Research Centre for AI and Education

1. AI-Driven Model Development for Educational Advancement 

Project Leader: Liu, Jingxin  (School of AI and Advanced Computing, Entrepreneur College (Taicang), XJTLU)

The project aims to develop specialized AI models for the education industry, with a focus on advancing teaching, learning, and administrative processes. Through collaborative efforts with experts and partners, we harness the power of AI technologies to optimize educational outcomes and create transformative tools. Our primary objective is to drive positive change in education by providing personalized learning experiences and streamlining administrative tasks.

2. AI and Game-based Learning (click to see more)

Project Leader: Liang, Hai-Ning  (Founding Head of Department (2019-2023), Deputy Director of the Suzhou Municipal Key Lab for Intelligent Virtual Engineering, the Suzhou Key Lab for Virtual Reality Technologies, and the XJTLU Virtual Engineering Center)

Digital games are very popular because they are fun to play. There is clear evidence that games have strong motivational properties, and when they are designed to support learning activities, students can learn while playing, that is, having fun and learning at the same time. Recent computing advances have enabled the rapid development and integration of AI into many activities that were previously not possible. In doing so, AI has empowered people to accomplish tasks more efficiently and effectively. This project explores how we can make games more supportive and engaging via AI-enhanced features. In short, we want to understand how to design and deploy AI-supported games to make them more fun and tailored to the needs of individual learners.

XJTLU AoFE DGBL SIG website: https://connect.xjtlu.edu.cn/user/na-li/educause-exemplar-xjtlu-digital-escape-room

3. AI and Language Learning

Project Leader: Zou, Bin(Department of Applied Linguistics, XJTLU)

AI and language learning project aims to explore using AI, including Generative AI, to enhance learning languages such as English, Chinese, Japanese, Spanish, etc. We welcome teachers, researchers, institutions, schools, companies and investors to join us to develop and share AI resources, practice and research.

4. AI and Organizational Learning 

Project Leader: Wang, Qian(Research Director, Academy of Future Education, XJTLU)

AI and Organizational Learning is a project aimed at leveraging artificial intelligence to optimize knowledge sharing and skill development within knowledge organizations, ultimately enhancing overall performance and adaptability. Through interacting with advanced AI and personalized learning, we seek to create a dynamic and responsive learning environment that fosters continuous growth and innovation.

5. AI as a Design Partner for Learning 

Project Leader: Dall’Asta, Juan Carlos(Department of Architecture, Design School, XJTLU)

The project seeks to explore the integration of AI Machine Learning in design education with the aim of enhancing creativity and design inspiration. The design process relies on a foundation of technical knowledge, accumulated experience, and an intuitive 'creative' component that often reflects the unique talent of the designer. Artificial Intelligence serves as a valuable collaborator in the creative phase by offering suggestions and ideas to designers.
The question arises, 'What do you think about this idea?' AI actively engages with designers during specific stages of their work. From this perspective, AI emerges as a valuable support system, capable of generating ideas and insights during the crucial conceptualisation phase, one of the most critical stages in the design process.

6. AI and Learner Emotion 

Project Leader: Craig, Paul(School of Advanced Technology, XJTLU)

The goal of this project is to investigate how Artificial Intelligence (AI) can be used to improve our understanding of learner emotion in order to improve the student learning experience and learning outcomes. Emotions and learning are closely related with learner emotion having the potential for either a positive or negative effect, depending on the type of emotion and the context in which it is experienced. For example, positive emotions such as joy, excitement, and curiosity can enhance learning by increasing motivation, engagement, and attention. Negative emotions such as anxiety, frustration, and boredom can hinder learning by reducing motivation, engagement, and attention. Being able to recognise and respond to learner emotion is key to fostering a supporting and engaging learning environment. This project aims to investigate how AI technology can improve the process for this type of emotionally empathetic learning support, particularly to improve our use of educational technologies where the normal channels for face-to-face recognition of human emotion may not be feasible.

7. AI and Learning Analytics 

Project Leader: Purwanto, Erick(Department of Computing, School of Advanced Technology, XJTLU)

Learning analytics systems leverage various types of data to gain insights into students' learning processes. They play a crucial role in identifying at-risk students and facilitating early, individualized intervention strategies. Traditional systems rely on Learning Management Systems and engagement data. This project aims to incorporate AI to additionally consider emotional states through multimodal data such as chat text, facial expressions, biometric sensor data, and voice recognition.

8. AI and Teaching Materials (click to see more)

Project Leader: Georgiev, Svetoslav(School of Intelligent Finance and Business, XJTLU); Tinsley, Joseph  (Educational Development Unit, XJTLU)

This project aims to explore students’ perceptions of teachers’ usage of generative AI (GAI) assisted teaching materials. Teaching materials created with the assistance of GAI have the potential to increase teacher efficiency and create value for students. Whilst the effectiveness of AI-assisted teaching materials has been demonstrated, there is a need for studies which examine students’ perceptions and acceptance of AI-assisted teaching materials. Students who have a positive perception of their learning environment, including teaching materials, are more likely to take a deeper approach to learning.
The project’s implications are two-fold. First, on the scientific front, we expect to enrich the literatures on student engagement, as well as on student perceptions of GAI usage in HE classroom environments. Second, on the industry front, we expect to aid companies developing AI tools for educational purposes to better understand students’ needs and perceptions of visual teaching materials, which would enable them to redesign and/ or readapt their software so that it can reflect the current needs of both students and teaching instructors.

9. AI and Childcare Quality Evaluation (click to see more)

Project Leader: Lu, Jinjin(Academy of Future Education, XJTLU)

The development of Childcare quality framework (3-6 years) is important for both EC teachers and parents. With the development of AI technology, we aim to provide a holistic method to evaluate the Childcare quality in the EC context. This powerful AI tool will be used as the first innovative tool in China.
We are recruiting PhDs who have experience in early childhood education, AI, computer science, and graph design, and user experience. Also, we are seeking for marketing and business partners in China and oversea.

10. AI and Assessment (click to see more)

Project Leader: Fan, Pengfei(Department of Intelligent Science, School of Advanced Technology, XJTLU)

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.

11. AI and Multimodal Learning (click to see more)

Project Leader: Varga-Atkins, Tünde(Centre for Innovation in Education, University of Liverpool, UK); Saunders, Samuel (Generative Artificial Intelligence Network, Centre for Innovation in Education, University of Liverpool, UK)

This project’s aim is therefore to explore and evaluate the opportunities brought by the use of GenAI tools within education to support disciplinary knowledge creation in the form of multimodal learning and the development of digital capabilities by students and staff.

Our interest in multimodality follows Kress’ (2010) social semiotic approach, which explores the ways in which different semiotic modes (text, speech, sound, image, moving image, touch, gesture etc.) are present and combined within one communication to produce a multimodal artefact. For instance, an infographic combines text and image in ways that produce extra meaning. Multimodal learning is concerned with designing learning utilising multimodal texts across media, forms and formats appropriately to the given context (van Leeuwen, 2017). Other examples of multimodal texts include posters, video, dance or virtual simulation. Multimodal learning also involves the use of ‘semiotic technologies’ such as collaborative tools, visualisation apps, online quizzes to support teaching practice. The ascent of GenAI offers further expansion to eductors’ repertoire of semiotic technologies.

GenAI is a form of technology that uses deep learning techniques to access a huge swathe of data and produce artefacts based on the prompt(s) provided to it by a human user, is increasingly capable of producing multimodal content, including (but not limited to) text, speech, audio, image, video and even three-dimensional models (Fui-Hoon Nah et al., 2023). Multimodal GenAI can be, and is being, used to create, manipulate, and adapt content and combine different semiotic forms together, to produce multimodal artefacts.
In this project, we focus on three strands:

• Teaching: teachers can potentially use GenAI to represent subject knowledge multimodally.
• Learning: students may be able to encounter, explore, evaluate and express ideas via multimodal GenAI, where technology can help manipulate, change, adapt or create artefacts that incorporate multiple semiotics forms.
• Assessment: students could use GenAI to create multimodal artefacts, or critique/reflect on existing ones for assessment.

12. Multi-university Experimental Research for Student-staff Exploration of Generative Artificial Intelligence in Higher Education (click to see more)

Project Leader: Reis, Charlie  (Director of Educational Development Unit, XJTLU); Sun, Yiqun  (Educational Development Unit, XJTLU)

In order to gain insights into the impact of AI on higher education and explore innovative applications for enhancing learning and teaching, it is proposed to establish an experimental research fund and project for student-staff exploration of generative artificial intelligence in higher education. Students, key stakeholders in the learning and teaching process, will collaborate with faculty members to create a “creative sandpit” that enables a comprehensive understanding of AI's impact on learning and teaching and an exploration of creative possibilities to harness the power of AI to enhance the educational experience.

13. AI and Project Fair Allocation (click to see more)

Project Leader: Song, Pengfei(Department of Mechatronics and Robotics, School of Advanced Technology, XJTLU)

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.

14. AI and Collaborative Learning (click to see more)

Project Leader: Kim, Jinhee   (Old Dominion University, US)

The aim of this project is to systematically investigate the process and outcome of SAI in classroom debate contexts and to introduce a conceptual framework guided by both self-regulated theory and AI methods used to advance research in human and AI collaboration in Education. 

15. Chinese Postgraduate Students’ Perceptions towards the ethics, challenges, and functions of using AI in their studies in Malaysia (click to see more)

Project Leader: Amini, Mansour (School of Languages, Literacies, and Translation, Universiti Sains Malaysia )

The proposed study aims to investigate Chinese postgraduate students' perspectives on the ethical considerations, challenges, and benefits associated with the use of artificial intelligence (AI) in their academic pursuits within the Malaysian higher education landscape. This study could hold significant relevance in understanding the nuances of AI adoption among Chinese students studying in Malaysia, and the insights garnered can contribute to informed decision-making in educational policies and practices related to AI integration. The research questions are structured to address key aspects: a) Ethical considerations perceived by Chinese postgraduate students in Malaysia regarding AI usage in academic studies. b) Challenges Chinese postgraduate students face when integrating AI technologies into research and coursework. c) Perceptions of the functions and benefits of AI in enhancing academic performance and research outcomes among Chinese postgraduate students in Malaysian universities.

16. The Role of Hofstede's National Culture in Influencing AI Adoption by University Teachers and Students - A Comparison Study between HSE and XJTLU (click to see more)

Project Leader: Koroleva, Diana (Institute of Education, HSE University); Li, Na (Academy of Future Education, XJTLU)

This study aims to explore the impact of Hofstede's national cultural dimensions on the adoption of AI tools by university teachers and students, comparing AI adoption patterns between the Higher School of Economics (HSE) and Xi'an Jiaotong-Liverpool University (XJTLU). This study can identify common challenges and opportunities in AI integration within the academic setting, and propose recommendations for enhancing AI adoption in higher education.

17.  AI-enhancement for Classroom as an Organisation Instructional Design: A prototype and Instructional Design Roadmap (practical-based research ) (click to see more)

Project Leader: LeCorre,  JeanYves (Researcher, University of Strasbourg, France)

This project evaluates alternative strategies for incorporating AI-driven tools into the classroom-as-organisation instructional design. AI can substantially elevate immersive learning environments. The project builds on an existing learning prototype supported by a learning management system (Moodle) and an immersive virtual reality (VR) platform. Along with developing the AI-enhanced prototype, a methodology roadmap will be proposed to assist educators in designing and implementing AI-enhanced immersive learning experiences based on classroom-as-organisation learning design in online or blended settings. The prototype features a course in management accounting, and the methodology roadmap could serve to design immersive learning experiences in other subject areas or disciplines. The project has applied for funding from XJTLU Summer Undergraduate Research Fellowship.

18. A quasi-experimental scenario-based study of the effect of using AI tools on self-regulation of learning

Project Leader: LeCorre,  JeanYves (Researcher, University of Strasbourg, France)

The literature suggests that learners demonstrate various behavioural intentions in response to socio-cognitive conflicts when interacting in groups to develop new knowledge. However, socio-cognitive conflicts benefit learning because they are regulated through specific mechanisms to ensure that the conditions under which confronting diverging ideas results in positive cognitive and relational outcomes are met. This project investigates the effects of incorporating AI-driven tools and learning activities on learning self-regulation. A causality model is examined through a series of scenario -based quasi-experiments to determine the antecedents of collaborative behaviours that affect behavioural intentions towards resolving socio-cognitive conflicts.The study uses an immersive learning prototype supported by a VR immersive learning platform and a learning management system in lab settings to support the research design.

19. Intelligent Interactive Mechanisms: Enhancing Student-Teacher Interactions with Virtual AI Tutors for Advanced Learning in Engineering Education (click to see more)

Project Leader: Quadir, Benazir (Academy of Future Education, XJTLU)

As the field of engineering education continues to evolve, the integration of Virtual AI Tutors (VAITs) presents a promising avenue to enhance learning outcomes. This research explores how VAIT-facilitated interactions influence learning outcomes in engineering education. Despite the importance of interaction in learning, studies on VAITs' design and efficacy in this field are limited. Interaction time is constrained in block-mode learning, and post-assignment feedback from teachers may not improve module scores. To address this, we propose "EnginAI Tutor," a platform tailored for engineering students, featuring AI-driven personalized learning and interactive problem-solving, and providing a a 24/7 real-time feedback mechanism for step-by-step interaction between students and virtual tutors. This study aims to inform educators and policymakers on optimizing AI integration to enhance learning in engineering education.

20. Creative AI’s ways to Enhancing Student’s Entrepreneurial Learning (click to see more)

Project Leader: Perez, Victor (Entrepreneurship and Enterprise Hub, XJTLU);

This research project aims to explore the innovative use of cutting-edge AI tools to improve students’ entrepreneurial learning by catering to their individual needs and strengths.

Fostering Mindset: Developing AI-powered products to boost students’ entrepreneurial behavior.

Skill Development: Implementing AI-driven platforms to enhance students’ skills in critical thinking, problem-solving, decision-making, and innovative thinking.

Data-Driven Insights: Using AI to analyze and interpret large datasets related to entrepreneurship trends, market dynamics, and successful business strategies, providing students with valuable insights that can inform their learning and entrepreneurial ventures.

Accessible Resources: Leveraging AI technologies to provide students with easy access to a wide range of resources, such as case studies, industry insights, and mentorship opportunities.

Collaboration and Networking: Integrating AI tools to facilitate collaboration among students, educators, and industry professionals, creating an environment that fosters networking, idea sharing, and real-world application of entrepreneurship principles.

21. Intelligent Interactive Mechanisms: Enhancing Student-Teacher Interactions with Virtual AI Tutors for Advanced Learning in Engineering Education (click to see more)

Project Leader: Lanze Vanermen (Department of Educational Studies, Academy of Future Education, XJTLU)

Educational leaders are increasingly confronted by discourses about the possibilities and threats of artificial intelligence (AI) in education. Leadership is typically framed as a learnable process of influencing a group to achieve a common goal and is embedded in an ecosystem of dynamic interconnections among people and resources. However, research has yet to fully show how non-human actors such as AI come to matter in educational leadership and, more specifically, leadership education.

This study investigates how generative AI (genAI) technologies reshape learning about leadership in everyday education practices, examining what it means to become a leader nowadays. It connects to studies on genAI in education by considering what AI consists of, what it does, and what it makes people do under certain conditions.

The study uses a sociomaterial approach to explore how becoming a leader is the result of relations between humans, technologies, and other material entities within a specific ecology. An ethnographic case study at a Chinese university reveals two main findings: the ecology promoting AI-driven leadership figures and the co-shaping of these figures by students and genAI. This study emphasises the importance of understanding leadership education beyond human-centric and technology-centric perspectives, introducing new analytical concepts for researchers, practitioners, and policymakers.

Note: The projects are listed chronologically with the most recent appearing last.