Emerging Engagement
Place-based Education and Preservice Teacher Training: Policy Practices to Enable Contextual Learning in South African Higher Education Institutions View Digital Media
Paper Presentation in a Themed Session Emma Barnett
Place-based education has the potential to contribute positively to the training of preservice teachers regarding contextual learning. Higher education institutions in South Africa continue to struggle to understand the significance of place-based education (PBE), into the curriculum for the training of preservice teachers (PST). This conceptual paper explores the policy practices that can enable contextual learning in South African higher education by embedding PBE into preservice teacher training programs. PBE is an umbrella term for pedagogical practices prioritising experiential, community-based, and contextual learning to cultivate greater connectivity to local contexts, cultures, and environments. The purpose of PBE is to enhance engagement and achievement and to promote democratic participation within local communities thus enriching the training of PST. Guided by place-based theory and employing a conceptual research methodology, selected policy texts in the Minimum Requirements for Teacher Education Qualifications (2015) have been analyzed to highlight the significance of PBE in creating meaningful and contextually relevant learning experiences for PST. The findings indicate that PBE is significant in the training of preservice teachers as it can prepare them to create more meaningful and contextually relevant learning experiences for their future. PBE should be woven into PST modules, demonstrating how to use local context to enhance teaching and learning and to develop skills to address the unique challenges of South African classrooms.
Leveraging AI for Pre-Service Teacher Development: Exploring its Impact on Lesson Design, Self-Reflection, and the Intersection of Human and Machine Learning in Literacy Education View Digital Media
Paper Presentation in a Themed Session Lisa Delgado Brown
This session describes a study exploring how pre-service teacher (PST) candidates majoring in Elementary Education integrate Artificial Intelligence (AI) tools into their lesson planning process and reflect on their teaching practices. Conducted over the Spring 2025 semester, the study examines how novice PSTs, new to lesson planning, use AI in lesson design. PSTs design literacy lessons for struggling elementary readers, utilizing AI tools for personalized and differentiated instruction. Participants employ a process writing framework including practicing voice-to-text, editing/proofreading, and using generative tools to brainstorm ideas, incorporate suggestions, and refine their drafts, while ensuring academic integrity. Throughout the study, they engage in iterative lesson revisions based on feedback, aiming to improve their teaching strategies. PSTs are surveyed and have their lesson plans analyzed at multiple points throughout the study to evaluate their effectiveness in using AI as a lesson planning tool. The final phase involves a course survey where PSTs assess their use of AI tools and reflect on their self-assessment and growth. This study is part of a larger, multi-institutional analysis involving researchers from small, medium, and R1 institutions. The broader study aims to develop an instructional design framework incorporating AI in lesson planning, focusing on pre-service teacher education. By examining AI’s impact on both lesson design and teacher self-reflection, this research will deepen our understanding of how technology can enhance teacher preparation and literacy education. It will also contribute to shaping an instructional design framework for teacher education programs across diverse institutions.
Integrating AI into Industrial Design Education: A Dynamic and User-Centered Development Process
Paper Presentation in a Themed Session Jinseup Ted Shin
This paper introduces a novel design development process tailored for industrial design education, integrating artificial intelligence (AI) tools to enhance learning and innovation. Building upon the traditional double diamond model, the proposed framework incorporates evaluation and validation points to ensure alignment with user and market needs while embracing a continuous, blended approach inspired by the Ying and Yang philosophy. The process emphasizes dynamic engagement across all design phases, allowing students to simultaneously explore the broader context of their projects while focusing on specific stages of development. By embedding AI tools such as data analysis, generative design, predictive modeling, and iterative validation, this framework equips students with the skills needed to address complex design challenges. These tools enable real-time insights, foster creativity, and streamline workflows, ensuring that students’ designs are both user-centered and market-relevant. Furthermore, the integration of AI promotes a deeper understanding of how human learning and machine learning can complement each other in solving real-world problems. This approach not only prepares students for professional practice but also demonstrates the transformative potential of AI in shaping the future of design education and innovation. By merging human creativity with machine intelligence, this framework offers a forward-thinking methodology for educators and practitioners alike.
