Towards Designing AI-Enabled Adaptive Learning Systems

Paper I, III, IV and V are not available as a part of the dissertation due to the copyright.Among the many innovations driven by artificial intelligence (AI) are more advanced learning systems known as AI-enabled adaptive learning systems (AI-ALS). AI-ALS are platforms that adapt to the learning strategies of students by modifying the order and difficulty level of learning tasks based on the abilities of students. These systems support adaptive learning, which is the personalization of learning for students in a learning system, such that the system can deal with individual differences in aptitude. AI-ALS are gaining traction due to their ability to deliver learning content and adapt to individual student needs. While the potential and importance of such systems have been well documented, the actual implementation of AI-ALS and other AI-based learning systems in real-world teaching and learning settings has not reached the effectiveness envisaged on the level of theory. Moreover, AI-ALS lack transferable insights and codification of knowledge on their design and development. The reason for this is that many previous studies were experimental. Thus, this dissertation aims to narrow the gap between experimental research and field practice by providing practical design statements that can be implemented in effective AI-ALSs.publishedVersio

Artificial intelligence for neurodegenerative experimental models

INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. HIGHLIGHTS: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery

Examining the Antecedents of Creative Collaboration with an AI Teammate

With the advent of artificial intelligence (AI), individuals are increasingly teaming up with AI-based systems to enhance their creative collaborative performance. When working with AI-based systems, several aspects of team dynamics need to be considered, which raises the question how humans’ approach and perceive their new teammates. In an experimental setting, we investigate the influence of social presence in a group ideation process with an AI-based teammate and examine its effects on the motivation to contribute. Our results show a multi-mediation model in which social presence indirectly influences whether human team members are motivated to contribute to a team with AI-based teammates, which is mediated by willingness to depend and team-oriented commitment

Predicting the expected behavior of agents that learn about agents: the CLRI framework

We describe a framework and equations used to model and predict the behavior of multi-agent systems (MASs) with learning agents. A difference equation is used for calculating the progression of an agent's error in its decision function, thereby telling us how the agent is expected to fare in the MAS. The equation relies on parameters which capture the agent's learning abilities, such as its change rate, learning rate and retention rate, as well as relevant aspects of the MAS such as the impact that agents have on each other. We validate the framework with experimental results using reinforcement learning agents in a market system, as well as with other experimental results gathered from the AI literature. Finally, we use PAC-theory to show how to calculate bounds on the values of the learning parameters