ASE 2027 Annual Conference

Is Generative AI fair for all science learners? The Department for Education names supporting personalised learning as a use case for generative AI in education, while warning that generative AI content can be inaccurate, biased or out of context. Science teachers carry responsibility for managing diversity, equity, inclusion and supporting identity formation in their classrooms. Generative AI is now sits inside that responsibility, often invisibly, as it infers things about students from their names, work, vocabulary and interests.

This accessible session widens the conversation from bias to fairness, looking at how generative AI shapes aspects such as cognitive challenge, assessment, the language and the framing different students encounter in their science learning. Building on research presented at ASE in 2026 (Hedlund, 2025) and on ongoing work into bias and fairness through names and assessment, the session turns research into practical guidance for science classrooms across all phases.

The session is built around being the informed human in the science learning loop. You will explore what to watch for, the quick wins that improve fairness, the mitigated prompts and processes that change what generative AI gives to your students, and how to model critical generative AI use to the students you teach. The session is pitched for a range of confidence levels with generative AI, and is grounded throughout in the realities of the science classroom.

Outcomes

Delegates will be able to:
• Recognise when generative AI is inferring student identity and changing science output as a result.
• Use specific strategies, prompts and processes that support fair generative AI use in their classroom.
• Model critical generative AI use to their students as part of wider digital literacy in the new Curriculum.
• Discuss the fairness implications of generative AI in science learning with colleagues, leaders and students.