The future of education is digital, and Artificial Intelligence (AI) is at the forefront of this transformation. The LICQual Level 3 Diploma in Artificial Intelligence in Education and Learning is designed for educators, instructional designers, training professionals, and academic leaders who wish to master advanced AI applications to enhance teaching, learning, and educational management. This diploma provides in-depth knowledge of AI tools and technologies that are reshaping modern education.
Learners will explore advanced concepts such as intelligent tutoring systems, predictive learning analytics, adaptive learning platforms, and AI-assisted assessment strategies. The course emphasizes practical implementation, enabling participants to design and integrate AI solutions that personalize learning experiences, improve student engagement, and optimize educational outcomes. Ethical considerations, data privacy, and responsible AI usage in education are also key components of this program.
Through interactive learning, case studies, and hands-on projects, participants will develop the confidence to lead AI-driven initiatives in classrooms, e-learning environments, and educational institutions. Completing this Level 3 Diploma in Artificial Intelligence in Education and Learning equips learners with professional competencies to innovate, enhance teaching effectiveness, and contribute strategically to the digital transformation of education.
This course also serves as a gateway for further advanced studies in AI and educational technology, empowering learners to become pioneers in shaping the future of learning.
Course Overview
Qualification Title
LICQual Level 3 Diploma in Artificial Intelligence in Education and Learning
Total Units
6
Total Credits
60
GLH
240
Qualification #
LICQ2200511
Qualification Specification
To enroll in the LICQual Level 3 Diploma in Artificial Intelligence in Education and Learning applicants must meet the following criteria:
|
Qualification# |
Unit Title |
Credits |
GLH |
|---|---|---|---|
|
LICQ2200511-1 |
Advanced Machine Learning and Deep Learning for Education |
10 |
40 |
|
LICQ2200511-2 |
Development of AI Algorithms for Adaptive Curriculum Delivery |
10 |
40 |
|
LICQ2200511-3 |
Research Methodologies for AI in Education |
10 |
40 |
|
LICQ2200511-4 |
Regulatory Compliance and Governance in AI-Driven Learning Systems |
10 |
40 |
|
LICQ2200511-5 |
Institutional Transformation through Artificial Intelligence |
10 |
40 |
|
LICQ2200511-6 |
Research Dissertation: AI Innovation for Educational Equity and Excellence |
10 |
40 |
By the end of this course,applicants will be able to:
1. Advanced Machine Learning and Deep Learning for Education
- Analyze advanced machine learning and deep learning models and their relevance to personalized education.
- Apply AI models to enhance adaptive learning, performance prediction, and intelligent tutoring systems.
2. Development of AI Algorithms for Adaptive Curriculum Delivery
- Design AI-driven algorithms tailored for dynamic and adaptive curriculum frameworks.
- Evaluate algorithm performance based on learner engagement, progress, and feedback mechanisms.
3. Research Methodologies for AI in Education
- Apply qualitative and quantitative research methods to investigate AI applications in education.
- Develop research proposals and data collection strategies aligned with AI-focused educational studies.
4. Regulatory Compliance and Governance in AI-Driven Learning Systems
- Interpret legal, ethical, and institutional policies governing AI in education.
- Assess the implications of data protection laws, algorithmic bias, and accountability in AI deployment.
5. Institutional Transformation through Artificial Intelligence
- Formulate strategic AI integration plans for education systems at the institutional level.
- Lead change initiatives that promote sustainable and scalable AI-driven transformation in learning environments.
6. Research Dissertation: AI Innovation for Educational Equity and Excellence
- Conduct an independent research project addressing a critical issue in AI and education.
- Present data-driven recommendations that support equity, inclusion, and innovation through AI solutions.
This diploma is ideal for:
- Senior educators and academic leaders aiming to lead AI-driven educational transformation
- Curriculum directors developing adaptive and data-driven instructional models
- Education technology specialists involved in deploying machine learning systems in schools or universities
- Instructional designers seeking advanced knowledge in AI-based curriculum delivery
- Education policy makers shaping national or institutional AI frameworks
- Researchers focusing on AI innovation, educational equity, and deep learning methodologies
- Data analysts in education exploring the predictive potential of big data and AI tools
- EdTech developers and solution architects designing intelligent educational platforms
- Higher education administrators overseeing AI integration across departments
- Corporate learning and development leaders implementing AI-based training systems
- Education consultants advising organizations on strategic AI adoption and compliance
- Quality assurance professionals evaluating the effectiveness of AI in teaching and learning
- Project managers responsible for institutional AI transformation initiatives
- NGO professionals working on AI-enhanced education programs for underserved communities
- Government officials driving AI policy and digital innovation in the education sector
- Professionals transitioning into educational AI roles from technology or research backgrounds
- International education leaders focused on scaling AI for global learning equity and excellence
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.