Earticle

본 연구는 지금까지 한국의 영어 교육 분야에서 발표된 챗봇 관련 연구들의 주제 동향을 탐색하고, 시간의 흐름에 따라 연구 주제의 변화를 확인하고자 실시되었다. 본 연구의 목표를 달성하기 위해 학술지 논문을 중심으로 관련 문헌을 선별하여 수집하였다. 그 결과 93편의 학술 논문이 최종 분석 대상으로 선정되었다. 아울러, 분석 대상 논문에서 제목과 초록을 추출하였으며, 이를 바탕으로 토픽 모델링 기법을 활용하여 챗봇 관련 연구들의 주제 동향 및 시간에 따른 주제 변화 추이를 관찰하였다. 본 연구 결과를 정리하면 다음과 같다. 첫째, 한국의 영어 교육 분야에서 실시된 챗봇 관련 연구들은 주로 학생들을 대상으로 하고 있으며, 사용된 챗봇 유형은 당시의 기술 동향이 반영되고 있는 것으로 나타났다. 둘째, 토픽 모델링 분석을 통해 "다양한 목적을 위한 챗봇의 다면적 응용(주제 1)", "챗봇 사용의 효과와 사용자 인식 이해(주제 2)", "챗봇 중심 과업, 과업 별 챗봇 개발 및 학습자-챗봇 상호작용(주제 3)"의 세 가지 연구 주제 동향이 관찰되었다. 셋째, 해당 주제들의 시간적 변화 추이를 살펴본 결과, 주제1의 비중은 시간이 지남에 따라 증가하는 추세를 보였고, 주제2는 반대로 감소하는 추세를 보였다. 주제3에서는 시간의 흐름에 상관없이 일관되게 낮은 관찰 빈도가 목격되었다. 본 연구는 한국의 영어 교육 맥락을 바탕으로 수행되었으나, 한국을 포함하여 유사한 외국어 학습 배경을 공유하는 학문 공동체에 역시 영어 교육에서의 효과적인 챗봇 통합을 위해 필요한 향후 연구 주제에 대해 다양한 방향과 지침을 제공해줄 수 있다.

This study aimed to investigate the specific topics of scholarly inquiries related to a chatbot within the domain of English pedagogy in South Korea and to track the changes in these topics over time. To achieve this goal, rigorous criteria were established to screen and gather relevant literature, resulting in the selection of 93 scholarly articles. From these articles, titles and abstracts were extracted and analyzed using topic modeling techniques. The findings of this investigation are as follows: first, it was observed that most chatbot-related scholarly articles in Korea predominantly involved students as subjects, and the types of chatbots used reflect sensitivity to current technological trends. Secondly, the topic modeling analysis identified three main topics: "the multifaceted application of chatbots for various purposes (Topic 1)," "understanding the impacts and perceptions of chatbot use (Topic 2)," and "chatbot-centric tasks, the development of task-specific chatbots, and learner-chatbot interactions (Topic 3)." Lastly, the temporal trajectory of these topics revealed a noticeable increase in the proportion of Topic 1, a decrease in Topic 2, and a consistently low frequency of Topic 3. Despite being limited to the context of South Korean English pedagogy, these insights provide several academic implications. Specifically, the findings can help researchers better understand and identify future research areas within and beyond the South Korean English education context.

가습기살균제특별법 제정으로 피해구제가 본격화됐지만, 아직 많은 피해자가 인과관계 입증에 어려움을 겪어 피해에 상응하는 보상을 받지 못하고 있다. 이에 본 연구는 특별법 이후 나타난 가습기살균제 피해구제의 문제점 을 검토하고 향후 개선점을 모색하고자 법이 시행된 2017년 8월부터 2023년 11월까지의 뉴스를 대상으로 텍스 트마이닝을 실시하였다. 시간의 흐름에 따른 주요 보도 이슈를 살펴봤을 때, 2017~2020년은 특별법 제정 후 나타난 피해구제의 실효 성에 대한 비판과 법령 개정을 통한 해결과정이 주로 조명됐으며, 2022~2023년은 가습기살균제 피해구제를 위 한 조정위원회의 결렬이 주된 이슈로 나타났다. 뉴스 본문에 대한 LDA 토픽모델링 결과, 협소한 피해구제 지적, 집단 피해구제의 한계, 피해구제 범위의 확대 노력, 민․형사 재판의 인과관계 문제 등의 쟁점 구조가 확인되었다. 상기 연구 결과를 바탕으로 본 연구는 가해기업과 정부가 책임을 인정하고 피해구제 사각지대를 좁히기 위해 각자의 배․보상액 몫을 확장할 것을, 신속하게 역학관계 입증을 완수하고 건강피해 인정기준을 유연하게 적용할 것을, 그리고 소송에서 엄격한 인과관계 입증보다 실질적 피해구제에 집중할 것을 제언하였다.

Despite the Special Act on Remedy for Damage caused by Humidifer Disinfectants, many victims are still unable to get redress for their damages due to difficulties in proving causation. In order to examine the problems in humidifier disinfectant redress that have emerged since the Special Act and to seek solutions, we conducted text-mining on the news from August 2017 to November 2023. When examining the main news issues in chronological order, the years 2017 to 2020 were primarily characterized by criticisms regarding the effectiveness of damage redress after the enactment of the Special Act, and the subsequent process of resolution through legislative amendments. Meanwhile, the years 2022 to 2023 were highlighted by the failure of the mediation committee for humidifier disinfectant damages redress, emerging as the primary issue during this period. As a result of the news topic modeling, the following topic structure was identified: the criticism of narrow damage redress, limitations of collective damage redress, efforts to expand the scope of damage redress, and causation problems in civil and criminal suits. Drawing on the above findings, we recommend that offending companies and governments acknowledge their responsibility and expand their respective shares of damage redress to narrow the gaps in compensation; expedite epidemiological proof and apply flexible criteria for recognizing health damages; and focus on substantive damages rather than strict causation proof in litigation.

This study applies topic modeling analysis to AI?dance research articles indexed in SCOPUS and KCI to identify the major themes and knowledge structures of international AI?dance scholarship, and, through comparison with domestic research, to suggest future directions for AI?dance research in Korea. First, while both international and domestic AI?dance research have experienced rapid growth since the pandemic, they exhibit distinct differences in research perspectives and methodologies. International research focuses on the structures, algorithms, and creative systems of AI technologies?such as generative AI, learning, movement modeling, neural networks, and human?computer interaction?thereby conceptualizing dance as a data-driven object of creation and analysis. In other words, AI is understood as a technological medium that performs practical creative functions within dance. In contrast, domestic research largely approaches AI as a tool for education, classroom practice, and applied case studies, remaining at an exploratory stage of potential utilization. AI is positioned not as a component of creative systems but as part of an educational discourse. Consequently, while international research has already entered a convergent and multidisciplinary research ecosystem, domestic research still remains in a foundational stage focused on educational introduction and preliminary application. To bridge this gap, it is necessary to strengthen the core competencies required for AI research, pursue more ambitious and forward-looking studies grounded in a deeper understanding of international scholarship, and expand domestic research into specialized educational topics as well as culturally and identity-based themes.

This study presents an automated content analysis of 2,379 abstracts of research articles published in four major journals from 1997 to 2017. A total of 45 topics were extracted using the structural topic model (STM), a statistical text analytic method that allows us to infer latent topics from abstracts and to conduct statistical significance tests regarding the relationships between topic prevalence and abstract features. We found that while some topics (e.g., public health or online social movement) demonstrate increasing trends, others (e.g., third-person effect and communication strategies) reveal decreasing trends; authors in non-US-based institutions are more likely than those affiliated with US-based institutions to focus on the societal consequences of media (e.g., agenda-setting effect or public deliberation). Additionally, interesting relationships were found between topic prevalence and the number of article authors (e.g., popular topics among multi-author papers are lab-based experiments or large-scale campaign studies). Finally, topic-journal associations were identified.

Purpose: The purpose of this study was to identify the supportive care needs of parents of preterm children in South Korea using text data from a portal site. Methods: In total, 628 online newspaper articles and 1,966 social network service posts published between January 1 and December 31, 2019 were analyzed. The procedures in this study were conducted in the following order: keyword selection, data collection, morpheme analysis, keyword analysis, and topic modeling. Results: The term "yirundung-yi", which is a native Korean word referring to premature infants, was confirmed to be a useful term for parents. The following four topics were identified as the supportive care needs of parents of preterm children: 1) a vague fear of caring for a baby upon imminent neonatal intensive care unit discharge, 2) real-world difficulties encountered while caring for preterm children, 3) concerns about growth and development problems, and 4) anxiety about possible complications. Conclusion: Supportive care interventions for parents of preterm children should include general parenting methods for babies. A team composed of multidisciplinary experts must support the individual growth and development of preterm children and manage the complications of prematurity using highly accessible media.

Purpose: This study analyzed research trends in infant and toddler rearing behavior among family caregivers over a 10-year period (2010-2021). Methods: Text network analysis and topic modeling were employed on data collected from relevant papers, following the extraction and refinement of semantic morphemes. A semantic-centered network was constructed by extracting words from 2,613 English-language abstracts. Data analysis was performed using NetMiner 4.5.0. Results: Frequency analysis, degree centrality, and eigenvector centrality all revealed the terms ''scale," ''program," and ''education" among the top 10 keywords associated with infant and toddler rearing behaviors among family caregivers. The keywords extracted from the analysis were divided into two clusters through cohesion analysis. Additionally, they were classified into two topic groups using topic modeling: "program and evaluation" (64.37%) and "caregivers' role and competency in child development" (35.63%). Conclusion: The roles and competencies of family caregivers are essential for the development of infants and toddlers. Intervention programs and evaluations are necessary to improve rearing behaviors. Future research should determine the role of nurses in supporting family caregivers. Additionally, it should facilitate the development of nursing strategies and intervention programs to promote positive rearing practices.

본 수영은 대중적인 생활체육으로 인식되고 있다. 하지만 아파트, 호텔 등에 설치된 실내수영장에서 종종익수사고가 발생하고 있다. 따라서 본 연구에서는 쇼셜미디어, 뉴스, 잡지, 학술논문에서 수집한 데이터를 대상으로 텍스트마이닝 기법을 활용하여 수영장 익수사고를 중심으로 키워드 분석을 수행하였다. 이를 통해 수영장에서 발생하는 사고의 원인, 결과와 밀접한 키워드를 분석하여 향후 사고예방을 위한 기술적 연구에 활용하는데 연구의 목적이 있다. 본 연구에서는 최근 5년간 소셜 미디어 동영상(102개)과 뉴스기사(30개)를 활용하여 키워드 및 토픽 모델링 분석을 수행하였다. 그 결과 첫째, 아동 익수 사고와 구조 활동, 둘째로 부주의와 그에 따른 법적 책임, 셋째, 수영장, 워터파크, 주거시설 내 수영시설에서의 아동 안전사고 위험으로 분류되었다. 네트워크 분석 결과는 ‘수영활동’과 ‘조사’와 같은 키워드가 매개 역할을 하는 것으로 나타났다. 본 연구 결과는 향후 수영장 익수사고의 인과관계 분석, 기술적 해결방안을 마련하는 기초자료로 활용성이 높을 것으로 판단된다.

Swimming is widely recognized as a popular recreational sport. However, drowning accidents frequently occur in indoor swimming pools located in apartments, hotels, and other facilities. Therefore, this study employs text mining techniques to analyze drowning accidents in swimming pools using data collected from social media, news articles, magazines, and academic papers. The purpose of the study is to identify keywords closely related to the causes and consequences of these accidents and to provide insights that can inform future technological research aimed at accident prevention. This study utilizes 102 social media videos and 30 news articles from the past five years to conduct keyword analysis and topic modeling. The results reveal three main themes: (1) child drowning accidents and rescue activities, (2) negligence and related legal responsibilities, and (3) child safety risks in swimming pools, water parks, and residential swimming facilities. Network analysis further indicates that keywords such as “water activity” and “investigation” serve as mediating nodes within the keyword network. These findings are expected to serve as valuable foundational data for future causal analyses of swimming pool drowning accidents and for developing technical solutions to enhance safety in swimming environments.

In this paper, we proposed a multi-subject-level sentiment analysis model for user reviews using the Latent Dirichlet Allocation (LDA) method targeting user-generated content (UGC). Data were collected from users' online reviews of hotels in major tourist cities in the world, and 30 hotel-related topics were extracted using the entire user reviews through the LDA technique. Six major hotel-related themes (Cleanliness, Location, Rooms, Service, Sleep Quality, and Value) were selected from the extracted themes, and emotions were evaluated for sentences corresponding to six themes in each user review in the proposed sentiment analysis model. Sentiment was analyzed using a dictionary. In addition, the performance of the proposed sentiment analysis model was evaluated by comparing the emotional values for each subject in the user reviews and the detailed scores evaluated by the user directly for each hotel attribute. As a result of analyzing the values of accuracy and recall of the proposed sentiment analysis model, it was analyzed that the efficiency was high.

본 연구의 목적은 토픽모델링 분석법을 통해 국내 트라우마 연구의 동향을 분석을 통해 주요 연구주제를 확인하고 성장 또는 쇠퇴하는 주제들을 규명하고자 하였다. 이를 위해 1996년부터 2016년까지 발간된 논문들 중 199편의 논문들을 선별하여 토픽모델링 분석을 실시하였다. 그 결과 총 39개의 주제가 추출되었고, 이는 다시 15개의 중범위 주제와 5개의 상범위 주제로 범주화되었다. 트라우마 연구논문의 수는 2008년, 2011년, 2014년을 기점으로 전반적인 증가 추세를 보임이 확인되었다. 특히, 2014년-2016년 논문 성과물이 전체 논문 실적의 55.78%였는데, 이는 ‘세월호 침몰’ 재난의 영향에 의한 것임을 확인하였다. 성장속도가 높은 주제는 ‘다양한 유형의 트라우마 사건에 따른 주요 심리적 증상들’과 ‘트라우마에 대한 심리사회적 개입’에 대한 주제였으며, 성장속도가 상대적으로 낮은 주제는 ‘PTSD의 생리심리사회적 기제’와 ‘트라우마 내러티브’에 대한 것이었다. 마지막으로, 본 연구의 주요 결과에 따른 논의에서 본 연구의 제한점과 함께 추후 트라우마 연구의 방향에 대해 제언하였다.

The purpose of this study is to analyze research trends related to psychological trauma in South Korea to identify upward and downward topics. A topic modeling analysis was conducted for 199 articles published from 1996 to 2016. The number of articles on trauma increased in 2008, 2011, and 2014 and those published between 2014 and 2016 covered 55.78% of the total because of Sewol ferry disaster. Based on the topic modeling analysis, 39 key topics were identified and categorized into 15 mid-range topics and 5 high-range topics. Hot topics were found to be ‘major psychological symptoms following various types of traumatic events’ and ‘psycho-social intervention for trauma’, while cold topics were ‘biopsychosocial mechanism of PTSD’ and ‘trauma narrative’. Implications and limitations of this study were discussed based on the results, along with the directions for future research on trauma.

In this paper, we present an e-book recommendation system using topic-modeling based on Latent Dirichlet Allocation (LDA) that is a probabilistic model increasingly used in various textual data analysis. Through the generated topics, we found the latent themes of the topics by estimating probability distributions for the topics in eBooks and words. We also address the basic concept of micro-segmentation used mainly in customer marketing field, which ensures that a variety of eBooks are recommended to users. The primary aim of our proposed method is to integrate the effective and efficient techniques with only using textual data of eBooks to improve recommendation performance in Content-Based Filtering (CBF) recommendation when it is unable to rely on the Collaborative Filtering (CF) utilizing ratings and reviews data obtained from a user's own past information. The experiment demonstrates the robustness of the presented method, and also shows that the method provides explainable recommendation results.

ESG management is essential to ensure the sustainability of companies in an environment where many risks such as climate change are impacting. It is essential to promote academic research that reflects social requirements so that ESG management can be implemented in all fields of our society. The purpose of this research is to identify social issues and trends linked to ESG and determine whether academic studies are being performed to address them. The study aimed to identify social issues and trends related to ESG, news article texts from Big Kinds, a news search site, were collected and topic modeling and semantic network-based text analysis were performed. In order to understand academic research topics related to ESG, summary texts of ESG-related academic papers were collected on the RISS site, and topic modeling and semantic network-based text analysis were performed. The article confirms an increasing interest in ESG from both social and academic perspectives. However, there is a slight disparity between the two, with social trends related to ESG management gaining attention from larger companies and then spreading to small and medium-sized enterprises, while academic research on ESG tends to focus mainly on the concept and principles.

Hong Kong cinema has undergone significant changes, shifting from an actor-driven, story-rich industry to one influenced by large-scale productions and global trends. This study applies topic modeling to analyze audience reviews of Hong Kong films from two periods: pre-2000 and post-2000. Using Latent Dirichlet Allocation (LDA), it identifies key thematic changes, highlighting a decline in acting quality and storytelling depth. Findings indicate that pre-2000 films were praised for strong performances and engaging narratives, while post-2000 films prioritized large-scale productions and high-budget visuals but faced criticism for weaker storytelling and predictable plots. The study suggests that Hong Kong cinema has struggled to retain its distinct identity amid globalization and competition from Hollywood and mainland Chinese productions. To regain its prominence, a balance between high production values and compelling storytelling is necessary. Future research should explore cross-cultural comparisons and industry perspectives to further understand audience engagement trends.

본 연구는 기후변화 관련 언론보도의 내용과 프레임을 토픽모델링과 프레이밍 분석을 통해 조사하고, 효과적인 기후변화소통 전략 도출을 목적으로 진행되었다. 2020년1월1일부터 2024년 12월 31일까지 조선일보, 동아일보, 중앙일보, 한국일보, 한겨레, 경향신문, 한국경제, 매일경제 등 총 8개의 신문에서 기후변화, 기후위기, 탄소중립, 온실가스 등의 키워드로 검색한 기사를 토대로 1차적으로 토픽모델링 분석을 진행하고, 이 중 2회 이상 기후 관련 키워드가 도출된 기사를 대상으로 프레임 분석을 진행하였다. 그 결과, 기후위기사회적대응, 그린뉴딜산업정책, 탄소중립정책, 재생에너지전환, 일사속탄소배출, 기업 ESG경영, 기후소송운동, 기후변화과학적증거, 코로나와기후, 국제기후협상 등 총 10개의 토픽이 도출되 었다. 프레임 분석 결과, 진단프레이밍이 37.6%, 결과프레임이 52.8%로 가장 높은 비중을 차지하였다. 본 연구는 기후변화 이슈와 관련하여 의제설정과 프레이밍의 상호작용을 실증적으로 탐구해보고자 했다는데 의의가 있으며, 기후변화소통을 위한 실천적 함의점을 제공해 줄 수 있을 것으로 기대된다.

This study examined the content and framing of climate change news coverage through topic modeling and frame analysis, aiming to derive effective climate change communication strategies. Articles from eight major Korean newspapers published between January 1, 2020, and December 31, 2024, were analyzed. Articles were initially searched using keywords such as climate change, climate crisis, carbon neutrality, and greenhouse gases. topic modeling analysis was first conducted, followed by frame analysis of articles in which climate-related keywords appeared two or more times. The analysis identified ten major topics. Fame analysis revealed that diagonostic framing accounted for 37.6% and consequence framing for 52.8%, represetning the highest proportions among the frames examined. This study is significant in that it empirically explores the interaction between agenda-setting and framing in relation to climate change issues.

본 연구는 에콰도르 대학교육에서 메타버스 도입 현황을 분석하기 위해 교수 및 학생 대상 인터뷰 자 료를 Latent Dirichlet Allocation(LDA) 기법으로 조사하였다. 주요 결과로 참여도, 사용성, 인프라 등의 핵심 주제 가 도출되어 기회와 도전 과제가 병존함을 확인하였다. 교수들은 학문 분야별 맞춤형 활용 가능성을 강조했으나, 교 강사 역량 강화, 네트워크 연결성, 기술적 한계 등을 주요 과제로 지적하였다. 학생들은 몰입형 학습의 장점을 긍정 적으로 평가하면서도 사용성과 접근성 측면에서 어려움을 호소하였다. 퍼플렉시티와 코히어런스 지표를 통해 LDA의 분석적 신뢰도를 확인한 본 연구는, 에콰도르의 디지털 격차 해소와 정책적 지원이 성공적인 메타버스 통합에 필수 적임을 시사한다.

This study applies Latent Dirichlet Allocation (LDA) to study metaverse adoption in Ecuadorian higher education through interviews with professors and students. Key findings reveal themes of engagement, usability, and infrastructure, highlighting both opportunities and challenges. Professors emphasize subject-specific customization but cite faculty training needs, connectivity issues, and technical limitations, while students value immersive benefits yet encounter usability and accessibility concerns. Perplexity and coherence values confirm the reliability of LDA in identifying critical adoption factors. The results underscore that bridging Ecuador’s digital divide and implementing supportive policies are pivotal for successful metaverse integration.

This study aims to reveal the popular features of blockchain games in the market and to identify which group of games perform better in terms of capitalization ability using a textmining based approach. We collected the information of over 2000 blockchain games including game descriptions and market performances from a popular blockchain game hub and employed topic modeling techniques to comprehensively explore the features. In total 38 topics were extracted from the game descriptions using BERTopic model and grouped into 4 themes: “blockchain games”, “game content”, “game format” and “others”. We further examined the relationships between these themes and market performances for the active 223 games. Preliminary results indicate that diverse market performances exist within each theme, Web3-focused and community-driven nature games exhibit a stronger performance. These findings provide insights for future research and help stakeholders understand key factors driving financial success in blockchain gaming.

This study conducts an in-depth analysis of user reviews from three leading metaverse platforms - Minecraft, Roblox, and Zepeto - using advanced topic modeling techniques to uncover key factors for business success. By examining a substantial dataset of user feedback, we identified and categorized the main themes and concerns expressed by users. Our analysis revealed that common issues across all platforms include technical functionality problems, user engagement and interest, payment concerns, and connection difficulties. Specifically, Minecraft users highlighted the importance of adventure and creativity, Roblox users expressed significant concerns about security and fraud, and Zepeto users focused heavily on the fairness of the in-game economy. The findings suggest that for metaverse platforms to achieve sustained success, they must prioritize the resolution of technical issues, enhance features that foster user engagement, ensure reliable connectivity, and address platform-specific concerns such as security for Roblox and payment fairness for Zepeto. These insights provide valuable guidance for developers and business strategists, emphasizing the need for robust technical infrastructure, engaging and diverse content, seamless user access, and transparent and fair economic systems. By addressing these key areas, metaverse platforms can improve user satisfaction, build a loyal user base, and secure long-term success in an increasingly competitive market.

Misinformation – content that lacks truth, but the motivation of falsehood is uncertain – on social media during a health pandemic presents a major concern for public health. Recently, the vast volume of news and information around COVID-19, which the World Health Organization refers to as “infodemic,” has led to an unprecedented increase of health misinformation woven into the online narrative about the pandemic. Online narratives, particularly on social media platforms, are critical objects of inquiry as narratives are fundamental to how people construct socially shared belief systems, and that can be the primary means to spread health misinformation online. Specifically, in the case of COVID-19, false social media narratives about the origin or unapproved or untested remedies can influence public health attitudes and behavior, potentially costing billions of dollars and numerous lives. For instance, the Centers for Disease Control and Prevention reports a sharp increase in poisoning cases related to cleaners and disinfectants in the US after the COVID-19 outbreak. While social media sites assume responsibility for moderating the platforms towards more meaningful and trustworthy content (for instance, see the joint statement from Facebook, Twitter, Google, YouTube, and others to combat fraud and misinformation about COVID-19), several fact-checking organizations across the world are also devoting their efforts to publish an evidence-based analysis of online narratives to convince audiences of its inauthenticity. However, there is a lack of research on how health misinformation could potentially impact individuals’ attitudes towards the pandemic. To that end, in this study, we develop a topology of health misinformation to understand the health behaviors of individuals.

This study systematically examines how research on AI-enabled human resource management (AI-enabled HRM) has evolved in terms of its thematic structure within organizational contexts. To this end, we applied BERTopic-based topic modeling to 797 articles on AI-enabled HRM retrieved from the Web of Science database. The analysis identified 20 topics, among which five dominant themes— adoption and effects, efficiency and strategy, managers and professionals, ethics and collaboration, and recruitment—each accounted for more than 5% of the total corpus and collectively defined the core structure of the AI-enabled HRM literature. A synthesis of the topic distributions and their temporal patterns indicates that AI-enabled HRM research has progressed from an initial focus on technological adoption and efficiency toward greater attention to organizational use, managerial interpretation, domain-specific applications, and governance-related concerns. In particular, the results suggest that the effects of AI-enabled HRM do not arise automatically from technological capabilities alone. Rather, they are contingent upon organizational preparedness, the interpretive and decision-making roles of managers and professionals, the characteristics of application domains, and management practices that support accountability and trust. This study provides a data-driven thematic map of AI-enabled HRM research that integrates previously fragmented research streams into a coherent field-level overview. The synthesis suggests that academic discussions of AI-enabled HRM are shaped not only by technological potential but also by organizational and governance conditions. Future research should move beyond general claims of effectiveness by identifying conditions and evaluating which governance arrangements support trustworthy AI-enabled HRM across application domains and stakeholder groups, including employees.