The Use of Artificial Intelligence in Learning Support: Evaluation of a Digital Tool Aimed at Academic Self-Regulation (#1373)

Authors

González, Rocío

Losano, Cecilia

Saravia, Pía

Morera, Luis

Rivarola, Pablo

Medrano, Leonardo Adrián

Abstract

Generative artificial intelligence (AI)–based educational platforms are increasingly used in higher education to support academic organization and self-regulated learning. This study describes user experience, adoption barriers, and self-regulated learning indicators associated with implementing an AI tool that assists students with academic planning and the development of self-regulation skills. The tool combines generative models and learning analytics to deliver personalized recommendations, improve time management, and encourage effective study habits. Fifty students participated; 19 reported using the tool and 31 reported not using it. Among users, perceived usability and integration were high: 84.2% indicated that platform functions were well integrated and 79.0% reported that the tool was easy to use. Confidence and intention to continue were also favorable, with 89.5% reporting that they felt confident using the tool, were interested in frequent continued use, and believed that most people could learn it quickly. Regarding self-regulated learning, 84.2% reported being able to understand and learn both basic and difficult content, and 78.5% felt capable of achieving self-set goals. However, sustained planning behaviors were less consistent: 68.4% reported keeping up with studying through a structured schedule, whereas 26.3% reported studying only before exams. Procrastination-related execution difficulties were common (42.1% delaying non-preferred tasks; 36.8% struggling to start important tasks; 31.6% waiting until the last moment for deadlines). Study strategies suggested room for improvement (42.1% relying on repetition; 15.8% memorizing without understanding). Overall, the tool was positively appraised for usability and integration, while highlighting opportunities to strengthen sustained planning and deeper learning strategies in future deployments.