Project 13: AI and Project Fair Allocation

AI and Project Fair Allocation

The aim of this project is to develop a personalized, and AI-assisted project recommendation system based on the Moodle fair allocation plugin to promote the effectiveness and satisfaction of students' Final Year Project learning experiences.

Project Leader: Song, Pengfei  (Department of Mechatronics and Robotics, School of Advanced Technology, XJTLU), Li, Na (Academy of Future Education, XJTLU)

Project Members:

Lim, Eng Gee (Dean, School of Advanced Technology, XJTLU); Leach, Mark (Deputy Dean, School of Advance Technology); Zhang, Xiaojun (Chief Officer of Education; Leader of Entrepreneur College (Taicang) Leadership Team; Executive Dean, Academy of Future Education; Acting Dean, Entrepreneurship and Enterprise Hub, XJTLU);

Research Grant:

• 2024 Natural Science Foundation of the Jiangsu Higher Education Institutions of China Programme - General Programme 2024年度江苏省高等学校自然科学研究面上项目
  
• 2024 Humanities and Social Science Program of Ministry of Education of PRC (MoE) Young Scientists Fund: Theoretical construction and empirical research on artificial Intelligence promoting fair allocation of project-based learning resources 2024年教育部人文社会科学青年基金：人工智能促进项目式学习资源公平分配的理论构建与实证研究

Journal paper:

• Li, N., Lim, E. G*., Leach, M*., Zhang, X*., & Song, P.* (2022). Role of perceived self-efficacy in automated project allocation: measuring university students’ perceptions of justice in interdisciplinary project-based learning. Computers in Human Behavior, 136. https://doi.org/10.1016/j.chb.2022.107381
• Yuan, H., Yuan, W., Duan, S., Yong, R., Jiao, K., Wei, Y., Leach, M., Li, N*., Zhang, X., Lim, E. G*., & Song, P*. (2024). Navigating the uncertainty: the impact of a student-centered final year project allocation mechanism on student performance. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-03324-7

Dataset:

• Li, N., Lim, E. G., Leach, M., Zhang, X., & Song, P. (2022). University students' perceived self-efficacy and justice datasets in a PBL project allocation context at a Sino-British international university in China Mendeley Data. https://doi.org/10.17632/w2kzmvfhb2.1

Research outreach:

• Li, N., Lim, E. G., & Leach, M (2024). Practical strategies for overcoming challenges in interdisciplinary project-based learning. https://www.timeshighereducation.com/campus/practical-strategies-overcoming-challenges-interdisciplinary-projectbased-learning
• Li, N., Zhang, X., & Song, P. (2023). Students with strong self-belief are happier and more successful – as our study shows. https://theconversation.com/students-with-strong-self-belief-are-happier-and-more-successful-as-our-study-shows-199645
• Li, N., Lim, E. G., Leach, M., Zhang, X., & Song, P. (2022). 大学生学习公平性和自我效能感：教育技术支持下的自动化跨学科项目分配https://mp.weixin.qq.com/s/3OSOYzLURN724cUSWnlVbQ

Conference presentations:

• Li, N., Lim, E. G*., Leach, M*., Zhang, X*., & Song, P.* (2022). Using Moodle for Fair and Effective Project-Based Learning: a Case Study in China. The Learning Ideas Conference 2022.

Data collection (Sep.2024-Nov. 2024):

1. Collect data on students' perceptions of Al-guided FYP (Final Year Project) using a scale.
2. Preliminarily organize the scale data for validation analysis in theoretical model construction.

Literature Review and Theoretical Model Construction (Dec. 2024 - Feb. 2025)

1. Systematically review domestic and international literature on project-based learning resource allocation and Al applications in education.
2. Build a preliminary theoretical model framework based on the scale data, using a grounded theory approach.
3. Optimize the model logic and refine core elements (such as perceptions of fairness, self-efficacy, etc) through expert interviews.

System Development and Data Integration (Mar. 2025 - May 2025)

1. integrate the collected FYP scale data with data from other dimensions (student abilities, project needs, etc.) to complete database construction.
2. Develop an Al resource allocation system based on the theoretical model

Empirical Research (un.2025-Aug.2025)

1. Deploy the FYP allocation system in universities for a small-scale pilot.
2. Evaluate the system's effectiveness through questionnaires (on perceptions of fairness, satisfaction) and in depth interviews (with teachers and students for feedback), and collect experimental data.
3. Optimize the system logic based on experimental data to enhance fairness and efficiency.

Outcome summary and Paper Writing (Aug.2025 -Oct. 2025)

1. Integrate experimental data and user feedback for paper writing.
2. Complete a draft of an empirical research paper (targeting the British Journal of Educational Technology for submission).
3. Make the dataset publicly available (on the Mendeley platform) and open-source the system code (on GitHub).