Building Knowledge
Featured Interrogating Classroom Safety and Deepfake Technology: Policy Implications in Education
Paper Presentation in a Themed Session
Janine Aldous Arantes
This paper explores the intersection of deepfake technology and the need for safe educational environments. It employs a critical feminist lens to interrogate the evolving concept of 'classroom safety' alongside the risks posed by synthetic media to prompt debate around the sufficiency of educational policy. Drawing on the insights of Feenberg’s Critical Theory of Technology and Manne’s Logic of Misogyny, the exploration considers the challenges introduced by deepfake technology in a broader neoliberal patriarchy and raises questions about the intersection of safety and the right to safe learning environments, guided by Rahm's work on educational imaginaries. I take the stance that the classroom is the teachers’ workplace, and both students and staff have the right to safe teaching and learning environments. Drawing on the Australian eSafety Commissioner's Safety by Design Principles, this discussion paper concludes by proposing policy considerations that emphasise service provider responsibilities, user empowerment, and transparency. With the increased shift towards an educational Metaverse, there is an urgent need for educational systems globally, to incorporate safety measures into policies around generated image and video, to mitigate against potential for novel forms of psychosocial harm from a critical feminist lens.
The Effects of AI-Enhanced Knowledge Building on Middle School Students' Scientific Competencies in Sustainability Education: A PISA-Based Quasi-Experimental Study
Paper Presentation in a Themed Session
Yian Chen,
Yiju Lin
This study investigates the impact of AI-enhanced knowledge building on middle school students' scientific competencies in sustainability education, based on the PISA scientific literacy framework. A quasi-experimental design was conducted with 119 students from high- and low-achievement regions. The students were randomly assigned to experimental (AI-supported) and control (traditional teaching) groups. The experimental group used Padlet and simulation tools in a thematic activity on sustainable coffee production, structured around the 6E instructional model (Engage, Explore, Explain, Engineer, Enrich, Evaluate). During the Engage phase, students examined connections between climate change and coffee production through videos and data analysis. The Explore phase involved simulations to analyze variables like temperature and humidity affecting coffee growth. In the Explain phase, students interpreted data and hypothesized outcomes. The Engineer phase focused on collaborative design of sustainable coffee production plans using Padlet, followed by presentations and iterative plan improvements in the Enrich phase. Finally, the Evaluate phase involved reflective reporting. A pre-test, post-test, and teaching intervention framework measured gains in explaining phenomena, designing inquiries, and applying information. Results revealed the experimental group outperformed the control group, with significant improvements in inquiry design and data application, particularly among low-achievement students. Qualitative analysis of extended responses showed that AI tools enhanced critical thinking and creative problem-solving. The findings suggest that AI-enhanced learning promotes scientific competencies and provides an innovative model for sustainability education, with implications for teaching practices and policy development.
Generative AI in Education: Course Contents Design
Paper Presentation in a Themed Session
Woei-jyh Lee,
Sung Jen Yen
This study explores the integration of Generative Artificial Intelligence (GenAI) into modern educational practices, emphasizing its transformative potential for enhancing student learning experiences. This work investigates how GenAI supports adaptive learning, fosters creativity, and enables personalized education. By adopting immersed methods, including theoretical analysis and case-based exploration, the study examines the effectiveness of GenAI-driven approaches such as step-wise learning and critical thinking frameworks in practical classroom scenarios. Key activities included testing the Interaction Granularity Hypothesis, using database management as example, where step-based GenAI tools like ChatGPT were evaluated for their ability to enhance SQL query development adaptive to each student, and analyzing GenAI’s role as a secondary teaching assistant using domain-specific materials. By examining step-based learning frameworks and critical thinking models, using programming language as example, the work highlighted GenAI’s ability to significantly enhance engagement and efficiency from designing algorithms to debugging and enhancing programming code, while uncovering areas for improvement in fostering deeper reasoning and innovative problem-solving. This work not only improved the analytical processes but also deployed possible solutions to support the practical decision making. These insights contribute to ongoing discourse on leveraging GenAI for education and propose future research directions for optimizing its integration.
