Master Award in
Artificial Intelligence and Sustainability
Master Award could transfer 20 credits and 50% tuition fees to Master’s programs of UKeU and/or Partner University.
Master Award in Artificial Intelligence and Sustainability
The aim of this award is explores how AI supports sustainability and the UN Sustainable Development Goals (UNSDGs). Learners will study AI’s role in resource efficiency, environmental impact reduction, and sustainable development, covering concepts like energy efficiency, bias mitigation, trustworthy AI, and applications in clean energy, waste management, and smart manufacturing.
Could transfer 20 credits and 50% tuition fee to the Master of Artificial Intelligence of UKeU.
Learning Outcomes:
1. Understand the role of AI in promoting sustainability
- 1.1. Describe the potential of AI in supporting the UNSDGs.
- 1.2. Explain the applications of AI in resource management.
- 1.3. Analyse the impact of AI on reducing environmental footprints.
- 1.4. Evaluate the opportunities and challenges of using AI for sustainability.
- 1.5. Assess the future potential of AI in supporting global sustainability efforts.
2. Be able to develop sustainable AI solutions
- 2.1. Develop AI models with a focus on energy efficiency.
- 2.2. Explain the importance of data efficiency in AI.
- 2.3. Critically analyse methods for identifying and mitigating bias in AI systems.
- 2.4. Evaluate the role of trustworthy AI in sustainable development.
- 2.5. Create a sustainable AI application for a specific industry or sector.
3. Understand the ethical implications of AI in sustainability
- 3.1 Describe the ethical challenges associated with AI deployment in sustainability initiatives.
- 3.2 Explain the importance of ethical AI governance in sustainability.
- 3.3 Critically analyse the societal impacts of AI-driven sustainability initiatives.
- 3.4 Evaluate strategies for ensuring ethical AI in sustainable development.
- 3.5 Develop recommendations for ethical AI use in sustainability projects.
4. Understand the application of AI in achieving specific UNSDGs
- 4.1 Describe the use of AI in clean energy initiatives.
- 4.2 Explain how AI can contribute to sustainable agriculture.
- 4.3 Analyse the role of AI in improving water management.
- 4.4 Critically evaluate the impact of AI on waste management and circular economy.
- 4.5 Critique the potential of AI in urba sustainability and smart cities.
Topics:
AI for Environmental Impact Reduction
Course Coverage:
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Overview of AI and Sustainability
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Definition and Scope of AI in Sustainability
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The Role of AI in Achieving the UNSDGs
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AI’s Potential in Driving Sustainable Development.
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AI in Environmental Monitoring and Protection
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AI in Sustainable Urban Development
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Challenges and Opportunities
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Opportunities for AI in Sustainability
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Challenges in Implementing AI for Sustainable Practices
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AI for Resource Efficiency
Course Coverage:
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Applications of AI in Resource Management
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AI in Energy Management: Smart Grids and Energy Efficiency
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AI in Water Resource Management: Monitoring and Optimization
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Enhancing Resource Efficiency with AI
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AI in Waste Management and Recycling
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AI in Agriculture: Precision Farming and Resource Optimization
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AI in Supporting Global Sustainability Efforts
Course Coverage:
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Future Potential of AI in Sustainability
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Emerging AI Technologies for Sustainability
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The Role of AI in Global Sustainability Initiatives
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Ethical Considerations and Sustainability
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Balancing AI Innovation with Ethical Responsibilities
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Ensuring Fair and Equitable AI Deployment
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Developing Energy-Efficient AI
Course Coverage:
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Techniques for Reducing Energy Consumption in AI
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Model Pruning and Optimization
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Hardware Solutions for Energy Efficiency
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Practical Applications
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Case Study: Energy-Efficient AI Models in Industry
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Implementing Energy-Efficient Algorithms in Practice
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Data-Efficient AI Techniques
Course Coverage:
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Approaches to Data Efficiency
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Data Augmentation and Transfer Learning
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Synthetic Data Generation and Its Applications
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Case Studies
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Examples of Data-Efficient AI in Real -World Applications
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Reducing Data Requirements in AI Model Training
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Bias Mitigation in AI Systems
Course Coverage:
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Identifying Bias in AI Algorithms
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Types of Bias: Data, Algorithmic, and Societal
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Tools for Detecting Bias in AI
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Mitigating Bias and Ensuring Fairness
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Techniques for Bias Mitigation: Re-Sampling, Re-Weighting
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Implementing Fairness-Aware AI Systems
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Trustworthy AI for Sustainable Development
Course Coverage:
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Principles of Trustworthy AI
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Transparency, Explainability, and Accountability in AI
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Ethical AI Frameworks and Guidelines
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Practical Implementation
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Building Trustworthy AI Solutions for Sustainable Industries
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Case Study: Trustworthy AI in Healthcare and Environment
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Developing Sustainable AI Applications
Course Coverage:
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Industry-Specific AI Applications
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AI in Agriculture: Precision Farming and Sustainable Practices
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AI in Energy: Renewable Energy Management
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Project Development
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Designing and Implementing a Sustainable AI Project
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Evaluating the Impact of AI Solutions on Sustainability Goals
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Ethics in AI for Sustainability
Course Coverage:
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Key Ethical Challenges
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Addressing Bias and Discrimination in AI
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Ethical Considerations in Data Privacy and Security
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Balancing Ethics and Innovation
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The Role of Ethics in AI Innovation
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Case Study: Ethical Dilemmas in AI Deployment
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AI Governance in Sustainability
Course Coverage:
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Importance of AI Governance
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Establishing Governance Frameworks for AI
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International Guidelines and Ethical Standards
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Practical Applications
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Implementing Governance Frameworks in AI Projects
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Ensuring Compliance with Ethical Standards
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Societal Impact of AI-Driven Sustainability Initiatives
Course Coverage:
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Social Implications of AI in Sustainability
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AI and Its Impact on Employment and Economy
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AI in Public Policy and Its Influence on Society
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Strategies for Minimizing Negative Impacts
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Promoting Inclusive AI Practices
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Engaging Communities in AIDriven Sustainability Projects
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Developing Ethical AI for Sustainability
Course Coverage:
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Best Practices for Ethical AI Development
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Embedding Ethical Principles in AI Design
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Stakeholder Engagement and Ethical Audits
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Policy Recommendations for Ethical AI
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Creating Ethical Guidelines for AI in Sustainability
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Case Study: Successful Implementation of Ethical AI
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AI in Clean Energy
Course Coverage:
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AI for Renewable Energy Optimization
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AI in Solar and Wind Energy Management
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Smart Grids and AI-Driven Energy Storage Solutions
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AI in Energy Efficiency
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Reducing Energy Consumption in Industries
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AI for Energy Demand Forecasting and Management
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AI in Sustainable Agriculture
Course Coverage:
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Precision Farming with AI
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AI in Crop Monitoring and Yield Optimization
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Sustainable Resource Management in Agriculture
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Reducing Environmental Impact with AI
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AI for Soil Health Monitoring and Conservation
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AI in Sustainable Pest and Disease Management
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AI in Water Management
Course Coverage:
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AI for Water Quality Monitoring
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Real-Time Monitoring and Prediction of Water Quality
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AI in Managing Water Resources and Distribution
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Sustainable Irrigation with AI
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Optimizing Irrigation Systems with AI
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AI in Reducing Water Waste in Agriculture
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AI in Waste Management and Circular Economy
Course Coverage:
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AI-Driven Waste Sorting and Recycling
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Automated Waste Sorting Systems
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Enhancing Recycling Efficiency with AI
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Promoting Circular Economy with AI
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AI in Resource Recovery and Reuse
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Sustainable Product Design and Lifecycle Management
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AI in Urban Sustainability and Smart Cities
Course Coverage:
AI for Smart Urban Planning
AI in Traffic Management and Urban Mobility
AI for Sustainable Infrastructure Development
AI in Enhancing Urban Quality of Life
AI in Public Health and Safety Monitoring
Smart City Initiatives: Energy, Waste and Water Management
Indicative reading list
Core texts:
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Floridi, L. (2014). The Ethics of Information. Oxford University Press
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Goodfellow, I., Bengio, Y., & Courville, A (2016). Deep Learning. MIT Press.
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Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson.
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Rolnick, D., Donti, P. L., Kaack, L. H., et al. (2019). Tackling Climate Change with Machine Learning. arXiv:1906.05433
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Chollet, F. (2018). Deep Learning with Python. Manning Publications.
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Marr, B. (2020). Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Wiley.
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Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
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Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
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Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.
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Floridi, L. (2019). The Logic of Information: A Theory of Philosophy as Conceptual Design. Oxford University Press.
Additional reading:
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United Nations Sustainable Development Goals (UNSDGs):www.un.org/sustainabledevelopment
Entry requirements
To enroll The Master Award, the learner must possess:
- Graduated with a Bachelor’s degree from an accredited university or achieved a Level 6 Diploma according to the European Qualifications;
- For a degree from non-recognized universities; The learner should have followed Accreditation of Prior Experiential Learning for Qualifications (APEL.Q) policy of MI Swiss and/or University Partners;
- Learners must be over 21 years old.
English requirements
If a learner is not from a predominantly English-speaking country, proof of English language proficiency must be provided.
- Common European Framework of Reference (CEFR) level B2 or equivalent;
- Or A minimum TOEFL score of 101 or IELTS 5.5; Reading and Writing must be at 5.5 or equivalent.
The UKeU reserves the highest decision-making authority regarding admissions and may accept or reject applicants following a thorough review of each applicant’s profile, ensuring that only those capable of benefiting from the course are admitted. Qualifications from diploma mills or fake universities/institutions will not be accepted by UKeU and/or Partner University.
After graduating with Master Award, learners receive all certified documents from the UKeU.
Certified Documents:
- e-Certificate from the UK eUni Worldwide (UKeU);
- Hard copy certificate from UK eUni Worldwide (UKeU) (Optional);
- Accreditation of Prior Experiential Learning for Qualifications (APEL.Q) certified from UKeU for credit and tuition fee transfer.
Because the course is accredited and recognized, learners can easily use their qualifications in the workplace and enjoy many opportunities for career advancement. In addition, if you wish to pursue a degree from UKeU and/or a Partner University, all credits and 50% paid tuition fees can be transferred.
The UKeU’ Master Award means:
UKeU Master Award is the award at the master level and is equivalent to:
- Level 7 certificate of Regulated Qualifications Framework (RQF) of UK
- Level 10 certificate of Scottish Credit and Qualifications Framework (SCQF)
- Level 7 certificate of Credit and Qualifications Framework (CQFW)
- Level 7 certificate of European Qualifications Framework (EQF)
- Level 9 certificates of the Australian Qualifications Framework (AQF)
- Level 7 certificate of ASEAN Qualifications Reference Framework (AQRF)
- Level 9 certificate of the African Continental Qualifications Framework (ACQF)
Learners can transfer all credits and 50% of their tuition fees when enrolling in UKeU and/or Partner University academic programs if they wish to pursue an academic degree.
Credits transfer:
Learners can transfer 20 credits from the Master Award course when participating in the Master program. Please see the credit transfer policy HERE.
Tuition fee transfer:
When enrolling in the Master program, graduates from the Master Award will receive a fee reduction equal to 50% of the tuition fees paid for the Master Award. Please refer to the tuition fee transfer policy HERE.
The UKeU Micro Degree course allows learners to transfer credits and 50% of their tuition fees toward full degree programs offered by UKeU and/or Partner University. UKeU reserves the right to limit admissions once enrollment exceeds the set quotas.
Apply Policy:
- To participate in the UKeU Micro Degree course, learners need to meet the entry criteria corresponding to each level. Please see the “Entry” tab for more details.
- UKeU will not accept applicants whose entry qualifications are from fake universities or institutions that are not accredited.
- For Master Award courses, if an entry bachelor is unavailable, learners must demonstrate a minimum of 5 years of work experience in the relevant field. Please note that a bachelor’s degree is required for the Master’s program at UKeU and Partner University so that you could study Master Award but could not move to the Master’s program of UKeU and/or Partner University.
- English is not a mandatory entry requirement for Micro Degree course, but candidates need to ensure that English is used in reading documents, listening to lectures, and doing assignments. Applicants should note that English is a mandatory requirement when switching to an academic program at UKeU and Partner University.
Apply Process:
- Step 1: To request a consultation for a course that best suits your needs, please email support@ukeu.uk. Our admissions department will contact you to guide you through the required documentation and the next steps in the application process.
- Step 2: Once your application documents are approved and the application fee is paid, UKeU will issue a Letter of Acceptance (LOA). You will then follow the provided instructions, including payment of the tuition fee.
- Step 3: After the tuition fee is paid, UKeU will issue a confirmation letter, provide your login details for the e-learning system, and send you all relevant documents.
- At this point, you have officially become a UKeU student. Welcome, and enjoy your learning journey!
The UKeU Micro Degree course is fully online, allowing you to study anytime and anywhere. You also have the option to attend live classes with UKeU. Final exams will be uploaded to the system and assessed by the UKeU academic board. Learners are required to submit assignments on time; failure to do so will require payment of a resit fee (with up to two attempts allowed). Continued non-compliance on a third occasion will result in being considered as having discontinued the course, and tuition fees will not be refunded.
Pricing Plans
Take advantage of one of our non-profit professional certified courses with favorable terms for your personal growing carreers.
- Live Class (Optional)
- Full online videos
- e-Books
- Self study contents
- Online tutor videos
- Assignment guide
- e-Certificate
- Hard copy certificate from UKeU and/or Partner Universities
- APEL.Q certified from UKeU for credit and tuition fee transfer
- Deliver hard copy certificate and all certified documents to your home
- Transfer full credits & 50% tuition fees to equivalent academic programs
- Opportunity to get scholarships when becoming Partner Universities' international students
UKeU MICRO DEGREE
Contact us
If you interested this micro credential course, please feel free to contact with us! Please note that this program is a not for profit and learning with full online model.