What Are Black-Box Algorithms?
AI tools deliver answers in seconds – but often without showing how they were produced. That makes them both convenient and risky in the classroom. This article explains what black-box algorithms are, why they are challenging in education, and how to use them responsibly.
TL;DR Summary
Black-box AI gives answers without showing the “how”. Handy for spellcheck, risky for tests or grading. Teachers & pupils should always ask: Where does this come from? What rules or sources? How can I check it?
What Is a Black-Box?
A black-box algorithm works like a closed box:
- Input: data goes in,
- Output: a result comes out,
- Process: remains largely hidden.
The opposite is a glass-box or white-box: models where the reasoning is visible, such as a decision tree or a simple formula.
With complex AI, such as neural networks or large language models, the workings are often so intricate that full transparency is impossible. Techniques exist to explain parts of the process (explainable AI), but they only provide limited insight.
Examples in the Classroom
- Test generators: create questions without showing which sources were used. Teachers cannot easily check whether they match the curriculum.
- Summarisation apps: shorten texts without explaining what information was removed or why. Pupils miss out on learning how to summarise effectively.
- Chatbots: such as ChatGPT and other generative AI systems give convincing answers without sources or reasoning. Pupils often accept these uncritically.
Why Is This a Problem?
- Teachers: difficult to judge whether pupils truly understand the material.
- School leadership: hard to justify the reliability of AI decisions to parents or inspectors.
- Pupils: risk of trusting AI outputs without checking them critically.
Not Always Bad
Black-box AI is not inherently useless. For routine tasks – such as spellchecking or sorting data – a black-box can be perfectly adequate. But when AI influences assessment, opportunities, or learning outcomes, transparency becomes essential.
Our View
Working with black-box AI requires more than technical knowledge. It is a new core skill in AI literacy: recognising when a black-box is acceptable, and when explanation and accountability are vital. Teachers need to look beyond outputs and help pupils to question, verify, and reflect on the use of AI.
What Can You Do?
Always ask three questions when using AI tools:
- Where does this output come from?
- Which sources or rules were used?
- How can I check if this is reliable?
Read the full article series
This article is part of our series on transparency in AI:
- Transparency in AI decisions: what schools should demand – why insight into AI outcomes is crucial for trust and fairness
- Explainable AI: core principles for schools – how XAI helps pupils and teachers remain critical and use AI responsibly
- What are black-box algorithms?> – the risks of opaque AI and how schools can deal with them wisely (you are here)