many young people. Postdoctoral researcher Abeer Badawi is exploring what that means for their mental health 鈥� and how to prevent harm.

Schulich alumni scale AI real estate investment tool聽
Learn how three Schulich School of Business graduates used 91亚色 mentorship and connections to launch Leni, an AI-powered tool that supports real estate investors in their decision making.

Know someone making an impact at 91亚色? Share your story ideas with YFile.

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In oncology clinics, diagnosing disease, monitoring treatment response and tracking tumour growth often begin with a meticulous review of medical images. For oncologists and radiologists, this can mean spending hours measuring tumours and comparing scans to understand how a patient's cancer has changed over time.

Much of that work is still done manually, even when clinicians are carrying out many of the same assessments across large numbers of cases. "There are many occasions where the time of oncologists and radiologists is spent reviewing cases that are very similar," says Sadeghi-Naini.

Spending significant time reviewing scans and performing routine assessments, he says, leaves less time for patients who require more urgent attention.

Sadeghi-Naini and his team at the QUANTIMB Lab are working to change that.

The 91亚色-based lab, led by Sadeghi-Naini, develops AI-powered imaging tools to support cancer care with a focus on helping clinicians process medical scans more efficiently.

鈥淭he idea is to automate things 鈥� to detect the location of the disease, quantify it and generate a report so the clinician can quickly review it,鈥� says Sadeghi-Naini, who teaches in the .

Rather than replacing clinical expertise, the goal is to develop AI tools that act as decision support systems, helping care providers assess scans and prioritize care. The tools are trained using large collections of medical scans, allowing them to identify patterns associated with disease progression and treatment response, which can then be flagged for clinical review.

One area of the lab's work focuses on monitoring brain tumours using MRI scans. For some patients whose cancer has spread to the brain, disease may appear as numerous small lesions scattered throughout the organ. In some cases, Sadeghi-Naini says, patients may have 10, 20 or 30 tumours that must be identified and monitored individually over time to determine whether treatment is working.

To help with that task, the lab has developed AI models that can analyze MRI scans, identifying and tracking small cancerous growths across a series of scans. The technology can automate much of that work and generate information to support treatment decisions, reducing the time clinicians spend manually locating and measuring each tumour over successive scans.

Another long-running project examines how tumours respond to treatment. For patients undergoing chemotherapy, one challenge is determining early on whether a tumour is responding to the drug. In some cases, this may only become clear after months of treatment, by which point valuable time and opportunities to adjust a patient's care may have been lost.

Using ultrasound data, Sadeghi-Naini and his team train AI models to look for biological changes that signal whether tumour cells are dying. By assisting in identifying those signals earlier than conventional monitoring approaches, the technology could give clinicians faster insight into whether a treatment is working. Detecting those changes earlier could help determine whether a different approach is needed.

鈥淚f a patient can be identified as not responding as early as possible, that spares months of ineffective chemotherapy and unnecessary side effects,鈥� says Sadeghi-Naini.

Although the projects focus on different clinical challenges, they are built around the same idea: using AI to extract useful information from medical images and help clinicians make decisions more quickly.

Across these projects, collaboration and human insight play a central role. The lab works closely with hospitals and clinicians, including researchers at Sunnybrook Health Sciences Centre, to access patient data, test new technologies and ensure the tools being developed address real-world clinical needs.

Feedback and guidance from clinical collaborators on the brain tumour monitoring technology have already been positive, says Sadeghi-Naini, who notes that some have expressed interest in using it as a support tool.

The lab is also designing interfaces that make the technology easier for practitioners to use, recognizing that ease of use is key to adoption in real-world settings.

Collaboration with those working directly in the field also helps accelerate the work. The ultrasound project, for example, has advanced to a clinical trial, a milestone that reflects growing interest among health care experts in evaluating the technology.

Although much of his work involves advanced technologies, Sadeghi-Naini says the motivation behind it is simple: helping patients receive the right care at the right time. Tools that monitor disease more efficiently, or determine sooner whether a treatment is working, could improve decision-making throughout the course of treatment and offer more time to focus on patient care.

"At the end of the day, this clinician time can be spent on somebody who actually urgently needs it," he says.

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Drawing on recent research findings, usage statistics and her own work, Badawi has observed that chatbot use is increasing, with many people turning to these tools not only for information and practical assistance, but also for advice, emotional guidance and support with personal decisions.

Badawi's interest in the issue grew out of her work with Kids Help Phone, Canada's national 24/7 e-mental health service for young people. While working at the Vector Institute, she gained exposure to the mental health challenges facing youth and saw firsthand how digital tools were increasingly being used to provide support.

The experience led her to pursue postdoctoral research at 91亚色 through the Connected Minds program with a focus on mental health and AI safety. Supervised by Assistant Professor Elham Dolatabadi and Laleh Seyyed-Kalantari from the , as well as Frank Rudzicz of Dalhousie University, Badawi鈥檚 research finds that youth, in particular, are turning to AI systems for help navigating everyday challenges, relationships and emotional concerns 鈥� even when they may not explicitly describe those interactions as mental health support.

Badawi's findings reinforced a growing concern about the role AI plays in people's lives. Chatbots, such as ChatGPT, are increasingly serving roles once filled by family members, friends and other trusted sources. Whether youth describe it that way or not, Badawi believes many of these exchanges have begun filling an emotional support role in people's lives.

鈥淓ven if they don't define it as mental health support, they are still using it as a type of mental health support,鈥� she says.

The concern, Badawi says, is that AI chatbots were not designed to serve that purpose.

Unlike therapists and other mental health professionals, general purpose large language models like ChatGPT are not designed to provide clinical care. As a result, users may receive advice or validation before sufficient context is gathered or alternative perspectives are explored.

鈥淵ou can end up with a system that always agrees with you,鈥� she says. 鈥淚t doesn't challenge you or ask questions in the same way a person would.鈥�

That dynamic can have consequences, she notes. Someone discussing a conflict with family, for example, may receive responses that validate their feelings without fully exploring the situation or offering alternative perspectives. Over time, Badawi worries that constant affirmation could contribute to overconfidence, one-sided thinking and an increasing reliance on AI systems.

Her research examines what she and her colleagues call 鈥� a potential pattern of interaction in which repeated reliance on AI may shift reflection, coping, emotional regulation and decision-making away from individuals and toward AI systems.

The concern, however, extends beyond questions of decision-making or dependence. She worries the same behaviours that encourage people to rely on AI for advice and emotional guidance may, in extreme cases, contribute to more serious harm. Models that consistently validate users, reinforce their perspectives and foster emotional attachment can become particularly problematic when people are vulnerable or in distress.

The issue became an important focus for Badawi following reported cases in which AI chatbot interactions were examined in connection with youth suicide. For her, these cases raised urgent questions about how chatbots respond when vulnerable users express distress, form strong emotional attachments to the system or require support beyond what an AI tool can safely provide. They also highlight the importance of designing models that recognize risk and encourage users to seek appropriate human help.

Those cases helped inform a research project supported through OpenAI's AI and Mental Health Grant Program, an initiative aimed at supporting research into how AI systems affect mental health and how they can be designed more safely.

As principal investigator, Badawi is leading Preventing Cognitive Atrophy in LLM Mental Health Support Through Psychometric Evaluation and Therapist-Aligned Design, a project that aims to better understand how AI systems can support mental health while reducing the risk of emotional dependency.

The team is developing new ways to measure how AI behaves in emotionally sensitive conversations. The goal is to design technology that will encourage users to seek help from people they trust and provide resources and responses that support independent coping and problem-solving.

To do that, researchers are analyzing conversations between users and chatbots and are working with experts in psychology and mental health to examine whether models gather sufficient context, encourage independent decicion-making, ask appropriate clarifying questions and respond with empathy.

One goal is to identify conversational patterns that can steer interactions into increasingly risky territory. Ultimately, Badawi hopes the work will help create safeguards that prevent AI systems from reinforcing harmful ideas, fostering unhealthy emotional attachment or encouraging dependence.

The obstacles, however, extend beyond the technology itself.

With more than a decade of experience working at the intersection of AI and health care, Badawi has collaborated with clinicians, mental health professionals and researchers across disciplines. Those experiences, she says, have underscored the importance of collaboration while also revealing how difficult it can be.

One obstacle is determining what a "good" response from an AI system actually looks like. As part of the project, and assess how the models respond in emotionally sensitive situations. Those evaluations help researchers identify the kinds of behaviours they want these tools to emulate 鈥� and those they want to avoid.

But unlike many areas of health care, there is no single correct answer. Different experts can interpret the same interaction in different ways, making it difficult to establish clear standards. A response one psychologist considers appropriate may prompt debate from another, not because either is wrong, Badawi says, but because they are approaching the situation from different perspectives.

鈥淚f humans don't agree, how are you expecting the model to follow a pattern?鈥� she asks.

Despite those challenges, Badawi believes progress is possible. She is also quick to emphasize that AI tools can still provide value.

鈥淲e can鈥檛 stop people from using AI tools,鈥� she says. 鈥淭he question is how to make them safer.鈥�

Her long-term goal is to develop AI-driven tools specifically , with safeguards that recognize when interactions are becoming problematic and encourage users toward reflection, autonomy and real-world sources of support.

鈥淭he real measure of progress is whether AI can recognize danger early, interrupt harmful patterns and connect a vulnerable person to human help before it is too late,鈥� she says.

Ultimately, the objective is simple: people are already using these systems every day. The task now is ensuring they can do so safely.

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is an AI platform built for real estate and asset management co-founded by Gaurav Madani and Arunabh Dastidar, who met as Schulich MBA students in 2017 and graduated together in 2019. The tool鈥檚 third co-founder, Zain Nathoo, is also a Schulich alum.

The platform was created to solve fragmented data challenges in real estate investing by using AI to organize information and provide customizable systems that help investment teams analyze data, automate tasks and make faster decisions. These were challenges both Dastidar and Nathoo encountered while working in real estate investment and helping to manage more than $1 billion in property assets.

Frustrated by delays in financial reports 鈥� which often arrived in spreadsheets weeks late and slowed decision-making 鈥� they turn to Schulich for guidance and support to bring their solution to market.

Early on, the founders brought their idea to Chris Carder, executive director of Schulich's Office of Innovation and Entrepreneurship. Madani recalls approaching Carder, who at the time was alone in the Schulich cafeteria, and sharing the team鈥檚 idea. Carder offered the team direction, facilitated important connections and amplified the company within his own network.

"He was a sounding board, which is what founders need in the early days," Madani says. "Especially in the early days when no one knows you, those kinds of things are really important."

Beyond Carder, the company drew on other forms of support at Schulich. Andre Kuzmicki, retired professor, introduced the team to senior professionals at large Canadian institutions who would become some of Leni's first clients.

As well, Schulich's Entrepreneurial Studies program has partnered with Leni to create student internships.

"Schulich gave us a community of people who believed in what we were building and opened doors that would have been much harder to access on our own," says Dastidar. "That early support was instrumental in helping us gain traction and credibility as we scaled."

While Leni began by connecting industry systems to track performance in real time, it evolved to integrate AI, which became central to its development from 2022 onward.

Today, it helps investment teams evaluate deals, conduct research and automate reporting tasks that would otherwise take days.

Madani says Leni stands out for its focus on accuracy. It is trained to break complex questions into parts, route them across multiple AI models, evaluate whether the responses meet its threshold, and surfaces an answer only when it passes 鈥� along with the reasoning and source data behind it.

"Leni will always treat any output as a hypothesis that needs to be proven," he says.

As the company expands, the relationship with Schulich continues in both directions. Leni's founders now serve as mentors, judges and guest lecturers with Schulich Startups, where they contribute to the same ecosystem that helped them get started.

For students who are thinking about building their own tool, Madani's advice is simple: "No two entrepreneurial journeys look the same," he says, adding that Schulich鈥檚 support was instrumental to Leni鈥檚 success.

With files from Mzwandile Poncana

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