Technology-enhanced learning and self-regulated learning: A pathway to enhance medical students’ outcomes

Abstract

Clinical teaching in internal medicine faces unique challenges owing to its highly complex knowledge system and rigorous reasoning logic, with medical students commonly experiencing cognitive overload and “clinical shock”. Traditional teaching struggles to address personalized needs and psychological support, whereas technology-enhanced learning (TEL) offers new opportunities to address these problems. However, introducing technology without self-regulated learning (SRL) mechanisms may lead to superficial learning. Based on Zimmerman’s SRL model, this review analyzes TEL mechanisms, explores the dual role of technology as scaffolding and mirror, and proposes three implementation pathways: Emotional regulation, cognitive optimization, and metacognitive awakening. By examining the effects of SRL training in TEL environments on professional burnout, professional identity, and sense of control, and combining empirical research from domestic and international studies over the past five years, this study discusses the positive impacts of this model on clinical reasoning ability, knowledge retention, and operational skills (Objective Structured Clinical Examination scores). It also summarizes the mediating role of psychological adaptation, forming a closed loop of “technical support - self-regulation - outcome improvement”. Finally, it identifies current challenges, including the risks of technology dependence, data privacy ethics, and “pseudo-learning”, and explores the potential of generative artificial intelligence as an SRL coaching partner. In this article, we propose that future medical education should shift from “technology instrumentalism” to “human-machine collaborative evolution”, strengthening students’ agency. This minireview provides a theoretical framework and practical pathway for internal medicine clinical teaching reform, aiming to enhance medical students’ psychological adaptability and learning outcomes (particularly during clinical internship stages), and offers references for cultivating high-quality medical talent.

Keywords: Technology-enhanced learning; Self-regulated learning; Medical students; Psychological adaptability; Clinical education

Core Tip: This minireview proposes an integrated model combining technology-enhanced learning and self-regulated learning to improve psychological adaptability and clinical outcomes in medical students. It highlights three pathways, emotional regulation, cognitive optimization, and metacognitive awakening, supported by tools like virtual simulation and learning analytics. The model forms a virtuous cycle: Technical support enhances self-regulation, which boosts learning outcomes and psychological resilience. Challenges and future directions, including generative artificial intelligence, are also discussed.