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Towards Designing AI-Enabled Adaptive Learning System

Adaptive learning systems, which personalize the learning experience to the needs and abilities of students, are a promising application of artificial intelligence in education.

Tumaini Kabudi


Tumaini Kabudi fra Fakultet for samfunnsvitenskap disputerer for phd.-graden med avhandlingen "Towards Designing AI-Enabled Adaptive Learning System" mandag 24. april 2023.

Hun har fulgt doktorgradsprogrammet ved Fakultet for samfunnsvitenskap med spesialisering i informasjonssystemer.

Slik oppsummerer Kabudi selv avhandlingen:

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.

Les hele avhandlingen i AURA.

Disputasen vil bli ledet av visedekan Hanne Haaland ved fakultet for samfunnsvitenskap.

Prøveforelesningen kl 10:15 og disputas kl 12:15 i B1-001.

Tittel på prøveforelesning: "Opportunities, challenges, and implications of generative conversational AI (e.g., ChatGPT) for adaptive learning in higher educational institutions”. 

Disputasen vil streames via Zoom, registrer deg her


Leder for evalueringskomiteen og internt medlem: Geir Inge Hausvik, professor, UiA 


Slik følger du disputasen digitalt:

Disputasen er åpen for alle, men for å følge prøveforelesning og disputas digitalt, må du melde deg via en Zoom-lenke som publiseres her. 

Du logger deg på tidligst 10 minutter før oppgitt tid - til prøveforelesningen kl. 10:05 og disputasen tidligst kl. 12:05. Etter dette kan du når som helst forlate og komme inn igjen i disputasen.

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