ASE 2027 Annual Conference

This session explores how Micro: bit technology can be used alongside case studies and practical classroom activities to help students investigate how diseases spread.

Developed during the COVID pandemic through collaborative teacher learning, the approach was designed to model both airborne and more traditional contact-based transmission. Participants will see how these activities can support engagement with current, real-world science through problem solving, discussion, and enquiry.

The session will share the background to the work, outline how the activities were developed for classroom use, and demonstrate how Micro: bits and disease-transmission scenarios can be used to promote STEM learning and scientific sensemaking. Resources will be shared digitally with attendees.

1. Use case studies and Micro: bit technology to engage students in investigating disease transmission through relevant, real-world contexts.
2. Plan opportunities for students to apply science inquiry and problem-solving approaches as they analyse evidence and identify possible sources of infection.
3. Adapt an integrative STEM activity that supports exploration, sensemaking, and discussion of disease transmission in the classroom.

This interactive workshop introduces a proof-of-concept student toolkit for using spatial computing to support climate change education in primary-aged multicultural settings. The session draws on my doctoral research at the University of Leeds, which examines how spatial computing can be co-developed within a climate change play curriculum with Indonesian and Malaysian children living in the UK.

The aim of the session is to share a research-informed and practice-facing approach to climate change education that is child-centred, culturally responsive and suitable for primary-aged learners. Rather than presenting technology as a novelty, the session considers how virtual reality, augmented reality, digital mapping, LEGO robotics and 3D modelling can help children explore climate change causes, impacts, mitigation and adaptation through active, visual and playful learning.

The session will begin with a short introduction to the research context and the meaning of spatial computing in primary climate change education. I will then share examples from six participatory workshops, showing how children’s discussions, drawings, digital artefacts, mapping activities and reflections shaped the toolkit’s content, learning approaches and activity design. These examples will include children’s use of VR to explore climate-affected environments, digital maps to compare climate issues across countries, LEGO sensors to model environmental systems, and AR/3D modelling to design climate solutions.

The main focus will be the student toolkit developed from the research. By the time of the conference, this toolkit will have been reviewed with children through a co-review workshop. This is not presented as a final validation exercise, but as part of the wider co-development process, where children’s responses help refine the clarity, relevance and usability of the resource.

Participants will examine selected toolkit activities and consider how they might work in primary classrooms, after-school clubs, teacher education or informal learning settings. Using a short feedback template, they will reflect on the toolkit’s climate change content, use of spatial computing, opportunities for children’s participation, cultural responsiveness and practical classroom feasibility.

By the end of the session, participants will be able to identify ways of using spatial computing to support climate change learning, explain how children’s contributions can inform curriculum development, and critically consider how a proof-of-concept toolkit can be adapted for their own educational context.

Artificial Intelligence (AI) is rapidly transforming how students access information, construct knowledge, and engage with scientific ideas. For science educators, this presents both exciting opportunities and important responsibilities. This session explores how AI can be meaningfully and ethically integrated into science teaching to support inquiry-based learning, deepen conceptual understanding, and strengthen assessment practices across primary and secondary contexts.

Rather than positioning AI as a replacement for traditional teaching approaches, the session emphasises its role as a powerful partner in learning. Participants will explore how AI tools can help students generate hypotheses, visualise abstract processes, interpret data, model scientific phenomena, and refine scientific explanations. Practical classroom examples will demonstrate how AI can scaffold thinking while maintaining the integrity of disciplinary knowledge and scientific reasoning.

A key focus of the session is supporting teachers in developing students’ scientific literacy in an AI-rich environment. As learners increasingly rely on intelligent systems to gather and synthesise information, they must also learn to question sources, evaluate outputs critically, and recognise limitations and bias within AI-generated responses. Strategies will be shared for helping students become reflective users of AI who can distinguish between evidence-based reasoning and plausible but inaccurate outputs.

Participants will also examine how AI can support inclusive science education. AI-powered tools can help differentiate instruction, provide accessible explanations, support multilingual learners, and offer alternative ways for students to demonstrate understanding. Examples will highlight how thoughtful integration of AI can increase participation and confidence among learners who may otherwise feel excluded from science learning.

Assessment is another important dimension addressed in the session. As AI tools become more widely available, educators are reconsidering how to design tasks that promote authentic scientific thinking. The session will present approaches for designing assessments that value process, reasoning, and interpretation over simple recall, while also using AI to support formative feedback and student reflection. These approaches encourage learners to engage more deeply with scientific practices such as modelling, argumentation, and evaluation of evidence.

Ethical considerations will be woven throughout the session, including responsible classroom use, transparency, data awareness, and supporting students to understand how AI systems function. Participants will leave with practical strategies for introducing AI in ways that align with curriculum expectations and uphold professional standards.

This interactive session is designed for educators who are curious about using AI in science education but want practical guidance grounded in pedagogy rather than technology alone. Through discussion, examples, and collaborative reflection, participants will develop confidence in identifying opportunities where AI can enhance—not replace—effective science teaching.

By the end of the session, participants will have concrete ideas they can adapt immediately within their own classrooms and departments to support thoughtful, future-ready science learning.

This session will explore how AI is influencing trainee and early career teachers beyond practical use, focusing on professional identity development in a changing educational landscape.

You will hear findings from research into how trainee teachers are currently using AI to support planning, learning, reflection, and professional practice, alongside key themes around confidence, dependency, authenticity, and perceptions of expertise.

The session will then examine how AI may shape identity development during the transition from training into independent practice, raising questions around autonomy, judgement, professional voice, and the evolving role of the teacher.

The final part of the lecture will introduce the next phase of research focusing on ECTs, exploring identity development in the early years of teaching and launching a wider survey to gather insight into lived experiences of AI use in practice.



Outcomes:
Following this session, participants will hopefully be able to:

* Recognise key patterns in how trainee teachers engage with AI.
* Critically consider how AI may shape professional identity and autonomy.
* Reflect on implications for teacher education, mentoring, and induction.
* Identify emerging challenges around authenticity, confidence, and dependency.
* Engage with research-informed discussion around AI and identity development.
* Consider how AI use may influence teachers beyond efficiency and workload.

This session is grounded in research conducted at the University of Warwick exploring how trainee teachers are engaging with AI and how this intersects with their developing professional identity. As AI becomes increasingly embedded in educational practice, it raises important questions not only about workload and efficiency, but about how emerging teachers construct confidence, autonomy, expertise, and authenticity.

The project has explored how trainee teachers use AI for planning, idea generation, academic support, and reflective practice, alongside wider considerations around ethics, professional boundaries, and dependency. This work has led to a broader interest in how AI may continue to shape identity formation beyond initial teacher training. What the research has shown is that trainees are using AI, and the project is investigating what this means for both ITE and schools going forward.

The next phase of this research expands to newly practising teachers, examining how Early Career Teachers navigate AI use while developing identity, confidence, and professional agency in their early years of practice and we hope to promote this phase at the ASE Conference.

The session will explore how student teachers currently use generative AI critically, ethically and practically as part of their developing professional practice. Rather than presenting generative AI as a shortcut or a threat, the session will position AI as a tool that requires teacher judgement, subject knowledge and careful pedagogical decision-making. The session will highlight research conducted into how student teachers currently use generative AI in their teaching practice.


Participants will be able to:
- describe emerging patterns in how student teachers are using generative AI in their preparation, planning and professional learning;
- identify key opportunities, concerns and support needs arising from student teachers’ current AI use;
- evaluate AI-generated educational materials using criteria such as accuracy, appropriateness, inclusion, bias and pedagogical value;
- distinguish between AI use that supports professional learning and AI use that risks over-reliance or weak pedagogical decision-making;

This interactive session will explore how collaborative, cross-institutional communities of practice can support higher education science educators in responding to emerging pedagogical challenges. The session will introduce the collaborative work of the BERG Higher Education Working Group through two current thematic areas: AI and assessment and decolonising the curriculum.

Through a series of short reflective case studies and emerging examples from participating HEIs, contributors will share approaches, challenges, and ongoing questions relating to inclusive curricula, assessment design, and the impact of generative AI on teaching and learning.

Participants will then be invited to discuss their own institutional experiences, challenges, and priorities within these areas. The session will encourage collaborative dialogue across disciplines and institutions, recognising that many of these challenges extend beyond bioscience education alone.

The session will conclude with a shared reflection on the role of collaborative networks and communities of practice in supporting responsive, inclusive, and evidence-informed approaches to science education in higher education.

Outcomes: - Participants will be able to:

Share current challenges and emerging practices relating to AI, assessment, and inclusive curricula within higher education science education.

Reflect on how different HEIs are responding to shared pedagogical and curricular challenges.

Discuss opportunities and barriers associated with collaborative approaches to educational change.

Consider how cross-institutional communities of practice can support ongoing professional dialogue and development.

Higher education science educators are navigating rapid pedagogical, curricular, and technological change. In response to shared challenges across the sector, members of the Biology Education Research Group (BERG) Higher Education Working Group, part of the Royal Society of Biology, established collaborative thematic groups focused on AI and assessment, and decolonising the curriculum. These groups bring together educators and academics from multiple HEIs to share practice, reflect on emerging challenges, and explore approaches to inclusive and responsive science education.

Game-Based Learning (GBL) offers powerful opportunities to enhance engagement, motivation, and conceptual understanding in science education. As science educators seek approaches that support curiosity, resilience, and active participation, well-designed learning games—both digital and non-digital—can create meaningful contexts in which students explore scientific ideas through investigation, collaboration, and problem-solving. This session explores how game-based approaches can be integrated thoughtfully into science teaching to support disciplinary knowledge, scientific practices, and inclusive classroom participation.

Rather than treating games as rewards or add-ons, the session focuses on using game-based strategies as structured learning experiences that align with curriculum goals and support progression in scientific understanding. Participants will explore how game mechanics such as challenge, narrative, feedback, role-play, and iteration can be used to strengthen students’ reasoning, modelling, and evidence-based thinking. Examples will include classroom-ready activities that support topics such as ecosystems, forces, energy, particle models, and scientific enquiry skills across primary and secondary settings.

A key emphasis of the session is the role of games in supporting inquiry-based science learning. Game environments naturally encourage experimentation, hypothesis testing, and revision of ideas—processes that mirror authentic scientific practice. Participants will consider how carefully designed gameplay can help learners explore complex systems, visualize abstract concepts, and develop persistence when working through challenging ideas. These approaches help students move beyond memorization towards deeper conceptual understanding.

The session will also highlight how game-based learning can support inclusion and participation. Games can provide alternative entry points into science for learners who may not respond as positively to traditional instructional formats. Collaborative gameplay can encourage discussion, peer explanation, and shared reasoning, helping to build confidence and scientific identity among a wider range of students. Examples will demonstrate how low-cost and accessible game strategies can be adapted for diverse classroom contexts without requiring specialist technology.
Assessment is another important focus. Participants will explore how teachers can use gameplay to gather evidence of student thinking through observation, dialogue, and reflective activities embedded within game experiences. Approaches will be shared for designing purposeful learning games that make students’ reasoning visible while maintaining high expectations for scientific accuracy and rigor. These strategies support formative assessment while promoting engagement and curiosity.

Importantly, the session will address how teachers can select or adapt games in ways that maintain strong pedagogical purpose. Participants will consider how to evaluate whether a game supports conceptual learning rather than distraction, and how gameplay can be integrated within sequences of lessons rather than used in isolation. Practical frameworks will help teachers identify opportunities where game-based approaches can enhance—not replace—effective science teaching.

This interactive session is suitable for educators across primary and secondary phases who are interested in expanding their teaching repertoire through evidence-informed and classroom-ready strategies. Through discussion, shared examples, and collaborative reflection, participants will leave with practical ideas they can adapt immediately to support engaging, rigorous, and inclusive science learning through game-based approaches.

Aims:
•Learn about a range of free digital platforms that can be used to create video content
•Understand how the use of video content can transform learning, as shown by the PIC/RAT model
•Learn how to create their own videos or templates to scaffold tasks for pupils
From TikTok to Teams calls, many platforms offer video editing software features for free, therefore creating video content has never been easier.

‘If you truly understand something, you can teach it to others’
Getting pupils to make their own videos gives every learner a voice and develops a variety of additional transferable skills, ranging from literacy and oracy to collaboration and digital skills.

Join Dr Ben Scott as he showcases examples of pupil’s work that demonstrate their understanding of science ranging from digital lab report videos, creating models of atomic concepts and revision videos. These tasks engage and challenge learners to be more creative in how they express their understanding. Apart from showcasing some of his favourite go to tools, he will also demonstrate how he uses rubrics to design and mark these types of assessments.

Outcomes:
-Be more confident about harnessing video editing software in their own classrooms
-Be able to design rubrics to create and mark their own pupil video assignments
-Record their own videos to help support their own lessons and learners

Since Covid, Ben has been passionate about the use of technology in teaching and found many digital tools remained in his classroom when he returned to face-to-face teaching. This led to him becoming a digital research and development coordinator as part of the Teaching and Learning school team. Ben has been selected as a UK Adobe Innovator for the last two years, recognised for his creativity in science teaching. He is also a founding member of the National PedTech Partnership, a UK focus group of digital thought leaders who are developing a pedagogy first, technology second approach within their school institutions. Using a variety of digital tools, he continues to implement innovative ways to challenge pupil’s learning, through engaging and creative means, to assess their knowledge and understanding. Creating YouTube style video content has been particularly powerful.

Science teachers are increasingly working with students who encounter AI-generated information every day, both inside and outside school. From chatbot explanations to AI-generated revision materials and scientific imagery, young people are already navigating a world where information can appear authoritative while containing inaccuracies, misconceptions or fabricated content. This session argues that AI literacy belongs within science education because scientific thinking provides one of the strongest frameworks for evaluating AI-generated information critically and responsibly.

This interactive workshop will explore how KS3 science classrooms can support students in developing critical disciplinary literacy in response to the growing influence of generative AI. Rather than focusing on specific tools or software, the session will examine how existing approaches in science education, including evidence evaluation, identifying misconceptions, questioning claims, and communicating scientific ideas accurately, can help students engage thoughtfully with AI-generated content.
The session also considers the increasing importance of supporting students to critically engage with misinformation, disinformation and misleading online content, including scientific conspiracy claims and AI-generated inaccuracies encountered beyond the classroom.

Participants will leave with:
a clearer understanding of the implications of generative AI for science education;
practical classroom strategies for developing critical AI literacy through science;
approaches for helping students evaluate scientific claims and information critically;
increased confidence in discussing AI, accuracy, misinformation and scientific communication within KS3 science contexts;
ideas for embedding AI literacy within existing science curricula and pedagogical approaches.

In science we predominantly represent data numerically or graphically. Teachers, and students, are comfortable with the idea that we can represent the world by a series of numbers or a line on a graph.

Sonification is an approach to treating and analysing data through sound to create an auditory display. One example that has been fairly widely used is the representation of astronomical data and concepts, including in planetaria. One benefit for astronomers of using sound is because the dataset are extremely large which can make it hard to plot the data in a conventional manner i.e. graphically. However, sound can have a range of parameters such as pitch, volume, tempo and type of instrument. This variety means that it is possible to represent and comprehend a much more complex data set using sound.

In addition, sonification also has use to support people who are blind or vision impaired to access data and visual information more readily.

In this session, participants will make their own (simple) sonifications of different data, and consider how they could use sound to help students explore data in their classrooms.

AI technology is changing the fabric of scientific knowledge building.  In biology, for example, AlphaFold predicts the structure of proteins and other biomolecules, changing how drugs are developed and diseases are  studied. In medicine, AI-powered image generation is changing medical diagnostics. In chemistry, AI tools are automating and accelerating organic chemistry and material discovery. In physics, complex AI-based simulations accelerate research across topics, while AI-based robotics is driving change in engineering. Further, scientific inquiry requires new skills for scientists, such as designing ‘AI experiments’, navigating data ethics and governance, evaluating AI models, and interpreting AI system output.

How, then, is school science changing to teach about this new way of scientific working and these new discoveries? What new AI-related real-world science concepts and skills might be taught?

The aim of this workshop is to start this discussion. We will lead a workshop to brainstorm candidate uses of AI in real-world science and consider what concepts and skills might be introduced at different stages of science education.
We will provide examples of how basic AI literacy concepts have been introduced in existing AI lessons covering topics such as ecosystems and healthcare.  We will use these to spark discussion on what content might be taught in classroom science lessons to showcase `AI in real-world science’.  We are particularly interested in stimulating discussion on how scientific inquiry in school might change in the light of AI technology.
 
The outcome from this workshop will be that participants will become more familiar with AI literacy concepts and begin to form ideas about what AI literacy for Science might look like. They will also start to develop ideas for lessons that introduce `AI in real-world science’ into their science classrooms. We do not have the answers to what should be taught and how about `AI in real-world science’, but as computer science education researchers, we would like to facilitate this discussion with science educators to help work towards this goal.

This year's Talking Science explores the evolving role of teachers in a world increasingly shaped by artificial intelligence. As AI transforms how young people learn, communicate and access knowledge, what does this mean for the future of science teaching? The session will consider both the opportunities and challenges facing science educators in supporting young people to become scientifically literate.
Following short stimulus inputs from our speakers, delegates will discuss and prioritise their recommendations for policy and practice that will help ensure the benefits of AI are realised equitably across science education. The discussion will explore implications for curriculum, assessment, teacher expertise, equity and students' future engagement with science.
SEPA intends to produce a short summary of key themes emerging from the discussion.

In common with our previous Talking Science sessions, this session promises thought-provoking insights and lively debate. We look forward to seeing you there and your contributions.

The Science Education Policy Alliance (SEPA) brings together the Association for Science Education, Institute of Physics, Royal Society, Royal Society of Biology and Royal Society of Chemistry to coordinate and collaborate on science education policy www.se-pa.co.uk

Refreshments will be served at the end of this session, courtesy of SEPA, for 25 minutes.

You may know the idea of the Third Teacher: the Reggio Emilia principle that the physical environment is as formative as any instructor. What might this mean for your science classroom?

This interactive workshop explores how conscious spatial design — layout, naturalness, stimulation, and pupil-controlled affordances — can deepen engagement, support scientific thinking, and strengthen collaborative inquiry. The session itself is designed to demonstrate the very principles it explores.

You'll move through three activities — individual reflection, small-group application, and collective visioning — surfacing answers grounded in your own classroom experience and actionable research. You'll leave with concrete techniques to apply immediately, and the opportunity to help shape what comes next: a founding circle for a UK-wide action research network exploring classroom design in secondary science at scale. This is the future of science education — and we'll build it together.

Participants will be able to:
•Reflect critically on how their classroom's physical features currently support or constrain pupil engagement, cognition, and behaviour
•Apply evidence-based spatial design techniques to enhance science learning outcomes immediately
•Join a founding action research network with shared resources, peer collaboration, and opportunities to both contribute to and benefit from emerging findings

This session aims to:
• convene a founding circle for a UK-wide action research network exploring the transformative potential of classroom design in secondary science settings
• empower teachers and practitioners with evidence-based, immediately applicable answers, built directly from their own classroom experience

Background: Across years of action researching transformative learning experiences, a pattern emerged: expert facilitators consistently build a deliberate relationship with the physical space before their sessions begin. This observation sparked a master's thesis — An Architecture for Empowerment: Exploring How Physical Environments Co-Create Human Capacity — synthesising environmental psychology, 4E Cognition, neuroscience, and architectural theory. Its core finding: physical environments are not backdrops to learning but active co-creators of cognitive, emotional, and relational capacity. Science classrooms — with their fixed benches, equipment-laden surfaces, and competing sensory demands — represent a largely untapped frontier for this work.

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.