In today’s rapidly evolving educational landscape, integrating Artificial Intelligence (AI) into teaching and learning has become essential for educators, trainers, and academic institutions. The LICQual Level 3 Certificate in Artificial Intelligence in Education and Learning equips learners with practical knowledge and skills to harness AI technologies effectively, enhancing the quality, efficiency, and personalization of educational experiences. This course is meticulously designed for educators, instructional designers, and learning facilitators who aim to stay ahead in the digital transformation of education.
Throughout this comprehensive program, learners will explore the fundamentals of AI, its applications in educational settings, and strategies for implementing AI-driven tools to improve student engagement, assessment, and learning outcomes. Key topics include intelligent tutoring systems, adaptive learning technologies, data analytics in education, and ethical considerations in AI deployment. By blending theory with hands-on activities, this course ensures participants gain practical skills that can be directly applied in classrooms, e-learning platforms, or institutional training programs.
Upon completion, learners will be confident in leveraging AI to design innovative teaching strategies, analyze learning data, and foster an inclusive and personalized learning environment. This Level 3 Certificate in Artificial Intelligence in Education not only enhances professional competencies but also empowers educators to become pioneers in the integration of AI in modern learning systems.
Course Overview
Qualification Title
LICQual Level 3 Certificate in Artificial Intelligence in Education and Learning
Total Units
6
Total Credits
24
GLH
120
Qualification #
LICQ2200508
Qualification Specification
To enroll in the LICQual Level 3 Certificate in Artificial Intelligence in Education and Learning applicants must meet the following criteria:
|
Qualification# |
Unit Title |
Credits |
GLH |
|---|---|---|---|
|
LICQ2200508-1 |
Designing AI-Based Learning Architectures |
4 |
20 |
|
LICQ2200508-2 |
AI for Multilingual and Cross-Cultural Education |
4 |
20 |
|
LICQ2200508-3 |
Leveraging Big Data and Predictive Analytics for Learner Outcomes |
4 |
20 |
|
LICQ2200508-4 |
Cognitive Computing and Intelligent Content Delivery |
4 |
20 |
|
LICQ2200508-5 |
Building AI-Infused Educational Apps (No-code/Low-code) |
4 |
20 |
|
LICQ2200508-6 |
Independent Project: Proposing an AI Innovation in Education |
4 |
20 |
By the end of this course,applicants will be able to:
1. Designing AI-Based Learning Architectures
- Develop frameworks for AI-integrated instructional design tailored to diverse learning environments.
- Evaluate the scalability and effectiveness of AI-driven learning infrastructures in educational institutions.
2. AI for Multilingual and Cross-Cultural Education
- Analyze how AI technologies support language diversity and cultural inclusivity in education.
- Apply AI tools to create adaptive learning pathways for multilingual and international learners.
3. Leveraging Big Data and Predictive Analytics for Learner Outcomes
- Use big data tools to analyze student behavior, performance, and engagement patterns.
- Apply predictive models to forecast learning outcomes and support targeted interventions.
4. Cognitive Computing and Intelligent Content Delivery
- Explain the principles of cognitive computing and its application in dynamic content personalization.
- Implement AI-driven content delivery systems that respond to real-time learner needs.
5. Building AI-Infused Educational Apps (No-code/Low-code)
- Design and prototype basic AI-powered educational applications using no-code/low-code platforms.
- Integrate AI functionalities such as chatbots, recommendation systems, and assessment tools into app designs.
6. Independent Project: Proposing an AI Innovation in Education
- Plan and present an original project that proposes an innovative AI solution for an educational challenge.
- Demonstrate the ability to conduct research, evaluate feasibility, and align AI applications with pedagogical goals.
This diploma is ideal for:
- Senior educators seeking to lead AI integration in schools, colleges, or training institutions
- Instructional designers developing intelligent and adaptive learning architectures
- Academic coordinators and program heads responsible for curriculum innovation
- Education technology specialists deploying AI solutions within LMS and digital ecosystems
- Corporate trainers aiming to build AI-driven learning pathways in professional development
- Digital learning consultants advising institutions on AI transformation strategies
- Researchers exploring the intersection of AI, pedagogy, and cognitive science
- Policy makers and education leaders shaping digital education strategies and frameworks
- Edtech entrepreneurs designing scalable AI-infused educational applications
- E-learning developers interested in no-code or low-code AI app development
- Education managers implementing predictive analytics for learner performance and retention
- Developers transitioning into the educational domain with a focus on AI integration
- Professionals working in multilingual or cross-cultural educational settings
- Quality assurance officers evaluating the effectiveness of AI-based instructional models
- NGO or non-profit staff promoting inclusive, tech-enabled education for underserved groups
- Academic advisors using data-driven insights to personalize student support
- Digital transformation officers managing the strategic adoption of AI in education
- Teachers interested in upskilling for future-ready, AI-enhanced classroom environments
Assessment and Verification
All units within this qualification are subject to internal assessment by the approved centre and external verification by LICQual. The qualification follows a criterion-referenced assessment approach, ensuring that learners meet all specified learning outcomes.
To achieve a ‘Pass’ in any unit, learners must provide valid, sufficient, and authentic evidence demonstrating their attainment of all learning outcomes and compliance with the prescribed assessment criteria. The Assessor is responsible for evaluating the evidence and determining whether the learner has successfully met the required standards.
Assessors must maintain a clear and comprehensive audit trail, documenting the basis for their assessment decisions to ensure transparency, consistency, and compliance with quality assurance requirements.