In a quiet lab nestled within the tree-lined campus of Sokoine University of Agriculture, a quiet revolution is underway. Catherine Francis Mangare, a 35-year-old assistant lecturer turned PhD researcher, is one of the most unlikely yet formidable forces shaping the future of artificial intelligence in Tanzania.
“Before, I didn’t even know about Zindi Africa,” she said, referring to the pan-African data science platform that has become a training ground for aspiring AI practitioners. “But once I joined the Youth Empowerment through the Establishment of Social Innovation (YEESI lab) — our innovation and entrepreneurship space for youth — everything changed.”
Mangare is currently on study leave, pursuing her PhD in computer science at Okayama University in Japan, with a focus on data mining. But her roots, and her mission, are firmly planted back home in Tanzania, where she plans to return by 2027 to continue mentoring students and leading practical research.
Her journey from dreaming of piloting aircraft to piloting deep neural networks reveals a deeper truth: Africa’s tech transformation is being driven from the ground up, by people who were never supposed to be in the room.
The Pivot That Sparked a Movement
Mangare’s path to AI wasn’t linear. “When I was young, I had a mentor who was a copilot. So I wanted to be a pilot,” she recalls. But life had other plans. Her high school grades derailed her aviation ambitions. She pivoted into ICT on a friend’s advice and never looked back. What began as a compromise soon became a calling.
“During my undergraduate, I studied informatics. For my master’s, it was computer science, but I focused on information systems — there was no AI back then,” she recalled. It wasn’t until she became a core member of the YEESI Lab that the world of machine learning opened up to her.
That exposure gave her more than just new knowledge — it gave her a role. “It made me automatically a mentor to my students,” she said. “And from then, I’ve never looked back.”
She now teaches computer science and informatics while researching data mining techniques for agriculture. Her real impact, however, lies in how she uses her platform to bring others along—especially young students with zero exposure to AI. “There’s no way you can run away from artificial intelligence,” she says, “so why not run toward it?”
Zindi: More Than Just a Competition
Zindi has been instrumental in shaping Mangare’s academic evolution. She joined the platform in 2022, participating in competitions that bring together coders, engineers, and data scientists to solve real-world problems using machine learning.
“What I know now is the division of problems, like starting from reading the data, going to cleaning the data, and then up to where a model is. Zindi gives us the template, like the Python template, where you just see the data flowing,” she says. “Before I didn’t know about that, but now Zindi has given me an opportunity to integrate the JSP files to Python, and to the Notebook files.”
In one challenge, she helped develop models to predict waterborne diseases, first using classical ML models, then pivoting to deep neural networks when results fell short. “I saw how model performance changed, and I learned how to structure a real AI workflow—from cleaning data to building production-ready models,” she says.
But Zindi’s true power lies in accessibility. With a Python notebook, a dataset, and a deadline, students are pulled into global-scale problems. Mangare used that momentum to revamp student learning at her university—adding AI content to the curriculum, creating tutorials on YouTube, and offering peer-led bootcamps.
Even so, the journey is not without challenges. “When I compete in Zindi challenges, and I see peers from Senegal or Tunisia ahead of us, I realize we still have work to do,” Mangare admits. “But it pushes me. I want to help close that gap.”
The results have been tangible. Two of Mangare’s former students, Margaret Malongo and Fikiri Matatizo, have begun applying AI skills in the real world. Malongo, now an IT technician in Bukoba, says her exposure to AI has made her more effective in her work. “She gives feedback all the time,” Mangare says proudly. “She can now use Python and machine learning to solve tasks that would’ve been hard with traditional tools like PHP or MySQL.”
From Beans to Bots: The AI-Agriculture Frontier
If you want to see AI in action in Tanzania, look to the fields.
Mangare’s research focuses on disease prediction in crops like maize and beans, combining plant biology with AI image classification. Her team collaborates closely with agricultural scientists to label datasets and validate model accuracy. “It’s hyperlocal work,” she explains. “These are real diseases affecting real farmers. The impact is immediate.”
And the ambition doesn’t stop at prediction. Her mentor, Dr. Kadege Fue, has built a robotic vehicle that autonomously distributes fertilizer based on soil moisture detected by sensors—essentially precision farming, built in a Tanzanian lab.
“It uses sensors to determine soil moisture and calculates how much fertilizer to apply,” she explains. “It’s more precise than a human — no fatigue, no shortcuts. Sure, the initial cost is high, but once it’s operational, it outperforms humans.”
This is where Zindi fits in again: many of the challenges on the platform mirror these real-world use cases. “What we test on Zindi, we bring to the farm,” says Mangare. “It’s not theory—it’s field-ready.”
The Swahili AI Revolution
One of the biggest hurdles facing AI in Africa is language. While global LLMs have expanded support for non-English languages, tools like ChatGPT are only just beginning to integrate Swahili. But Tanzanian developers aren’t waiting around.
Mangare’s colleagues are building Swahili chatbots and NLP models from the ground up. One project, Mkulima GPT, developed by her colleague Dr Theofrida Maginga, helps farmers in rural areas get answers to crop questions in their native language.
“The chatbot advises them through Swahili. Our project which we proposed in Morocco helps to give patients medical prescriptions using Swahili language,” she explains. “Especially for those people who are required to go for the clinical check-ups. And since Swahili is now coming to the East African community, we need to work hard on integrating AI to Swahili language.”
Another effort involves Swahili news classification, with datasets partially sourced from Zindi competitions.
The goal is bold: to create a Swahili-centered AI ecosystem that doesn’t just translate global tech, but originates local innovation.
When she returns to Tanzania in 2027, Mangare plans to make simulations the core of her teaching. “We don’t have access to a lot of equipment back home,” she said. “But simulations allow students to use software to recreate real-world scenarios. It’s cost-effective and still impactful.”
She’s already designing modules to help students model systems like the fertilizer robot or disease classifiers in virtual environments. “It’s about making learning practical. Students need to feel like they’re solving real problems.”
Scaling AI From the South
Zindi may not have the brand cachet of Kaggle or OpenAI, but its impact across Africa is tangible. It’s a platform that democratizes access to cutting-edge problems—water sanitation, climate data, public health—and it does so in a way that prioritizes African languages, infrastructures, and realities.
Mangare’s story is a microcosm of this movement. A single lecturer, motivated by a missed childhood dream, is now inspiring dozens of young Tanzanians to become leaders in AI. Some might end up building the next big LLM. Others might design a robot that saves harvests. All of them are proof that talent is everywhere—opportunity isn’t.
When asked what advice she has for young Tanzanians curious about AI, her answer is both poetic and practical. “Programming is like mathematics,” she said. “You can only learn by practising. Competitions like Zindi will show you where you are — and where you need to go. Use AI for the future. Take the framework and put it into reality.”
As the sun rises over the red hills of Morogoro, it casts a long shadow over a university lab where AI isn’t just a futuristic ideal — it’s a tangible path to empowerment. And at the centre of that lab, in silence and persistence, Catherine Francis Mangare is rewriting what’s possible for a new generation.
That’s where Zindi comes in. It’s not flashy. It’s not VC-funded hype. It’s just working.
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Emmanuel Benson 
ImpactAiNews