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critical questioning with gen-ai
This project investigates how generative AI can foster critical thinking and questioning skills among students. By interacting with AI models as a conversation, students are encouraged to challenge assumptions, pose complex questions, and engage in deeper inquiry. The project explores AI's potential to create dynamic, interactive learning environments where students critically evaluate AI-generated content to develop AI literacy. This approach aims to empower learners to develop a more sophisticated understanding of knowledge construction in the digital age.
This project is supported by Curtin School of Education Research and Innovation Grant.
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dialogic interaction with gen-aI
This project explores how customized AI chatbots can be leveraged to enhance students' critical thinking, argumentation, and co-construction of knowledge through dialogic interaction. Various chatbots are created to embody a dialogic learning principle of treating generative AI as a dialogic partner, instead of an authoritative knowledge provider. The analysis of students' interaction with these chatbots aims to contribute pedagogical knowledge and insights into the meaningful use of generative AI in education.
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science reading with gen-ai
Focusing on science literacy, this project leverages generative AI to enhance students' engagement and comprehension in reading complex scientific texts. It fills a critical gap by examining how emerging GenAI tools can support high school students to develop a deeper understanding of scientific principles and concepts through their reading. The project aims to make science reading more accessible and engaging, helping students build confidence and competency in interpreting scientific discourse.
This project is supported by the Australian Government Research Training Program (RTP) Scholarship for International PhD students.
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Translanguaging with Gen-AI (Starting soon)
This project explores the application of generative AI to support translanguaging, where students fluidly use multiple languages in learning. By creating multilingual resources, AI assists students inaccessing content across languages, fostering language inclusivity and cultural relevance in the classroom. This research examines how AI-driven translanguaging tools can empower multilingual students, enhancing their learning experiences and embracing linguistic diversity as a strength in the education landscape.
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special needs with gen-ai
Among the first of its kind in educational research, this project focuses on using generative AI to support students with Autism Spectrum Disorder (ASD). By providing personalized support, the project explores how AI-driven tools can enhance educational accessibility and offer tailored pathways that foster comfort and growth, while enabling educators to better understand and support the diverse needs of ASD learners.
This project is supported by Curtin School of Education Research and Innovation Grant.
redesigning assessment with gen-aI
In a transformative approach to evaluation, this project explores the integration of generative AI in assessment design. Moving beyond traditional testing, it considers how AI can support creative, formative, and adaptive assessments that reflect real-world problem-solving. By generating complex scenarios and challenges, AI can help educators develop assessments that are more aligned with holistic and future-ready learning evaluations.
This project is supported by Curtin School of Education Research and Innovation Grant.
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multimodal science education with gen-ai
This project explores the integration of generative AI to create multimodal learning experiences in science education, combining text, visuals, and interactive elements to engage students in diverse ways. By using AI-generated multimedia, such as visual explanations, simulations, and interactive text, the research aims to enhance student engagement and understanding with complex scientific ideas. modes. This project leverages on Gen-AI as a multimodal tool to make science learning moredynamic and interactive.
This project is supported by the Australian Government Research Training Program (RTP) Scholarship for International PhD students.