AI systems can reflect and amplify biases present in their training data. CurricuLLM takes a proactive approach to detecting, mitigating, and responding to bias.

Our approach

CurricuLLM uses curriculum-grounded content as the primary knowledge source, which reduces the surface area for bias compared to general-purpose AI tools. The AI tutor is designed to guide learning through questioning and scaffolding rather than presenting opinions, which further limits the pathways through which bias can manifest.

Controls

Content filters screen AI outputs for harmful, discriminatory, or stereotyping language before they reach students.
Curriculum alignment ensures responses are grounded in authoritative, reviewed educational content rather than unconstrained model generation.
Safety Centre monitoring detects patterns that may indicate systematic bias across student interactions over time.
Incident classification — bias incidents are classified and managed through our AI Incident Management Plan, with defined escalation and remediation procedures.
Ongoing review of AI outputs across subjects, year levels, and demographics to identify emergent bias.

Recognising bias

Teachers and students should be alert to AI-generated content that:

Consistently presents one perspective while omitting others
Uses stereotyping language about any group (gender, ethnicity, disability, socioeconomic status, location)
Makes assumptions about a student's background, ability, or interests
Frames certain cultures, viewpoints, or experiences as default or "normal"

Reporting bias

If you notice content that seems biased, stereotyping, or unfair:

Students can use the thumbs down button on any message to flag it
Teachers can report concerns to hello@curricullm.com
All reports are reviewed and, where bias is confirmed, trigger our incident management process

Critical evaluation of AI-generated content — including being alert to potential bias — is an important skill that we encourage both teachers and students to develop.

AI systems can reflect and amplify biases present in their training data. CurricuLLM takes a proactive approach to detecting, mitigating, and responding to bias.

Our approach

Controls

Content filters screen AI outputs for harmful, discriminatory, or stereotyping language before they reach students.
Curriculum alignment ensures responses are grounded in authoritative, reviewed educational content rather than unconstrained model generation.
Safety Centre monitoring detects patterns that may indicate systematic bias across student interactions over time.
Incident classification — bias incidents are classified and managed through our AI Incident Management Plan, with defined escalation and remediation procedures.
Ongoing review of AI outputs across subjects, year levels, and demographics to identify emergent bias.

Recognising bias

Teachers and students should be alert to AI-generated content that:

Consistently presents one perspective while omitting others
Uses stereotyping language about any group (gender, ethnicity, disability, socioeconomic status, location)
Makes assumptions about a student's background, ability, or interests
Frames certain cultures, viewpoints, or experiences as default or "normal"

Reporting bias

If you notice content that seems biased, stereotyping, or unfair:

Students can use the thumbs down button on any message to flag it
Teachers can report concerns to hello@curricullm.com
All reports are reviewed and, where bias is confirmed, trigger our incident management process

Critical evaluation of AI-generated content — including being alert to potential bias — is an important skill that we encourage both teachers and students to develop.

10.6 Bias

Our approach

Controls

Recognising bias

Reporting bias

10.6 Bias

Our approach

Controls

Recognising bias

Reporting bias