Education

Overview and Inspirations

Inspired by the belief that knowledge is the catalyst for change, our education outreach initiative is designed to bridge the scientific community and the public for a sustainable future. In an era with complex global challenges like plastic pollution, we cannot allow innovative solutions to remain confined to laboratories and academic papers. Through a variety of approaches including interactive sharing sessions, hands-on project exhibitions, data science experience courses, clean recycling events, tailored educational booklets, and surveys to collect feedback and suggestions about the educational booklet and project exhibition, as well as board game design, we not only disseminate knowledge but also listen to public concerns and values through two-way dialogue.

Our approaches are tailored to resonate with a wide spectrum of learners, including primary school students, secondary school students, parents, and the general public. Our mission is to translate cutting-edge science and technology into accessible, engaging, and actionable knowledge, empowering everyone to be part of the solution. The greatest challenge and most important goal is to make complex concepts simple without being simplistic. We break down the key messages as follows:

  1. How does our engineered biosensor work?
  2. How to raise public awareness about environmental justice, a concept that is not yet widely known?
  3. How do the AI models Mask-RCNN and YOLOv8 work? How do these AI models detect and quantify microplastics?
  4. How to achieve clean recycling?


Sharing Sessions in Primary and Secondary Schools

Our team conducted various sharing sessions at primary and secondary schools, reaching 600 F.1 to F.5 students in G.T. (Ellen Yeung) College Secondary Section on 7th July; 300 P.4 to P.6 students in G.T. (Ellen Yeung) College Primary Section on 8th July; 600 F.1 to F.5 students from Queen Elizabeth School on 9th July; and 287 P.3, P.4, and P.6 students from Salesian Yip Hon Primary School.


Inspired by suggestions from Dr. David Jones, we adopted flexibility in our presentation approach. We adjusted our presentations to different age groups, ensuring a diverse range of audiences could engage. For primary school students, we made the sharing sessions more fun, interactive, and easy to understand.


To simplify the concept of biosensors, we used an analogy to explain what a biosensor is and how it can be used to detect microplastics. The analogy was as follows: We compared a biosensor to a typical Lego Mindstorm piece. On the screen, there were two Lego sensor pieces and a light bulb. The Lego sensor on the left side represented LasR in the sensory module, while the Lego sensor on the right side represented pLasRL in the reporting module. They were connected to a light bulb (hydrogel of X-gal assay). When a beam of light (AHL molecule secreted from bacteria adhering to microplastics) entered the Lego sensor on the left side, it (the LasR-AHL complex) transmitted a signal to the Lego sensor on the right side, which converted the signal into a current, causing the light bulb to light up (resulting in blue coloration in the hydrogel).


Secondly, we modified our presentation by using larger, colorful pictures and breaking down the concepts into separate slides. For example, we used colorful shapes to represent the basic components of the BioBrick circuit and taught students the sequence of the basic BioBrick circuit: Promoter → Ribosome Binding Site → Coding Sequence → Terminator. We then invited two groups of four students each to come on stage. Each student was given a cardboard with one of the four components of the BioBrick circuit. They had to arrange the components in the correct sequence of the circuit. The students actively participated in the game and were able to arrange the correct sequence successfully.


We brought up the idea of environmental justice with some examples, such as the illegal plastic recycling factories in Jenjarom and the Flint water crisis. Then we used an innovative way to encourage active participation of students, which is an interactive video about environmental justice. Students were fully engaged in a choice-driven video in which they were told to be a boss of a restaurant and have to make choices on several decision such as what packaging of straw, plastic or paper to choose and how to dispose of plastic waste, dump into Less Developed Country which is cheaper or go recycle which is more expensive. They create a unique story and ending to the video. Students can raise up their hands to decide how the story goes on, and different choices lead to different endings. We hope to make new and difficult topics much easier for the students to understand by letting them really participate as a character in the story.


To make students understand the working principles of AI models, we used “Where is Wally”, a series of children's books where the goal is to find a distinctively dressed character, Wally hidden within highly detailed illustrations to explain the one step and two steps process of AI models, Mask-RCNN and YOLOv8. We also used robot performance to show the comparison results of three AI model that our team used in building the suitable model installed in the robotic car. Robot with the best score represent the best AI model performance which is YOLOv8 in our project.


We also educated students how to clean recycle beverage carton by live demonstration during sharing session. In all sharing sessions, students were fully engaged and gave very positive responses.



Data Science Experience Course for S1 to S5 students

We organized Data Science Experience Course on 23rd April, a course designed by our Technology Team members. The course aimed to allow students to learn more about the AI models, Mask-RCNN, RT-DETR and YOLO v8, and have a hands-on experience about plastic tagging. We hope the activity will allow students to acquire practical skills on training an AI Model and how to make good use of large datasets.


The course began by contrasting the traditional, manual method of microplastic collection with the potential of AI-driven automation, followed by an introduction to the viable AI models (Mask RCNN, YOLOv8, and RT-DETR) for detecting microplastics. We also displayed some work from previous research and what is being done by the Technology Team and showed the results for the images of the microplastics after they were processed by the AI models.


The course was a great testing ground for us; we identified key areas for improvement. Our content was too technical, with terms such as 'RolAlign' and 'FCN', which were proven to be too advanced for participants. Thus, we made our content less in-depth and using more analogies in subsequent courses in September, which were less demanding without removing the core contents and key points we wanted to deliver. It was proved successful, as the participants gained a good understanding of the use of AI in microplastic detection and the integration of synthetic biology with AI.


Benefiting from the experience we gained and our overall growth in knowledge during the summer, we revamped our presentation by delivering complex ideas through analogies, such as using the popular "Where's Wally?" game to demystify how AI models like Mask-RCNN and YOLOv8 perform object detection. We also explained the processes of developing an effective AI model (train, verify, test) by using the way students prepare for exams. Besides, there was a plastic tagging activity to teach students how to use Roboflow annotation tool to tag the microplastics taken by our team.


Furthermore, we split the event into 3 days, one for Grade 7 students, one for grade 8 students, and the final day for grade 9 to 11 students. This arrangement allowed us to adjust our difficulties based on students’ academic level and maturity. This was especially beneficial for explaining machine learning where mathematics abilities are involved. For example, the equation y=mx+c may be relatable to gradient descent for G9 to 11 students.


We conducted pre-surveys and post-surveys to study the responses of participants. The surveys showed strong evidence that participants had a substantial increase in their understanding of our contents. From the first question, it can also be concluded that participants, after the course, have a strong understanding of the fundamental regarding the AI models as the number of students understand the working principle of the two models increased from 55.6% to 88.6%.


Pre-survey


Post-survey


At the end of the course, we collected valuable feedback together with the post-survey. Nearly all participants strongly agree or agree that they learned more about different AI models’ algorithms. Also more than 95% participants strongly agree and agree that they understand how plastic tagging could be done. Together with the performance on the post-survey, indicates the event was a success.




iGEM Board Game

Our iGEM Board Game is an augmentation to our education booklet and sharing sessions. Inspired by "Snakes and Ladders," the game board is color-coded to represent key project categories: Plastic Pollution (brown), Technology (blue), Synthetic Biology Research (green), and Environmental Justice (yellow). Players answer category-specific questions when landing on a colored tile, reinforcing knowledge from our Education Booklet and Sharing Sessions. Each category features unique gameplay mechanics, such as building a synthetic biological circuit in Research or analyzing case studies in Environmental Justice, ensuring content recall and engagement.


We have had a preliminary trial of the board game within our team. The game was executed smoothly, and we can’t wait for our students to try out our game



Elementary Education & Communications booklet

The elementary Education & Communications booklet, targeted for upper primary students and junior secondary students aims to educate and raise readers’ awareness of plastic pollution and environmental justice. The booklet also introduce them to our project integrating biosensors and AI vehicles. There are interactive games, such as quiz corners, crosswords, mazes, and spot the plastic games, which transform the booklet into a new, innovative medium to make challenging synthetic biology topics easy and fun to understand. On 2nd September, we invited F.1 to F.2 students of our school to read the educational booklet during class reading period and fill in the survey to provide us suggestion to improve.



Here is the summary of feedback:

We received 204 responses, of which approximately 96% were from junior form students (Forms 1 to 2), with the remainder of around 4% were from senior form students.

Among the 204 responses, 66% of the respondents rated our educational booklets with the clarity of writing and explanations of 4 or 5 (maximum: 5). It shows that most of the junior form students of GT College could understand the contents of our educational booklets.


For the depth and technicality of the content, 68% of the respondents rated our educational booklets with the rated 4 or 5 out of 5 for our educational booklets. This shows that the technical contents level of our educational booklets is suitable for their readings.


For the usefulness of diagrams/ illustrations used in our educational booklets, 60% respondents rated 4 to 5 (maximum: 5).


For the logical flow and structure of our educational booklets, over 66% of respondents rated 4 or 5 (maximum: 5).


For the overall readability of our educational booklets, over 66% of the respondents rated 4 or 5 (maximum: 5).


From the overall readability and the logical flow and structure ratings, we can see that our educational booklets are suitable for most junior form students in which they can understand the content of our booklets. We will further revise the booklet by providing more relevant illustration to make the contents more easy to understand.


In addition, we also received valuable suggestions from the survey, such as including more pictures or photographs instead of text. Some respondents wrote comments on “not wanting microplastic to pollute the ocean” or “don’t want to use much plastic” after reading our booklets. Also, some suggest us to add a brief glossary in the booklets. All these comments and suggestions are recorded for the improvement of our booklets. Overall, nearly all respondents are satisfied with our educational booklets.


Some valuable respondents’ comments:


We also conduct a short quiz to study the level of understanding of readers about booklet contents. Here are the summary:


Most of the people (around 95%) answered question 1 about biosensors correctly. It shows that nearly all respondents acquire knowledge on what biosensors can detect for pollution.


For questions 2, 46% of respondents answered the question about biosensors correctly. It indicated that more illustration about biosensor is needed.


For question 3, over 86% of the respondents got the answer correct. It shows that most of the respondents understand what P.aeruginosa is attracted to after reading our educational booklet.


For question 4, about 39% of respondents got the answer correct. It indicated the readers was not familiar with the use of biosensor and its relationship with the bacteria.


For question 5, over 67% of the respondents got the answer correct. It shows most of the respondents understand the use of AI models in our project.


For question 6, over 42% of respondents answer this question correct. It indicates readers did not understand the steps in AI model detection from the booklet.


For question 7, over 59% of respondents answer this question correctly. It shows most of the respondents understand the working process of Mask-RCNN, YOLOv8 and RT-DETR are not the same after reading our educational booklet.


In conclusion, most of the basic questions of our survey were answered correctly, with over 80% or even 90% accuracy. However, contents about the use of biosensor and its relationship with bacteria need to have more illustrations as there were only around 40% of students answered correctly. Besides, concepts about the step in AI model detection also need to be emphasized. The study shows that most junior form students gained the basic knowledge from our educational booklet, but the harder content may require more time to digest. Therefore, we will revise our booklets to improve their understanding of those specific parts. The surveys also allow us to find out the contents that students do not familiar with and we can introduce the contents in the project exhibition through interactive activities such as poster exhibition, games and demonstration.



Project exhibition

We organized a project exhibition during our school’s lunch recess and the Alumni Consultation Night, reaching audiences that included students from grades 7-12, as well as parents. The content was based on the concepts that students do not familiar with as indicated in the educational booklet survey. To make students learn in an interactive and fun way, we designed different minigames for each team to further consolidate their understanding.


In the exhibition, each team made a poster, with the research team focusing on the components of a BioBrick circuit, the design of biosensor and their project findings; the human practice team focusing on environmental justice and plastic pollution, and the technology team focusing on the introduction of instance segmentation methods and other AI models. We included games and activities in each subteam’s booth, with a game to build a BioBrick circuit using toy building bricks, simulating BioBricks for the research team. The human practice team designed a game where participants answer questions by throwing ping pong balls into cups of the correct answers. While the technology team introduced AI models and showcased their physical robotic car to students.

We designed a survey to collect students feedback regarding to our exhibition. We found that there were great improvements in the understanding of concepts that students were not familiar with previously.

59.1% students obtained the correct answer that a biosensor will show a blue colour when it detects a microplastic. Before the exhibition was held, a pre-survey showed that only 36.5% of students got the correct answer. The improvement in the percentage of students (about 22.6%) obtaining the right answer reflects that the exhibition was successful in increasing students’ understanding of synthetic biology and our project findings. In the exhibition, we especially highlighted this part in posters with diagrams, and use toy building bricks to simulate BioBricks circuit and biosensor design.

77.3% of students obtained the correct answer of the accurate sequence of biobricks in a standard biological circuit. In the pre-exhibition survey, only 36.5% of students obtained the right answer, proving the effectiveness of our exhibition. In the exhibition, the research team members designed a game where, after students learn the sequence from reading the research team’s poster with team members’ explanations, they play with toy building blocks which simulate the different biobricks in a biological circuit. The game is especially designed to strengthen students’ memory of the biological circuit’s sequence by building one physically using toy bricks.

50% of participants gave the correct answer of AHL, which is a 17.7% increase from the 32.3% of students who gave the correct answer in the pre-survey. This increase shows the effectiveness of the information displayed by the research team, where the part relating to the design of our biosensors states clearly that the biosensor's receptor binds to AHL resulting in a blue colour.

The response shows that 54.5% of participants responded with the correct answer of only 9% of our plastic waste being recycled, while the pre-survey showed that only 35.4% of participants knew the answer. The increase of 19.1% shows that the information displayed in the human practice team booth is absorbed effectively by the audience, most notably because of the game in which students answered questions regarding plastic pollution and environmental justice by choosing the best answer and throwing a ping-pong ball into the corresponding cup. The game ensures that the audience truly understood the material provided in the booth via an interactive experience, which is proven to be effective from the increase in correct responses.

45.5% of students correctly identified the 1982 Warren County landfill protests as the cause for the environmental justice movement. There is a 4% improvement in accuracy compared to the pre-exhibition survey results, but the rise is not significant. This demonstrates that students are still unfamiliar with environmental justice, which is one of the key ideas in our project. To increase the understanding of students in future activities, we will offer a more in-depth illustration on the topic.

36.4% of students correctly answer the definition of extended producer responsibility (EPR). In the previous survey conducted before the exhibition, only 24.5% of students correctly responded. The improvement of students obtaining the correct answer is about 12%. This indicates that our exhibition successfully enhanced students’ understanding about the concept of extended producer responsibility, as it is one of the key topics in our education booklet, which was presented during our exhibition.

40.9% of students obtained the correct answer for the computer vision technique used in the deep learning model. A pre-survey conducted before the exhibition showed that only 18% of students had the correct answer. The improvement of about 22.9% reflects that more students understood this aspect of our project. At the exhibition, we highlighted this aspect on posters and used more diagrams with explanations. Members of our team also explained the concepts to students using the real model from our project. Most of the participants gained a better understanding of our project’s model through the demonstration of the real one at the exhibition.

27.3% of students obtained the correct answer for question 8, compared to the accuracy rate of 33.8% in the pre-exhibition survey. The decrease of 6.5% indicates that most participants still could not identify the most appropriate model for the specific goal. This outcome highlights that there is still room for improvement on the clarification of the technical side in our exhibition.

68.2% of students obtained the correct answer of choosing to use which AI model in the specified situation. In the pre-exhibition survey, only 45.4% of students obtained the correct answer, proving the effectiveness of education about the features of different AI models in our exhibition. In the exhibition, we used examples such as finding the specific character “Wally” out of a photo with lots of different characters by AI models to explain how different AI models work and comparing them with each other. This helps the students to understand more easily and get a clear idea about the differences between each AI model.


From the post exhibition survey, we found out almost all questions have a higher percentage of correct responses compared to the responses in the pre-exhibition survey. In the research part, we can see students understand more about the standard biological circuit and how our biosensor works such as showing a blue colour when microplastics are detected, after playing with a toy building block set which represents different BioBricks in a biological circuit in our exhibition. In the human practice part, we found out more students know about recycling plastics, environmental justice and specific terms such as extended producer responsibility (EPR) after playing the Q&A game in our exhibition. In the technology part, we can conclude that more students know about the AI models, especially YOLOv8 and Mask-RCNN and the differences between these AI models after explaining to them about the AI models with a simple example.


Overall, we can see the improvements in students’ understanding about our project in all aspects such as our biosensor, environmental justice, AI models and more. The exhibition was effective in educating and successful as a whole.


Clean Recycling Event

From the interview with EPD reply and the visit to Green@SaiKung, we realize that there are challenges in the implementation of recycling such as how to encourage the public to practise recycling and how to educate them to achieve clean recycling. Besides, cross contamination with non-recyclable items into the recycling bin will lower the quality of the recycled products. Thus, our team collaborated with Environmental Organization initiated from our school, Zero Plastic League to organize a Green Event by hosting a green booth in our school events, Alumni Consultation Day and Parents’ Night to educate students, teachers and parents how to achieve clean recycling and promote environmental sustainability through recycling. We educate the public the steps in recycling beverage carton. We encourage the public to clean recycle plastic bottles, plastic wastes, beverage cartons and paper cups. We also built slogans to spread the message of clean recycling.


Conclusion

In conclusion, our educational outreach successful resonate with a wide spectrum of learners including primary school students, secondary school students, parents and the general public. We tried every effort to make complexity concepts into simple in order to engage everyone to foster a sense of responsibility towards our environment. We embrace participant feedback which enhance our future planning and initiatives. The active participation and positive feedback from participants demonstrated the impact of our efforts to extend knowledge to the next generation.

Team Members | HKGTC - iGEM 2025