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

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.