(1) Objective: To review the existing evidence on pain education in patients with pain derived from an oncological process. (2) Methods: A systematic review was conducted using the databases Pubmed, Web of Science, PEDro, and Scopus. The selected studies had to incorporate instruction about the neurophysiology of pain into their educational program. The target population was cancer patients who had suffered pain for at least one month. The methodological quality of the articles collected was assessed using the PEDro scale. (3) Results: Some 698 studies were initially identified, of which 12 were included in this review. Four different models of pain education programs were found in the studies’ interventions. Pain intensity, pain experience, quality of life, pain tolerance, and catastrophism were the variables that appeared most frequently. (4) Conclusions: This review demonstrates that pain education in patients with cancer pain may produce effects such as decreased pain intensity and catastrophism. Knowledge about pain also seems to increase. However, no benefit was reported for patients’ overall quality of life. Therefore, more research is needed to clarify the effects of these interventions on the oncology population.

Generative AI virtual patients (VPs) introduce a new kind of variability into clinical training: each session is unique and shaped by what the student brings. This study analyzes 56 session transcripts from a 2 × 2 mixed-design experiment in which psychology postgraduate students (N = 59) conducted a single 50-minute interview with a generative VP, with or without an AI supervisory copilot providing real-time feedback. Transcripts were processed with a 161-variable linguistic pipeline covering productivity, lexical richness, pronouns (including morphological extraction of Spanish first-person plural markers), therapeutic technique, emotion (NRC lexicon and contextualized BETO), and dyadic features. Three findings emerged. First, the VP showed systematic behavioral coherence across sessions (TF-IDF similarity M = 0.459, 95% bootstrap CI [0.455, 0.463]), supporting its validity as an experimental stimulus. Second, first-person plural verbal language by the student (vamos a, trabajemos, podemos) predicted five of six experiential outcomes with large effects (r = .425–.569, FDR-significant). Third, the effect of collaborative language on post-session self-efficacy was mediated by perceived closeness with the VP (standardized indirect effect β = 0.224, 95% bootstrap CI [0.077, 0.451]; 73.6% of total effect). Neither the copilot nor any other variable altered these relationships, although the copilot produced a local effect on turn-to-turn thematic tracking (d = 0.67). Results indicate that learning in AI-patient simulations is fundamentally relational, carried by the linguistic microstructure of the student’s contribution rather than by the technical scaffolding of supervisory tools. Implications for designing generative VPs and AI-assisted clinical training are discussed.

Virtual Reality is an interactive technology that creates immersive, computer-generated three-dimensional environments for therapeutic and rehabilitative purposes. In neurorehabilitation, it has been increasingly adopted to support motor, cognitive, emotional, and functional recovery via task-oriented, multisensory, and feedback-driven interventions. Originally developed for exposure-based therapies, its applications have expanded to neurological conditions such as stroke, traumatic brain injury, Parkinson’s disease, multiple sclerosis, spinal cord injury, and chronic pain. Its therapeutic rationale is thought to rely on mechanisms including neuroplasticity, multisensory integration, motor learning, embodiment, and adaptive feedback. Despite the fact that standardized clinical protocols are still evolving, VR is considered a complement to conventional neurorehabilitation, with advances in artificial intelligence and wearable technologies expected to further enhance its clinical applications.