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Revolutionise your teaching methods with artificial intelligence

Professional Certificate in AI-Enhanced Pedagogy

Professional Certificate in AI-Enhanced Pedagogy

The Professional Certificate in AI-Enhanced Pedagogy is an innovative programme designed to equip teachers with the knowledge and skills to effectively integrate AI into their teaching practices. This programme aims to enhance the educational experience by leveraging AI technologies to create more personalised, efficient, and engaging learning environments.

 At the end of the programme, learners will be well-equipped to harness the power of AI in their educational environments, ensuring that they are prepared for the future of teaching and learning.

  • Contextualised: By drawing on real-world examples and textbooks used in local schools, the curriculum integrates activities and examples directly relevant to the Singaporean educational context.
  • In-Depth Learning: This programme goes beyond the basics by delving into advanced AI concepts such as machine learning and natural language processing.
  • Automation: Learn how to automate administrative tasks to boost productivity in the classroom.
  • Personalisation: Be equipped with the skills to tailor learning experiences to the individual needs, preferences, and pace of students.
  • Convenience: This programme utilises synchronous e-learning, allowing learners to attend lessons from anywhere, eliminating the need for commuting.
Course TitleObjective 


Course 1
Python Programming for Teachers

Python is ranked the top programming language both in the recent IEEE Spectrum annual ranking of the top programming languages and in the Popularity of Programming Language Index. Its popularity is driven by the vast number of specialised libraries available for it and its simple syntax which results in high code readability.

Learners will be taught computational thinking concepts using Python, develop and implement Python applications and create data visualisations using Matplotlib.

At the end of the course, learners will be able to:

  • Write Python programmes and execute them using three different methods: Jupyter Notebooks, Python’s Integrated Development and Learning Environment (IDLE) and the command line.
  • Import and use the Python standard and external libraries.
  • Visualise 2D data using Matplotlib.

Course 2
Data Analytics for Teachers

In today’s rapidly evolving educational landscape, the ability to harness data effectively has become increasingly vital for teachers striving to optimise learning outcomes. This course offers a comprehensive exploration into the practical applications of data analytics within educational settings, equipping learners with the tools and knowledge needed to leverage data for informed decision-making and enhanced student engagement.

Learners will delve into various facets of data analytics, from understanding different types of educational data to mastering essential data analysis techniques. Learners will gain hands-on experience in collecting, cleaning, analysing, and interpreting data, which enables them to extract meaningful insights.

At the end of the course, learners will be able to:

  • Master data collection and preparation techniques.
  • Gain proficiency in descriptive, inferential, and predictive analytics methods, enabling them to identify patterns, make data-driven decisions, and predict student outcome.

     

    Course 3
    Machine Learning for Teachers

    Machine learning has become a transformative force with profound implications across various industries, including education. This course is designed to empower teachers with the knowledge and skills to understand and harness the potential of machine learning, enabling them to introduce their students to this exciting field and prepare them for future careers.

    Learners will get to explore the fundamentals of machine learning and its applications. From supervised and unsupervised learning techniques to model evaluation and real-world case studies, this course will provide learners with a solid foundation in machine learning concepts and equip them with practical skills to incorporate machine learning into teaching practices.

     At the end of the course, learners will be able to:

    • Understand the fundamentals of machine learning and how it can be applied in education.
    • Apply supervised and unsupervised learning techniques to analyse and interpret data.
    • Evaluate and validate machine learning models using appropriate metrics and techniques.
    • Explore real-world applications of machine learning through case studies.
    • Apply machine learning techniques to a real-world dataset through a project.

      Course 4
      Natural Language Processing for Teachers


      In today’s digital age, vast amounts of textual data are generated every day, presenting both opportunities and challenges for teachers. Natural Language Processing (NLP) is a powerful set of techniques that enables computers to understand, interpret, and generate human language, opening new possibilities for enhancing teaching and learning experiences.

       At the end of the course, learners will be able to:

      • Understand the fundamentals of NLP.
      • Preprocess and prepare text data for analysis.
      • Represent text data using different techniques.
      • Apply NLP techniques for text classification and sentiment analysis.
      Explore advanced NLP topics such as Named Entity Recognition and Topic Modelling.



      This programme is ideal for teachers who are:
      • Looking to integrate AI tools into their teaching practices to enhance student engagement and learning outcomes.
      • Interested in leveraging AI for research and advanced teaching methodologies.
      • Seeking to implement AI-driven strategies to improve school management and decision-making processes.
      • Aiming to design AI-integrated educational content and resources.
      • Keen to gain a deeper understanding of AI applications in the education industry.

      Venue: Virtual (Online), NTU e-Learning platform
      Time: 8:30 am - 12:00 pm 

      CourseCourse Dates
      Python Programming for Teachers
      4, 11, 18, 25 Jan 2025
      Data Analytics for Teachers8, 15, 22 Feb, 1 Mar 2025
      Machine Learning for Teachers
      		29 Mar, 5, 12, 19 Apr 2025
      Natural Language Processing for Teachers
      10, 17, 24 and 31 May 2025


      Note:       NTU reserves the right to change the date, venue, and mode of delivery due to unforeseen circumstances.

      Apply by 14 Dec 24

      Upon completing all four courses, participants will be awarded a Professional Certificate in AI-Enhanced Pedagogy, demonstrating their proficiency in these essential areas of AI-enhanced teaching tools.

       

       

       Fee and Funding  
      Course Standard Fee
      (Inclusive of 9% GST)      		Singapore Citizens
      (aged 21-39) / PR (aged ≥21)
      70% Funding       		Singapore Citizens (aged ≥40)
      MCES1 - up to 90%
      		SME-sponsored Singapore
      Citizens / PR
      ETSS2​ - up to 90%
      Professional Certificate in AI-Enhanced PedagogyS$7,848S$2,354.40S$914.40S$914.40

      Funding Requirements:
      - You must achieve a minimum of 75% attendance for each course.
      - You must complete and pass all assessment components.​​​

      Read more about funding here. 

      • NTU/NIE alumni may utilise their $1,600 Alumni Course Credits. Click here for more information.

      Dr. Lee Chu Keong
      Programme Director/Trainer

      Dr Lee is currently the Assistant Chair (Lifelong Learning and International Relations) and programme director of the MSc (Knowledge Management) programme at the Wee Kim Wee School of Communication and Information at the Nanyang Technological University (NTU) in Singapore. He is a chemical engineer by training, but furthered his studies in the areas of information science and knowledge management. In addition to NTU, he has also held teaching positions at Singapore Polytechnic and Temasek Polytechnic.

      His current teaching assignments include graduate courses in the areas of knowledge management, business information sources and services, and data science. He strongly believes that everyone should be able to think computationally.