Kim, writing from Atlanta at the age of seventy-six and drawing on a formation that spans Korea and America, Christian theology and Chinese thought, describes working with AI as being surrounded by thirty exceptionally capable doctoral students. One offers a term, another restructures the argument, a third suggests what the writer really means, and others return carrying notes from Peirce, Wittgenstein, Confucius, or contemporary sociolinguistics. The experience is exhilarating, and Kim does not deny it. But the metaphor breaks down at a crucial point. Thirty real doctoral students are thirty centers of resistance. One will dislike the argument, another will say Peirce has been misunderstood, a third will accuse the writer of romanticizing East Asia. Someone, sooner or later, will deliver the sentence every scholar needs to hear: the work is beautiful, and it is also wrong.

AI can imitate such objections, but imitation of plurality is not plurality. Current systems, Kim argues, inhabit overlapping textual worlds and share habits of exposition. They are gifted at coherence. Give them two distant ideas and they will often find a bridge before asking whether one ought to exist. This, in the essay’s framing, may be generative AI’s characteristic epistemic danger: the texture of consensus without the evidential weight of consensus. Five machines admiring an idea do not constitute peer review, yet five machines can help that idea put on a suit and tie by afternoon. The old difficulty of scholarship was that a living question might die before a scholar could pursue it. The new difficulty is that a foolish question can become a polished manuscript before dinner.

The essay’s sharpest formulation concerns what AI has actually democratized. Public discussion often claims that these tools have democratized intelligence. Kim counters that they have done something more unsettling: subsidized the appearance of intelligence. Eloquence can now be rented. Erudition can be rented. Structure can be rented. Even the external appearance of judgment can be rented by the hour, or by the token. This does not mean the renter is stupid, and Kim is emphatic on that point, describing a daily practice of using AI gratefully as an extraordinary workshop. A question that once required weeks of preparation can now be tested while it is still warm. Confucian zhengming, the rectification of misalignment between name and reality, can be placed beside Peircean semiotics, Wittgenstein can be invited to disturb the arrangement, and a sociolinguist’s objection can be summoned in seconds. The library is illuminated at three in the morning. The translator is awake.

Speed, Kim acknowledges, preserves the temperature of a question, and that is no small gift. But speed also creates a new temptation: linguistic completion before intellectual completion. The machine closes sentences, fills conceptual gaps, smooths awkward transitions, and reconciles tensions with extraordinary politeness. What once remained visibly unresolved can now be made to look settled. Fluent closure has become almost free, and the institutional consequences follow directly. When half-formed thoughts can be dressed for submission in an afternoon, journals inherit part of the cost that AI has removed from authors. Editors and peer reviewers become unpaid inspectors of premature coherence. The manuscript becomes cheaper to produce precisely when judgment becomes more expensive to supply.

Kim’s personal response is to cultivate a virtue that sounds almost trivial but carries considerable technical weight: the ability to say almost. Almost right. Almost clear. Almost mine. Almost worthy of publication. The discipline involves a battery of resistant responses to fluent output: the draft is too smooth, the problem has been Westernized, the concepts should not be reconciled yet, the word is technically correct but existentially wrong, the English has been improved while the thought has been weakened, the concept is right but the center of gravity is wrong. Kim increasingly suspects that this capacity will matter more than brilliant prompting. Prompt engineering, in the essay’s wry framing, is becoming a minor priesthood, complete with incantations and claims to esoteric skill. Learn the spells by all means, but the deeper human competence is recognizing when an elegant answer has betrayed the question.

The problem is especially acute for thinkers who work across languages and civilizations. A thought may arrive with a Korean emotional temperature, a Chinese conceptual skeleton, a Christian theological horizon, and a Western philosophical question, and academic English asks it to pass through customs. AI is a magnificent customs broker, Kim writes. It can move the cargo quickly. It can also quietly replace the cargo. To guard against that substitution, Kim has adopted a deliberately primitive discipline: before inviting AI into an important thought, two or three ugly sentences of the original intuition are preserved on the page, sometimes in Korean, sometimes grammatically incomplete. The purpose is not legal ownership, since the mythology of the solitary author was always exaggerated and people think with teachers, enemies, books, languages, lovers, traditions, and ghosts. The purpose is intellectual provenance, the ability to trace a refined phrase back to the crude perception from which it grew.

That provenance question is illustrated with a phrase from Kim’s own writing, indexicality without occupancy, and the Korean intuition behind it: AI speaks like a person of authority, but there is no one behind the words to bear responsibility; there is a sign, but no person there. The important question, Kim argues, is not whether the machine offered the elegant phrase or whether the human refined it through forty exchanges, but whether the language clarified the original intuition or replaced it. Formal governance will not settle this. Academia will need disclosure rules for AI use, publishers will invent policies, and universities will produce forms, all necessary and all predictably bureaucratic. The more intimate problem will remain unregulated: whether scholars will still recognize their own thoughts after the machines have improved them.

Kim is less worried that AI will become intelligent than that human beings will become satisfied with the signs of intelligence, and academia already rewards those signs: a polished abstract, correct vocabulary, a symmetrical framework, six boxes in a diagram, a confident conclusion beginning with a call for fundamental reconceptualization. AI did not invent academic vanity, the essay observes; it simply industrialized some of its preferred costumes. The antidote is not nostalgia, and Kim explicitly refuses to dismiss a technology that enables what the essay calls speculative adjacency, bringing distant bodies of thought into rapid, exploratory contact. When the hallways become short enough, the architecture of thinking changes. But the cheaper inquiry becomes, the more expensive discernment must become. Not every connection deserves development, not every curiosity deserves a paper, and not every publishable paragraph contains a thought worth publishing. In the old economy of scholarship, scarcity imposed crude discipline through limited books, research trips, conversations, and years. AI has removed many of those restraints, and what is needed now, Kim writes, is intellectual fasting.

The essay closes on a question that reframes the entire debate. Rather than asking whether AI will make us more intelligent, a question that flatters machine and human alike, Kim asks whether, surrounded by nearly free fluency, we are becoming more discriminating. Are our questions improving? Can we recognize a false harmony? Can we preserve an inelegant intuition long enough to discover what it means? Can we throw a curveball that breaks the window of premature coherence and then inspect the glass? Above all, can we still say almost when the machine gives us exactly the beautiful answer we hoped to hear? That small word, Kim suggests, may become one of the last defenses of formed intellect. AI has made fluency cheap. Judgment is now expensive, and we should spend accordingly.

Subject of Research: The epistemic impact of generative AI on human judgment, intellectual provenance, and scholarly writing

Article Title: The ability to say “almost”

Article References: Kim, S. (2026). The ability to say “almost”. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03297-y

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03297-y

Keywords: generative AI, artificial intelligence, epistemology, academic writing, peer review, judgment, prompt engineering, intellectual provenance, AI & Society, scholarship, language models, critical thinking

The study, conducted by Fei Song and Michael Dunn of the Centre for Biomedical Ethics at the National University of Singapore together with Zhenni Hu of Nanjing Normal University, examined 43 articles indexed in the Chinese Social Sciences Citation Index along with four policy reports. The analysis reveals that Chinese scholars have made substantial contributions to policy and governance discussions around trustworthy AI, but that these contributions remain conceptually scattered, with different disciplines approaching the question of trust from radically different angles. The researchers set out to synthesize this dispersed knowledge into a coherent conceptual account, and in doing so they arrived at a framework that treats trustworthiness not as a property of algorithms alone but as an emergent condition spanning technology, institutions, and human relationships.

The first layer of the framework concerns technical foundations. This is the layer most familiar to engineers and computer scientists, encompassing properties such as reliability, robustness, transparency, explainability, fairness, and safety. Chinese-language scholarship in this domain is extensive and spans a striking range of applications. Researchers have examined explainable learner models as the technical key to trustworthy personalized learning, explored blockchain-driven approaches to financial security intelligence, and investigated how algorithmic transparency in personalized recommendation systems shapes users’ perceptions of trustworthiness. Work on data annotation governance has highlighted backstage risks in the labor-intensive processes that feed machine learning systems, while archival science perspectives have been brought to bear on the question of algorithm provenance, asking how the origins and histories of algorithms can be documented and verified. These contributions demonstrate that the technical layer of trustworthiness is not merely a matter of model architecture but extends deep into the data pipelines, documentation practices, and supply chains that underpin AI systems.

The second layer addresses governance and regulatory arrangements. Here the Chinese literature is particularly distinctive, reflecting the country’s active policy discourse on AI regulation. Scholars have assessed pathways for trustworthy AI legislation, explored innovation-friendly regulatory designs that balance safety with technological development, and analyzed governance practices in terms of models, actors, objects, and tools. Policy documents play an important role in this layer, including the Ministry of Science and Technology’s 2019 governance principles for developing responsible AI, a white paper on trustworthy artificial intelligence produced by the China Academy of Information and Communications Technology, and more recent industry ecosystem reports and governance surveys. The review shows that Chinese scholars have been grappling with the same fundamental questions that animate regulators in Europe and the United States: how to translate abstract ethical principles into enforceable rules, how to assign liability when AI systems fail, and how to design oversight mechanisms that keep pace with rapidly evolving technology. One striking example is legal scholarship on liability when human–machine co-piloting fails, examining the negligence of safety operators through the lens of the principle of reliance.

The third layer, and the one the authors argue is most often neglected in purely technical accounts, concerns the conditions of human–AI interaction under which trust is formed and calibrated. The Chinese-language literature here is remarkably rich, drawing on empirical psychology, communication studies, and philosophy. Researchers have studied trust in automated vehicles, safety trust in intelligent domestic robots, and human–AI mutual trust in the era of artificial general intelligence. A dual-pathway model of trust calibration distinguishes between trust dampening and trust promoting mechanisms, offering a nuanced picture of how users adjust their reliance on machines. Other studies examine how anthropomorphism affects trust in AI agents, with a meta-analysis synthesizing evidence on how humanlike features in AI systems influence trust and how contextual factors moderate that effect. Work on large language models has explored how machine hallucinations impair human–machine trust and what recalibration mechanisms might restore it, a question of obvious urgency as chatbots and AI assistants become ubiquitous.

What emerges from this synthesis is the insight that these three layers are not independent. An AI system can be technically excellent, with high accuracy and robust performance, and still fail to be trustworthy if the governance arrangements around it are inadequate or if the conditions of human interaction systematically miscalibrate trust. Conversely, strong institutions and well-designed interaction conditions cannot compensate for fundamentally unreliable technology. The authors’ central claim is that an AI system is trustworthy only if it meets the requirements at all three layers simultaneously. This conjunctive structure has practical implications: it means that trustworthiness cannot be certified by any single test, audit, or label, but requires integrated assessment across the full stack from model internals to regulatory environments to the design of the encounters between humans and machines.

The framework also speaks to a long-running philosophical debate about whether trust in AI is even coherent. Some Western philosophers argue that trust, properly understood, requires mutual expectations and moral agency that machines cannot possess, and that what we call trust in AI is really a form of reliance or confidence. Others contend that it is entirely possible to trust medical AI systems and that denying this misunderstands the functional role of trust in clinical decision-making. The Chinese literature adds distinctive voices to this debate, with scholars asking directly whether artificial intelligence can serve as a trustee and defending the notion of trustworthy AI from a logical perspective. The review also engages with a network account of trustworthy AI, which holds that trustworthiness is distributed across the networks of actors, artifacts, and institutions surrounding an AI system rather than residing in the system alone. The three-layer framework can be read as a structured elaboration of this network idea, specifying the technical, institutional, and interactional nodes that such a network must contain.

The methodological significance of the study lies partly in its source base. Scholarship published in Chinese remains largely absent from mainstream academic debates, a gap reinforced by publication incentives that push Chinese humanities and social science researchers toward international English-language venues. By drawing on the Chinese Social Sciences Citation Index, the authors accessed a body of work that includes empirical studies using artificial neural networks and Q methodology to map the dimensions of human–AI trust, philosophical analyses grounded in Confucian traditions, and domain-specific investigations in education, journalism, healthcare, and finance. The result is a more globally representative picture of what the research community actually knows about trustworthy AI. The authors also note distinctive features of the Chinese context, including a practice-oriented view of science and technology that shapes how trustworthiness is framed, and a governance culture in which state-led principles, industry white papers, and academic analysis interact closely.

The study concludes by identifying three directions for future research. First, the conceptual fragmentation of the field calls for continued theoretical work to integrate technical, governance, and interactional perspectives into unified accounts of trustworthiness. Second, the rapid emergence of generative AI and large language models creates new empirical challenges, from hallucination-induced trust erosion to the trust dynamics of human–machine dialogue, that demand updated frameworks. Third, cross-linguistic and cross-cultural comparative research is needed to test whether the three-layer structure holds across different scholarly traditions and regulatory cultures, or whether trustworthy AI means genuinely different things in different contexts. As AI systems increasingly mediate news consumption, medical diagnosis, education, and financial services, the stakes of getting trustworthiness right could hardly be higher. This review makes the case that answering the question well requires listening to conversations that have been happening in Chinese all along, and that the resulting framework, grounded in arguments advanced in the Chinese context, offers a more robust conceptual account of how disparate claims about trust fit together than either technical or governance approaches alone can provide.

Subject of Research: Trustworthy AI beyond the technical: a three-layer framework informed by Chinese-language scholarship

Article Title: Trustworthy AI beyond the technical: a three-layer framework informed by Chinese-language scholarship

Article References: Song, F., Hu, Z., & Dunn, M. (2026). Trustworthy AI beyond the technical: a three-layer framework informed by Chinese-language scholarship. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03366-2

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03366-2

Keywords: Trustworthy, beyond, technical, three-layer, framework, informed, Chinese-language, scholarship, scientific research

The methodological design of the study is itself notable for its rigor in a field where such designs are often missing. The researchers used a sequential mixed-methods approach, labeled QUAN-qual, meaning the quantitative phase came first and directly informed the qualitative phase that followed. In the quantitative phase, the team surveyed 71 faculty members who held postgraduate training in medical education, drawn from institutions across Pakistan, and achieved a striking 93.4 percent response rate. Such a high rate matters because it reduces the risk that the survey reflects only the views of the most motivated or most frustrated respondents. The qualitative phase then used two focus group discussions, one with junior faculty and one with senior faculty, to dig beneath the survey numbers and understand the lived experience behind them.

The results reveal a generational split in the obstacles that matter most. Among junior faculty, the primary challenges in conducting medical education research were lack of time, lack of training, and lack of support. These are, in many respects, the classic barriers reported by early-career researchers everywhere, but in Pakistan they take on a particular intensity because medical educationists often carry heavy teaching and administrative loads with no protected time carved out for scholarship. For senior faculty, the picture shifted: their main challenges were lack of funding for publication, the absence of well-reputed local journals devoted to medical education research, and a pervasive culture of mistrust that undermines collaboration. In other words, even those who had survived the early career bottleneck found themselves facing a publishing ecosystem and a professional culture that offered little reward or scaffolding.

The qualitative analysis distilled these findings into overarching themes that applied to both junior and senior faculty. At the systemic level, participants described the absence of protected time and inadequate institutional support as structural features of their working lives, not temporary inconveniences. At the individual level, they acknowledged gaps in their own preparation, including insufficient training in research methods and in the theoretical frameworks that give educational scholarship its analytical backbone. This dual diagnosis is important because it suggests the problem cannot be fixed by training alone or by institutional reform alone; the barriers operate at multiple levels simultaneously and reinforce one another. A well-trained educationist with no protected time produces nothing, while an educationist with generous time but no methodological grounding produces work that journals will not accept.

The context in which these findings sit is a global one. Medical education research is overwhelmingly produced in high-income countries, with limited contribution from low- and middle-income countries such as Pakistan. This asymmetry matters for science as a whole because educational questions in Lahore or Karachi differ in funding realities, class sizes, student demographics, and cultural expectations from those in Boston or London. When the evidence base for how to teach medicine is built almost entirely in resource-rich settings, its findings travel poorly to the places where most of the world’s medical students actually study. Pakistan, as the study’s authors note, has made departments of medical education mandatory in every medical and dental college, creating a large pool of trained professionals whose insights are, in principle, exactly what the global literature lacks.

The study was conducted with formal ethical oversight, receiving approval from the Ethics Review Committee at Aga Khan University Hospital under study ID 2022-7738-23209, with informed consent obtained from all participants and the research conducted in accordance with the ethical principles of the Declaration of Helsinki. The researchers report that the work received no specific grant from any funding agency in the public, commercial, or non-profit sectors, a detail that is perhaps telling in itself: even a study about the lack of research funding had to proceed without it. The authors declare no competing interests, and the article is published open access under a Creative Commons license, making the full findings freely available to the very community of educationists whose circumstances it documents.

What makes the study’s conclusions actionable is the specificity of its recommendations. The authors argue that scholarship in medical education in Pakistan is hindered by individual, institutional, and systemic factors, and they direct their prescriptions accordingly. Institutions, they conclude, need to focus on providing protected research time, bolstering training in medical education research, and establishing robust mentorship and collaborative networks to strengthen the research culture. Protected time, in particular, is a recurring theme in faculty development research worldwide, but the Pakistani data give it local weight: when 93 percent of trained educationists in a national survey point to time as a barrier, the case for institutional scheduling reforms becomes difficult to ignore.

The mistrust that senior faculty identified as a barrier to collaboration deserves particular attention, because it points to a problem that no amount of funding can solve on its own. Collaborative research depends on norms of credit-sharing, transparent authorship, and confidence that intellectual contributions will be respected. Where those norms are weak, researchers retreat into silos, small studies go unpublished, and the cumulative building of knowledge stalls. Similarly, the absence of well-reputed local journals means Pakistani educationists face a choice between competing in high-income-country journals with rejection rates shaped by different priorities, or publishing in venues with limited visibility. A credible regional publication infrastructure, the findings imply, is not a vanity project but a structural prerequisite for a self-sustaining research community.

The implications extend well beyond Pakistan’s borders. The World Health Organization and international medical education bodies have repeatedly emphasized that health workforce education in low- and middle-income countries must be evidence-driven rather than imported wholesale from contexts that differ fundamentally. Studies like this one provide the diagnostic foundation for that ambition, identifying exactly where the pipeline from trained educationist to published researcher breaks down. The senior authors, including Naveed Yousuf, and co-author Syed Moin Ali of Aga Khan University’s Department for Educational Development, along with Rafay Iqbal of the university’s Department of Family Medicine, frame their work as a mapping exercise, and the map they produce is one that institutional leaders, policymakers, and international funders can all read. For junior faculty wondering whether their struggles are personal failings, and for deans wondering why their education departments generate so little publishable output, the answer documented here is neither indolence nor incapacity, but the accumulated weight of missing time, missing training, missing money, missing journals, and missing trust.

Beyond its specific findings, the study offers a useful illustration of how research capacity itself can be built in resource-constrained settings. The near-complete survey response suggests that Pakistani medical educationists were eager to have their professional difficulties documented, an appetite for engagement that institutions could harness. The sequential design also demonstrates a pragmatic model for LMIC researchers: a modest survey followed by focused group discussions can generate publishable, policy-relevant evidence without the large budgets that often deter early-career scholars in such settings.

The timing of the work is also worth noting. The article was released as an accepted manuscript ahead of its final version of record, a publishing route that shortens the lag between peer review and public availability. For a field whose participants explicitly identified slow, difficult publication as a barrier, faster dissemination models have practical relevance. The open access license further means the findings can circulate freely among the very institutions expected to act on them, including smaller colleges that may lack journal subscriptions.

There is also a measurement lesson embedded in the study. By separating junior and senior faculty experiences, the researchers avoided treating the workforce as a monolith, revealing that career stage changes which barriers dominate. That distinction has direct implications for intervention design: mentorship schemes and protected time may matter most early on, while funding access, journal infrastructure, and trust-building become decisive later. Capacity-building programs imported from high-income countries frequently assume a uniform faculty trajectory, and this evidence argues for tailoring support to career stage. The study thus functions both as a diagnosis of Pakistan’s medical education scholarship landscape and as a template for how other low- and middle-income countries might map their own.

Subject of Research: Mapping the landscape: current status, challenges, and perspectives on scholarship of medical educationists in Pakistan

Article Title: Mapping the landscape: current status, challenges, and perspectives on scholarship of medical educationists in Pakistan

Article References: Sabzwari, S. R., Ali, S. M., Iqbal, R., & Yousuf, N. (2026). Mapping the landscape: current status, challenges, and perspectives on scholarship of medical educationists in Pakistan. BMC Medical Education. https://doi.org/10.1186/s12909-026-10078-0

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10078-0

Keywords: Mapping, landscape, current, status, challenges, perspectives, scholarship, medical, educationists, Pakistan, scientific research