Cogtech.AI

ADAPTIVE . ENGAGING . MODULAR

Using machine learning, Cogtech.AI employs research and learning pedagogy to create an adaptive learning platform that customises the content to a user’s learning style — all while fine-tuning itself based on the learner’s responses. It also enables training management personnel to seamlessly upload and update training content, generate questions using AI, track the learner’s progress, and gather insights on training effectiveness.

Introducing Cogtech.AI

Cogtech.AI addresses a critical need in the corporate training sector, offering an innovative solution that makes training courses more engaging and effective for individuals. This solution stands as a testament to the potential of adaptive learning in transforming professional development. This leads to our problem statement: How might we design a modular system that leverages adaptive learning to make vocational training more engaging and tailored to the diverse learning styles of individuals?

Team members

Wise Lim (DAI), Chen Jiasen (DAI), Kat Yong Jie (CSD), Lim Kai Feng, Jared (DAI), Adriana Ng Elynn (DAI), Goh Yu Fan (CSD)

Instructors:

  • Kwan Wei Lek

Writing Instructors:

  • Delfinn Tan Sweimay

Attention Corporate Training Managers!

With Cogtech.ai you can effortlessly upload content and generate questions, saving time and effort. Along with emulating the course before it goes live. Compatible with existing tools, allowing you to seamlessly integrate into your workflow!
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Powered by Machine Learning

Through leveraging cutting-edge Machine Learning technologies developed in recent years, Cogtech.ai aims to revolutionise corporate training. By incorporating Kolb’s Learning Style Framework, Cogtech.ai identifies each trainee’s preferred learning method, enabling a more tailored and effective learning experience. This is coupled with the ease of question generation for training managers, allowing for their focus on catering their expertise to the trainees rather than on content dissemination.

Kolb’s Experiential Learning Style
Question Generation
Adaptive Learning

Kolb’s Experiential Learning Style is grounded in David A. Kolb’s four-stage learning cycle: concrete experience (CE), reflective observation (RO), abstract conceptualization (AC), and active experimentation (AE). This cycle posits that effective learning occurs when a learner progresses through each of these stages, which can be entered at any point, following a logical sequence. Kolb Learning Style Inventory 4.0 (KLSI 4.0) introduces a novel nine-style typology that refines the understanding of individual learning styles beyond the original four categories.

One of the goals of our AI solution was to be able to generate questions based on a given input such as content material from PDF files, which involves extracting relevant information from the documents and generating coherent questions. To achieve this task, we implemented Retrieval Augmented Generation. This involves the process of optimising the output of a large language model by referencing an authoritative knowledge base outside of its training data before generating a response.

Another goal for our AI solution is to be able to identify and adapt to a user’s preferred learning style and provide them with optimal learning conditions via appropriate question types. To achieve this we used a neural network to consider the different input factors before adjusting the user’s preferred learning style and selecting the future questions given to the user.

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Vote for our project at the exhibition! Your support is vital in recognizing our creativity. Join us in celebrating innovation and contributing to our success. Thank you for being part of our journey!

Team members :

Wise Lim
Design and Artificial Intelligence (DAI)
Chen Jiasen
Design and Artificial Intelligence (DAI)
Kat Yong Jie
Computer Science and Design (CSD)
Lim Kai Feng, Jared
Design and Artificial Intelligence (DAI)
Adriana Ng Elynn
Design and Artificial Intelligence (DAI)
Goh Yu Fan
Computer Science and Design (CSD)

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Contact the Capstone Office :

+65 6499 4076

8 Somapah Road Singapore 487372

8 Somapah Road Singapore
487372

www.sutd.edu.sg/Capstone

capstone@sutd.edu.sg

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Contact the Capstone Office :

+65 6499 4076

8 Somapah Road Singapore 487372

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Contact the Capstone Office :

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About Capstone Design Programme

At Singapore University of Technology and Design (SUTD), we believe that the power of design roots from the understanding of human experiences and needs, to create for innovation that enhances and transforms the way we live. This is why we develop a multi-disciplinary curriculum delivered v ia a hands-on, collaborative learning pedagogy and environment that concludes in a Capstone project.

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