Lecturers’ perceptions of the influence of AI on a blended learning approach in a South African higher education institution
In this study, the researchers explore lecturers’ perspectives on the impact artificial intelligence (AI) has on blended learning within the context of South African higher education. AI is transforming traditional teaching and learning by enabling academic institutions to offer computerised, effect...
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Veröffentlicht in: | Discover education 2024-09, Vol.3 (1), p.1-18, Article 135 |
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Sprache: | eng |
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Zusammenfassung: | In this study, the researchers explore lecturers’ perspectives on the impact artificial intelligence (AI) has on blended learning within the context of South African higher education. AI is transforming traditional teaching and learning by enabling academic institutions to offer computerised, effective, and objective educational processes. The research was conducted to address the growing need to understand lecturers’ viewpoints on how AI can enhance educational practices and overcome existing challenges in blended learning environments. To investigate this phenomenon, the researchers applied the Substitution, Augmentation, Modification, and Redefinition (SAMR) model as theoretical framework for the study. Their qualitative research undertaking employed a singular case study design focusing on 15 lecturers from the College of Education at a selected academic institution, to arrive at an in-depth understanding of lecturers’ experiences and perceptions of how AI is integrated in blended learning. The researchers examined both the benefits and challenges associated with a blended teaching and learning mode, in the context of AI integration. The data collection process involved semi-structured focus group interviews that allowed for in-depth discussions to be conducted. This was complemented by detailed document analysis to analyse the course materials and teaching methods used by the lecturers. Homogeneous, purposeful sampling was applied to select participating lecturers who shared specific characteristics relevant to the study. Data analysis involved coding through the induction method, which helped to reveal relevant codes that were subsequently categorised. The study also included a comprehensive literature review of recent research findings, which were correlated with the collected data. The findings underscored the critical need for supportive measures, such as management backing, enhanced training opportunities, professional development initiatives, reliable technological infrastructure, improved internet connectivity, and additional time allocation, for the successful implementation of blended learning which integrates AI. This study contributes valuable insights into, and discussions on, the implications of adopting AI in a hybrid learning environment. |
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ISSN: | 2731-5525 2731-5525 |
DOI: | 10.1007/s44217-024-00235-2 |