Leveraging Artificial Intelligence (AI) to Facilitate Student-centred Active Learning

TY - CONF

T1 - Leveraging Artificial Intelligence (AI) to Facilitate Student-centred Active Learning

AU - Hassan, Abubakar

A2 - Smith, Julie

A2 - Duke-Williams, Emma

A2 - Holme, Richard

A2 - Srinivasan, Sundar

A2 - Spark, Gordon

A2 - Martindale, Linda

A2 - Kinnear, Susan

N1 - © 2025 The Author.

PY - 2025/10/6

Y1 - 2025/10/6

N2 - This study explores the pedagogical impact of artificial intelligence (AI) tools on student engagement and academic performance within two undergraduate economics and business modules at a UK university. Using a mixed-methods design, the research compares three distinct periods: the Pre-AI Era (2022/23), the AI Era (2023/24, featuring unguided AI use), and the AI Intervention period (2024/25, involving structured pedagogical guidance). Quantitative data from institutional learning platforms, combined with qualitative survey responses, inform a two-stage mediation analysis to evaluate engagement as a mediator between AI use and student outcomes. Findings reveal that unguided AI use does not consistently improve academic performance and may even negatively impact problem-solving in summative assessments. In contrast, structured AI interventions significantly increase independent study hours, improve problem-solving, critical thinking, and communication skills, and enhance overall performance. Engagement is validated as a robust mediator, confirming that the pedagogical framing of AI use is critical to its effectiveness. The results underscore the need for instructional scaffolding to guide AI use, especially in disciplines requiring analytical reasoning and applied knowledge. This study contributes to the literature by offering empirical evidence on the mediating role of engagement in AI-driven learning, while providing actionable insights for institutions seeking to integrate AI technologies responsibly. It advocates for structured, context-sensitive interventions that promote active learning, student autonomy, and deeper cognitive engagement in AI-enhanced educational environments.

AB - This study explores the pedagogical impact of artificial intelligence (AI) tools on student engagement and academic performance within two undergraduate economics and business modules at a UK university. Using a mixed-methods design, the research compares three distinct periods: the Pre-AI Era (2022/23), the AI Era (2023/24, featuring unguided AI use), and the AI Intervention period (2024/25, involving structured pedagogical guidance). Quantitative data from institutional learning platforms, combined with qualitative survey responses, inform a two-stage mediation analysis to evaluate engagement as a mediator between AI use and student outcomes. Findings reveal that unguided AI use does not consistently improve academic performance and may even negatively impact problem-solving in summative assessments. In contrast, structured AI interventions significantly increase independent study hours, improve problem-solving, critical thinking, and communication skills, and enhance overall performance. Engagement is validated as a robust mediator, confirming that the pedagogical framing of AI use is critical to its effectiveness. The results underscore the need for instructional scaffolding to guide AI use, especially in disciplines requiring analytical reasoning and applied knowledge. This study contributes to the literature by offering empirical evidence on the mediating role of engagement in AI-driven learning, while providing actionable insights for institutions seeking to integrate AI technologies responsibly. It advocates for structured, context-sensitive interventions that promote active learning, student autonomy, and deeper cognitive engagement in AI-enhanced educational environments.

U2 - 10.20933/100001444

DO - 10.20933/100001444

M3 - Paper

T2 - University of Dundee Scholarship Conference 2025

Y2 - 2 September 2025 through 3 September 2025

ER -