Postgraduate Research Interdisciplinary Collaborative Core (PGR ICC) course

The Postgraduate Research Interdisciplinary Collaborative Core (PGR ICC) course at NTU is aligned with the NTU 2025 Education Strategy and focuses on the 3Cs expected of all NTU graduates: Character, Competence and Cognitive Agility.

In today's rapidly evolving world, technological advancements and changing work environments demand a new approach to education. Our ICC courses emphasize key transferable skills and address global challenges through an interdisciplinary and collaborative learning model.

By breaking down traditional academic barriers, the ICC program integrates various disciplines, offering a comprehensive and future-ready higher education experience. Designed to enhance the academic and professional skills of our doctoral candidates, the course prepares them for their research endeavours and beyond.

GP8000: Artificial Intelligence Literacy

  1. Course information

    Course Coordinator Prof Erik Cambria
    Course Code GP8000
    Course Title Artificial Intelligence Literacy
    Academic Units NA (Non-credit bearing)
    Grading Pass/Fail
    Course Start Aug 2024
    Target Audience Compulsory for PhD (Aug 2024 Intake onwards),
    Optional for Master’s by Research (subject to vacancy availability)

     Artificial Intelligence Literacy is a 9-contact hour course consisting of:

    1. 5 Lectures comprising 4 modules (hybrid mode, flipped classroom)
      • Each module consists of recorded lecture(s) with individual assessment (MCQs)
    2. Independent group projects (*5-6 students per project)
    3. Group project presentation and discussion:
      • Compulsory Briefing Session in Week 3: Introduction to the group project outlines and meeting your group members.
      • Optional Tutorials in Weeks 6 and 9: Two sessions for groups to collaborate on their projects and consult with tutors.
      • Compulsory Tutorials in Weeks 12 and 13 (2 x 2 hours): Group project presentations, facilitated discussions, and peer evaluations, including assessments of individual contributions to the group work and presentation.

2. Course Aims     

The Artificial Intelligence (AI) Literacy course aims to give an overview of AI tools, ethical issues as well as applications in both STEM and HSS. This is to equip the postgraduate students with knowledge of AI since AI is fast becoming an integral component for all industries.

3. Intended Learning Outcome

By the end of this course, you should be able to:

  1. Explain what Artificial Intelligence (AI) is about and appreciate its relevance and importance for IT and society.
  2. Describe the human cognitive organization in problem-solving and appreciate the ethics involved in the application of AI techniques.
  3. Endow with knowledge of the societal impact and governance complexity and apply the appropriate solutions when using AI.
  4. Understand the different emerging AI applications in STEM and HSS and the use of these tools to increase work productivity.
  5. Be aware of the ethical issues and complications when using AI.

4. Course Lectures

  1. Module 1 - Basics of artificial intelligence (1h lecture) with compulsory quiz
  2. Module 2 - Emerging applications (2h lectures)
    • Complete first 5 compulsory bite-size lectures on applications of AI with compulsory quizzes; and
    • Choose 1 out of 2 for the last two bite-size lectures without quiz
  3. Module 3 - Ethics and artificial intelligence (1h lecture) with compulsory quiz
  4. Module 4 - Societal impact and governance (1h lecture) with compulsory quiz
  5. A minimum score of 70% is required to pass each individual quiz, and the overall passing threshold for the quizzes is also set at 70%.
  6. The quizzes will contribute 40% to the total course grade.

5. Physical Tutorials

  1. Compulsory: Briefing session in Week 3, a 2-hr tutorial in Week 12 and a 2-hr tutorial in Week 13.
  2. The tutorials in Week 6 and Week 9 are optional. These sessions are facilitated for groups to collaborate on their projects and consult with tutors if necessary.
  3. The independent group projects (about 5-6 students per project) will take up 60% of the overall passing score of the course.
  4. Group project presentations, facilitated discussions, and peer evaluations, including assessments of individual contributions to the group work and presentation, will be conducted during the tutorials in Week 12 and 13. 

 

FAQs for GP8000: Artificial Intelligence Literacy

1. Course Registration

You will be pre-registered by the School & Graduate College in Year 1 Sem 1 (Aug 2024 intake onwards). You will need  to approach your School for assistance on queries and matters related to course registration.
  • The ICC course is compulsory, and you are required to complete it before your Qualifying Examination (QE).
  • You are strongly encouraged not to defer and follow the curriculum plan to ensure that you take this course in time before your QE.
  • Any deferment must be approved by the School with strong justifications and Graduate College must be informed.
  • School must seek approval from Graduate College for more than 2 deferments.
  • You will have to complete the course before QE otherwise your QE will be delayed.
  • You should approach your School should you have queries related to course registration.

The ICC course is compulsory, and you are required to complete and pass it before your QE. You are not allowed to drop the ICC courses. 

  • Students from various schools and disciplines are registered for the tutorial class based on proportional distributions. To maintain the balance and effectiveness of this plan, you are strongly encouraged to adhere to the initially assigned schedule. This adherence supports the ICC program's goal of integrating various disciplines and providing a comprehensive, future-ready higher education experience through smooth and effective collaboration among students from diverse academic backgrounds.
  • While your School and Graduate College have the discretion to allocate you to different tutorial slots if you are unable to attend your pre-assigned classes, it is expected that such changes are made only for valid reasons and subjected to approval.
  • You should approach your School should you have queries related to course registration or wish to deviate from your study plan. Please note that this is subjected to vacancy availability and approval.

Please express your interest to your School to be placed on the waitlist. With the School’s endorsement, you will be put on the waitlist, and we will notify you if a vacancy becomes available. Please note that this is a non-credit bearing course. Therefore, this course will be an elective that is outside of your Coursework requirement.

Only PhD students who are matriculated from Aug 2024 onwards need to take GP8000 course. This means If you are matriculated before Aug 2024 as a Master by Research student and have later converted to PhD, you will not need to take the course. If you are matriculated from Aug 2024 onwards and have converted to PhD, you would need to take the course.

2. Attendance & MCs

Attendance is compulsory for the tutorials that are indicated as compulsory. Absence from class without an MC, hospitalisation leave or officially approved Leave of Absence (LOA) will affect your overall course grade. 

Absence from class must be covered by an MC, hospitalisation leave or officially approved LOA. Email your tutor and course coordinator to inform them of your absence with the attached documentary proof (e.g. MC, hospitalisation leave, approved LOA) by Friday of Week 13. You will also have to submit your MC to your School to get official approval for a short LOA. Informing your tutors, course coordinator, or Graduate College of your absence will not be accepted without the aforementioned documentation.  

3. Grading matter

The ICC course is a non-credit bearing, pass-or-fail module. The components of the assessment are:

Assessments (40%) Group Project (60%)

Students are required to watch the videos and complete all MCQ assessment quizzes to check their understanding of the concepts and information covered in the videos. 

The allocated group of 5-6 students from at least 2 Colleges form a Team. The team will select one of the real-life problems they encounter and work on developing an AI solution for this problem. 

The passing mark is 70%. Students are given two attempts for each MCQ test, and the higher score will be recorded.

The team will submit a Problem-Solving Analysis to demonstrate their understanding, identify the problems and validate probable causes for these problems before developing a viable solution.

 

Each team will give a 10-minute presentation on the problem and suggested solution to the class and submit a group report.

 

Students can contribute their expertise in finding solutions and apply their problem-solving, critical and creative thinking, teamwork and presentation skills. 

 

Components for this group project:

  • Problem-Solving Analysis (Report) – 10%
  • Group Presentation– 50%
  • Group discussion & Interaction (peer evaluation of group mates)) – 30%
  • Peer group evaluation – 10%   
  • Students who fail must re-take the course immediately in the following semester.
  • Students must complete the course before QE, failing which the QE will be delayed.
Course-related queries
  • Questions related to course registration as well as the semester you should take your ICC course should be directed to your School.
  • General PGR ICC course-related questions can be directed to Graduate College office at [email protected]. 
Course-related feedback
  • Email the Graduate College office at [email protected].
  • All feedback will be taken seriously, and any identifying information will be kept strictly confidential to protect your identity