TY - ADVS
T1 - Teaching Development Fund (TDF): Collaborative Industry-Academic Internships: Advancing Pedagogical Innovation and Experiential Learning through the Synergy of Work-Integrated Learning (WIL) and AI-Driven Methodologies
AU - Chen, Jian
PY - 2025/6
Y1 - 2025/6
N2 - This pedagogical research project explores the integration of Work-Integrated Learning (WIL) and Artificial Intelligence (AI) to enhance authentic assessment practices in XJTLU. By embedding WIL projects into internship modules (e.g., EDS406), the initiative aims to bridge theoretical knowledge with real-world application, fostering students’ applied research capabilities and problem-solving skills. The project employs qualitative research to evaluate the effectiveness of WIL projects in addressing workplace challenges and investigates AI’s role in supporting students’ evaluative judgment through tools like XIPU AI. Data will be collected over two semesters (with two different student cohorts) via interviews and coursework materials, focusing on seven dimensions of applied research skills. Expected outcomes may include evidence-based recommendations for refining WIL projects, an adaptable WIL assessment framework, a concise handbook on WIL design and AI-driven assessment, and scholarly outputs such as journal articles and workshops. Aligning with XJTLU’s strategic priorities in community engagement, syntegrative education, and education + AI, the project seeks to advance pedagogical innovation, promote industry-education collaboration, and equip students with the competencies needed for professional success.
AB - This pedagogical research project explores the integration of Work-Integrated Learning (WIL) and Artificial Intelligence (AI) to enhance authentic assessment practices in XJTLU. By embedding WIL projects into internship modules (e.g., EDS406), the initiative aims to bridge theoretical knowledge with real-world application, fostering students’ applied research capabilities and problem-solving skills. The project employs qualitative research to evaluate the effectiveness of WIL projects in addressing workplace challenges and investigates AI’s role in supporting students’ evaluative judgment through tools like XIPU AI. Data will be collected over two semesters (with two different student cohorts) via interviews and coursework materials, focusing on seven dimensions of applied research skills. Expected outcomes may include evidence-based recommendations for refining WIL projects, an adaptable WIL assessment framework, a concise handbook on WIL design and AI-driven assessment, and scholarly outputs such as journal articles and workshops. Aligning with XJTLU’s strategic priorities in community engagement, syntegrative education, and education + AI, the project seeks to advance pedagogical innovation, promote industry-education collaboration, and equip students with the competencies needed for professional success.
M3 - Textual work
ER -