This research endeavours to demonstrate that generative artificial intelligence (AI), through the creation of lecture content and assessment quizzes, can drive significant advancements in teaching practices and learning outcomes within higher education (Holmes et al., 2022; Bates, 2019). Such platforms also provide scope for sustainable, scalable model for AI integration that firstly supports educators and secondly, enhances the overall educational experience (Luckin et al., 2016; Zawacki-Richter et al., 2019; Nature, 2024).
The project will focus on the development and evaluation of AI-driven systems designed to automatically generate lecture content and knowledge-checker-quizzes as educational materials (Luckin et al., 2016). By engaging with credible AI platforms, the research seeks to create interactive, adaptive resources that tailor resources to modular learning outcomes (Holmes et al., 2022).
Drawing on comprehensive reviews of AI applications in education, this research extends the discussion to novel generative AI models (Zawacki-Richter et al., 2019). These models can produce lecture content that is dynamic and contextually relevant, while simultaneously creating quizzes that serve as continuous formative assessments (Kasneci et al., 2023).
Adaptive learning systems have been shown to improve student outcomes significantly, with improvements in performance of up to 62% reported (Nature, 2024). Such generated content not only enhances the learning experience but also reduces the workload on educators, allowing them to refocus on pedagogical strategies and student support (Selwyn, 2020).
Building on established frameworks for AI integration in education, this study employs a mixed-methods approach (Creswell & Creswell, 2018). Student performance metrics will be measured quantitatively, with additional insights gathered through qualitative methods (Luckin et al., 2016; Creswell & Creswell, 2018). Research strategies include implementation of generated AI content within live module delivery of Business and Management and Digital Marketing Master's students.
The research also aims to address ethical and practical concerns associated with AI deployment in education (Williamson & Eynon, 2020). Fundamental issues such as content accuracy, are addressed by integrating insights from educational technology research, including the Technological Pedagogical Content Knowledge (TPACK) framework (Mishra & Koehler, 2006) and digital pedagogy guidelines (Bates, 2019). These frameworks underscore the importance of deploying technology in a manner that is both pedagogically sound and aligned with learning outcomes (Dillenbourg, 2016).
Conference themes: Interdisciplinary Collaboration; Digital Collaboration; Equality, Diversity & Inclusion; Collaborative Research in Education