University of Cambridge
Enhancing Engagement in Online Business Learning: Exploring the Role and Impact of Artificial Intelligence and Dialogic Pedagogy
Abstract
dc:description.abstractThis doctoral research investigates the enhancement of student engagement in online business education, with a particular focus on Executive Master's in Business Administration (EMBA) programs designed for adult learners. Grounded in Bakhtin’s dialogic theory and Wegerif’s notion of dialogic space, this study examines how dialogic pedagogy can be reimagined in asynchronous, digitally mediated learning environments. A central innovation is the use of Artificial Intelligence in Education (AIED)—particularly conversational agents like ChatGPT—to stimulate dialogic learning and foster critical thinking, responsiveness, and a stronger sense of learner agency and community. Adopting a Design-Based Research methodology, the study iteratively designed, implemented, evaluated, and refined pedagogical interventions across multiple cycles. Mirroring Bakhtin’s view of dialogue as a dynamic, co-constructive process, each cycle leveraged empirical insights to adapt AIED tools, exploring how ChatGPT mediated student dialogue, responded to learner needs, and influenced engagement in real time. The study engaged 145 EMBA participants from nine countries across English-medium online courses and explored ten conjectures concerning the structure of dialogic spaces, the optimisation of asynchronous discussion, and the potential of AIED to enhance learning outcomes. To frame and analyse learning complexity, the Structure of Observed Learning Outcomes (SOLO) taxonomy was applied, enabling the classification of student contributions based on increasing levels of cognitive depth and integration. The analytical framework also incorporated the Online Student Engagement Scale (OSE) to evaluate behavioural, emotional, cognitive, and social dimensions of engagement. Additionally, the Online Engagement Framework and Chain-of-Thought reasoning were used to assess the processes through which learners interacted with AI-supported dialogue. Data collection included Moodle interaction logs, semi-structured interviews, and reflective commentaries, integrating both qualitative and quantitative dimensions. Dialogue quality and interaction depth were assessed using an adapted Tech-SEDA coding scheme, while NVivo facilitated thematic analysis of interview data. Machine learning approaches—specifically Naïve Bayes classification and sentiment analysis were used to identify engagement trends and emotional tone in student contributions. The findings reveal that ChatGPT significantly enhanced engagement by reducing cognitive load, promoting learner autonomy, and expanding dialogic space. Students described the AI as an accessible and responsive interlocutor that scaffolded their inquiry and supported deeper interaction with course content. Key contributions of this research include the development of the ENGAGE Framework, a design model for supporting AI-mediated student engagement, and an Integrated Approach to Executive Online Education, offering actionable strategies for implementing dialogic pedagogy in asynchronous digital environments. Despite its strengths, the study acknowledges methodological limitations, including the lack of experimental controls, which constrain causal inference. Nevertheless, its design-centric approach yields valuable insights for iterative educational innovation. This research contributes to the evolving discourse on AIED by demonstrating how dialogic theory, AI technologies, and educational frameworks like SOLO and OSE can be integrated to promote meaningful, inclusive, and cognitively rich learning experiences. It offers practical guidance for educators, designers, and policymakers seeking to transform online learning into a more engaging and intellectually vibrant experience for adult learners. Future research should explore longitudinal impacts of AIED on learner development and further investigate AI's evolving role as a co-participant in education.
Degree
thesis:*- Name dc:type.qualificationname
- Doctor of Education (EdD)
- Level dc:type.qualificationlevel
- Doctoral
- Grantor dc:publisher.institution
- University of Cambridge
- Year dc:date.issued
- 2025
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- English, Vincent
- Advisors dc:contributor.advisor
-
- Wegerif, Rupert
- Hennessy, Sara
Subjects
dc:subject × 13Rights
dc:rightsIdentifiers
dc:identifier.*- Author Identifier
- 0009-0002-1375-5982
- OAI identifier oai:identifier
- oai:www.repository.cam.ac.uk:1810/393896