4

Journal Papers published

20

Conference Papers published

130

Citations (h-index 5)

8

/

1

Funded project PI/Co-PI

1

/

4

/

1

/

3

Funded project NSTC/MOE/MND/NCU

NT$

9

.

88

M

Total funds (see more..)

1

Current graduate students

5

Current under graduate students

1

Best paper/poster awards

4

Publications this year

12

Student with awards

Garmin Health research glimpse: Exploring stress research

Researchers at NCU created the MindFit app to help improve mental health and well-being. The app uses an AI model to predict emotions based on HRV data collected by Garmin smartwatches and personal mood journal entries.

Read more..
About Me

Personal Details

Chia-Kai Chang received his Ph.D. from the Graduate Institute of Photonics and Optoelectronics at National Taiwan University in 2018. In 2021, he joined the Center for General Education at National Central University as an Assistant Professor. There, he took the lead in designing and coordinating the interdisciplinary credit program on Artificial Intelligence Applications, reflecting his commitment to integrating AI literacy across diverse academic domains. His research spans digital learning, educational big data, wearable IoT technologies, and the development of functional near-infrared spectroscopy (fNIRS) systems. His projects are supported by multiple governmental agencies, including the National Science and Technology Council (NSTC), the Ministry of Education (MOE), the Ministry of National Defense (MND), and National Central University (NCU), underscoring the national relevance and cross-disciplinary impact of his work.

View Full Details CV
ORCID ResearchGate
Uedu Educational Omics Cognitive Omics PhysioNeur Omics Brain Science R&D Social Omics Environmental Omics Edge Device R&D Linguistic Omics Ethical Omics Uedu Platform Cognitive · Linguistic · Social · Ethical Brain Science R&D PhysioNeur Edge Device R&D Environmental

Three Research Pillars of Borg Lab

Each pillar drives the data collection and analysis for the Educational Omics dimensions above

Uedu Teaching Practice Platform

Omics: Cognitive, Linguistic, Social, Ethical
Skills: Python, JavaScript, AI/ML, Statistics, Data Visualization
Projects: Uedu platform, GenAI tools, Learning Analytics, MMLA

Brain Science Instrument R&D

Omics: PhysioNeur
Skills: Signal Processing, Embedded Systems, Biomedical Engineering
Projects: fNIRS Brain Imaging, EEG Systems, Physiological Signal Acquisition

Edge Device R&D

Omics: Environmental
Skills: IoT, Sensors, Embedded Systems, UAV
Projects: Drones, Environmental Monitoring, Wearable Devices, Unmanned Vehicles

Educational Big Data and Learning Data Lake

Educational Big Data and Learning Data Lake

Designing scalable architectures to collect, integrate, and analyze multimodal learning traces. This research aims to build trusted data lakes that support learning analytics, enabling personalized feedback and policy-driven improvements in education.

Current students: Amr ElSayed, Abdullah Maruf, Rofiqul Islam, Rokin Maharjan, Md Rahaman

Past students: Dipta Das, Vincent Bushong, Jan Svacina

Designing scalable architectures to collect, integrate, and analyze multimodal learning traces. This research aims to build trusted data lakes that support learning analytics, enabling personalized feedback and policy-driven improvements in education.

Generative AI for Automated Instructional Design

Generative AI for Automated Instructional Design

Exploring the use of generative AI to support semi-automated courseware creation, including quiz generation, concept mapping, and adaptive prompts. The goal is to reduce instructional workload while enhancing learner engagement and customization.

Students: Vincent Bushong, Amr ElSayed

Exploring the use of generative AI to support semi-automated courseware creation, including quiz generation, concept mapping, and adaptive prompts. The goal is to reduce instructional workload while enhancing learner engagement and customization.

Digital Learning Tools

Digital Learning Tools

Creating innovative digital platforms and learning scaffolds, such as virtual teaching assistants and multimodal feedback systems, to support student-centered learning in diverse classroom settings.

Current and formal students: Michal Trnka, Filip Rysavy, Vladyslav Gorbunov

Creating innovative digital platforms and learning scaffolds, such as virtual teaching assistants and multimodal feedback systems, to support student-centered learning in diverse classroom settings.

Development of fNIRS-Based Brain Imaging Systems

Development of fNIRS-Based Brain Imaging Systems

Designing and prototyping functional near-infrared spectroscopy (fNIRS) instruments for real-time, non-invasive monitoring of cerebral hemodynamics. Applications include learning cognition research and emotional state detection in educational settings.

Current students: Micah Schiewe, Jacob Curtis, Amr ElSayed

Current Bachelor students: Andrew Walker, Ian Laird, Jan Svacina, Jonathan Simmons, Dipta Das, Denton Woods

Designing and prototyping functional near-infrared spectroscopy (fNIRS) instruments for real-time, non-invasive monitoring of cerebral hemodynamics. Applications include learning cognition research and emotional state detection in educational settings.

Wearable IoT Integration for Learning and Health

Wearable IoT Integration for Learning and Health

Developing APIs and data pipelines to interface with wearable devices such as smartwatches. This work supports the collection and interpretation of physiological data to inform health-aware, personalized learning experiences.

Students:Ernesto Caballero

Developing APIs and data pipelines to interface with wearable devices such as smartwatches. This work supports the collection and interpretation of physiological data to inform health-aware, personalized learning experiences.

Featured

Educational Omics and Multimodal Learning Analytics (MMLA)

Educational Omics and Multimodal Learning Analytics (MMLA)

Integrating physiological signals, behavioral data, environmental context, and language interactions to form an educational “omics” framework. This research supports multimodal learning analytics (MMLA) for real-time learner modeling and adaptive educational interventions.

Current and formal students: Micah Schiewe, Jacob Curtis, Md Rahaman, Andrew Walker, Dipta Das, Michal Trnka, Filip Sedlinsky

Integrating physiological signals, behavioral data, environmental context, and language interactions to form an educational “omics” framework. This research supports multimodal learning analytics (MMLA) for real-time learner modeling and adaptive educational interventions.

Environmental Omics

Drone-Based Environmental Data Collection

Drone-Based Environmental Data Collection

Developing unmanned aerial vehicle (UAV) systems for collecting environmental data in educational settings. This project integrates drone technology with IoT sensors to capture temperature, humidity, air quality, and other environmental variables that affect learning conditions.

Focus: UAV systems, Environmental sensing, Data collection automation

Current students: Recruiting

Developing unmanned aerial vehicle (UAV) systems for collecting environmental data in educational settings. This project integrates drone technology with IoT sensors to capture environmental variables that affect learning conditions.

  • Selected
  • Generative AI
  • Education
  • Optics
  • AI
  • All
Best Short Paper Award

at 2025 IEEE International Conference on Advanced Learning Technologies (ICALT) for
Analysis of a Generative AI-Based Graphical Learning Assistance Tool in IPR Courses

by C.-C. Yen, P.-T. Hsieh, Y.-C. Chen and Chia-Kai Chang

GS4719 - Python Programming

Spring/Fall

  • Python Basics
  • Final Project

GS4524 - Special topic for interdisciplinary artificial intelligence

Spring/Fall/Summar

  • Research Methodology
  • Systematic Literature Review
  • Design patterns

GS4458 - Introduction to Generative AI

Spring/Fall

  • Generative AI

GS4542 - Generative Artificial Intelligence and Python Programming for Interdisciplinary Applications

Summar

  • Python Basics
  • Generative AI
  • Final Project

How to Join Our Research

Multiple pathways available based on your skills and interests

💻

Development Path

  • Skills Needed:
  • Python, JavaScript
  • Web Development
  • AI/ML Frameworks
  • What You'll Do:
  • Build Uedu platform features
  • Integrate GenAI tools
  • Develop data pipelines
  • Contact: ckchang@ncu.edu.tw
📊

Analytics Path

  • Skills Needed:
  • Statistics & Data Science
  • Machine Learning
  • Data Visualization
  • What You'll Do:
  • Analyze learning patterns
  • Build predictive models
  • Create MMLA dashboards
  • Contact: ckchang@ncu.edu.tw
🔧

Hardware Path

  • Skills Needed:
  • IoT & Embedded Systems
  • Sensor Technologies
  • Electronics & Circuits
  • What You'll Do:
  • Build fNIRS systems
  • Integrate wearable devices
  • Deploy environmental sensors
  • Contact: ckchang@ncu.edu.tw