HAIC Specific Requirements

Specific Guidelines for HAIC Courses

Common core courses under the Human-AI Co-Creation and Data Literacy (HAIC) area [link to “Common Core Areas” for the 2026-27 cohort in Section N] in the Foundations group aim to equip students with essential skills to navigate a rapidly evolving technological landscape and to provide baseline training in Artificial Intelligence (AI) fluency for all students, fostering a mindset transformation that enables students to understand, reason with, design through, and lead amidst AI’s uncertainties.

This HAIC area is categorized into two levels, with specific requirements outlined below for your reference in developing these courses:

First-level HAIC Courses

Common core courses under this category are designed to equip students with foundational knowledge and skills in AI in their first year of study. The Center for Education Innovation (CEI) [link to CEI’s new webpage] has developed the first course, AISC 1000 Foundations of AI Literacy: Human-AI Orchestration, as a fully online course that includes a blended-learning section and uses a Distinction/Pass/Fail (DI/PA/F) grading system. Schools may adapt this course to face-to-face format while maintaining identical course intended learning outcomes (ILOs). If you are interested in making such adaptations, please consult with your School about specific arrangements.

Second-level HAIC Courses

Common core courses under this category refer to subsequent HAIC courses developed based on the Center for Education Innovation’s (CEI) [link to CEI’s new webpage] pioneering First-level course, AISC 1000 Foundations of AI Literacy: Human-AI Orchestration, allowing students to further enhance their AI skills after completing the first course.

Building upon AISC 1000

In addition to achieving at least one of the area ILOs of the HAIC area [link to “Common Core Areas” for the 2026-27 cohort in Section N], you must reference the content of CEI’s pioneering First-level course, AISC 1000, to avoid content overlaps and ensure students build upon what they learned to enhance their AI cognitive development. While the competency of using AI will be introduced at the First-level courses, the Second-level courses should focus more on the reinforcement and mastery of AI skills through discipline-specific applications.

Course credit and offering schedule

The University requests Schools to offer Second-level HAIC courses for one credit. Each course must be offered three times within an academic term, structured into three identical sessions (L1, L2, and L3) scheduled sequentially, with L1 scheduled for Weeks 1 to 4, L2 for Weeks 5 to 8, and L3 for Weeks 9 to 12.

Example:

Course Code	Week 1-4	Week 5-8	Week 9-12
ABCD 1234	L1	L2	L3

General requirements

Open Enrollment: Your Second-level HAIC course should be open to all students from all disciplines and should have no prerequisites other than the eligible First-level HAIC courses, including AISC 1000.
Course Focus: Ensure that your course is neither as narrow as advanced discipline-specific courses nor as broad as “AI for Science” or “AI for Engineering”. For example, a course like “AI in Civil Engineering” could be considered if it is designed to be introductory and broadly accessible to all students.
Grading: You may adopt either a Pass/Fail (P/F) system or regular letter grades.
AI Fluency Aspects: To ensure coherence across Schools in offering the Second-level HAIC courses, please pay attention to the following AI fluency aspects for mapping with your course:

First-level HAIC Course (3 credits, Taken in Year 1)		Second-level HAIC Course (1 credit, Taken after completion of the First-level HAIC course)
Characteristic	Example: AISC 1000 Foundations of AI Literacy: Human-AI Orchestration	Characteristic	Example: AI for Smart Cities (SENG)	Example: AI, Media, and the Public Sphere (SHSS)	Example: AI for Creative and Design Innovation (AIS)
Domain/Disciplinary Knowledge Understanding how AI transforms the concepts, methods, and knowledge structures of a specific discipline. This includes knowing what AI can and cannot do within a field, how AI-generated outputs relate to disciplinary standards of evidence, and how AI reshapes professional practice. Courses with this aspect help students see AI through their disciplinary lens – not just as a generic tool, but as something that changes what counts as knowledge in their field.
All First-level HAIC courses must contribute to all aspects.	✓	Individual Second-level HAIC course can contribute to any aspects. Students will equip all 4 aspects upon completion of the 3 x 1-credit Second-level HAIC courses.	✓	✓
Procedural Knowledge Knowing how to work with AI effectively – the practical techniques, workflows, and collaboration patterns for human-AI interaction. This includes prompt engineering, workflow design, delegation strategies, validation routines, and iterative refinement. Procedural knowledge answers the question: “How do I actually use AI to accomplish a goal?”
All First-level HAIC courses must contribute to all aspects.	✓	Individual Second-level HAIC course can contribute to any aspects. Students will equip all 4 aspects upon completion of the 3 x 1-credit Second-level HAIC courses.	✓		✓
Technical Skills Understanding the underlying mechanisms of AI systems – how models are trained, how data shapes outputs, what architectures enable different capabilities, and how to evaluate system performance. Technical skills range from conceptual understanding (e.g., the difference between supervised and reinforcement learning) to hands-on competency (e.g., building a Retrieval-Augmented Generation (RAG) chatbot or fine-tuning a model).
All First-level HAIC courses must contribute to all aspects.	✓	Individual Second-level HAIC course can contribute to any aspects. Students will equip all 4 aspects upon completion of the 3 x 1-credit Second-level HAIC courses.	✓
Cognitive and Employability Skills Higher-order thinking abilities developed through working with AI – critical evaluation of AI outputs, metacognitive awareness, ethical reasoning, systems thinking, and the capacity to adapt as AI evolves. These are transferable skills that prepare students for a workforce where AI is embedded in every role, regardless of discipline.
All First-level HAIC courses must contribute to all aspects.	✓	Individual Second-level HAIC course can contribute to any aspects. Students will equip all 4 aspects upon completion of the 3 x 1-credit Second-level HAIC courses.	✓	✓	✓