Towards Designing AI-Enabled Adaptive Learning System

Tumaini Kabudi of the Faculty of Social Sciences has submitted her thesis entitled "Towards Designing AI-Enabled Adaptive Learning System" and will defend the thesis for the PhD-degree on Monday 24 April 2023.

She has attended the PhD programme in Social Sciences with a specialisation on Information Systems. 

Summary of the thesis by Tumaini Kabudi:

Artificial Intelligence (AI) is increasingly becoming an  important component of the education sector, both in primary education and in higher education. The rapid advancement of computing technologies and big data processing techniques has made it possible to implement AI in education. AI and machine learning techniques are now being used to deliver chatbots like ChatGPT; intelligent tutoring systems; automatic grading systems; and other advanced learning  systems like AI Adaptive Learning Systems, in the education sector. These advanced learning systems, AI enabled Adaptive Learning Systems (AI-ALS), is the focus of my doctoral dissertation.

AI-ALS are the most advanced generation of adaptive learning systems that use more sophisticated data analytics (learning analytics) and AI  to provide just-in-time insights and embed ongoing data collection to improve the quality of adaptive learning. Adaptive learning is the personalization of learning for students in a learning system. Thus, AI-ALS are learning platforms that adapt to the learning strategies of students by modifying the order and difficulty level of learning tasks.

Adaptive learning systems, which personalize the learning experience to the needs and abilities of students, are a promising application of artificial intelligence in education. A growing body of research suggests that the use of AI-ALS in educational contexts has the potential to enhance teaching and learning skills as well as human performance. However, the effectiveness of these systems in real-world settings is not yet clear. While the potential and importance of such systems are well documented, the actual implementation status of AI-ALS and other AI-based learning systems in real-life teaching and learning settings is low and not fully understood. Also, the research on AI-ALS lacks insights and codification of its design and development knowledge. In addition, misuse of AI due to algorithm biases and lack of governance could also exacerbate inequalities in our educational institutions.

Overall, I consider the lack of information and  research on AI-ALS, especially in the field of Information Systems (IS), to be a significant gap. It is necessary to address the identified research gaps by engaging in more inclusive conversations to identify specific and minimally viable requirements for AI-ALS applications. Thus, as part of my contribution to IS research, my thesis aims examines how AI-enabled adaptive learning systems (AI-ALS) should be designed and developed. I investigate and establish a set of propositions for design and development of AI-ALS, based on both qualitative and quantitative methods that support the interpretive nature of the research.

The thesis uses a systematic mapping of literature, expert interviews in conjunction with ranking-type analysis to identify core propositions for designing AI-ALS that can deliver effective adaptive (personalized) learning. The dissertation consists of five published academic articles that focus on understanding the core research problems and educational practice concerns in AI-ALS, identifying the underlying principles for designing AI-ALS, integrating and implementing AI technologies to promote quality education, and understanding the important aspects of designing and developing AI-ALS. The findings provide insights for practitioners, systems developers, and educators in educational settings who are interested in AI-ALS. Practical recommendations are presented, such as the need for interdisciplinary and transdisciplinary collaborations, addressing algorithm bias and ethical and privacy issues, using human centric approach to design AI-ALS, and a thoughtful, creative, and incremental approach to deploying AI-ALS. The theoretical contributions advance the field by providing an overview of AI-ALS design and implementation and by drawing on a theoretical model to understand the nature of AI-ALS as a mediating tool for improved learning.

Read the thesis in AURA.

What to do as an online audience member:

The disputation is open to the public, but to follow the trial lecture and the public defence online, transmitted via the Zoom conferencing app, you have to register (link will be published here). 

A Zoom-link will be returned to you. (Here are introductions for how to use Zoom: support.zoom.us if you cannot join by clicking on the link.)

We ask online audience members to join the virtual trial lecture at 10:05 at the earliest and the public defense at 12:20 at the earliest. After these times, you can leave and rejoin the meeting at any time. Further, we ask online audience members to turn off their microphone and camera and keep them turned off throughout the event. You do this at the bottom left of the image when in Zoom. We recommend you use ‘Speaker view’. You select that at the top right corner of the video window when in Zoom.

Opponent ex auditorio:

The chair invites members of the public to pose questions ex auditorio in the introduction to the public defense. Deadline is during the break between the two opponents. The person asking questions should have read the thesis. For online audience the Contact Persons e-mail are available in the chat function during the Public Defense, and questions ex auditorio can be submitted to Cecilie Rygh Mawdsley.