Medical education faces challenges in providing scalable, consistent clinical skills training. Simulation with standardized patients (SPs) develops communication and diagnostic skills, but remains resource-intensive and variable in feedback quality. Existing AI-based tools show promise yet often lack comprehensive assessment frameworks, evidence of clinical impact, and integration of self-regulated learning (SRL) principles. Through a multi-phase co-design process with medical education experts, we developed MedSimAI, an AI-powered simulation platform that enables deliberate practice through interactive patient encounters with immediate, structured feedback. Leveraging large language models, MedSimAI generates realistic clinical interactions and provides automated assessments aligned with validated evaluation frameworks. In a multi-institutional deployment (410 students; 1,024 encounters across three medical schools), 59.5% engaged in repeated practice. At one site, mean Objective Structured Clinical Examination (OSCE) history-taking scores rose from 82.8 to 88.8 (p < 0.001, d = 0.75), while a second site’s pilot showed no significant change. Automated scoring achieved 87% accuracy in identifying proficiency thresholds on the Master Interview Rating Scale (MIRS). Mixed-effects analyses revealed institution and case effects. Thematic analysis of 840 learner reflections highlighted challenges in missed items, organization, review-of-systems, and empathy. These findings position MedSimAI as a scalable formative platform for history-taking and communication, motivating staged curriculum integration and realism enhancements for advanced learners.

Despite the importance of speaking practice in language learning, most students struggle to find low-stakes opportunities for authentic oral communication. ChitterChatter addresses this challenge by providing an AI-powered tool that enables instructors to create curriculum-aligned, voice-enabled conversation activities for students. Built on OpenAI's Realtime API and designed through iterative feedback from language education experts, ChitterChatter offers personalized, adaptive speaking practice while maintaining a judgment-free environment that promotes student comfort and confidence. Our pilot study with university-level Spanish learners shows that students value the platform's ability to provide authentic conversation practice without fear of judgment, although barriers to adoption remain. This paper presents ChitterChatter's design, preliminary evaluation results, and future directions for enhancing the system. Our findings demonstrate the potential of AI conversation partners to support classroom language instruction by increasing both the quantity and quality of speaking practice opportunities for students.

Simulation-based learning (SBL) is a foundational component of clinical education, yet its implementation often varies in authenticity and educational value. Through semi-structured interviews with ten medical students across three U.S. institutions, we examined how students engage with SBL within their broader learning contexts. Our thematic analysis identified adaptive learning strategies developed in response to time constraints, limited formal guidance, and a fragmented educational landscape. Students described challenges including gaps in simulation realism, inconsistent assessment objectives, and difficulty obtaining actionable feedback. This study provides critical learner-centered design insights intended to inform the development of scalable solutions-particularly digital or AI-driven platforms-that can address these limitations and better support learning in high-pressure professional education.