Overview
AiCE students must satisfy multiple requirements before the Bachelor of Engineering degree is certified. The AiCE undergraduate curriculum requires at least 360 credit units. This includes 66 units of arts, humanities, social science and communication electives, 90 units of math/science, 132 units of core technical fundamentals, and 54 units of undergraduate research and development.
The maximum number of credits a student can earn is 400.
All requirements are expressed via the AiCE competency scheme. Some competencies are required, meaning that every AiCE student must demonstrate this competency in order to graduate. Others are optional. In some cases, optional competencies form a set from which the student is required to select a subset to complete.
Because of the individualized nature of the AiCE program, the pattern of study for each student will be different. Furthermore, students who receive credit upon entry to the program for advanced placement or other prior experience will experience a different learning path than students without these pre-existing skills.
The section below entitled AiCE Competencies presents the full set of currently defined competencies and the amount of credit associated with each one, with an indication of whether the competency is required in order to graduate. We expect that these lists will change somewhat as the AiCE program becomes more established.
Undergraduate Research and Development
The AiCE program is designed to provide students with opportunities to solve real world problems in collaboration with external stakeholders from industry, government and/or non-governmental organizations. Starting in their first semester, students will devote at least 7-9 hours per week working on projects in three tracks:
We call this project work “Undergraduate R&D”, though depending on the project, the work may tend more toward research or more toward development. The students are expected to participate for at least two semesters in each track. AiCE students must complete all three tracks to satisfy their graduation requirement. AiCE program will designate a track manager to help facilitate relevant activities and stakeholder-matching for each track. Expected output from the completion of each of these projects is comparable to completing a capstone design project. Hence AiCE graduates will have significantly more real-world research and development experience than students who earn bachelor’s degrees from most other computer engineering programs.
The projects will be designed by the organizational stakeholders in collaboration with CMKL University faculty; however, students will be encouraged to provide input and suggestions. If they have some original ideas they would like to pursue, students can also propose their own project topics to potential stakeholder organizations.
Once the scope and high-level problem for a project has been defined, the CMKL faculty will map the project to a set of associated competencies, based on the knowledge and skills that the project requires. These competencies may be either required or optional competencies. Thus, project work may overlap with more traditional study activities in satisfying the graduation requirements.
Credit for undergraduate R&D work will depend on the underlying competencies. Even if a project specifies a competency that the student has already demonstrated via more traditional study activities, the student can receive additional credit for this competency. This recognizes the fact that the level of mastery required by a real-world project will usually be higher than necessary to pass the initial competency assessment.
AiCE Competencies
The AiCE competencies are organized according to knowledge pillars. Each pillar represents a broad set of topics and concerns. There are five central pillars in the program: Artificial Intelligence Core, Human-Centered Design, Cybersecurity, Scalable Systems, and Entrepreneurship and Innovation. In many cases these major categories are divided into subcategories called subdomains.
In addition to these central pillars, we also incorporate science, mathematics, humanities, and other competencies, which would normally be viewed as external electives or general education, into our scheme. Additional competencies may be available through AIEI university network and student can request for competency credit transfer through AIEI system.
The tables below list the currently defined competencies for each pillar.
Pillar: Artificial Intelligence Core
Total credits: 130 Required: 90
Subdomain
Competency
Credits
Required?
Programming Fundamentals
Algorithmic Thinking
2
Yes
Intro to Programming
4
Yes
Programming Multi-module Applications
4
Yes
Object Oriented Programming***
4
Yes
Functional Programming***
4
Dataflow Programming***
4
Algorithms and Data Structures
8
Yes
Mathematics for AI*
Probability and Statistics
12
Yes
Discrete Mathematics
12
Yes
Matrices and Linear Transformations
12
Yes
Data Domains | Time/Frequency Domain
4
No
Artificial Intelligence
Logic-based Models
6
Yes
Probability-based Models
4
No
Planning and Search Strategies
4
Yes
Neural Networks and Deep Learning and CNN
4
Yes
Data Mining
Information Extraction and Retrieval, Search and Indexing
4
No
Proximity Measurement and Cluster Analysis
4
No
Classification and Regression
4
No
Machine Learning
Supervised and Unsupervised Learning
6
Yes
Reinforcement Learning
4
Yes
Transformer Network
4
Yes
AI Applications **
Recommendation Systems
4
Natural Language Processing (NLP)
4
Autonomous Agents
4
Computer Vision
4
* Mathematics for AI competencies are counted toward 90 credits of math and science competency requirement.
** Students must complete at least one AI Application competency. This may be in the context of undergraduate R&D.
****Students must choose 1 of these optional programming competencies.
Pillar: Human-Centered Design
Total credits: 42 Required: 18
Subdomain
Competency
Credits
Required?
Analysis and Presentation
Visualization
4
Yes
User Experience and Interface Design
4
Yes
User Interface Design and Evaluation
6
No
Immersive Environments (AR/VR)
6
No
Understanding Context of Use
Accessibility and Universal Design
4
Yes
User Research Methodologies & Data
4
No
Design for Human-Machine Teaming
Ethics in Computer Engineering
2
Yes
Creating Explainable AI
4
Yes
*** Human Psychology for User Interface Design
4
No
Engaging in Critical Oversight
*** Ethical Principles for AI (Fairness, Accountability, Transparency, Ethics)
4
No
*** Competencies can be counted toward communication, humanities and social sciences distribution requirements
Pillar: Cybersecurity
Total credits: 42 Required: 24
Subdomain
Competency
Credits
Required?
Data Acquisition, Management and Governance
Data Acquisition, Preparation, Transformation and Cleaning
4
No
Data Reduction and Compression
4
No
Data Governance
2
No
Data Privacy, Security and Integrity
Data Privacy, Security and Integrity
4
Yes
Creating Secure Software
4
Yes
Securing System Infrastructure
6
Yes
Security Policy and Processes
4
No
Distributed Ledger and Blockchain
4
No
AI System Security
Security Challenges in Modern AI Systems
4
Yes
Robustness of AI Components and Systems
6
Yes
Pillar: Scalable Systems
Total credits: 78 Required: 32
Subdomain
Competency
Credits
Required?
Computing and Computer Fundamentals
Operating Systems (Unix Basics)
4
Yes
Real Time Operating Systems*
4
Yes
Linux Kernel Hacking*
4
Computer Architecture (assembly programming)
4
Yes
Computer Design: Processor Architectures**
2
Yes
Computer Design: Digital Design using HDLs**
2
Web Architecture
4
No
Storage and File Systems Fundamentals
2
No
Networks
4
No
Software Development and Maintenance
Software Engineering Processes
6
Yes
Software Testing
4
No
Software System Design
4
No
Designing and Implementing Data Bases
6
No
Computer System Fundamentals
Cyber-Physical Systems
4
Yes
Cloud Computing
4
Yes
Scalable Management of Data and Models
4
No
Scalable Algorithms and Infrastructure
4
No
Big Data Systems
Parallel Computing
4
Yes
Distributed Data Storage
4
No
Big Data Computing
4
No
*Students must choose 1 of these operating systems optional competencies.
**Students must choose 1 of these computer architecture optional competencies.
Pillar: Entrepreneurship and Innovation
Total credits: 68 Required: 26
Subdomain
Competency
Credits
Required?
Entrepreneurship and Innovation
Create Innovation-driven Enterprise (Path Selection)****
4
Yes
Product Design and Development (Including Design Thinking)
8
Yes
Intellectual Property
2
No
Startup from Idea to Impact
8
Yes
Building Effective Teams to drive Innovation
2
Yes
Entrepreneurial Finance
2
No
Strategic Innovation Development
4
Yes
Strategy and CEO****
2
No
Platform Strategy
2
No
Inclusive Leadership
2
No
Persuasive and Leadership Communication***
4
No
Negotiation
4
No
Business Application Domains ***
Retail and Services Applications
4
No
Logistics
4
No
***** Biomedical, Bioinformatics and Health
4
No
Gaming and Creative Industries
4
No
***** Fintech
4
No
Educational Technology
4
No
** Students will usually choose one of these application domains in the context of undergraduate R&D. Other competencies in this pillar may also be addressed via R&D projects.
*** Competencies can be counted toward 66 credit social science/communication distribution requirement.
**** The list of business application domains may be expanded. Students will normally address these competencies in the context of undergraduate research projects.
***** Competencies can be counted toward math and science requirements. Other scientific application domains proposed by students or stakeholders may also provide math/science credits
Pillar: Science
Total credits: 60
Subdomain
Competency
Credits
Required?
Science
Fundamentals of Biology
12
No*
Fundamentals of Chemistry
12
No
Physics I
12
No
Physics 2
12
No
Quantum Physics
12
No
* Students must complete a total of 90 credits of math and science competencies. This includes the Mathematics for AI competencies under the AI Core pillar as well as some other designated competencies within the five main pillars.
Pillar: Mathematics
Total credits: 36
Subdomain
Competency
Credits
Required?
Mathematics
Differential Equations and Approximation
12
No*
Differential and Integral Calculus
12
No
Calculus in Three Dimensions
12
No
* Students must complete a total of 90 credits of math and science competencies. This includes the Mathematics for AI competencies under the AI Core pillar as well as some other designated competencies within the five main pillars.
Pillar: Communication and Presentation
Total credits: 41
Subdomain
Competency
Credits
Required?
Communication and Presentation
Research and Technical Writing
8
No
Creative Writing
8
No
Improvisational Acting
9
No
Graphics and Visual Storytelling
8
No
Thinking skills
8
No
Pillar: Humanities, Arts and Social Sciences
Total credits: 72
Subdomain
Competency
Credits
Required?
People, Places and Cultures
Sociology and Cultural Anthropology
9
No
Social Psychology
9
No
Political Studies
9
No
Human Geography
9
No
Global Histories
9
No
History of Visual Arts
9
No
History of Music
9
No
Economics
9
No
Pillar: Soft Skills
Students do not sign up for the competencies under the “soft skills” pillar. However, all students are required to demonstrate these competencies during their undergraduate career. Usually, soft skills will be evaluated by instructors or industry mentors as part of the student’s work on projects or undergraduate R&D.
To facilitate this evaluation, the full AiCE curriculum model breaks down each of these soft skill competencies into a set of observable behaviors that will allow objective assessment of the degree to which the students demonstrate these competencies.
Subdomain
Competency
Credits
Required?
Adaptability
Creative flexibility
Yes
Working flexibility
Yes
Empathy
Human-centered focus
Yes
Respect for diversity
Yes
Ethics
Social consciousness
Yes
Honesty
Yes
Fairness
Yes
Respect for privacy and confidentiality
Yes
Proactiveness
Service orientation
Yes
Continuous improvement focus
Yes
Professionalism
Responsibility
Yes
Compliance with organizational norms
Yes
Time management
Yes
Quality focus
Yes
Professional awareness
Yes
Interpersonal relations
Yes
Self-Learning
Motivation to learn
Yes
Active learning
Yes
Teamwork
Attention
Yes
Respect and courtesy
Yes
Openness
Yes
Team spirit
Yes
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