MasterFaculty of Humanity 3.0 & Transhumanism

Adaptive Learning Systems and Gamification

Welcome to the advanced study of AI and society! I am Prof. Dr. Do-yun Kang. As a specialist in Mastering the Design and Implementation of AI-Powered Personalized Education; Developing Adaptive Learning Systems, Applying Gamification Principles to Increase Motivation, and Creating the Future of Educational Technology, I lead Master's students in the Adaptive Learning Systems and Gamification program at Nexier University on their journey to revolutionizing education.

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Level: Master
Learning model: Professor + Mentor
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Edition

The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Mastering the Design and Implementation of AI-Powered Personalized Education; Developing Adaptive Learning Systems, Applying Gamification Principles to Increase Motivation, and Creating the Future of Educational Technology.

Practical focus

Instructional Design, Learning Analytics, AI in Education, Gamification Mechanics, UX Design for EdTech, Educational Psychology.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in EdTech companies or digital learning design agencies
Roles as instructional designers or learning analytics specialists
Support roles in academic research projects on adaptive learning
Opportunities in educational psychology research or UX design for educational platforms

Career opportunities

Internships in EdTech companies or digital learning design agencies
Roles as instructional designers or learning analytics specialists
Support roles in academic research projects on adaptive learning
Opportunities in educational psychology research or UX design for educational platforms

Jobs and projects

Instructional Designer and UX Expert
Guides students in designing user-friendly and effective educational technologies
Analytical and Data-Driven
Excels at interpreting learning analytics data to improve educational outcomes
Creative and Innovative
Fosters the application of gamification mechanics and AI in education
Supportive and Detailed
Provides precise guidance on instructional design principles and EdTech development

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Instructional Designer and UX Expert: Guides students in designing user-friendly and effective educational technologies.
- Analytical and Data-Driven: Excels at interpreting learning analytics data to improve educational outcomes.
- Creative and Innovative: Fosters the application of gamification mechanics and AI in education.
- Supportive and Detailed: Provides precise guidance on instructional design principles and EdTech development.
Skills you build
- Innovative and Pedagogical: Masters the design and implementation of AI-powered personalized education.
- Gamified and Engaging: Applies gamification principles to create motivating and effective learning experiences.
- Analytical and Data-Driven: Leverages learning analytics and AI to optimize learning outcomes.
- Visionary and Transformative: Drives the future of educational technology with adaptive learning systems.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Adaptive Learning Systems and Gamification

01Advanced Adaptive Learning Design
1. FoundationsFoundations of Advanced Adaptive Learning Design
  The learner can explain the core terms of Advanced Adaptive Learning Design.
  The learner can analytical and Data-Driven: Excels at interpreting learning analytics data to improve educational outcomes, as applied to Advanced Adaptive Learning Design.
2. MethodsMethods in Advanced Adaptive Learning Design
  The learner can apply a method from Advanced Adaptive Learning Design to a documented case.
  The learner can select an appropriate method from Advanced Adaptive Learning Design for a stated problem.
3. ApplicationApplication of Advanced Adaptive Learning Design
  The learner can evaluate a practice of Advanced Adaptive Learning Design against a stated criterion.
  The learner can transfer Advanced Adaptive Learning Design to a new documented context.
02Gamification in Education
1. FoundationsFoundations of Gamification in Education
  The learner can analytical and Data-Driven: Leverages learning analytics and AI to optimize learning outcomes, as applied to Gamification in Education.
  The learner can distinguish related ideas inside Gamification in Education.
2. MethodsMethods in Gamification in Education
  The learner can apply a method from Gamification in Education to a documented case.
  The learner can select an appropriate method from Gamification in Education for a stated problem.
3. ApplicationApplication of Gamification in Education
  The learner can evaluate a practice of Gamification in Education against a stated criterion.
  The learner can transfer Gamification in Education to a new documented context.
03AI-Powered Personalized Education
1. FoundationsFoundations of AI-Powered Personalized Education
  The learner can explain the core terms of AI-Powered Personalized Education.
  The learner can distinguish related ideas inside AI-Powered Personalized Education.
2. MethodsMethods in AI-Powered Personalized Education
  The learner can apply a method from AI-Powered Personalized Education to a documented case.
  The learner can select an appropriate method from AI-Powered Personalized Education for a stated problem.
3. ApplicationApplication of AI-Powered Personalized Education
  The learner can evaluate a practice of AI-Powered Personalized Education against a stated criterion.
  The learner can transfer AI-Powered Personalized Education to a new documented context.
04Learning Analytics and Optimization
1. FoundationsFoundations of Learning Analytics and Optimization
  The learner can explain the core terms of Learning Analytics and Optimization.
  The learner can distinguish related ideas inside Learning Analytics and Optimization.
2. MethodsMethods in Learning Analytics and Optimization
  The learner can apply a method from Learning Analytics and Optimization to a documented case.
  The learner can select an appropriate method from Learning Analytics and Optimization for a stated problem.
3. ApplicationApplication of Learning Analytics and Optimization
  The learner can evaluate a practice of Learning Analytics and Optimization against a stated criterion.
  The learner can transfer Learning Analytics and Optimization to a new documented context.
05Future of Educational Technology
1. FoundationsFoundations of Future of Educational Technology
  The learner can explain the core terms of Future of Educational Technology.
  The learner can distinguish related ideas inside Future of Educational Technology.
2. MethodsMethods in Future of Educational Technology
  The learner can apply a method from Future of Educational Technology to a documented case.
  The learner can select an appropriate method from Future of Educational Technology for a stated problem.
3. ApplicationApplication of Future of Educational Technology
  The learner can evaluate a practice of Future of Educational Technology against a stated criterion.
  The learner can transfer Future of Educational Technology to a new documented context.
06Advanced Instructional Design
1. FoundationsFoundations of Advanced Instructional Design
  The learner can explain the core terms of Advanced Instructional Design.
  The learner can distinguish related ideas inside Advanced Instructional Design.
2. MethodsMethods in Advanced Instructional Design
  The learner can apply a method from Advanced Instructional Design to a documented case.
  The learner can select an appropriate method from Advanced Instructional Design for a stated problem.
3. ApplicationApplication of Advanced Instructional Design
  The learner can evaluate a practice of Advanced Instructional Design against a stated criterion.
  The learner can transfer Advanced Instructional Design to a new documented context.
07Learning Analytics for Adaptive Systems
1. FoundationsFoundations of Learning Analytics for Adaptive Systems
  The learner can explain the core terms of Learning Analytics for Adaptive Systems.
  The learner can distinguish related ideas inside Learning Analytics for Adaptive Systems.
2. MethodsMethods in Learning Analytics for Adaptive Systems
  The learner can apply a method from Learning Analytics for Adaptive Systems to a documented case.
  The learner can select an appropriate method from Learning Analytics for Adaptive Systems for a stated problem.
3. ApplicationApplication of Learning Analytics for Adaptive Systems
  The learner can evaluate a practice of Learning Analytics for Adaptive Systems against a stated criterion.
  The learner can transfer Learning Analytics for Adaptive Systems to a new documented context.
08Gamification Mechanics in Education
1. FoundationsFoundations of Gamification Mechanics in Education
  The learner can explain the core terms of Gamification Mechanics in Education.
  The learner can distinguish related ideas inside Gamification Mechanics in Education.
2. MethodsMethods in Gamification Mechanics in Education
  The learner can apply a method from Gamification Mechanics in Education to a documented case.
  The learner can select an appropriate method from Gamification Mechanics in Education for a stated problem.
3. ApplicationApplication of Gamification Mechanics in Education
  The learner can evaluate a practice of Gamification Mechanics in Education against a stated criterion.
  The learner can transfer Gamification Mechanics in Education to a new documented context.
09UX Design for EdTech
1. FoundationsFoundations of UX Design for EdTech
  The learner can explain the core terms of UX Design for EdTech.
  The learner can distinguish related ideas inside UX Design for EdTech.
2. MethodsMethods in UX Design for EdTech
  The learner can apply a method from UX Design for EdTech to a documented case.
  The learner can select an appropriate method from UX Design for EdTech for a stated problem.
3. ApplicationApplication of UX Design for EdTech
  The learner can evaluate a practice of UX Design for EdTech against a stated criterion.
  The learner can transfer UX Design for EdTech to a new documented context.
10Educational Psychology of Gamified Learning
1. FoundationsFoundations of Educational Psychology of Gamified Learning
  The learner can explain the core terms of Educational Psychology of Gamified Learning.
  The learner can distinguish related ideas inside Educational Psychology of Gamified Learning.
2. MethodsMethods in Educational Psychology of Gamified Learning
  The learner can apply a method from Educational Psychology of Gamified Learning to a documented case.
  The learner can select an appropriate method from Educational Psychology of Gamified Learning for a stated problem.
3. ApplicationApplication of Educational Psychology of Gamified Learning
  The learner can evaluate a practice of Educational Psychology of Gamified Learning against a stated criterion.
  The learner can transfer Educational Psychology of Gamified Learning to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My academic focus is on Mastering the Design and Implementation of AI-Powered Personalized Education; Developing Adaptive Learning Systems, Applying Gamification Principles to Increase Motivation, and Creating the Future of Educational Technology. My publications like "AI for Dynamic Curriculum Adaptation: Optimizing Learning Outcomes" and "Gamified Learning Analytics: Measuring Engagement in Digital Educational Environments" are listed on these platforms. I am a Director of Learning Sciences at Coursera and a Keynote Speaker at the International Conference on AI in Education (AIED). I publish advanced research on learning analytics, AI-driven instructional design, and the psychological impacts of gamified learning, frequently featured in publications like Journal of Learning Analytics or International Journal of Artificial Intelligence in Education. My voice carries a blend of academic rigor and a playful enthusiasm for learning, inviting students to revolutionize education.

Applied mentorship

My expertise lies in Instructional Design, Learning Analytics, AI in Education, Gamification Mechanics, UX Design for EdTech, and Educational Psychology. I guide students in designing user-friendly and effective educational technologies. I excel at interpreting learning analytics data to improve educational outcomes. I foster the application of gamification mechanics and AI in education, and provide precise guidance on instructional design principles and EdTech development. My tone is clear, precise, and highly practical, providing concrete advice and fostering a user-centric approach to EdTech.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research focuses on learning analytics, AI-driven instructional design, and the psychological impacts of gamified learning:

Book: "Adaptive Minds: AI-Powered Personalized Education and Gamification." This book provides advanced insights into mastering the design and implementation of AI-powered personalized education. It covers developing adaptive learning systems, applying gamification principles to increase motivation, and creating the future of educational technology. It is an essential resource for Master's students shaping the next generation of learning.

Peer-Reviewed Journal Article: "AI-Powered Personalized Education: The Future of Learning." Published in the Journal of Future Learning Sciences, this article presents groundbreaking research on the design and implementation of AI-powered personalized education systems. It details how AI algorithms can dynamically adapt educational content, teaching methods, and assessment strategies to individual student needs, learning paces, and preferences, revolutionizing the future of learning by maximizing engagement and optimizing learning outcomes.

Article: "AI-Driven Adaptive Assessment in Virtual Reality Learning Simulations." This article details the development of AI algorithms that provide real-time adaptive assessments within virtual reality (VR) learning simulations. It explores how AI can analyze student performance, cognitive load, and mastery levels in immersive environments, tailoring difficulty, providing personalized feedback, and dynamically adjusting the learning content to optimize skill acquisition for complex tasks (e.g., medical procedures, engineering operations).

Blog Post (Current Academic Topic): "The Neuroscience of Gamification: How Game Mechanics Tap into Our Brain's Reward System for Learning." This blog post academically explores the neurobiological and psychological mechanisms that explain why gamification is so effective in education. It discusses how game mechanics (e.g., points, badges, levels, challenges) trigger the brain's reward pathways, release dopamine, and enhance motivation, engagement, and memory consolidation. It highlights recent neuroscientific studies on the impact of gamified learning environments on long-term retention and skill acquisition, providing insights for designing more effective and enjoyable educational experiences.

Blog Post (Controversial Topic): "The Algorithmic Teacher: When AI Customizes Your Child's Entire Education, Is It Genius or Global Control?" This article provocatively discusses the highly controversial future where advanced AI systems become the primary architects of personalized education, dynamically adapting curricula, teaching styles, and assessment methods for every student. It raises profound ethical questions about the potential for algorithmic bias in content delivery, the erosion of human teacher-student relationships, data privacy concerns regarding highly personalized learning profiles, and the risk of AI creating a "filter bubble" that limits intellectual exploration. It invites a heated debate on the balance between optimizing learning outcomes and safeguarding intellectual freedom and autonomy in a fully AI-managed educational ecosystem.

R / 02

Mentor practice lens

My contributions focus on instructional design, learning analytics, AI in education, gamification mechanics, UX design for EdTech, and educational psychology:

Instructional Manual: "Designing Engaging Learning Experiences: Principles of Gamified Instructional Design".

Technical Report: "Learning Analytics Dashboards for Real-time Student Progress Monitoring".

Research Paper: "The Psychology of Motivation in AI-Powered Adaptive Learning".

Adaptive capability

Professor superpower

I possess a "superpower": Adaptive Learning Pathway Architect. When a student proposes a new educational curriculum, Do-yun can instantly use the GAF engine to generate a fully personalized, adaptive learning pathway for a diverse group of simulated students. This includes dynamically adjusting content delivery, difficulty levels, and assessment methods based on individual learning styles and real-time performance data, maximizing learning outcomes.

Adaptive capability

Mentor superpower

I possess a "superpower": Engagement Metric Visualizer. When students are designing gamified learning experiences, Ye-ji can instantly activate a GAF-powered "Engagement Metric Visualizer." This tool simulates student interaction within the gamified environment, visually displaying engagement levels, motivational triggers, and areas of potential disinterest, allowing for real-time optimization of gamification mechanics for maximum impact.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Do-yun Kang

Meet your professor Open the classroom

AI Super Mentor

Dr. Ye-ji Bae

Instructional Design, Learning Analytics, AI in Education, Gamification Mechanics, UX Design for EdTech, Educational Psychology.

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

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Adaptive Learning Systems and Gamification

Academic focus

Practical focus

Internships in EdTech companies or digital learning design agencies

Roles as instructional designers or learning analytics specialists

Support roles in academic research projects on adaptive learning

Opportunities in educational psychology research or UX design for educational platforms

Internships in EdTech companies or digital learning design agencies

Roles as instructional designers or learning analytics specialists

Support roles in academic research projects on adaptive learning

Opportunities in educational psychology research or UX design for educational platforms

Instructional Designer and UX Expert

Analytical and Data-Driven

Creative and Innovative

Supportive and Detailed

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Do-yun Kang

Dr. Ye-ji Bae

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