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Leveraging on AI Tools in Hybrid Learning for Inclusive Learning Analytics: Evidence from a South African Disadvantaged University
Abstract
Background: The rapid integration of Artificial Intelligence (AI) into higher education has transformed hybrid learning environments by enabling personalised, data-driven, and inclusive teaching and learning practices. AI-powered learning analytics offer opportunities to monitor student engagement, predict learning outcomes, and provide timely interventions, thereby addressing the diverse needs of students in increasingly complex educational settings. Despite these advancements, there is limited understanding of how AI-enabled learning analytics can effectively promote inclusivity and equitable learning experiences within hybrid learning environments.
Purposes: This study explores the utilisation of AI tools in hybrid learning environments to advance inclusive learning analytics. Specifically, it examines how AI-enabled technologies support personalised learning, enhance student engagement, improve academic performance, and facilitate informed pedagogical decision-making in higher education.
Method: A qualitative research approach was adopted to gain in-depth insights into participants' experiences and perceptions of AI-supported learning analytics. Data were collected from participants across two university campuses through semi-structured interviews and analysed using thematic analysis. The study was informed by the theoretical foundations of learning analytics, inclusive education, and educational data science.
Results: The findings revealed two dominant themes: personalised learning and academic performance enhancement. Participants reported that AI-powered tools, including adaptive learning platforms, predictive analytics, automated feedback systems, and intelligent dashboards, enabled tailored learning experiences, continuous feedback, and early identification of at-risk students. These capabilities improved learner engagement, supported academic success, and empowered educators to implement targeted interventions. The findings further demonstrated that AI-driven learning analytics can foster greater inclusivity by accommodating students with diverse socio-economic, educational, and technological backgrounds, thereby promoting equitable access to learning opportunities.
Conclusion: The significance of this research lies in its contribution to the growing body of knowledge on AI-enhanced learning analytics and its potential to move beyond traditional student performance monitoring towards more equitable, student-centred educational practices. The study also responds to the increasing demand for flexible and inclusive learning models in higher education, particularly within contexts characterised by large class sizes, diverse student populations, and limited resources.
Keywords
References
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