ASE 2027 Annual Conference

We've all seen it: the student who can't focus for ten minutes in class will happily watch twenty or thirty science videos in a row on their phones.

Online science education has grown into a high-quality, mainstream part of how young people learn, sitting alongside the classroom rather than at its edges, and the best creators are now among the most influential science educators in the country. So what do they know that those in the classroom can borrow?

This session, chaired by Dave Allen, brings together some of the most successful science educators working on YouTube, TikTok and beyond for an open Q&A. Ask them how they grab and hold attention, turn a tricky topic into something memorable, and keep the science accurate under the pressure of the feed. It's a practical, honest conversation about the craft of engagement, the quality of learning that online education can deliver, and what genuinely transfers from a phone screen to a classroom.

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.

This interactive workshop will give you a grounded, practical exploration of how AI can support workload reduction without creating additional stress, unrealistic expectations, or dependence on technology.

You will explore real examples of where AI can save time, including planning support, summarising information, drafting routine communications, adapting content for different audiences, generating starting points for resources, and reducing repetitive administrative tasks. Through discussion and short practical activities, you will consider how to evaluate when AI is genuinely useful, when it may be inefficient, and where caution is needed.

A key focus will be on critical use rather than blind adoption. You will consider issues such as accuracy, bias, safeguarding, privacy, and the importance of maintaining professional oversight.

Designed for educators and professionals who are curious about AI but want realistic, manageable approaches rather than technical deep dives or exaggerated claims, this session will leave you with practical ideas, increased confidence, and a clearer understanding of how AI can support, but not replace professional expertise.

Following this session, participants hopefully will be able to:

* Identify appropriate tasks where AI may reduce workload effectively.
* Critically evaluate whether AI is the right tool for a specific task.
* Apply simple AI-supported strategies to improve efficiency in planning, communication, and organisation.
* Recognise common risks and limitations when using AI in professional settings.
* Use AI more intentionally, ethically, and with clearer professional boundaries.
* Trial at least one realistic AI workflow within their own practice.

AS students increasingly turn to tools like ChatGPT and Gemini, we introduce a student-agency framework to help learners use AI more critically rather than over-relying on it.

In this workshop computer science education researchers will work with science educators to begin adapting the agency framework for use in the science classroom.

Generative AI produced images are quickly becoming part of how primary teachers prepare and run lessons, but how do we know if these images are scientifically sound? In this session we share findings from our research showing how generative AI can both reinforce known misconceptions and introduce new ones, particularly when teachers ‘personalise’ content for their pupils, which is a specified use case by the DfE for Generative AI use in education.

During the session you'll see worked examples from common primary science topics. You'll then have the chance to generate, examine and discuss images with other delegates, with prepared images available if you don't have a device.

The session is pitched for a range of confidence levels with AI, including those just starting out, while respecting that many primary colleagues already have significant generative AI expertise. You'll leave with a framework and checklist to evaluate generative AI produced images for misconceptions, plus prompt strategies to improve the science quality of what you (and your pupils) create.

Delegates will be able to:
• Recognise common misconceptions reinforced or introduced by generative AI produced primary science images.
• Use a clear framework and checklist to appraise generative AI visuals before using them in lessons.
• Apply prompting strategies that reduce the risk of misconception-rich outputs.
• Confidently discuss generative AI image quality with colleagues and pupils as part of wider digital literacy.

Background: Helen Harden, former chemistry lead on the University of York's BEST project and chemistry curriculum consultant, brings extensive expertise in primary and secondary science misconceptions. Victoria Hedlund, founder of GenEd Labs and contributor to the DfE and Chartered College's Safe and Effective Use of Generative AI in Education guidance, brings critical oversight of generative AI in classroom use. Our shared interest in this area is set out in our article “Looking deeper into AI-generated visual explanations” to be published in the July 2026 edition of SSR with a primary-focused article planned for ASE’s Primary Science journal. This forms the precursor to a larger ongoing research project.