Technology is most useful when people can understand it in the language they use every day. As FAIRIST explores AlifBot, an early-stage AI learning concept for non-native Arabic speakers, one question has become especially important: how can a modern digital tool communicate naturally with Urdu-speaking users?
Urdu and the language of technology
Urdu has a rich literary and cultural tradition. Yet many everyday technology terms still arrive in English, often written in Urdu script. Words such as “app”, “download”, “cloud”, and “settings” may be familiar to many users, but they do not always explain a feature clearly or consistently.
This is not a weakness in Urdu. Languages grow when communities, educators, designers, and institutions develop shared ways to describe new ideas. For digital services, clearer terminology can help users understand what a tool does, make informed choices, and use it with confidence.
What AlifBot is exploring
AlifBot is a FAIRIST prototype concept that explores how AI-supported tools may help non-native Arabic speakers practise and memorise Quranic recitation. The project is still being developed and has not yet launched as a public service.
Its intended interface raises practical language questions. How should a user understand a prompt for practice, feedback, or a correction? Which Urdu words feel natural, respectful, and clear? A direct translation may be technically accurate but still feel unfamiliar in an app interface.
Keeping language clear without losing meaning
During early design discussions, terms such as “recitation mode”, “practice mode”, “speech feedback”, and “real-time correction” need careful treatment. The aim is not to replace every borrowed word. It is to choose wording that helps a person understand the action they need to take.
For example, “recitation mode” can be expressed through a familiar phrase centred on تلاوت, while a label such as “practice and repeat” may explain a feature more clearly than a literal technical translation. Testing these choices with intended users will be essential before any pilot.
Technical terms in plain English
AlifBot is exploring speech-based AI approaches that could support practice and feedback. Two terms often used in this work are:
- LSTM-RNN: a type of AI model designed to recognise patterns over time. In simple terms, it can help a computer consider the sequence of sounds in speech rather than treating every sound separately.
- MFCC: a way of turning audio into measurable sound features that a computer can analyse. It helps a system focus on characteristics of speech that may be useful for recognition.
These methods are research tools, not a guarantee of accuracy. Any future AlifBot pilot would need careful testing, expert guidance, and clear communication about what the tool can and cannot do.
A wider opportunity
The AlifBot concept points to a wider opportunity for Urdu in digital technology. Consistent terminology, plain-language design, and user testing can make AI tools more approachable for people who prefer Urdu. This work also supports a larger FAIRIST principle: technology should adapt to people, rather than asking people to adapt to technology.
As Urdu-speaking communities engage with more digital services, developing clear and usable language will help strengthen access, participation, and trust. AlifBot is one small exploration within that wider conversation.