AI.EDU Research Lab 2.0: Support of Competencies for Research-based Learning Using Recommender Methods.

• Funded by the CATALPA - Center of Advanced Technology for Assisted Learning and Predictive Analytics at the FernUniversität in Hagen.

• Role and Participation:

  • Senior Researcher: 11.2023 - present

The AI.EDU Research Lab is exploring the use of AI in university teaching. In version 2.0, the research focuses on supporting students' competencies, especially in deriving a term paper topic and a related guiding question with recommender systems (RecSys) as well as with generative AI tools. For this purpose, the project is based on the results and experiences of the first research funding.

RecSys, based on different recommender methods, is used as a context-bound combination of AI technologies and didactic design for the purpose of transmitting recommendations to educational stakeholders. In the project, they are used to research and evaluate suitable AI methods to support students in finding a topic and generating a guiding research question for their term paper. A central research topic is, among other things, the transparency and trustworthiness of self-developed AI systems and those already in use. Comparatively, current tools and tasks for innovative applications with generative AI are explored.

This project is a cooperation with Research Cente CATALPA – Center of Advanced Technology for Assisted Learning and Predictive Analytics, FernUniversität in Hagen.

Software:

1. Rahul Rajkumar Bhoyar, Xia Wang, Nghia Duong-Trung (2024). KaggleGPT: Prompt-based Recommender System for Efficient Dataset Discovery. Link.

Publications:

1. Nghia Duong-Trung, Xia Wang, Milos Kravcik (2024). BloomLLM: Large Language Models Based Question Generation Combining Supervised Fine-tuning and Bloom's Taxonomy. In Proceedings of the 19th European Conference on Technology Enhanced Learning (ECTEL 2024). Springer LNCS.  September 16-20, 2024. Krems, Austria. Link. DOI. Q2 SCImago.

MILKI-PSY: Multimodal Immersives Lernen mit künstlicher Intelligenz für Psychomotorische Fähigkeiten (Multimodal Immersive Learning with Artificial Intelligence for Psychomotor Skills). 

• Funded by the Federal Ministry of Education and Research of Germany (BMBF). Grant number 16DHB4015.

• Role and Participation: 

  • Senior Researcher: 09.2022 - present

  • Project Lead: 05.2023 - present

A research consortium around the Cologne Game Lab at TH Köln is developing a learning environment with artificial intelligence to support the independent learning of psychomotor skills. Project partners are the Institute for Product Development and Design Technology (IPK) at TH Köln, the German Research Center for Artificial Intelligence (DFKI GmbH), RWTH Aachen University, the Leibniz Institute for Human Development and Educational Information (DIPF), and the German Sport University Cologne (DSHS).

Anyone who wants to learn a sport trains in new psychomotor skills; until now, this has required role models such as on-site teachers who explain, demonstrate, and assess specific procedures. As part of the joint research project MILKI-PSY, a consortium led by the Cologne Game Lab at TH Köln, is developing a learning environment with artificial intelligence (AI) to support the training process. 

The MILKI-PSY project designs an innovative environment for independent learning of psychomotor skills. For this purpose, the correct movement sequences are recorded by trainers using cameras and sensors. A virtual avatar generated from this recording will serve as a model for the learners. For example, it can be displayed on a large screen in an augmented or virtual reality environment. With the help of artificial intelligence and automated error detection, the learning progress is analyzed, and individual feedback is generated.

Publications:

1. Abhishek Samanta, Hitesh Kotte, Patrick Handwerk, Khaleel Asyraaf Mat Sanusi, Mai Geisen, Milos Kravcik, Nghia Duong-Trung (2024). IMPECT-POSE: A Complete Front-end and Back-end Architecture for Pose Tracking and Feedback. In Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP 2024). ACM Digital Library. 1 - 4 July 2024. Cagliari, Sardinia, Italy.  link. DOI.

2. Hitesh Kotte, Florian Daiber, Milos Kravcik and Nghia Duong-Trung (2024). FitSight: Tracking and Feedback Engine for Personalized Fitness Training. In Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP 2024). ACM Digital Library. 1 - 4 July 2024. Cagliari, Sardinia, Italy. Acceptance ratio 22.6%.  link. DOI.

3. Hitesh Kotte, Milos Kravcik, Nghia Duong-Trung (2023). Real-Time Posture Correction in Gym Exercises: A Computer Vision-Based Approach for Performance Analysis, Error Classification and Feedback. MILeS 2023 International Multimodal Immersive Learning Systems Workshop at Eighteenth European Conference on Technology Enhanced Learning (ECTEL2023), September 4-6, 2023, Aveiro, Portugal.

4. Jan Schneider, Khaleel Asyraaf Mat Sanusi, Bibeg Limbu, Nghia Duong-Trung (2023). Novices make more noise! But how can we listen to it? MILeS 2023 International Multimodal Immersive Learning Systems Workshop at Eighteenth European Conference on Technology Enhanced Learning (ECTEL2023), September 4-6, 2023, Aveiro, Portugal.

5. Nghia Duong-Trung, Hitesh Kotte, Milos Kravcik (2023). Augmented Intelligence in Tutoring Systems: A Case Study in Real-time Pose Tracking to Enhance the Self-Learning of Fitness Exercises. In Proceedings of the Eighteenth European Conference on Technology Enhanced Learning (ECTEL2023). Springer LNCS.  September 6-8, 2023. Aveiro, Portugal.

Tech4compKI: Personalized competence development through scalable mentoring processes.

• Funded by the Federal Ministry of Education and Research of Germany (BMBF), the second phase of the Tech4comp project. Grant number 16DHB2208. 

• Role: Senior Researcher.

• Participation: 09.2022 - present

The joint project tech4comp develops and researches design concepts to make the proven quality of individual mentoring for acquiring competencies measurable. In addition, digital tools are being developed in mentoring learning and exam rooms that teachers can use to supervise many learners and automate mentoring processes through AI-based knowledge services.

Project partners are the University of Leipzig; Technical University Dresden; German Research Center for Artificial Intelligence (DFKI GmbH); Martin-Luther-University Halle-Wittenberg; Technical University of Chemnitz, University of Technology; Economy and Culture Leipzig; Free University of Berlin; RWTH Aachen.

Publications:

1. Matteo Orsoni, Alexander Pögelt, Nghia Duong-Trung, Mariagrazia Benassi, Milos Kravcik, Martin Grüttmüller (2023). Recommending Mathematical Tasks Based on Reinforcement Learning and Item Response Theory. 19th International Conference on Intelligent Tutoring Systems (ITS2023). June 5-8, 2023, Corfu, Greece. Best Full Paper Award.

Completed Projects

KIWI-biolab: International future laboratory for AI-based bioprocess development 

• Funded by the Federal Ministry of Education and Research of Germany (BMBF). link. Grant number 01DD20002A.

• Task Force 1 (96 PM): Active Learning for High Throughput Data and Operation

• Task Force 1 Members: Prof. Dr. Dr. Lars Schmidt-Thieme (University of Hildesheim, Germany), Dr. Stefan Born (TU-Berlin, Germany), Dr. Nghia Duong-Trung (TU-Berlin, Germany), Prof. Dr. Andrey Ustyuzhanin (Faculty of Computer Science, HSE University, Russia), Randolf Scholz (University of Hildesheim, Germany).

• Role: Machine Learning Scientist

• Participation: 12.2020 - 08.2022.

The KIWI-biolab – Künstliche Intelligenz für Wissensbasierte Integrierte Biolabore – brings international top scientists in artificial intelligence, machine learning and bioprocess engineering together to explore the opportunities and challenges of automation in biotechnological processes.

Our Future Lab aims to use AI and ML technologies to automatize data analysis on microorganisms and enable computers to plan and optimize experiments independently. The long-term target is to advance towards automatization for a more efficient and sustainable production.

The KIWI-Biolab is a 3 years initiative funded with EUR 4.5 million by the Federal Ministry of Education and Research (BMBF), within the Federal Government’s Strategy for the Internationalization of Education, Science and Research, and the Federal Government’s High-Tech Strategy 2025 frameworks.

Publications:

1. Nghia Duong-Trung, Stefan Born, Jong Woo Kim, Marie-Therese Schermeyer, Katharina Paulick, Maxim Borisyak, Mariano Nicolas Cruz-Bournazou, Thorben Werner, Randolf Scholz, Lars Schmidt-Thieme, Peter Neubauer, Ernesto Martinez (2022). When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development. Biochemical Engineering Journal, Biochemical Engineering in the Era of Machine Learning and Automation Special Issue. SCIE Web of Science. IF 4.446. Q2 SCImago. link. DOI.  

2. Kiran Madhusudhanan, Johannes Burchert, Lars Schmidt-Thieme,  Nghia Duong-Trung, Stefan Born (2022). Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting.  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022).

Deployment of a learning management system for Can Tho University of Technology (CTUT)

• Funded by CTUT, Vietnam

• Duration: 

  • Phase 1: 7 months (From 03/2018 to 09/2018)

  • Phase 2: 10 months (From 09/2019 to 06/2020)

• Role: Project manager

• Tasks: Budget management, human resource management, business analysis, courses structure development

• Project members: Ha Le Ngoc Dung (co-manager), Pham Yen Nhi, Nguyen Trung Kien

In this project, we aim to deploy a learning management system that can be integrated into CTUT’s teaching and studying activities, through a combination of formal, social, mobile and experiential learning. It facilitates the management, delivery, engagement, and measurement of all CTUT’s elearning courses. The system is going to support more than 100 teachers and 4000 students yearly. 

System deployment: https://elearning.ctuet.edu.vn/

Engineering and information technology development and application in aquaculture and fisheries

• Funded by Japan's ODA. A cooperation between Can Tho University (Vietnam) and Tokyo University of Marine Science and Technology and Kyushu University (Japan)

• Duration: 36 months (From 10/2018 to 09/2021)

• Role: Machine learning developer

• Tasks: Developing machine learning models for shrimp classification, shrimp's disease prediction, food recommendation. Deploying images database collection, sensor systems. Deploying mobile application. Conducting research papers.

• Key project members: Assoc. Prof. Chi-Ngon Nguyen, Assoc. Prof. Truong Quoc Phu, Prof. Akio Okayasu, Prof. Tsuyoshi Ikeya, Assoc. Prof. Daisuke Inazu.

The overall objectives are to study, develop and apply of engineering technology and IT for sustainable development of shrimp farming in the Mekong Delta. The specific objectives are (i) acquiring the pond environment parameters for shrimp farming management, (ii) eExpert System development for aquaculture extension on mobile communication networks, (iii) using of renewable energy in shrimp ponds, and (iv) building an Information system for Aquaculture and Fisheries management.

Publications:

1. Nghia Duong-Trung, Luyl-Da Quach, Chi-Ngon Nguyen (2020). Towards Classification of Shrimp Diseases Using Transferred Convolutional Neural Networks. Advances in Science, Technology and Engineering Systems Journal (ASTESJ). 

2. Nghia Duong-Trung, Luyl-Da Quach, Chi-Ngon Nguyen (2019): Learning Deep Transferability for Several Agricultural Classification Problems. International Journal of Advanced Computer Science and Applications (IJACSA) 10(1).

3. Nghia Duong-Trung, Luyl-Da Quach, Minh-Hoang Nguyen, Chi-Ngon Nguyen (2019): A Combination of Transfer Learning and Deep Learning for Medicinal Plant Classification, in Proceedings of ACM International Conference on Intelligent Information Technology (ICIIT 2019). Da Nang, Vietnam.

4. Nghia Duong-Trung, Luyl-Da Quach, Minh-Hoang Nguyen, Chi-Ngon Nguyen (2019): Classification of Grain Discoloration via Transfer Learning and Convolutional Neural Networks, in Proceedings of ACM International Conference on Machine Learning and Soft Computing (ICMLSC 2019). Da Lat, Vietnam. link. DOI. Best Presentation Award.