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Article
Publication date: 15 January 2025

Henry Adeyemi Aluko, Ayodele Aluko, Goodness Amaka Offiah, Funke Ogunjimi, Akinseye Olatokunbo Aluko, Funmi Margareth Alalade, Ikechukwu Ogeze Ukeje and Chinyere Happiness Nwani

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…

Abstract

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.

Details

International Journal of Organizational Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1934-8835

Keywords

Article
Publication date: 17 May 2024

Khalid Samara, Gary Mulholland and Akinseye Olatokunbo Aluko

The intricate and unpredictable nature arising in higher education institutions (HEIs) implementing technology-driven change for e-learning environments demands a much closer…

Abstract

Purpose

The intricate and unpredictable nature arising in higher education institutions (HEIs) implementing technology-driven change for e-learning environments demands a much closer examination of individuals’ interpretations and interactions as they undergo these changes. Through a micro-foundational lens, this study aims to examine the micro-level inhibitors and supporting factors of individual’s readiness for change by investigating technology-driven change in HEIs.

Design/methodology/approach

A two-phased research design using grounded theory methodology was used to collect and analyse data incorporated within a single-case study in an HEI. Data was collected using semi-structured interviews with 22 participants, followed by a focus group with eight participants centered on factors affecting their readiness for change during technology-driven change in e-learning environments. The data analysis followed an iterative constant comparative approach and its three-phased coding process: open, axial and selective coding.

Findings

This study revealed that staff with little awareness of the organisations expectations towards a technology-driven change or who are poorly communicated with can exhibit higher resistance and lower individual readiness for change. While macro-level factors of organisational structure can contribute to the success or failure of technology-driven change, the fundamental features related to individuals’ readiness for change are integral to understanding the micro-level causal behaviours underlying these macro-level phenomena.

Originality/value

The dominant model of change is often focused on drivers that are centered on examining macro-level constructs rather than individuals who are facing the change. This study presents theories on readiness for change and offers a micro-foundational view to bring new perspectives into the current literature on how individual-level micro-foundations enhance understanding of technological change in HEIs.

Details

International Journal of Organizational Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1934-8835

Keywords

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