Can Ai Understand Culture

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Oct 22, 2026

5 minute read

Can AI understand culture? A retired teacher and high schooler put AI to the test

Consider this age-old riddle: “What kind of house do you enter blind and leave seeing?” While English speakers may instantly recognize the answer as “a school,” AI models can struggle with the same riddle if asked in another language. A Cohere Labs open science project revealed the complex nature of interpretation, and the results were surprising.

The word "riddle" comes from the Old English "rædan" which means "to interpret" or "to advise,” suggesting that riddles are about interpretation and insight rather than just guessing. Riddles thrive on ambiguity but have specific solutions. Just as humans get better at riddles through practice and feedback, AI improves its abilities through machine learning. “We could not believe our eyes… It is not ‘just’ research anymore, it IS a movement.”

• Boczka Károly

Hungarian diplomat Boczka Károly was actively exploring opportunities in AI research. When he decided to build tricky, Hungarian trivia-style riddles to test if AI models recognized cultural clues, he received stunning results. The models’ answers sounded right, but they weren't right. The models were missing culturally specific facts, and in certain instances, were hallucinating. In an open-science community discussion around culture in AI hosted by Cohere Labs, Boczka shared his findings with Julia Kreutzer. Kreutzer, the Staff Research Scientist at Cohere Labs, saw immediate potential to test the gap between AI fluency and cultural knowledge. Together, they globally scaled a project to test the fluency, logic, and reasoning within models trained mostly on English data and the blind spots behind each answer.

Participation in the project did not require expertise beyond identification with a culture and a commitment to writing and evaluating riddles, which opened the door for research participants of any age and background. An open call on social media and within the Cohere Labs existing research communities invited people to join the project with no entry criteria, deliberately removing barriers that typically restrict participation. The community gathered on Discord and built its own benchmarks across cultural segments like sports, history, and politics, with each question designed as a riddle.

Mr. Boczka's idea quickly grew into an international initiative. “We could not believe our eyes,” he says. “We had more than 100 contributors from more than 50 countries. It is not “just” research anymore, it IS a movement.” He continues, “Our collective was so dedicated, independent, and cooperative that the whole project grew like a well-planted plant.” Two contributing members within that well-rooted community were 16-year-old American Henry Gagnier and Canadian retired schoolteacher Jane Smith.

While most teenagers might be hesitant to join a global research team of this magnitude, Henry Gagnier’s infectious enthusiasm outweighed any preconceived notion of what a “typical” research team member should be. “I'd say that AI research is for everybody. There are so many different aspects of AI research. We needed people from many different cultures, and it's not ‘AI skills’ that are helpful for building this benchmark, it is real cultural knowledge.” When asked if he was intimidated or surprised by the group, he says, “What surprised me most was their inclusivity and openness, because I was always kind of intimidated as a high school student, joining this project and saying I want to be a lead for this team. But everyone was so nice. It was an amazing experience.”

This project, while grounded in cultural and semantic nuance, provided him with more insight into research and a possible future within it. “It gave me a nice sneak peek into what actual researchers are doing,” Gagnier says. “I want to continue research in this field. I'm doing some projects to further my knowledge in this field now, so I think this project is a good educational experience, and it's definitely changed my outlook on where I want to go in the future.”

As a retired teacher, Jane Smith took what she calls a “teacher approach” to her research. “When I would evaluate kids if they got something wrong, I always tried to back think it, like okay, ‘Where did it go wrong?’ ‘What led to this completely wrong answer?’ There...