Bias and Representation in AI Generated Teaching Material

AI systems are trained on vast collections of text and images from the internet and other sources. These datasets reflect historical biases, underrepresentation of certain groups, and skewed coverage of topics. When an AI system generates text or images, these biases are present in the output. A system asked to generate images of a teacher might produce primarily images of white women. A system asked to generate examples of criminals might produce names and characteristics skewed toward particular ethnic groups. A system asked to generate role models might focus disproportionately on men or on narrow types of achievement.

Teachers who use AI-generated content without reviewing it for bias may inadvertently teach pupils biased perspectives. This is not an accident; it is a predictable consequence of training data. The responsibility for checking generated content falls on the teacher.

Common Biases in Generated Content

Gender bias is documented in AI systems, with women underrepresented in professional roles and overrepresented in caregiving roles. Ethnic bias leads to underrepresentation of certain groups in positive contexts and overrepresentation in negative contexts. Disability bias leads to erasure or tokenistic representation of people with disabilities. Age bias can lead to underrepresentation of older people in valued roles. Sexual orientation and gender identity bias leads to underrepresentation of non-heterosexual and non-cisgender people.

These biases do not occur because the system is deliberately programmed to discriminate. They occur because the training data reflects historical discrimination. The system learns to replicate patterns in the data.

Checking AI Generated Content

Before using AI-generated teaching materials with pupils, read them for representation and bias. If you are using images, look at diversity of gender and ethnicity, and whether different ages, abilities, and other characteristics are represented. If you are using text examples, check whether they include diverse names and professions, as well as varied family structures and perspectives. If an AI tool has generated role models or exemplars, check whether they represent a range of groups or whether they are skewed.

A simple check: if the AI system has generated examples of doctors and teachers, engineers and other professionals, look at whether the examples include women, people of colour, older people, and people with disabilities. If not, the output is reflecting bias and should not be used without modification.

Modifying and Supplementing

If AI-generated content contains bias, modify it before using it. Rewrite examples to include diverse names and backgrounds. Supplement AI-generated images with additional images that provide more diverse representation. Ask the AI tool to regenerate content with explicit instructions to include diverse representation. These steps require effort, but they prevent biased teaching.

Teaching Pupils About Bias in AI

Teach pupils to notice bias in AI-generated content. Have them evaluate AI-generated images and text for representation. Discuss why systems might produce biased output. This is valuable AI literacy that helps pupils become critical consumers of AI-generated content in their own lives.

Your Responsibility

Your responsibility is to ensure that teaching materials, whether human-created or AI-generated, present pupils with diverse representations and avoid perpetuating bias. Using AI-generated content does not reduce this responsibility; it requires more active checking and curation.