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DIES National Multiplication Trainings 2025-2026

Topic: Innovative Artificial Intelligence in Quality Assurance Management for Higher Education in Vietnam (InnoAIQA-VN)

1. Program Background

Over the years, Vietnam’s higher education sector is rapidly evolving to meet global and regional standards, with an increasing emphasis on developing and implementing internal quality assurance (IQA) as a strategic component for enhancing institutional governance, fostering autonomy, and promoting continuous improvement across higher education institutions (HEIs). However, this process faces numerous challenges, including a lack of advanced tools, limited capacity for data-driven decision-making, and the very limitations of professional communities fostering innovation in QA practices.

Artificial Intelligence (AI) offers unprecedented opportunities to address these challenges and enhance the quality of higher education by automating routine QA tasks, analyzing large datasets for institutional improvement, optimization of accreditation and evaluation processes, and the promotion of transparency and efficiency in internal quality assurance (IQA) systems. Yet, despite the global trend toward integrating AI in higher education, its adoption in Vietnam remains limited due to a lack of specific practical guidelines and a supportive technical ecosystem tailored to domestic conditions.

To address this critical demand, the VNU - HCM Center for Education Accreditation (VNU-HCM CEA), in partnership with distinguished experts from Vietnamese universities, and with the esteemed support of the DIES (Dialogue on Innovative Higher Education Strategies) program—a collaborative initiative led by the German Academic Exchange Service (DAAD), the German Rectors’ Conference (HRK), and the University of Potsdam, Germany—has developed and launched the “Innovative Artificial Intelligence in Quality Assurance Management for Higher Education in Vietnam” (InnoAIQA-VN) program. This National Multiplication Training (NMT) initiative exemplifies the strategic adaptation of global innovations to response the specific requirements of Vietnam’s higher education system.

2. Program Objectives

1.  General Objective

To empower educational leaders and management staff in higher education institutions (HEIs) across Vietnam to effectively integrate Artificial Intelligence (AI) into internal quality assurance (IQA) processes; To develop a professional community on quality assurance and AI; promote experience sharing, innovation, and sustainable collaboration among universities in Vietnam.

2. Specific objectives:

• Equip participants with practical knowledge and advanced skills to apply AI tools in specific areas of quality assurance, such as program/curriculum development, database management for IQA, monitoring, and institutional research.

• Develop actionable, ethical, and context-relevant AI implementation guidelines tailored to the Vietnamese higher education landscape.

• Strengthen participants’ capacity to design, lead, and manage change projects that incorporate AI for improving QA processes.

• Establish a professional learning network to facilitate the exchange of best practices and innovations in AI-driven QA management.

• Address regional disparities in AI adoption by providing tailored support for HEIs with limited technological resources, particularly in underserved areas.

Please click the link below to apply:  LINK;   Appendix available here: LINK

3. Expected learning outcome

• Apply AI tools (e.g., data analytics, natural language processing…) to optimize specific quality assurance activities, such as academic program reviews, compliance monitoring, and reporting.

• Create institutional AI guidelines that address responsible use, data privacy, and ethical considerations in QA processes, aligned with national, international standards and The UNESCO Ethics of AI.

• Lead change projects by employing project management frameworks and strategies for integrating AI into QA systems, overcoming resistance, and ensuring scalability.

• Collaborate effectively to establish and sustain a professional learning network (with key members from the participants of this project and other relevant stakeholders) for sharing AI-based QA solutions, focusing on practical challenges and innovations.

•  Tailor AI applications to address the unique challenges faced by their institutions, particularly those in rural, mountainous, and disadvantaged regions. By strengthening the capacity of leaders and administrators to effectively utilize AI in quality assurance processes, these efforts aim to enhance access, equity, and inclusivity, enabling underprivileged institutions to improve their quality standards and better serve their communities.

4. Target Group

• Middle-level managers at institutes/faculties/departments/offices/centers (e.g., Quality Assurance Office, Testing Center, R&D Department, Training Institute, Faculty) or units responsible for quality assurance/IQA functions at higher education institutions (HEIs) (maximum of 3 participants per institution).

• Priority will be given to:

• Institutions located in disadvantaged, central, mountainous, or highland regions.

• Female candidates in leadership/management/coordination roles.

• Applicants with a change project idea that applies AI in quality assurance.

Notes: Participants should not have taken part in any DIES training (funded by DAAD) in the past.

4. Program Structure, Topics and Timeframe

Workshop I: 

• Date: 26th - 28th November, 2025 (3 days)

• Place: Ho Chi Minh City

• Topics:

• AI application for curriculum development and scientific research.

• AI application for quality monitoring and database management for IQA.

• Establishment of AIHE Community of Profession in quality assurance.

Online support phase: includes mentoring, supplementary training, and monthly progress sharing sessions from December 2025 to March 2026.

Workshop II: 

• Date: 10th - 12th April, 2026 (3 days)

• Place: Other Provinces (excludiing Ho Chi Minh City)

• Topics:

• Ethics and challenges in applying AI to quality assurance.

• Presentation of change projects by participants and sharing of practical experiences.

5. Application Requirements

• Fully filled “Change project application form” (Appendix 1). The change project can be implemented individually or in groups (up to 3 members per group). Members of a change project team may come from one or multiple higher education institutions (HEIs).

• Curriculum vitae (CV) of one or more applicants including date and signed.

• Letter of commitment from the leadership of the HEI including date and signed (Appendix 2).

Note: The application must be submitted in Vietnamese or English. The training will be conducted in both Vietnamese and English.

6. Application Procedure and Key Dates

• Kick-off: August 2025 (online), with detailed registration guidance provided.

• Application deadline: From August 11, 2025, until 11:00 PM on September 14, 2025.

• Notification of selection results: Between October 6 and October 11, 2025, via email.

• All applications must be submitted through the online application form below: LINK

• Official website: https://iqadvanceteam.blogspot.com/

7. Selection Criteria 

• Relevance to AI and QA Objectives, feasibility, innovation, and potential impact of the change project.

• Participant Qualifications: Professional roles and experience, AI Knowledge or Willingness to Learn

• Support from institutional leadership and internal consensus.

• Regional and gender diversity.

8. Funding and Expenses

The program outlines the following funding and expense provisions:

• Training fees: Fully waived for all participants.

• Accommodation: Accommodation will be arranged for all participants during both contact phases in Ho Chi Minh City and other provinces.

• Meals: Participants will be provided with three daily meals throughout both contact phases.

• Travel expenses: Participants, or the nominating higher education institutions, are respectfully requested to cover travel expenses to and from the training locations and daily allowances during workshops..

9.  Contact

• Website: https://iqadvanceteam.blogspot.com/

• For more information, please contact: 

Huynh Huu Phuoc Tho (MA)

Center for Education Accreditation, VNU-HCM

Email: InnoAIQAVN@gmail.com; C.C.: hhptho@vnuhcm.edu.vn;

Mobile phone: (+84) 987109179

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InnoAIQA-VN Project Participates in DAAD ToT Workshop in Germany

Within the framework of the National Multiplication Training (NMT) program funded by the German Academic Exchange Service (DAAD), representatives of the project team "Application of Artificial Intelligence in Quality Assurance Management in Vietnamese Higher Education" (InnoAIQA-VN), including two members—Ms. Ho Dac Hai Mien, MA, Deputy Director of the Center for Education Accreditation, Vietnam National University Ho Chi Minh City (VNU-HCM CEA) and Project Leader, and Dr. Le Phuong Truong, Head of the Office of Educational Testing & Quality Assurance at Lac Hong University and Project Team Member—traveled to Berlin, Germany, to participate in the Training of Trainers (ToT) workshop held from July 21 to 25, 2025. The InnoAIQA-VN project is led by VNU-HCM CEA in collaboration with partner universities across Vietnam.

The 5-day training program was held in Berlin and Potsdam and aimed to build the capacity of project teams from various countries, including Bolivia, Cameroon, Colombia, Indonesia, Kenya, Mozambique, the Philippines, Senegal, South Africa, Zambia, and Vietnam. German experts directly provided lectures, guidance, and project consultation through thematic sessions, group activities, and personalized mentoring. Participation in the ToT workshop serves as a crucial preparatory step toward the successful implementation of the InnoAIQA-VN project. The project seeks to enhance the capacity of Vietnamese higher education institutions in integrating AI into quality assurance processes such as data analysis, curriculum development, quality monitoring, and the advancement of professional QA communities.

 With financial support from DAAD and academic support from the University of Potsdam, the InnoAIQA-VN project team hopes to disseminate positive values, contributing to digital transformation, innovative governance, and the enhancement of higher education quality in Vietnam.

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AI Pedagogy: Rooted in Pedagogy, Centered on Humans

In modern education, as technology advances rapidly, AI (Artificial Intelligence) has become an essential tool in teaching and learning activities. However, for successful AI integration, we must always remember the principle: “rooted in pedagogy, centered on humans.” This means that AI is a means to serve educational goals, while the core remains the pedagogical methods and the interaction between educators and learners. This article will analyze the concept of "AI pedagogy" and provide user-friendly examples that resonate with learners.

1. What is AI Pedagogy?

“AI Pedagogy” refers to the application of AI in teaching and learning from a pedagogical perspective. AI acts as a supporting tool to implement learning and training activities but does not replace the central role of humans and pedagogical principles. In other words, AI is “integrated” into the teaching process to optimize and personalize learning while adhering to the fundamental values of education: humanity, interaction, and a focus on holistic learner development.

 

Training on AI Pedagogy for LHU and Hanoi University of Pharmacy

2. Rooted in Pedagogy

2.1 Teaching methods based on Kolb’s Learning Styles

Challenges in the past: Designing lessons based on Kolb’s learning styles (with four stages: Concrete Experience, Reflective Observation, Abstract Conceptualization, and Active Experimentation) often required educators to spend significant time creating scenarios, activities, and various materials tailored to different learner groups (e.g., those who prefer direct experience versus those who prefer analysis).

AI Integration: AI can suggest learning activities tailored to each learner’s style, create real-life simulations, or provide diverse materials (videos, readings, interactive games). This allows lesson planning to become faster and more flexible while preserving the core value of Kolb’s method: connecting learners with experiences and reflection.

Applying AI to Plan Lessons Based on Kolb Learning Style

2.2 Blended Learning

Challenges in the past: Designing a blended learning course (combining in-person and online learning) required educators to carefully balance teaching time, select online platforms, organize group activities, and assess learners. Ensuring that online content was cohesive and engaging was a major challenge.

AI Integration: AI can track learners' progress and recommend schedules, content, or interactive activities tailored to individual abilities and preferences. This enables learners to engage in online learning comfortably, while in-person sessions focus on interaction and discussion. Thus, the essence of blended learning—a harmonious combination of in-person and online learning—is enhanced, not overshadowed by technology.3. Centered on Humans

3.1 Virtual tutors and friendly learning assistants

Virtual tutors: These are learning assistants available anytime, anywhere. For instance, when learners need to review knowledge or ask questions outside classroom hours, virtual tutors can provide answers, suggest materials, or assign practice exercises.

Human-centric value: While AI can respond quickly, educators remain crucial in guiding, sparking critical thinking, and offering emotional and psychological support. Virtual tutors cannot replace the motivation and personal care educators provide.

3.2 Chatbots trained on educators’ curricula

Learning chatbots: Chatbots built on official course materials help learners quickly search for information, address common questions, and review knowledge. Learners can interact with chatbots, asking questions ranging from simple to complex.

Human-machine interaction: Since chatbots are trained from materials developed by educators, their answers align closely with classroom teaching and the pedagogical intentions of educators. Additionally, educators can track frequently asked questions to adjust lessons, enrich examples, or design appropriate learning activities.

Core value: Humans (educators) remain the ones who “ignite inspiration,” crafting lively curricula, encouraging learning enthusiasm, and ensuring the quality and standards of content. Chatbots act as bridges, helping learners grasp and reinforce knowledge more effectively.

4. Benefits and Challenges

The integration of AI into education offers numerous benefits and presents certain challenges. On the positive side, AI optimizes time and resources for educators by streamlining lesson planning and assessment, while also personalizing and enhancing interactions with learners. It creates engaging learning experiences through simulations, games, and visual aids, empowering learners to take charge of their own learning and revision.

However, challenges include ensuring that AI serves as a means to support education, rather than becoming the ultimate goal. Safeguarding learners’ personal data, such as information and learning outcomes, is a critical concern. Additionally, educators must be flexible and equipped with technological skills to effectively use AI. Continuous updates and maintenance of AI tools are essential to prevent inaccuracies or irrelevant chatbot responses, ensuring that the technology remains a valuable and reliable educational tool.

5. Conclusion

“AI Pedagogy” is not just about using AI for technical purposes but, more importantly, about maintaining the core values of education “rooted in pedagogy, centered on humans.” No matter how powerful and versatile AI becomes, the central role lies with educators and learners. AI is a companion, helping us design creative and personalized lessons and learning environments, without overshadowing the essential elements of interaction, empathy, and mutual understanding—the foundations of education.

The application of AI from a pedagogical perspective empowers learners to take charge of their education, becoming more active and engaged in acquiring knowledge. At the same time, it allows educators to devote more time to fostering the holistic development of learners. This approach paves the way for a modern, high-quality, and humane vocational education system.

Lê Phương Trường

 

 

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AI in Quality Assurance

 In the past, when designing learning outcomes for a course or educational program, educators often had to spend significant time researching and memorizing the verbs in Bloom’s taxonomy. Memorizing or manually referencing Bloom’s verb table across various materials was not only time-consuming but also prone to errors. For example, if a teacher wanted to express the competency of "analyzing," they would have to look up suitable verbs in Bloom’s taxonomy and then cross-check their alignment with learning objectives.

Today, with the support of AI models specifically trained in quality assurance, educators no longer need to “commit to memory” every detail of Bloom’s taxonomy. Instead, they can interact directly with AI to quickly identify appropriate verbs and pinpoint areas for improvement. This significantly reduces the effort required to consult resources or verify the coherence of training programs. For instance, if an educator aims to adjust a learning outcome from the level of "understand" to "apply," AI can suggest verbs like "perform," "apply," or "execute," accompanied by a clear explanation of their relevance.

Additionally, AI can provide detailed advice and guidance to help educators better understand accreditation standards such as AUN-QA or the Ministry of Education and Training’s requirements. Rather than navigating through numerous legal documents or accreditation manuals, teachers simply need to ask questions and receive immediate, accurate answers. This makes the process of skill enhancement more flexible and engaging, allowing educators to explore quality assurance knowledge in their own proactive and creative way.

In summary, AI does not replace the role of educators but acts as an intelligent “assistant” that collaborates with them to design and refine educational programs. It helps save time, accelerates workflows, and, most importantly, sustainably enhances the quality of education.

To learn more about the application of AI in quality assurance, visit:

 Link:   ChatGPT - Trợ lý đảm bảo chất lượng

Le Phuong Truong

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The AI Competency Framework for Students

 

The AI Competency Framework for Students, developed by UNESCO, aims to equip learners with the knowledge and skills needed to understand, apply, and innovate with AI in modern education and society. The framework is structured around three levels—Understanding, Applying, and Creating—and focuses on four key domains:

Human-Centered Mindset

Critical Reflection on AI: Encourages students to critically examine the role and impact of AI in society.

Safe and Responsible Use: Guides students on using AI ethically, securely, and responsibly.

Self-Development in the AI Era: Helps students understand how AI can support personal and professional growth.

Ethics of AI

Human Autonomy: Ensures students understand that AI should support, not replace, human decision-making.

Ethical AI Design: Promotes the design of AI systems in line with ethical principles that respect human rights and values.

AI Citizenship: Prepares students to actively and responsibly participate in AI-supported societies.

AI Foundations

Data, Algorithms, and Models: Provides foundational knowledge on how AI operates, including data collection, algorithms, and model building.

Programming and Data Analysis: Develops skills in programming and data analysis to create basic AI applications.

Modeling and Visualization: Teaches students to visually represent AI data and results in clear and understandable formats.

AI Skills

AI Techniques and Applications: Equips students with skills to utilize AI tools and techniques in practical scenarios.

AI Programming: Trains students to develop AI programs tailored to specific needs.

AI Product Development: Encourages creativity and practical application in designing and implementing AI projects or products.

AI for Problem-Solving

Identifying Problems: Guides students in recognizing issues that can be addressed using AI.

Co-Designing Solutions: Promotes collaboration in designing AI solutions.

Co-Creation and Continuous Feedback: Encourages the iterative improvement of AI solutions based on real-world feedback.

This framework aims to holistically develop students, not only in technical competencies but also in ethical considerations and social responsibility when utilizing and developing AI. It provides a foundation for students to navigate the AI-driven future effectively and responsibly.

(unesdoc.unesco.org)

Link: Download here

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Redesigning Assessments in the AI Era

 

Introduction

In the technological era, particularly with the explosion of Artificial Intelligence (AI), numerous fields have been profoundly impacted, such as content creation, data analysis, and marketing. Education is no exception. One significant advantage of AI is its ability to reduce administrative tasks and personalize the learning process for each individual. However, a major challenge educators face is assessment. As AI becomes more prevalent in learning, students can rely on AI tools to complete assignments and exams more quickly and effortlessly. This raises an urgent question: How can fairness and integrity in assessments be ensured?

This article will discuss redesigning assessment systems in the AI era and propose solutions to address the challenges posed by students using AI in exams.

The Impact of AI on Assessment

Generative AI provides numerous helpful tools for learners, ranging from solving complex problems, generating text content automatically, to offering detailed answers to academic questions. While these advancements bring significant benefits to learning, they also present serious challenges in assessing the actual competencies of students. Specifically:

• Cheating: Students can use generative AI tools such as ChatGPT, Claude, and Copilot to generate essays or answer complex questions effortlessly without conducting their own research or critical thinking.

• Difficulty in Determining Actual Competence: Without appropriate control measures, assessments may fail to reflect students' actual competencies, as AI can help them complete tasks without in-depth understanding. Over time, this reliance on AI may erode their critical thinking and deep comprehension of the topics under study.

Solutions for Redesigning Assessment in the AI Era

1. Design Assessments Based on Critical Thinking

Instead of focusing on knowledge-based or straightforward questions, assessments should evaluate learners’ critical thinking, problem-solving, and creativity. Open-ended questions requiring reasoning, analysis, and personal opinions are more challenging for AI tools to address effectively.

2. Integrate Direct and Practical Assessments

An effective solution is to combine theoretical tests with practical or in-person assessments. For instance, after completing a written essay or exam, students could be required to present orally or perform related tasks on the spot to verify their understanding and ability to apply knowledge. This ensures that students genuinely grasp the content rather than relying on AI.

3. Leverage Technology to Detect AI-Generated Content

AI-detection software is being developed and can be integrated into assessment systems. These tools help identify whether an essay or answer has been generated by AI, reducing the risk of cheating.

4. Focus on Learning Processes Instead of Final Results

Rather than concentrating solely on final exams, educators can evaluate students through their learning processes. Long-term projects, stage-by-stage reports, or continuous group activities can assess progress and capabilities comprehensively. This makes it harder for students to rely on AI throughout all phases without demonstrating genuine understanding.

5. Encourage Using AI as a Learning Tool, Not for Cheating

Educators should design teaching and assessment activities where AI is a support tool rather than a means of cheating. For example, students may be asked to use AI for information retrieval but then analyze and provide personal insights based on the results. This promotes responsible and creative use of AI while still accurately assessing learners’ abilities.

6. Use Presentation Methods

Presentations are one of the most effective tools to discourage students from over-relying on AI. Presentations require students to understand the subject matter and demonstrate their communication and logical content delivery skills. Through this method, educators can easily evaluate students' actual understanding and their ability to address arising questions during the presentation. Benefits of this method include:

• Enhancing Critical Thinking and Analysis: Students must synthesize information, think critically, and explain concepts clearly.
• Fostering Creativity: Presentations provide an opportunity for students to showcase creativity through interpretation and content illustration.
• Developing Communication Skills: Communication skills are essential not only in learning but also in future careers.

Conclusion

In the AI era, assessments face numerous challenges but also present opportunities to redesign and improve systems to enhance learners’ critical thinking and adaptability. By applying innovative assessment methods, leveraging technology, and promoting critical thinking, educators can build fair assessment systems that accurately reflect students’ real abilities while encouraging responsible use of AI. Teachers and educational administrators must proactively adjust their approaches, not only to address the challenges AI brings but also to turn it into an effective learning support tool.

Le Phuong Truong

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AI Competence Framework for teacher

 The AI Competency Framework for Teachers by UNESCO is designed to equip educators with the necessary skills and knowledge to effectively integrate artificial intelligence (AI) into their teaching practices. This framework emphasizes a human-centered approach, prioritizing inclusivity, human agency, and respect for diversity.

The framework is structured around five key areas:

1. Human-Centered Mindset: Encouraging teachers to understand and assert their agency in relation to AI.

2. Ethics of AI: Promoting responsible use, ethical principles, and safe practices in AI applications.

3. AI Foundations and Applications: Providing foundational AI knowledge and skills necessary for educators.

4. AI Pedagogy: Supporting teachers in leveraging AI for innovative teaching methods and enhancing learning experiences.

5. AI for Professional Development: Outlining how educators can utilize AI to drive their own lifelong professional growth.

These competencies are categorized into three progression levels: Acquire, Deepen, and Create, allowing for a structured development pathway for teachers at different stages of AI integration.

For a comprehensive understanding and to access the full document, you can download the framework from UNESCO's official repository.

Link: Download here

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