AI Equity Data Challenge 2025: Meet the Top Five Finalists

Over the past three months, TECHNATION’s AI Equity Data Challenge brought together over 200 post-secondary students from across Canada, forming 86 teams eager to explore how AI is reshaping the future of work. Through data storytelling and labour market research, students examined which groups stand to benefit from AI adoption and which may face new or growing barriers. 

On November 18, the journey reached its final stage as the top five finalist teams, comprised of 18 outstanding students, presented their live pitches to our panel of judges: Melanie Chai (Humber Polytechnic), Kelvin Kung (BluePrint.AI), and Lauren McVeigh (NAIT – Northern Alberta Institute of Technology). 

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🏆 Selecting This Year’s Winners 

After five powerful presentations, the judges deliberated for nearly one hour because of how close the scores were. Each team brought forward thoughtful research, strong technical execution, and a genuine desire to build a fairer AI future in Canada. 

The judges praised all finalists for their creativity, clarity, and impact-driven approach, noting that each solution had the potential to become a real-world initiative. It was a standout year, and the energy, talent, and commitment from students was truly unmatched. 

Meet this year’s three winning teams and the powerful ideas they brought to the final pitch.
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🥉 Third Place – Otters 2.0 
Team members: Tracy Do | Rowena Chen | Prudence Cheung | Kim Parsons ️

Rounding out the top three, Team Otters 2.0 shed light on the experience of mid-career workers, highlighting how many feel uncertain about which skills matter most, and how AI is reshaping the paths available to them. 

Problem & Insight 
Interviews with workers across healthcare, finance, and service roles revealed a shared feeling of being “stuck,” uncertain which skills matter, which training is worth their money, or which roles offer real stability in an AI-driven economy. 

Solution 
Otters is a lightweight browser extension that turns every new tab into a personalized career dashboard: skill analysis, job outlooks, salary trends, recommended courses, curated resources, and community networks. 

Vision 
Their goal is to democratize reskilling by making career planning accessible, affordable, and intuitive, beginning with a Toronto area pilot of 500 workers and scaling through partnerships with employment agencies and colleges.
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🥈 Second Place – Team Designerscore  
Team members: Amaka Nwegbu | Dave Adams | Goury Prathap | Papa Yaw Adjei-Larbi 
Following closely, Team Designerscore presented Dreamcatcher, an AI-powered, offline-first learning platform designed specifically for Indigenous women in rural communities, a group disproportionately impacted by AI-driven job disruption. 

Problem & Insight 
Their research revealed a deep digital divide: while urban AI-readiness sits at 80–85%, rural regions average around 40%. Only 24% of Indigenous communities have reliable high-speed internet, leaving many women without access to training, upskilling, or wage growth. 

Solution 
Dreamcatcher delivers low-bandwidth, downloadable learning modules; culturally relevant role-based learning paths co-created with Indigenous mentors; and a gamified rewards wallet that offsets cost-of-living barriers by allowing points to be redeemed for essentials. 

Vision  
Their rollout strategy begins with three pilot communities, scaling to support thousands of Indigenous women within three years, unlocking over $13.5M in wage gains and building long-term digital confidence and economic resilience.
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🥇 FirstPlace – Team bob 
Team members: Winnie Xie I Putu Pramana Putra | Agin Joshi | Rahul Paul 
Leading the winning lineup, Team bob delivered an equity-focused analysis of Canada’s labour data, revealing a cycle where those most threatened by automation (youth, newcomers, and marginalized groups) are also the least able to benefit from AI-complementary roles. 

Problem & Insight 
AI-driven wage gaps are widening as many workers lack access to training pathways and equitable employer networks. This results in low upward mobility and persistent underrepresentation in AI-related roles. 

Solution  
AI Bridge is a digital tool that uses open labour data to recommend personalized learning pathways, wage-lift projections, equity-verified employers, lived-experience mentorship, and micro-credential opportunities. 

Vision 
Using their example “Tina,” they demonstrated how the tool could raise wages from $22 to $35/hour and support job placement within 6-12 months, scaling nationally to increase mobility and close access gaps.
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Honourable Mentions 
Together with the top three winners, the remaining two finalist teams helped shape an exceptional top five, each offering unique insights into Canada’s AI equity landscape. 

▪️ Team Solo Data 
Team members: Khuong Nguyen | Huong Thao Nguyen | Ha Nhi Tran
Team Solo Data tackled Canada’s widening gender gap in AI-exposed roles and proposed AIXHER, a national AI upskilling and apprenticeship program designed to help women transition confidently into AI-augmented careers. 

▪️ Team AIOTU 
Team members: Zainab Lawal | Ali Hakkani | Shayaan Kashif
Team AIOTU built Equity Decoder, a bilingual Chrome extension that detects bias in job postings, rewrites them into inclusive versions, and helps employers meet Canadian equity standards through transparent scoring and accessible design.
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A Final Thank You 
We extend our sincere thanks to the judges, mentors, and all students who joined this year’s AI Equity Data Challenge. Your data-driven thinking, strong collaboration, and commitment to responsible innovation stood out across every phase. This year’s submissions set a high standard and showcased the strength of Canada’s emerging talent. ______________________________________________________________________

👩🏽‍💻 Canadian post-secondary students curious about tech or AI can visit TECHNATION’s Career Ready page to stay tuned for our next Student Innovation Challenge.  

💡Member Opportunity: Partner with us on Student Innovation Challenges that align with your business needs here.
✉️ Contact Tim Sidock at tsidock@technationcanada.ca to discuss future opportunities.  

We look forward to seeing your ideas in the spotlight soon!