Bronze Awards are typically completed by students aged 11+. They complete a ten-hour project which is a perfect introduction to STEM project work. Over the course of the project, teams of students design their own investigation, record their findings, and reflect on their learnings. This process gives students a taste of what it is like to be a scientist or engineer in the real-world.
Silver Awards are typically completed by students aged 14+ over thirty hours. Project work at Silver level is designed to stretch your students and enrich their STEM studies. Students direct the project, determining the project’s aim and how they will achieve it. They carry out the project, record and analyse their results and reflect on the project and their learnings. All Silver projects are assessed by CREST assessors via our online platform.
Gold Awards are typically completed by students aged 16+ over seventy hours. Students’ projects are self-directed, longer term and immerse them in real research. At this level, we recommend students work with a mentor from their chosen STEM field of study. All Gold projects are assessed by CREST assessors via our online platform. There are more CREST approved resources that have been developed by our partners and providers specific to your region.
To browse the briefs, click the buttons below or scroll down.
Teacher guide Disease detection Machine learning is already being used to diagnose lung cancer, pneumonia, and other diseases by the healthcare industry. It has proven to be incredibly accurate and efficient at diagnosis, and it is likely that continued development will mean that these technologies will be even more accurate in the future. However, awareness and understanding of machine learning amongst the general population is still relatively low, and trust is a big issue for the use of artificial intelligence and machine learning in the healthcare industry. In this project, students will create a communication campaign to raise awareness about how machine learning can fight disease. Students will need to think about what form their campaign will take, how to conduct a fair test to evaluate the effectiveness of their campaign, and how to collect and analyse their data. Prompts • What format(s) will your communication campaign take? Have you thought about the most effective way to reach your target audience? Find out what kinds of media your target audience consumes. • What will be the key messages? Research what makes an effective campaign and consider how many messages are you trying to get across? What wording will work best? • How will you use your understanding of people’s hopes and fears to help shape your work? How will you involve people in the decision making? 6
Student brief Disease detection Machine learning is already being used to diagnose lung cancer, pneumonia, and other diseases by the healthcare industry. It has proven to be incredibly accurate and efficient at diagnosis, and it is likely that continued development will mean that these technologies will be even more accurate in the future. But awareness and understanding of machine learning amongst the general population is still relatively low, and trust is a big issue for the use of AI and machine learning in the healthcare industry. Imagine you work for the public health department of your local council. The doctors in your area are unhappy because when they send test samples to the laboratory for analysis the results are taking longer and longer to come back. Other areas are starting to use machine learning tools to analyse all kinds of test results - x-rays, MRI scans, eye tests, and more. You want to start using these tools in your area too, but before you do, you want to consult local people. Create a communication campaign to raise awareness about how machine learning can fight disease and start a conversation about how machine learning should be used in healthcare. Getting started In this project, you will need to create a communication campaign to raise awareness about how machine learning can be used to detect illnesses and diseases. You will also need to think about ways of including different stakeholders in the decisions about how machine learning should be used. You could do a survey of people from different demographics about their views on machine learning and healthcare. Find out what their hopes or concerns are, and design a process that engages with those concerns. Useful resources • Spotting early warning signs https://www.bbc.com/future/article/20190116- the-invisible-warning-signs-that-predict-yourfuture-health • Eye disease https://www.bbc.com/news/health-44924948 • AI pacemakers https://www.bbc.com/future/article/20191216- how-hacking-the-human-heart-could-replacepill-popping • Malaria, cancer and sight loss https://www.bbc.com/future/article/20170914- spotting-cancer-stopping-shootings-how-aiprotects-us • Trust and machine learning in healthcare https://edition.cnn.com/2019/07/15/business/art ificial-intelligence-healthcare/index.html • Towards trustable machine learning: https://www.nature.com/articles/s41551-018- 0315-x Health and safety To avoid any accidents, make sure you stick to the following health and safety guidelines before getting started: • Find out if any of the materials, equipment or methods are hazardous using http://science.cleapss.org.uk/Resources/Stu dent-Safety-Sheets/ to assess the risks (think about what could go wrong and how serious it might be). • Decide what you need to do to reduce any risks (such as wearing personal protective equipment, knowing how to deal with emergencies and so on). • Make sure there is plenty of space to work. • Clear up slip or trip hazards promptly. • Make sure your teacher agrees with your plan and risk assessment. 7