Minimally invasive D2 lymphadenectomy for gastric cancer is technically demanding, and its quality varies across patients, surgeons, platforms, and institutions. Conventional endpoints, including lymph node yield, margin status, operative time, blood loss, postoperative morbidity, and survival, remain essential but do not consistently capture intraoperative procedural fidelity, safety-critical deviations, or case complexity. This narrative framework review synthesized evidence from MEDLINE, Embase, and Web of Science from inception to May 10, 2026, focusing on technical difficulty, surgical quality assessment, pathology-centered oncologic adequacy, risk-adjusted outcomes, and artificial intelligence (AI)-enabled audit. Technical difficulty was conceptualized as a case- and context-dependent risk-adjustment layer shaped by anatomical complexity, vascular variation, therapy-altered tissue planes, visceral adiposity, operative platform, team workflow, and learning stage. Surgical quality was defined as a multidomain construct integrating process metrics, pathology metrics, and risk-adjusted clinical outcomes. The proposed framework provides audit-oriented guidance for identifying a simplified minimum dataset, prioritizing high-risk D2 segments for selective process review, and interpreting process, pathology, and outcome indicators together after adjustment for technical difficulty. AI may support scalable audit through video indexing, phase and step recognition, extraction of high-risk operative segments, assisted event logging, and structured feedback. However, AI outputs should be treated as candidate measurement signals requiring human confirmation, expert surgical review, pathology-based assessment, external validation, governance, and post-deployment monitoring. Future validation should proceed stepwise, from feasibility testing and inter-rater reliability assessment to prospective workflow evaluation and multicenter assessment of audit efficiency, benchmarking validity, and process- or patient-level outcomes. Quality assessment in minimally invasive D2 lymphadenectomy should shift from isolated surrogate endpoints toward an auditable, difficulty-adjusted framework that makes “D2 achieved” more measurable, reviewable, and clinically meaningful.
Yasheng Xue, Xiaoyun Dai, Xi Wang et al.· World Journal of Surgical On...· 0 citations
Title: Generative AI-Powered Pedagogical Agents in Immersive Environments for Social Sciences and Humanities Education: A Scoping Review Purpose This research project investigates how generative artificial intelligence (GenAI)-powered pedagogical agents and virtual instructors are being used within immersive virtual reality (VR) and augmented reality (AR) environments, specifically in the context of social sciences and humanities education. While research on generative-AI agents in immersive learning environments has grown rapidly since 2023, no existing systematic or umbrella review has yet mapped this specific intersection — most prior reviews either predate the generative-AI/large-language-model (LLM) era or focus predominantly on STEM, medical, and engineering education. Given that social sciences and humanities education (e.g., history, geography, civics) involves distinctive pedagogical and ethical demands — such as historical empathy, multi-perspective reasoning, and open-ended interpretive dialogue — that differ meaningfully from technical or procedural training domains, the project aims to determine what is currently known about this intersection, how mature the field is methodologically, and where meaningful gaps remain. To address this aim, the study was designed as a scoping review, following Arksey and O'Malley's (2005) five-stage methodological framework and reported according to the PRISMA-ScR (PRISMA Extension for Scoping Reviews) guideline. A scoping-review design was chosen deliberately over a systematic review or meta-analysis because the objective is to map the breadth, characteristics, and trends of an emerging body of literature — rather than to statistically synthesize effect sizes or assess a narrow effectiveness question — which is appropriate given how new and heterogeneous this specific research area still is. Research Questions The project is guided by four research questions: RQ1: What are the design features (embodiment, mode of interaction, role assumed) of GenAI-based pedagogical agents/virtual instructors used in immersive VR/AR environments in social and humanities education? RQ2: What learning outcomes have been reported in studies on these agents, and in which direction do the findings trend? RQ3: At which educational levels and in which social sciences/humanities subfields have these studies been conducted? RQ4: What are the methodological trends and limitations in the field, and what directions are recommended for future research? Methodology A systematic search was conducted across Scopus, Web of Science, and ERIC (August 2026), combining terms related to pedagogical agents/virtual instructors, generative AI/LLMs, immersive VR/AR/XR technologies, and education. The search was restricted to English-language, peer-reviewed journal articles published between 2023 and 2026 — a window chosen to capture the generative-AI/LLM era specifically. Of 105 records initially identified, a multi-stage screening and eligibility process (title/abstract screening, full-text assessment, and data-charting verification) resulted in 9 studies meeting all inclusion criteria. Data extracted from each study included agent design characteristics, technology used, research design, educational level and subject area, reported learning outcomes, and author-stated limitations and future-research recommendations. Findings were synthesized narratively (rather than statistically) around the four research questions and subsequently interpreted through the theoretical lenses of Presence Theory and Embodied Cognition. Expected/Actual Outcomes The review's findings indicate that the included agents are predominantly designed as embodied 3D characters built on GPT-family models, most often assuming peer or mentor roles within VR environments. Reported effects on learning outcomes (motivation, engagement, partner perception, and, in some cases, academic performance) trend positive overall, though effect sizes vary considerably across studies and are notably smaller in the few studies employing control-group comparisons than in single-group, pre-/post-test designs. A key substantive finding is that the existing literature is concentrated almost entirely at the higher-education level and clusters around language education and AI ethics/literacy — it has not yet reached classic social-studies subfields such as history, geography, or civics education, despite the conceptual gap the study set out to address. Interpreted through Presence Theory and Embodied Cognition, the findings further suggest that an agent's educational impact depends less on its technical sophistication (e.g., visual realism) than on whether an appropriate balance between presence and embodiment has been achieved relative to the nature of the learning task — an "embodiment paradox" identified across several included studies. The project's broader contribution is threefold: (1) it provides the field's first dedicated mapping of the generative-AI/LLM generation of pedagogical agents within the social sciences/humanities education context, filling a gap left by earlier, pre-generative-AI-era reviews; (2) it offers a theoretically grounded interpretive lens (Presence Theory/Embodied Cognition) for understanding why and how these agents affect learning, rather than only cataloguing whether they do; and (3) it identifies concrete directions for future research — including extending investigation to K-12 contexts, directly targeting classic social-studies content, adopting more rigorous control-group designs, and incorporating physiological/multimodal measures alongside self-report data. The review also transparently documents its own methodological limitations (a single-researcher screening stage, no prior protocol registration, no formal quality/risk-of-bias appraisal, and a modest final sample of nine studies), consistent with the exploratory nature of scoping reviews and intended to guide readers in appropriately weighing the strength of the evidence presented.
Veysel Dağdemir, Erhan Görmez· Open Science Framework· 0 citations
This systematic literature review examines research on artificial intelligence (AI) in mathematics education published between January 1, 2021, and August 1, 2025. Searches of Scopus and Google Scholar identified 922 records; after deduplication, screening, and full-text eligibility assessment, 42 peer-reviewed journal articles and conference proceedings were included. The review used descriptive quantitative summaries and a deductive-inductive thematic synthesis. Two independent reviewers conducted screening (Cohen's kappa = 0.88), and methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT). The included literature indicates increasing attention to generative AI, personalised support, feedback, teacher practice, academic integrity, and equity. Evidence for educational benefits varies substantially across study designs and contexts, and technical capability should not be equated with demonstrated classroom effectiveness. Geographic patterns in the selected sample are described without attributing them to regulatory, economic, or infrastructural causes that were not directly tested. Because the 2025 search covered only January through August 2025, publication counts are treated as partial-year data and are not directly comparable to complete prior years. Key limitations include reliance on two databases, English- and Russian-language restrictions, reproducibility constraints in Google Scholar, methodological heterogeneity, and limited long-term evidence. Overall, AI shows potential to support mathematics teaching and learning, but stronger longitudinal and comparative evidence is needed to establish effectiveness, equity, and sustainable implementation.
Fariza Omirzakova, Sarsenkul Sh. Tleubai· European Journal of STEM Edu...· 0 citations
The fields of human-robot interaction (HRI) and embodied conversational agents (ECAs) have long studied how empathy could be implemented in machines. One of the major drivers has been the goal of giving multimodal social and emotional intelligence to these artificially intelligent agents, which interact with people through facial expressions, body, gesture, and speech. What empathic behaviors and models have these fields implemented by mimicking human and animal behavior? In what ways have they explored creating machine-specific analogies? This chapter aims to review the knowledge from these studies toward applying the lessons learned to today’s ubiquitous, language-based agents such as ChatGPT.
Angelica Lim, Özge Nilay Yalçın· Cambridge University Press e...· 0 citations
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Advances in the performance of large language models (LLMs) have led some researchers to propose the emergence of theory of mind (ToM) in artificial intelligence (AI). LLMs can attribute beliefs, desires, intentions, and emotions. But, rather than employing the characteristically human method of empathy, they learn to attribute mental states by recognizing behavioral and linguistic patterns in a dataset. We ask whether LLMs’ inability to empathize precludes them from honoring an individual’s right to be an exception, from assessing character with appropriate sensitivity to a person’s individuality. We defend the following claims: (1) Both humans and LLMs can honor the right to be an exception. (2) Humans can honor that right through empathy, and LLMs cannot empathize. (3) There is distinctive moral value in honoring the right to be an exception through empathy.
Will Kidder, Jason D’Cruz, Kush R. Varshney· Cambridge University Press e...· 0 citations
Artificial Intelligence systems increasingly match or surpass human performance across a wide range of cognitive tasks. Recent advances in large language models have yielded conversational abilities often indistinguishable from human dialogue. In this chapter, we argue that whether artificial agents should be considered empathetic depends on how empathy itself is defined and how it relates to subjective experience. We show that different philosophical schools produce distinct, and often incompatible, answers to the question of whether such a system should be considered genuinely empathetic. On one hand, empathy is a functional trait that can be fully characterized by observable behavior; on the other hand, empathy is inseparable from subjective experience and conscious feeling. Drawing on the philosophical notion of the “zombie” we introduce a thought experiment involving a hypothetical chatbot that exhibits perfect empathic behavior across all conceivable benchmarks while lacking any subjective experience. This framing allows us to disentangle functional performance from conceptual attribution and to examine whether empathy judgments depend on observable behavior alone or on assumptions about inner experience. As artificial agents become increasingly integrated into emotionally contexts, understanding how and why humans attribute empathy to machines becomes a pressing scientific, ethical, and societal question.
Ariel Goldstein, Gabriel Stanovsky· Cambridge University Press e...· 0 citations
This article examines the urgency of reconstructing the Islamic Religious Education (IRE/PAI) curriculum in facing the challenges of Society 5.0, which is laden with technological disruption, particularly the emergence of Artificial Intelligence (AI). Using a qualitative approach based on library research, this study systematically analyzes various scientific literature, education policy documents, and contemporary theories concerning technology integration in religious education. The findings indicate that the conventional Islamic religious education curriculum has not been able to adequately respond to the challenges of the destructive 5.0 era, making reconstruction necessary across three main dimensions: (1) integration of artificial intelligence as a pedagogical tool without sacrificing spiritual values; (2) strengthening Islamic ethical values as a moral foundation amid the flow of digitalization; and (3) developing sustainable competencies relevant to twenty-first-century needs. This research recommends an integrative PAI curriculum model that combines digital-spiritual literacy, contextual Islamic ethics, and adaptive competencies as a strategic response to contemporary challenges.
Sunhaji, Yanti Nurdiyanti, Muhamad Muzaki· International Journal of Inn...· 0 citations
In 2023, the arrival of Chat GPT 3o heralded many viral conversations about what it means to interact with artificial intelligence (e.g., Roose, 2023). People read about individuals having extended conversations with chatbots, often leading to complex consequences that raise deeper questions. What do we mean by “empathy” in these spaces? What are the ethical consequences for building or using technology for emotional support? There have been cases of large language models giving better and more empathetic medical advice than trained medical workers (Ayers et al., 2023; see later replication by Ovsyannikova et al., 2025). There have also been popular write-ups about people developing relationships with Artificial Intelligence (AI) platforms explicitly positioned as romantic partners (Patel, 2024). In this introductory chapter, we will highlight core themes that unite the chapters throughout the volume, highlighting connections across social sciences, humanities, and engineering and computers science to juxtapose perspectives and illustrate connections and ongoing controversies.
C. Daryl Cameron, Anat Perry· Cambridge University Press e...· 0 citations
Most definitions of empathy stress its interpersonal nature: Empathy requires a human sender and a human receiver, where all parties involved have the theoretical capacity to accurately understand each other’s minds. Because empathy is inherently interpersonal, some scholars have questioned whether empathy between humans and artificial intelligence as it currently exists (e.g., large language models) is “real.” However, the requirement for two minds, each with the theoretical capacity to fully understand the thoughts and emotions of the other, is also not clearly met in other domains where empathy is discussed, such as empathy between humans and non-human animals or empathy with nature. This chapter critically examines these assumptions about what is needed for empathy to occur, offering several possible sets of empathy requirements that researchers might engage with, and outlining potential challenges researchers might face if they adopt any particular set of requirements in their work. Ultimately, we pose the question of whether two minds and/or the ability for minds to understand each other are truly necessary for empathy, or whether all that is required for empathy to occur is an entity’s perception of giving or receiving it.
Sean M. Laurent, Iris Sooyun Chung· Cambridge University Press e...· 0 citations
With artificial intelligence (AI) becoming increasingly integrated into healthcare systems, questions have emerged regarding how patients’ and clinicians’ views of AI in these high-stakes environments are shaped by its perceived moral competence and adherence to ethical principles, including respect for personal autonomy. To examine this question, this chapter focuses on how people evaluate medical decisions made by AI versus humans and the role that ascriptions of various moral and compassionate traits play in these judgments. We discuss current applications of AI in healthcare settings, existing empirical evidence on public perceptions of AI-assisted versus human decision-making, and offer speculative explanations for the widely documented asymmetrical preference for human over AI decision-makers in patient medication, triage, and life support decisions. As AI evolves, understanding its impact on ethical choices in healthcare is vital for balancing technological advancement with compassionate care.
Michael Laakasuo, Kathryn Francis, Marianna Drosinou et al.· Cambridge University Press e...· 0 citations
Who – or what – people engage for supportive conversations is rapidly evolving as technology becomes more sophisticated and users adapt their expectations and perceptions of communication. Empathy is central to understanding and responding to emotions along with communicating sensitive supportive messages. Questions surrounding the role of empathy when communicating with artificial intelligence, however, have yielded mixed and interesting conclusions. This chapter discusses perspectives on empathy, supportive communication, and the influence of machine agents in the process of communicating sensitive, empathic messages. We consider whether machine agents can simulate empathy and convey sensitive support or merely produce reactive content that might simply appear empathic. This chapter offers insights into the relationship between empathy and supportive interactions with artificial intelligence.
Austin Beattie, Andrew C. High· Cambridge University Press e...· 0 citations
A question that arises when contemplating artificial intelligence and empathy is whether it would matter to the empathy recipient if AI genuinely feels or cares. This is a seemingly novel issue, born of the capacity of computers to simulate human empathy. I will suggest, however, that questions concerning “artificial empathy” should not be limited to AI chatbots, as such forms of empathy that are void of feeling are widespread in human relationships as well. This chapter offers a short history of artificial empathy and how it became widely accepted, well before AI, beginning with the operationalization of empathy in mid-twentieth-century clinical psychology. Evaluations of AI’s empathetic abilities are often based on comparisons to human or “real” empathy – a romanticized form of empathy that is, sadly, less common than we appreciate. This chapter offers an alternative perspective that might prove valuable, comparing AI’s “artificial empathy” to human “artificial empathy.”
Shai Satran· Cambridge University Press e...· 0 citations
