CAI conducts research that applies to e-learning initiatives.

Some of our current Education projects are outlined below.


Learning is a major activity for most children aged 3 to 22 years old and is therefore a key period in human life. Researchers in Education always strive to make learning an enjoyable activity.

Game based learning has a long history. However, classic game based learning requires a lot of extra effort and resources (including human resources), thus are only applied in a limited way in very early year education. The current widespread usage of computer games provides a great opportunity to design edutainment systems for kids to gain knowledge and skills in a fun way.

In our experiments of embedding learning content in virtual world games have shown its significant power in retaining students learning interests and improving their performance. In edutainment, there are two types of knowledge to be modeled: the knowledge to be learned and the knowledge on how learning should be facilitated.

This project was supported by a VU grant (2008-2009).

Research team

Learning through negotiation

Argumentation plays an important role in promoting deep learning, fostering conceptual change and supporting problem solving. The new “learning by arguing” paradigm leads to new learning opportunities. However, due to the difficulties in modelling human cognition, there are few learning systems that can facilitate argumentation dialogues between systems and learners.

Towards the goal of providing an easy to use effective knowledge model to facilitate learning through (automatic) negotiation, we apply a number of computational models including cognitive maps and classic rule based systems. Particularly, cognitive maps are a family of computational models for capturing human knowledge and facilitating machine inferences. They have gained increasing popularity among domain applications because CMs are easy to use, can easily model domain experts’ knowledge, have a visualised presentation of the modelled knowledge, and have a clear mapping between vertices in the model and the corresponding factors in the real system.

Based on these knowledge models we design intelligent software agents to facilitate argumentative learning. The agents are able to simulate a peer learner and automatically conduct argumentative dialogues with learners. All knowledge based interaction can be viewed as a form of learning process, or an argumentation based learning. The argumentative agents can be applied in general school education as well as special domains like diabetes education and eHealth decision support.

Research team