The Impact of Automated Response Systems on Meeting Holistic Student Learning Outcomes

This dissertation investigates the impact of Automated Response Systems (ARS) on achieving holistic student learning outcomes in higher education. In response to increasing pressures on educators—including staff shortages, expanding curriculum demands, and the disruptions of the COVID-19 pandemic—this study explores how pedagogically grounded ARS can bridge the gap between technological innovation and effective teaching practice. Through a Design-Based Research (DBR) methodology, the study was conducted across four iterative phases: problem analysis, design model development, implementation, and theory-building. Central to the study is the AI Teacher Teaching Task Spectrum (AITTTS), a conceptual framework that aligns ARS capabilities with varied pedagogical roles to enhance both academic and socio-emotional student outcomes. The findings highlight that a well-designed ARS, when grounded in educational theory and developed with practitioner input, can positively influence student engagement, reflective thinking, and academic achievement. Importantly, the study expands the current understanding of holistic outcomes by integrating critical pedagogical values such as empowerment, adaptability, and digital literacy. Methodologically, the study demonstrates the value of DBR in AI-enhanced education and contributes a replicable model for future ARS development. Practically and policy-wise, the study provides actionable guidelines for educators, developers, and policymakers seeking to implement AI technologies that support whole-person education. This study offers a compelling case for repositioning ARS as transformative, student-centred tools in the evolving landscape of higher education.