Project Name
AI and Assessment
Project Description
The project aims to investigate AI's transformative role in educational assessment. This project seeks to explore the potential benefits, challenges, and implications of integrating AI technologies into various aspects of assessment, including automated grading, customised feedback, and testing methodologies.
Project Members
Project Leader:
Fan, Pengfei (Department of Intelligent Science, School of Advanced Technology, XJTLU)
Responsibilities:
Overall project oversight and leadership.
Coordination of activities across different schools and units.
Collaboration with AI experts on the technical aspects of the AI-enabled tool.
Liaison with industry partners for collaboration opportunities.
Ensuring project alignment with the university's strategic goals.
Technical Leader:
Purwanto, Erick (Department of Computing, School of Advanced Technology, XJTLU)
Responsibilities:
Leading the technical development of the AI-enabled assessment tool.
Collaborating with AI experts to implement advanced features.
Overseeing the integration of the tool into educational platforms.
Conducting technical evaluations and ensuring system scalability.
Providing technical guidance to the project team.
Project Members:
Jia Wang (School of Advanced Technology, XJTLU) - Data Scientist
Responsibilities:
Conducting data analysis using machine learning algorithms.
Extracting meaningful insights from assessment data.
Collaborating with researchers on the mixed-methods research design.
Ensuring the accuracy and reliability of data-driven findings.
Presenting data-related insights to the project team.
Li, Na (Department of Educational Studies, Academy of Future Education, XJTLU) – Industry Liaison
Responsibilities:
Establishing and nurturing collaborations with industry partners.
Aligning assessments with real-world challenges through industry connections.
Exploring funding opportunities and industry-supported initiatives.
Ensuring the project's relevance to industry demands.
Facilitating knowledge transfer between academia and industry.
Ting Ting Tay (Academy of Future Education, XJTLU) - Pedagogical Design Specialist
Responsibilities:
Providing expertise on educational design and pedagogical best practices.
Collaborating with faculty members on integrating AI into teaching practices.
Designing effective learning experiences aligned with project goals.
Facilitating faculty training sessions on AI-enhanced assessment.
Ensuring the pedagogical soundness of implemented strategies.
Ling Wang (Educational Development Unit, Academy of Future Education, XJTLU) - Educational Researcher
Responsibilities:
Conducting educational research on the impact of AI-enhanced assessment.
Collaborating with researchers on the project's mixed-methods design.
Collecting and analyzing qualitative data on teaching quality and student outcomes.
Contributing to publications and disseminating research findings.
Ensuring ethical considerations in research activities.
Project Outcome
Existing outcomes: a talk was given at the 2024 Pedagogic Research Conference on Technology and AI in Learning and Teaching.
Project Goals and Expected Outcomes
| Project Goals | Expected Outcomes | |
| One Year (Short-term) | 1. AI Tool Development: Successfully develop and implement the initial version of the AI-enabled assessment tool with basic functionalities. 2. Pilot Kaggle Method: Implement the Kaggle method in selected courses to test the effectiveness of the AI tool in real-world educational settings. 3. Initial Evaluation: Conduct initial assessments and gather qualitative and quantitative data to understand the tool's impact on student engagement, learning outcomes, and teaching quality. 4. Feedback and Iteration: Collect feedback from educators and students to identify areas of improvement in the AI tool and adjust functionalities accordingly. |
1．A functional AI-enabled assessment tool integrated into selected courses. 2. Preliminary data on the tool's impact on student engagement and learning outcomes. 3. Identification of areas for tool enhancement based on user feedback. |
| Three Years (Mid-term) | 1. Tool Refinement: Continuously refine the AI-enabled assessment tool based on feedback and insights gained from the initial implementation. 2. Scale Implementation: Expand the use of the Kaggle method and AI tool to a broader range of courses and departments within the university. 3. Comprehensive Evaluation:Conduct a thorough mixed-methods evaluation to assess the tool's impact across various disciplines, learning environments, and student demographics. 4. Industry Collaboration: Establish strong collaborations with industry partners to ensure the relevance of assessments to real-world challenges. |
1. A refined and enhanced version of the AI-enabled assessment tool. 2. Expanded implementation of the Kaggle method across multiple courses and departments. 3. Comprehensive research findings on the tool's impact on diverse student groups and disciplines. 4. Active collaboration with industry partners, ensuring alignment with industry demands. |
| Five - Ten Years (Long-term) | 1. Institutional Integration: Fully integrate the AI-enabled assessment tool into the university's standard assessment practices, making it a core component of the educational experience. 2. Pedagogical Transformation: Witness a transformative shift in pedagogical practices, with educators leveraging AI for personalized tutoring and real-time feedback in various disciplines. 3. National Recognition: Attain recognition at the national level for pioneering innovative AI-based assessment practices in higher education. 4. Sustainable Funding: Establish sustainable funding models, including ongoing government support and industry partnerships, to ensure the long-term viability of the project. 5 .Broader Educational Impact: Share successful practices and outcomes with other educational institutions, contributing to a broader adoption of AI-enhanced assessment strategies. |
1. Full institutional adoption of the AI-enabled assessment tool. 2. Pedagogical practices across the university transformed with AI integration. 3. National recognition as a leader in AI-driven education. 4. Sustainable funding models supporting ongoing innovation. 5. Influence and contribute to a broader transformation in educational assessment practices nationally and internationally. |
Student Recruitment Requirements
- Educational Background:
Preferred background in computer science, data science, educational technology, or a related field.
For students with an education background, a strong interest in technology and data science is desirable.
- Technical Skills:
Proficiency in programming languages such as Python, Java, or similar.
Basic knowledge of NLP, prompt engineering, data analysis tools and techniques.
- Research Interest:
Demonstrated interest in educational technology, AI, or related research areas.
Previous involvement in research projects or coursework related to AI and education is a plus.
- Collaboration and Communication:
Ability to work collaboratively within a multidisciplinary team.
Strong communication sk
- Discuss potential industrial partnership opportunities.
In the preliminary stage of engagement with the company
- Discuss any other items.
Comments
Add comment