The incorporation of generative artificial intelligence (AI) in doctoral supervision signifies a transformative evolution in higher education. This has been significant, particularly within intricate and emotionally complex research such as sexuality studies. This reflective, collaborative autoethnographic study investigates the experiences of a doctoral student and her supervisor. They explored AI generative tools to enhance research processes, quality of supervision and intellectual inquiry. Anchored in Kolb’s Experiential Learning Theory and reconceptualised through an augmented experiential learning framework, the study elucidates how AI tools like ChatGPT encourage critical thinking. These tools were also used to foster methodological innovation and facilitate ethical reflexivity. Through iterative engagements, AI supported the formulation of sophisticated research questions and bolstered academic writing. It also aided emotional resilience in traversing heteronormative and interdisciplinary landscapes. The study highlights the evolving role of supervisors, not as gatekeepers but as collaborators in AI-informed learning. Significant emphasis was placed on prompt engineering, scholarly scrutiny and academic integrity. Ethical guidelines and rigorous documentation practices ensured a responsible AI application without sacrificing originality. Contribution: The findings reveal that AI-augmented supervision promotes deeper theoretical engagement and enhances self-directed learning. It also introduces new pedagogical possibilities for complex research endeavours. Nonetheless, the study also underscores the challenges of bias, overreliance and contextual insensitivity inherent in AI outputs. By suggesting actionable strategies for ethical integration, this paper contributes to emerging global discussions on AI in higher education. It presents a framework for inclusive, transformative and contextually aware supervision practices.

The article presents a systematic literature review examining how artificial intelligence methods (LLM/GenAI, computer vision, deep learning, and multi-agent architectures) accelerate mobile application development and testing within the mobile SDLC. The objective is to address a deficit of domain-specific systematisation for mobile engineering and to answer three classes of questions: which AI approaches are applied across development and QA stages, which acceleration and efficiency metrics are empirically substantiated, and which quality/security risks accompany the adoption of generative tools. The relevance is driven by the growing complexity of mobile ecosystems and the limited scalability of manual testing and script-based automation, particularly due to brittleness under GUI changes. The novelty of the review lies in synthesising evidence from 28 selected studies from 2019 to 2025, with an explicit focus on mobile-specific constraints, and in juxtaposing speed gains against a trust contour. The principal findings are as follows: AI assistants demonstrate a substantial acceleration of routine tasks (up to ~55%) and a reduction in timelines for large-scale migrations. In testing, a transition toward LLM agents is observed, enabling high coverage at both scenario and element levels, as well as resilience to UI evolution. Concurrently, a trust crisis is documented due to a significant share of vulnerabilities in generated code, dependency hallucinations, and increased technical debt, necessitating the institutionalisation of verification practices and secure-by-default principles. The article is intended to be beneficial to software engineering researchers and to mobile development/QA practitioners integrating GenAI into workflows.

This paper examines how generative artificial intelligence (AI) reproduces colonial visual tropes when tasked with representing Aotearoa New Zealand's historical past. Using OpenAI's Sora as a case study, the analysis investigates AI‐generated images prompted to depict (1) precolonial landscapes, (2) first contact between Māori and Europeans, (3) British colonial rule, and (4) Māori figures from the 1860s. Drawing on iconographic methods, the study finds that Sora‐generated outputs closely mirror dominant settler‐colonial visual conventions. These include portrayals of the land as terra nullius, colonisation as peaceful and consensual, and Māori as timeless, passive figures. Rather than offering disruptive alternatives, Sora reinforces hegemonic memory frameworks learned from biased training data. As generative AI tools become increasingly influential in shaping public understandings of the past, such depictions matter; they naturalise myths of benevolent colonisation and undermine Māori claims to political sovereignty, redress, and cultural revitalisation. The paper concludes by evaluating possible interventions at three stages of the AI development pipeline—preprocessing, model training, and postprocessing—while also highlighting the importance of AI literacy in enabling users to critically prompt and repurpose these technologies for decolonial ends.

Artificial intelligence (AI), including rapidly expanding generative AI tools, is increasingly shaping how school-age students search for information and complete learning tasks. Yet comparative evidence on AI awareness, use, and attitudes across school levels—especially among under-18 learners—remains limited in Central and Eastern Europe. Guided by the Technology Acceptance Model (TAM), this cross-sectional survey study examined Hungarian elementary and high school students’ AI use and school-related applications, focusing on perceived usefulness and willingness to use AI in learning contexts. Data were collected from 183 elementary and 127 high school students using a structured questionnaire. AI use was widespread in both groups, but marked school-level differences emerged. High school students reported more frequent and academically oriented AI use, greater reliance on AI tools when seeking help, and a stronger willingness to use AI during classroom activities. In contrast, elementary students more often relied on familiar platforms such as social media and YouTube and reported comparatively more recreational or conversational uses of AI. Across school levels, students generally viewed AI as useful and potentially engaging for learning, while many also expressed uncertainty about the reliability of AI-generated responses. These findings underscore the need for age-appropriate AI literacy education aligned with students’ developmental characteristics and digital habits, and they highlight the importance of teacher support and training to integrate AI meaningfully and responsibly into classroom practice.

This study investigates the perceptions of future English as a Foreign Language (EFL) teachers in Bali, Indonesia regarding artificial intelligence (AI), particularly generative AI tools like ChatGPT and Gemini. Recognizing the potential of AI to enhance instructional practices, the research employs an embedded mixed-method design with 150 participants, utilizing surveys and semi-structured interviews. The instruments demonstrated content validity and reliability, with quantitative data analyzed for frequency distributions and qualitative data subjected to interactive model analysis. Findings reveal a predominantly positive perception of generative AI among EFL students in a blended learning context, who recognize its utility while expressing concerns about implementation. As prospective educators, participants are beginning to contemplate pedagogical strategies for integrating AI into their future classrooms. The study highlights the urgency of establishing AI policies grounded in critical digital pedagogy principles to optimize educational experiences. This research contributes to the discourse on AI in education, emphasizing the need for context-specific approaches to leverage AI’s potential while addressing pedagogical, ethical, and technical challenges.

Generative AI-enhanced nomogram predicts left without being seen among ambulance-transported emergency department patients.

In the context of global efforts to preserve cultural diversity and the expanding role of artificial intelligence in creative industries, this study presents an artificial intelligence–driven framework for extracting and innovatively reinterpreting cultural factors embedded in the costume patterns of the Miao people in Qiandongnan, China. By integrating ethnographic fieldwork with computational analysis, the research systematically examines the visual, semantic, and symbolic dimensions of Miao pattern culture. A comprehensive database (MiaoPattern-DB) of 1,600 high-resolution images of embroidery, weaving, and silver ornaments was compiled from 16 Miao-inhabited counties, including Kaili, Leishan, Taijiang, Liping, and Congjiang. We applied multimodal fusion techniques—feature detection with YOLOv9, hierarchical representation using Swin Transformer, and cross-modal semantic alignment via CLIP—to identify five principal cultural factors (pattern, structure, color, semantics, and morphology) and seventeen sub-factors. The database maps relationships among visual form, cultural meaning, and regional identity, and these relationships were further formalized in a Neo4j-based knowledge graph. Generative design tools (ComfyUI and Stable Diffusion) were employed to reconstruct traditional motifs and translate them into contemporary design products, enabling creative revitalization of ethnic visual heritage. This study bridges computational design and intangible cultural heritage preservation, offering a scalable methodological paradigm for technology-enabled research in ethnic arts and contributing to the sustainable transmission and contemporary renewal of Miao visual culture.

Sulphur (S) is an essential secondary macronutrient that plays a wonderful role in measuring the oil content, yield, and quality of crops. It is integral part of protein synthesis, enzyme activation, oil biosynthesis, chlorophyll formation, and sulphur-containing production of secondary metabolites like glucosinolates. In nowadays, sulphur deficiency has emerged as a major nutritional restriction in oilseed-based agroecosystems due to exhaustive cropping, decreasing atmospheric sulphur emission, extensively use of sulphur-free fertilizers, and reducing organic inputs. Soil sulphur exists in multiple organic and inorganic forms that undergo continuous biogeochemical transformation processes broadly controlled by soil microbes. Sulphur-cycling based microbiomes mediate mineralization, immobilization, oxidation, and reduction, thereby regulating sulphur availability and plant acquisition. Beyond nutrition, sulphur plays a vital role in enhancing resilience to stresses through regulation mechanism of redox homeostasis, antioxidant and induce systemic defense systems. Recent advancement of in next-generation technology, especially next-generation sequencing (NGS), multi-omics, metagenomics, transcriptomics, and precision nutrient management strategies, have revolutionized the studying of sulphur-cycling microbial community assembly and their important functions in oilseed cropping. This review highlights current understanding on sulphur biogeochemistry, sulphur-cycling based microbiomes, stress resilience systems, and advancing next-generation technology, exposing their integrative potential for enhancing and improving oilseed crop yield, quality in sustainable agriculture, and eco-sustainability.

ABSTRACT This study examines how AI image generation tools influence design thinking, creativity, and self-reflection in architectural education. Using a 15-week integrated studio with fourth-year undergraduates, it analyzes AI engagement and cognitive shifts across conceptual, developmental, and detailed design stages through mixed methods, including Creativity Support Index (CSI) analysis and comparisons between student self-assessments and tutor evaluations. The studio adopted a process-oriented pedagogy emphasizing iterative feedback, stage-based design logs, and a collaborative whiteboard platform. Leveraging AI’s capacity for low-cost failure – rapid, low-risk experimentation with multiple alternatives – it accelerated experimental loops and reinforced the cycle of outcome – interpretation – problem redefinition. AI fostered divergent idea generation, nonlinear thinking, and externalization of concepts while challenging conventional typologies of program, form, and materiality. A key pedagogical aim was to channel AI’s unpredictability into productive learning opportunities. Over time, students shifted from perceiving AI as uncontrollable to engaging with it as a design partner requiring interpretation and adaptation. Findings suggest that AI integration under this framework enhanced self-reflection, critical thinking, and creative decision-making. The study repositions AI from a mere visualization tool to a cognitive catalyst that broadens design thinking and supports co-discovery, proposing strategies for cultivating reflective designers in AI-integrated studios.

Simulation-based learning (SBL) has become standard practice in educating health care professionals to apply their knowledge and skills in patient care. While SBL has demonstrated its value in education, many educators find the process of developing new, unique scenarios to be time-intensive, creating limits to the variety of issues students may experience within educational settings. Generative artificial intelligence (AI) platforms, such as ChatGPT (OpenAI), have emerged as a potential tool for developing simulation case studies more efficiently, though little is known about the performance of AI in generating high-quality case studies for interprofessional education. This study aimed to generate geriatric case scenarios across 5 AI platforms by a transdisciplinary team and systematically evaluate them for quality, accuracy, and bias. Ten geriatric case studies were generated using the same prompt from 5 different generative AI platforms (N=50): ChatGPT, Claude (Anthropic AI), Copilot (Microsoft), Gemini (Google), and Grok (xAI). An evaluation tool was developed to collect evaluative data to assess the content and quality of each case, sociodemographic data of the featured patient, the appropriateness of each case for interprofessional education, and potential bias. Case quality was evaluated using the Simulation Scenario Evaluation Tool (SSET). Each case was evaluated by 3 team members who had experience in SBL education. Assessment scores were averaged, and qualitative responses were extracted to triangulate patterns found in the quantitative data. While each AI platform was able to generate 10 unique case studies, the quality of studies varied within and across platforms. Generally, evaluators felt that the content in the cases was accurate, though some cases were not realistic. Some patient populations and common conditions among older adults were underrepresented or absent across the cases. All cases were set within traditional health care settings (eg, hospitals and routine medical visits). No cases featured home-based care. Based on the average SSET scores, reviewers assessed ChatGPT to be the highest overall performer (mean 3.27, SD 0.45, 95% CI 2.95-3.59) while Grok received the lowest scores (mean 1.61, SD 1.26, 95% CI 0.71-2.51). Platforms performed best at generating learning objectives (mean 3.35, SD 1.08, 95% CI 3.04-3.65) and lowest on their ability to describe supplies and materials that may be available in hypothetical scenarios (mean 1.27, SD 0.84, 95% CI 1.03-1.51). This study is the first to systematically evaluate and compare multiple generative AI platforms for case study generation using a validated assessment tool (SSET) and provides evidence-based guidance on selecting and using AI tools effectively. The findings offer practical direction for educators navigating available generative AI tools to enhance training for health care professionals, including specific strategies for prompt engineering that can improve the quality of SBL resources in interprofessional education. These insights enable educators to leverage AI capabilities while maintaining pedagogical rigor.

Generative AI tools like ChatGPT are rapidly reshaping how students access and engage with knowledge. While these technologies can support learning, their influence depends heavily on students’ epistemic beliefs—their views about the nature, source, and justification of knowledge. Research has shown that epistemic orientations shape whether learners critically interrogate or passively accept information, yet little is known about how this plays out in AI-mediated contexts, particularly in higher education settings outside the West. This study addresses that gap by examining how undergraduate students in Oman engage with ChatGPT through the lens of Hofer and Pintrich’s (1997) epistemic belief framework. Using a qualitative interpretive approach, semi-structured interviews were conducted with thirteen undergraduate students from diverse disciplines to explore how their epistemic beliefs influenced their interactions with ChatGPT. The thematic analysis revealed four engagement patterns: Naïve Believers, Over-Reliant Users, Strategic Skeptics, and Critical Evaluators. These patterns reflect not only individual beliefs but also cultural norms, educational experiences, and workload pressures. The findings advance AI–epistemic beliefs scholarship by showing how cultural context and academic conditions shape trust, verification, and reasoning with AI outputs. The study argues for embedding AI literacy into curricula through strategies such as argumentation-based learning, claim–evidence coordination, and metacognitive scaffolding to foster critical digital literacy and prepare students for an AImediated knowledge environment.

Effective debriefings in simulation-based education require accurate observation of team interactions, yet facilitators face challenges due to cognitive load, observer bias, and the complexity of team dynamics. Generative artificial intelligence (AI) tools offer a potential means to support this process by analyzing verbal communication and providing structured feedback. This study explored how AI tools can contribute to teamwork observation and debriefing in immersive medical simulations. We conducted a qualitative, exploratory study using thematic analysis of simulation participants' and debriefers' experiences with AI-generated teamwork reports. Forty-one participants (anesthesia nurses, residents, and attendings) participated in immersive scenarios at the University Hospital Zurich simulation center. Verbal interactions were transcribed with AI-assisted speech recognition and analyzed using two large language model-based systems (Isaac and ChatGPT-4o) guided by a prompt based on the Team-FIRST framework. Structured reports were generated for each scenario and reviewed by four simulation experts. Semi-structured interviews captured learners' perspectives on being observed by AI tools. A total of 26 AI-generated reports and 27 learner interviews were analyzed. Experts valued the detailed transcripts and illustrative quotes, which supported structured feedback and captured observations that might otherwise be missed. Limitations included inaccuracies in categorization, misattribution of speakers, overly generalized interpretations, and the absence of contextual or nonverbal information. Learners expressed openness and optimism about AI's potential benefits: efficiency, objectivity, and enhanced perception, while also raising concerns about transparency, data protection, interpretation errors, and risks of overreliance. Both groups emphasized the necessity of human oversight. Generative AI tools can complement simulation debriefings by structuring communication data and highlighting teamwork patterns, supporting reflective practice. Current limitations highlight the need for multimodal approaches, refined prompting strategies, and integration with expert facilitation to ensure AI functions as a support tool rather than a replacement in simulation-based education. BASEC ID: Req-2024-01642.

Abstract The rapid growth of digital technologies has transformed the way businesses communicate with customers and generate sales leads. Social media platforms have emerged as one of the most effective tools for business lead generation due to their extensive reach, targeting capabilities, and cost efficiency. Among various platforms, Facebook and Instagram are widely used by businesses of all sizes. This research paper aims to analyse and compare the role of Facebook and Instagram in business lead generation with special reference to Amravati city. The study adopts a descriptive research design and is based on both primary and secondary data. Primary data was collected through a structured questionnaire from business owners, entrepreneurs, and marketing professionals operating in Amravati. Secondary data was sourced from research journals, books, reports, and online publications. The study evaluates these platforms based on parameters such as reach, engagement, cost-effectiveness, lead quality, and conversion rate. Statistical tools like percentage analysis and hypothesis testing have been used for data interpretation. The findings reveal that Facebook is more effective in generating a higher volume of leads, while Instagram is superior in terms of engagement and brand visibility. The study concludes that an integrated use of both platforms can significantly enhance business lead generation. The paper also provides practical recommendations for businesses to improve their social media marketing strategies. This study compares Facebook and Instagram for business lead generation in Amravati, highlighting Facebook’s reach and Instagram’s engagement. Keywords Social Media Platforms, Business Lead Generation, Facebook, Instagram, Digital Marketing, Amravati

Critical Thinking in Vietnamese EFL Students’ Use of Generative AI Tools for Academic Learning: Qualitative Insights from a Brief Intervention

ABSTRACT Adopting generative artificial intelligence tools (GAITs) in higher education leads to opportunities that require a better understanding of the motivations influencing student usage behaviour. Thus, this study introduces a comprehensive research model, which integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) with trust and risk perceptions to examine students' intention to use GAITs in education settings and its effects on actual use behaviour. An online survey was conducted with 1122 university students. The structural results indicate that students' perceived trust enhances performance expectancy and effort expectancy, fostering greater acceptance, while perceived risk negatively affects effort expectancy and social influence. Effort expectancy is one of the notable determinants of students' behavioural intention, highlighting the significance of usability and accessibility in AI-driven learning environments. These findings offer both practical and academic insights for designing AI-driven educational tools that examine usability, trust, and ethical considerations.

The application of Generative Artificial Intelligence (GenAI)—defined as a class of AI systems capable of autonomously generating new content such as images, texts, and design solutions based on learned data patterns—has become increasingly widespread in creative design. By supporting ideation, rapid trial-and-error, and data-driven decision-making, GenAI enables designers to explore design alternatives more efficiently and enhances human–computer interaction experiences. In design practice, GenAI functions not only as a productivity-enhancing tool but also as a collaborative partner that assists users in visual exploration, concept refinement, and iterative development. However, users still face a certain learning curve before effectively adopting these technologies. Within the framework of human-centered artificial intelligence, contemporary design practices place greater emphasis on inclusivity across diverse user groups and on enabling intuitive “what-you-think-is-what-you-get” interaction experiences. From a sustainable design perspective, GenAI’s capabilities in digital simulation, rapid iteration, and automated feedback contribute to more efficient design workflows, reduced collaboration costs, and broader access to creative participation for users with varying levels of expertise. These characteristics play a crucial role in enhancing the accessibility of design resources and supporting the long-term sustainability of creative processes. Focusing on the context of China’s digital design industry, this study investigates the application of GenAI in design workflows through an empirical case study of Zhitu AI, a generative design tool developed by Beijing Didi Infinity Technology Development Co., Ltd. The study conducts a literature review to outline the role of GenAI in visual design processes and employs observation-based experiments and semi-structured interviews with users of varying levels of design expertise. The findings reveal key pain points across stages such as prompt formulation, secondary editing, and asset generation. Drawing on the Kano model, the study further identifies potential design opportunities and discusses their value in improving efficiency, supporting non-expert users, and promoting more sustainable and inclusive design practices.

Abstract BACKGROUND The Lincoln Ostomy Output Consistency Scale for jejunostomy, ileostomy and colostomy (LOOCS) is a medical instrument providing standardized stool descriptors for the ostomate community (Acad J Gastroenterol Hepatol 2021; 3:554). A limitation of many medical instruments is language accessibility. Language barriers in a clinical setting can hinder quality of care, lead to miscommunications, and decrease patient and physician satisfaction. Understanding these barriers, the Bristol Stool Form Scale has been translated in over 40 languages. Traditional translation involving human experts is often time-consuming and resource-intensive. Neural machine translation and generative artificial intelligence tools pose an opportunity to enhance translation efficiency. This project aimed to increase accessibility of the LOOCS. METHODS Using Ethnologue data, the top 100 most spoken languages were selected for translation, as well as several languages requested by bilingual speakers. The translation tools used included Google Translate, DeepL, ChatGPT, and MistralAI. Two separate translation tools were selected, labeled Tool A and Tool B, and four routes of forward and backward translation were used to determine the most effective and accurate route. The translation process included 7 steps. Step 1 involved preparing key phrases. Step 2 was forward translation using Tools A and B. Step 3 compared the two tool’s output. Step 4 was backward translation to English using Tools A and B on their respective forward translations. Step 5 was comparison to the original LOOCS. If the backward translation did not adequately represent the original LOOCS, steps 2-5 could be performed using different translation tools. Step 6 was selection of the most accurate translation based on likeness to the original content, fluency, and medical appropriateness. Step 7 (optional) entailed human validation. If verification of the AI translation by a human speaker is deemed invalid, steps 2-7 can be repeated using different translation tools. RESULTS The LOOCS was translated into 116 languages. Currently, 14 translations have been evaluated by bilingual speakers with 13 deemed accurate. According to the Urdu speaker, none of the AI tools produced an accurate Urdu translation. ChatGPT was deemed most accurate in 82 target language translations, Google Translate in 22, MistralAI in 7, and DeepL in 5. ChatGPT was able to translate 8 languages not able to be translated by any other tool, including Farsi and Karen. CONCLUSION This project demonstrated the utility and effectiveness of AI translation tools in a clinical healthcare setting. Though it appears AI translation tools can successfully translate the LOOCS, which uses simple and concise phrases, further research would be needed to determine their effectiveness in the translation of longer, more scientifically complex texts.

Generative AI tools have introduced literacy demands in English as a Foreign Language (EFL) writing instruction that existing frameworks do not fully address. This narrative review synthesizes empirical and theoretical studies published between 2020 and 2025 to examine prompt literacy as a core dimension of AI literacy in EFL writing pedagogy. Drawing primarily on research from Asian EFL contexts, the review pursues three objectives: conceptualizing prompt literacy, examining the linguistic and cognitive challenges EFL learners encounter when formulating prompts, and identifying pedagogical approaches to integrate prompt literacy into writing instruction. Analysis across the reviewed literature indicates that effective prompting requires linguistic precision, rhetorical awareness, and metacognitive monitoring rather than technical procedures alone. Current AI literacy frameworks offer limited guidance on text-based interaction with generative systems. Language proficiency shapes prompting practices: lower-proficiency learners rely on generic requests, while higher-proficiency learners engage in strategic refinement. Cognitive load from simultaneous language formulation and AI interaction may constrain performance, though metacognitive support mitigates these effects. Four pedagogical dimensions emerge: systematic instructional frameworks, iterative interaction patterns, teacher professional development requirements, and assessment approaches addressing both processes and products. The review argues that prompt literacy functions as a foundational competency for effective AI-mediated composition in EFL contexts.

Abstract This research seeks to empirically explore the mediating role of governance quality on the nexus between generative artificial intelligence and macro-level financial performance. This empirical study employs cross-country panel data from 2021 to 2024, encompassing an analytical sample of nine emerging markets. Initially, various static panel data techniques were employed. Afterward, to alleviate potential endogeneity bias and support the reliability of the results, the one-step system GMM approach was implemented. The results reveal that while fixed-effects estimates indicate a partial mediating role of governance quality in the nexus between generative artificial intelligence and macro-level financial performance, the dynamic system GMM estimations support full mediation once endogeneity and path dependence are controlled for. Taken together, these findings underscore the central role of governance quality as the primary transmission channel through which AI readiness translates into macro-level financial performance. The novelty of this research is reflected in its application of mediation techniques to elucidate the relationship between generative AI and macro-level financial performance. Moreover, this study pays rigorous attention to offer multidimensional insights for regulators and policymakers to design solid regulatory frameworks that enhance the adoption of generative AI tools, uphold high governance standards, and ultimately strengthen macro-level financial performance.

Purpose This study critically examines the epistemological and ethical implications of artificial intelligence in digital archival systems. It introduces the concept of the “algorithm of silence” to interrogate how automated processes reproduce historical exclusions through biased data structures. Through a systematic literature review and conceptual modeling, the research proposes a framework for ethically responsive archival curation, grounded in auditability, explainability, reversibility and informational justice. The article aims to reconceptualize digital archives as contested epistemic spaces, advocating for inclusive algorithmic governance and the redefinition of curatorial authority in the age of computational memory. Design/methodology/approach This research employs a systematic literature review across interdisciplinary databases, integrating conceptual modeling and visual schematization to interrogate algorithmic bias in digital archival systems. Using Boolean logic and strategic refinement, 28 peer-reviewed sources were selected and analyzed via Rayyan. Generative AI tools were utilized to construct visual frameworks that enhance interpretative clarity and methodological transparency. The approach foregrounds auditability, reproducibility and epistemic rigor, positioning artificial intelligence not as a neutral tool but as a cognitive extension subject to ethical scrutiny within archival epistemologies. Findings The study identifies three critical findings: (1) the “algorithm of silence” as a conceptual tool to expose how AI systems perpetuate historical exclusions under the guise of neutrality; (2) a paradigmatic rupture in archival mediation, now governed by opaque algorithmic processes; and (3) the imperative for ethical-algorithmic governance grounded in auditability, explainability, reversibility and cultural inclusiveness. These insights reframe digital archives as contested epistemic spaces and advocate for a justice-oriented curatorial paradigm responsive to sociotechnical asymmetries. Originality/value This article offers a novel theoretical framework, the “algorithm of silence”, to critically interrogate how artificial intelligence perpetuates archival exclusions. By integrating digital ethics, critical epistemology and information science, it reconceptualizes digital archives as contested spaces of memory and power. The study's originality lies in its ethical reframing of algorithmic curation, challenging technocratic neutrality and advocating for inclusive, auditable and culturally responsive archival models. It contributes to the emerging field of critical archival AI by proposing a paradigm of curatorial justice in the age of computational memory.