Framework for Research Narratives
This website shows how the framework of storytelling, analogy and metaphor (SAM) contributes to better science communication at a newly established interdisciplinary university in China. We aim to inspire instructors at various educational institutions use narrative and rhetorical devices to help students understand concepts, use tools, and experience virtual reality journeys. It also includes case studies, resources, and tools, as well as user feedback and assessments to improve understanding and use of these technologies and tools.
Artificial Intelligence
- Machine Learning (ML)
- Natural Language Processing (NLP)
- Computer Vision
- Speech Recognition
- Deep Learning
Robotics and Autonomous Systems
- Autonomous Systems of Robot Swarms
- Integration of Robotics and Artificial Intelligence
- Robotics Manipulation and Grasping
- Underwater Robotics
- Medical Robots and Human-Robot Interaction
- Design and Control of Robotic Actuation Systems
- Robots for Artistic Applications
- Robots for Architecture and Agricultural Applications
Language Landscape
- Language Ecology and Endanger
- Cultural Identity and Language
- Digital Language Landscapes
- Language and Power
- Language in Urban Spaces
- Cultural Heritage and Preservation
- Language and Education
- Globalization and Linguistic Diversity
About the SAM Model for Better Science Communication
We are a team of action researchers dedicated to the challenge of effectively teaching and communicating complex science concepts to general public. Traditional teaching methods do not allow students to take full initiative in building their understanding of complex science concepts. In addition, communicating science to the public can be challenging for students due to knowledge gaps. We aim to address these issues by using a methodology of storytelling, analogies, and Science and Technology
2024 PLACE Research Narrative Presentation Guidelines
Please read the following carefully.
The PLACE Summer Camp, initiated by the Hong Kong University of Science and Technology (Guangzhou), is excited to introduce the 2024 Research Narrative Presentation to celebrate and nurture the talents of young researchers who can transform complex research ideas into compelling narratives.
HKUST(GZ) champions the art of narrative in interdisciplinary science communication, recognizing the power of storytelling, analogy, and metaphor in inspiring insights and creative ideas. This initiative celebrates our dedication to clear, coherent, and engaging science communication in knowledge exploration. The presentation is designed to blend the rigor of scientific research with the engaging art of narrative, urging participants to explore a wide array of interdisciplinary subjects through storytelling, analogies, and metaphors, making complex concepts accessible for non-professionals.
Presentation Details
On one page of A4 paper or 1 page of PPT slide, describe the following elements (not necessarily all) of your interested research using a story, metaphor, or analogy. Visualization with AIGC (AI Generative Content) tools is highly encouraged.
Research gap which shows the impact/contribution of the research to the society
A key jargon/technical mechanism in the research
An intended product/prototype of the research
Categories
Artificial Intelligence, Robotics and Autonomous Systems, Language Landscape
Entry Requirements
Each entry must address only one of the research questions in your chosen subject category and must not exceed 350 words on 1 A4 page.
The filename of your pdf must be in this format: Team Number-Date of submission-Category.pdf.
Assessment
Our rubric is proprietary. Entries will be judged on depth of knowledge of the chosen category, the quality of research gap, the originality of communication and the persuasive force. The very best entries are likely to be those which would best clarify the research gap and of changing somebody’s mind about the complexity of scientific research.
Quality of Research Gap:
Does the gap address a significant issue within the field? Is the gap clearly defined, making it apparent what has not been explored? Is there a methodological path forward for investigating the gap.