The rapid advancement of artificial intelligence (AI) presents transformative possibilities for redesigning how students engage with academic content, manage their study workflows, and receive personalised instructional support. This research investigates the design, implementation, and effectiveness of an AI-powered academic assistant engineered to enhance student productivity and enrich the overall learning experience at the tertiary level. The study adopts a convergent mixed-methods, quasi-experimental design, involving a structured quantitative survey administered to 250 students across undergraduate and postgraduate levels at a tertiary institution. Stratified random sampling ensured demographic representativeness across four academic cohorts. Data were analysed using paired t-tests, chi-square tests, and one-way ANOVA to evaluate four principal hypotheses addressing the measurable impact of AI assistance on academic performance, student engagement, time management, and cross-disciplinary adaptability. Results confirm statistically significant improvements across all productivity dimensions, with AI assistance yielding mean gains of 6–9%. Student satisfaction recorded a mean rating of 4.4 out of 5.0. The study identifies critical design principles — including personalisation, real-time feedback, contextual comprehension, and ethical transparency — essential to effective academic AI deployment.
Oza Archana Nayan Rajeshkumar (Fri,) studied this question.