2025/05/27 by Shuaibu, Halima, Sule, Ibrahim Mohammed, Magashi, Salim Bashir +4
#AI-enhanced feedback #student learning and distance learning
paper · doi:10.60787/nodel.v2.29
In open, distance, and e-learning (ODeL) environments, students often face challenges such as limited personalized interaction, delayed feedback, and inadequate support, which hinder their academic performance and engagement. This study investigates the impact of AI-enhanced feedback systems on student learning outcomes in distance education, focusing on the dimensions of personalization, adaptability, timeliness, multimodality, context awareness, and engagement strategies. The theoretical framework integrates the Technology Acceptance Model (TAM), Cognitive Load Theory (CLT), and Self-Determination Theory (SDT). The study involves students enrolled in the distance learning program at Ahmadu Bello University. The study adopted non-experimental design of correlational research type. The population of the study comprised 4,500 MBA students. Simple random sampling technique was used to select 426 students as sample. Data was analyzed using partial least square structural equation model (PLS-SEM) to test the hypotheses of the study. The findings revealed that higher levels of personalization, adaptability, timeliness, multimodality, context awareness, and engagement strategies with t-statistics of 3.224, 2.744, 4.038 and 6.483 in AI-enhanced feedback systems positively correlate with improved student learning outcomes. The study delved into the dynamics of AI-enhanced feedback systems in distance learning, exploring their impact on student learning outcomes and recommend that distance learning institutions should establish mechanisms for regular assessments, seeking feedback from both students and educators, and adapt the systems based on evolving technological advancements and educational needs. The practical outcomes provide valuable insights for educational institutions seeking to leverage technology to improve student outcomes in remote learning environments.