Exploring the effectiveness of AI-generated learning materials in facilitating active learning strategies and knowledge retention in higher education

Purpose This study aims to explore the intersection of AI-generated learning materials and active learning strategies in higher education artificial intelligence (AI) is bringing about changes and opening up new possibilities for an improved and more efficient higher education. However, the argument is that its use in education/classroom should be informed by verifiable evidence as well as best practice, which this scholarly work helps build evidence-based research to assess this technology in higher education. Design/methodology/approach Primary data was collected through structured questionnaire administered online via Google form. Based on the non-probability sampling technique, 300 higher education tutors and students across the UK were purposively targeted, out of which 218 (72.7%) response rate was achieved. Data was analyzed using descriptive statistics with the aid of Statistical Package for Social Sciences, whereby regression, correlation and Chi-square tests were conducted to determine the statistical significance, direction and strength of the relationship between the measured variables. Findings This study revealed that AI-generated learning materials support active learning strategies that enable students to actively engage in their learning, likewise enabling students to develop deeper understanding of their course content with significantly better knowledge retention, which is critical to the learning process. However, findings further revealed that acceptance/regular use of AI-generated learning materials is still below par across the higher education institutions, and there is major concern that the benefits may not be fully realized due to barriers to adoption. Research limitations/implications There are limitations that future studies can improve on, especially in terms of methodology. Pragmatism is a philosophical research stance that integrates quantitative data collection with qualitative data (such as interviews) and will ask in-depth questions to gain holistic quality data for such empirical. Future studies can also improve on the research scope to allow for generalizability of findings and check for potential biases in the data collection, analysis and interpretation processes. Originality/value Despite the huge anticipation regarding how AI technology could transform teachers’ roles in higher education, concrete research into AI-generated learning materials and actual impact in facilitating active learning strategies and knowledge retention is currently lacking. This study presents theoretical models on AI acceptance in higher education and explored the Technology, Pedagogical and Content Knowledge framework to inform empirical information on how AI can support active learning strategies and students’ knowledge retention.