The earlier editorial, by E. Qama, had made a compelling case that tools such as ChatGPT can help people manage chronic conditions only if those people possess what has come to be called prompt literacy: the ability to frame effective queries and then critically interpret what the model returns. In that framing, prompt literacy functions as an extension of digital health literacy, and gaps in it become a new source of disparity. Users who prompt skillfully can shape a plan suited to their own situation, while weaker prompting tends to return advice so generic, or so inaccurate, that acting on it becomes risky. Saad and Yudkin accept the observation as valuable, but they argue it rests on a hidden assumption that collapses precisely where the stakes are highest.

That assumption is access. Qama’s argument implicitly presumes the user has a healthcare system available as an alternative or complement to AI. Where that presumption fails, the authors contend, the disparity that matters most is not a gap in skill but a gap in access. They distinguish between what they call a second-order inequality, differences among users who already have a choice between AI and professional care, and a first-order inequality, the question of whether any accessible guidance exists at all. In low- and middle-income countries, and among under-resourced populations inside wealthy countries, the comparison confronting many patients is not AI versus a clinician. It is AI versus nothing. Under that comparison, even an imperfect AI response may represent a genuine improvement over the counterfactual.

The distinction is not merely philosophical; it maps onto the empirical literature in revealing ways. A scoping review by Alon and Levkovich covering 27 studies of generative AI and health information seeking found that credibility and trust concerns were the most commonly reported barrier, appearing in 48.1 percent of included studies. The same review found that people typically wove AI into a broader information routine rather than treating it as a replacement for other sources, and patients managing low back pain used AI-generated information while seeking professional oversight as the perceived stakes rose. These are exactly the patterns a prompt-literacy framework predicts, because they describe populations for whom professional care remains reachable. The framework, in other words, was built from the experience of people who already have options.

The geographic footprint of the evidence is itself part of the problem. In the Alon and Levkovich review, the largest shares of studies came from the United States, with eight, followed by Saudi Arabia, the United Kingdom, and China, with three each. The reviewers explicitly noted a concentration of evidence from English-speaking and high-income settings, with limited representation from low- and middle-income countries, sub-Saharan Africa, Latin America, and South and Southeast Asia. Only one study came from an African country, a Nigerian study by Adeyemi and colleagues, and its own authors cautioned that their online-recruited convenience sample of university students likely overrepresented people with greater internet access and digital literacy. The theoretical picture of who uses health AI, and why, has been assembled largely from the connected and the insured.

What evidence does exist from under-resourced populations suggests that structural factors, not literacy alone, drive the pattern. A survey of United States users by Ayo-Ajibola and colleagues found that ChatGPT users were more likely than nonusers to report lower income and lower educational attainment, and they also scored lower on a validated eHealth literacy scale. Other studies indicate that chatbots can reduce embarrassment when people seek information about sensitive symptoms, an effect most pronounced for embarrassing sexual symptoms. Yet acceptance cannot be assumed. McLeod and colleagues found that underserved United Kingdom participants valued immediacy and privacy in online support but often rejected chatbots specifically, citing scripted responses and a preference for a live human. Research with older Black adults shows that credibility, cultural tailoring, and historic mistrust of medical institutions shape how chatbot design is received. Taken together, these findings point to access-adjacent and structural forces as the dominant explanatory variables.

If access rather than skill is the binding constraint for many users, the policy problem changes shape entirely. Saad and Yudkin argue that the harm attributed to low prompt literacy in an access-constrained setting is not only the harm of using AI less effectively than a skilled peer; it is also the harm of having only one channel of guidance and using it imperfectly. These are not equivalent problems and they do not share a solution. The former can be addressed primarily through user training. The latter demands improving what the AI channel offers by default: uncertainty signaling that tells users when the model is guessing, red-flag escalation that pushes urgent symptoms toward emergency care, and safer design for users with no route to verification anywhere else. A poorly formed question answered by a well-designed system can still be survivable; a well-formed question answered by a brittle system, with no fallback, may not be.

The equity implications run deeper still. Prompt literacy describes a graded inequality operating on a shared floor of care access. Where that floor does not exist, because of geography, cost, or local health system capacity, the more consequential inequality may be binary rather than graded: whether a person has any accessible source of guidance at all. Treating both situations as the same literacy gap, the authors warn, risks systematically under-weighting the more severe access disparity. The same problem infects intervention design. Prompt literacy workshops embedded in rehabilitation curricula or delivered through existing care programs presuppose contact with a healthcare system. Where that contact is scarce, such interventions may fail to reach the very people who need them most, a design flaw the authors compare to teaching navigation skills to people who have no boat.

The commentary therefore proposes an access-sensitive agenda in which safety is built into the tool and its surrounding delivery infrastructure rather than delegated to the user’s ability to ask the right question. On tool design, that means uncertainty signaling, red-flag symptom escalation, plain-language outputs, multilingual and low-literacy interfaces, and voice-based options. On infrastructure, it means low-bandwidth design, offline-compatible features where feasible, and transparent data and privacy protections. Clinical localization calls for alignment with local guidelines and context-specific medication and referral warnings adapted to what services actually exist nearby. Community delivery would enlist community health workers, clinics, hospitals, pharmacies, schools, universities, libraries, non-governmental organizations, and public health agencies, while referral pathways would connect the chatbot’s output to real escalation options, from emergency care to hotlines and community-based supports. Evaluation, crucially, should measure baseline care access, affordability, appointment availability, travel time, language concordance, and whether AI was used before, instead of, or after professional care.

For researchers, the message is that access constraint should not be treated as one barrier among many but as a variable that changes the meaning of outcomes already being measured. Intention to use, verification behavior, and downstream escalation to a clinician all mean something different for someone who can see a doctor tomorrow than for someone who cannot see one at all. Future studies, the authors urge, should stratify AI adoption and reliance by insurance status, affordability, geographic distance, provider-patient concordance, and sequencing relative to professional care, and should test which access-sensitive designs reduce harm and improve appropriate escalation in real-world self-management. The authors are careful to close the loop on the original argument: prompt literacy remains a useful construct, and better-formed queries do produce more useful outputs while critical interpretation remains essential. Their claim is simply narrower and, in an era when conversational AI is racing into the hands of people the health system has left behind, more urgent. Skill shapes the quality of advice; access determines whether any advice exists. Public health, they argue, cannot afford to confuse the two.

Subject of Research: Healthcare access versus prompt literacy in AI-supported health self-management for low-resource populations

Article Title: Access before literacy: reframing conversational AI in health self-management for low-resource contexts

Article References: Saad, A., & Yudkin, J. S. (2026). Access before literacy: reframing conversational AI in health self-management for low-resource contexts. International Journal of Public Health, 71, Article 1610302. https://doi.org/10.3389/ijph.2026.1610302

Image Credits: AI Generated

DOI: 10.3389/ijph.2026.1610302

Keywords: conversational AI, health self-management, health disparities, healthcare access, prompt literacy, low- and middle-income countries, digital health literacy, chatbots, public health, health equity, generative AI, scoping review

The study surveyed 649 undergraduate and postgraduate students across Indian higher education institutions, using a cross-sectional questionnaire design that captured how often and how thoughtfully students engaged with generative AI tools. Rather than treating AI use as a single behavior, the researchers decomposed it into distinct competencies. Prompt literacy — the craft of formulating effective instructions for AI systems — was measured as one construct. Critical AI literacy — the capacity to question, verify, and ethically evaluate what an AI produces — was measured as another. Assessment design, meaning the degree to which coursework is structured in ways that make genuine understanding visible, formed the third pillar of the model. The outcome variable, epistemic transparency, captured whether students disclose AI involvement, justify their sources of knowledge, and take responsibility for the claims they make in academic work.

To analyze the relationships among these constructs, the team employed partial least squares structural equation modeling, a statistical technique well suited to testing networks of direct, indirect, and interacting effects among latent variables that cannot be observed directly. The modeling rigor was considerable: the authors reported reliability checks using Cronbach’s alpha, discriminant validity assessment following the HTMT criterion established by Henseler and colleagues, and common method bias screening through Kock’s full collinearity approach. Missing data were handled with modern imputation strategies, and the measurement model passed the Fornell-Larcker benchmarks. In plain terms, the statistical machinery was strong enough that the findings deserve attention rather than dismissal.

The headline result is a chain of influence that runs from the humble prompt to the philosophy of knowledge. Prompt literacy, the study found, has a strong positive effect on critical AI literacy. Students who learn to write precise, contextualized, well-structured prompts do not merely get better outputs; they develop a more questioning stance toward the technology itself. The act of engineering a prompt forces a learner to articulate what they actually want to know, and that articulation appears to sharpen their skepticism about what comes back. This finding aligns with a growing body of literature, including work by Federiakin and colleagues framing prompt engineering as a genuine twenty-first-century skill, and research by Knoth and colleagues showing that AI literacy shapes prompting strategies in a reciprocal loop.

Intriguingly, prompt literacy’s direct effect on epistemic transparency was statistically significant but modest. Knowing how to talk to a chatbot does not, by itself, make a student candid about using one. The pathway runs instead through critical AI literacy, which partially mediated the relationship between prompting skill and transparent behavior. In other words, students become epistemically open when they can critically evaluate AI outputs — spotting hallucinations, recognizing training-data biases, and understanding that a fluent paragraph is not the same thing as a verified fact. The study’s authors suggest this is why prompt training alone, a popular quick fix in university workshops, is unlikely to change disclosure behavior on its own. It builds the tool skills but not the epistemic conscience.

The strongest practical lever, according to the data, was assessment design. When assignments are structured so that process matters — drafts, reflections, oral defenses, documented iterations, and evidence of reasoning — students report significantly higher epistemic transparency. Assessment design showed the largest direct contribution of any variable in the model, edging out both literacy constructs. This echoes a substantial international literature on evaluative judgment, including work by Bearman, Tai, Dawson, Boud, and Ajjawi arguing that assessment in the AI era should cultivate students’ capacity to judge quality rather than merely produce outputs, and studies by Bretag and colleagues linking poorly secured assessment designs to contract cheating. The new study extends that logic into the generative AI age: assessments that reward visible thinking make AI use legible, and legibility is the precondition for transparency.

One of the study’s more sobering findings is what did not work. The interaction between assessment design and critical AI literacy was not statistically significant, indicating that these two factors operate independently rather than synergistically. Transparent AI-supported learning, the data suggest, is not produced by combining clever assignments with critical students in some multiplicative fashion. Each factor contributes on its own track. For educators, this carries a practical implication: institutions cannot assume that strong assessment reform will automatically cultivate critical AI literacy, or that critical literacy curricula will compensate for opaque assessment practices. Both need deliberate, parallel investment.

The conceptual contribution of the paper may prove as influential as its statistical results. By defining epistemic transparency as a distinct, measurable learning outcome — grounded in the epistemology of social knowledge developed by Goldman and the ethics of knowing articulated by Fricker — the study gives universities something concrete to target. Transparency here is not a vague aspiration but an observable behavior: disclosing AI assistance, tracing claims to defensible sources, and exhibiting what the authors, drawing on Flavell’s metacognition tradition, describe as reflective monitoring of one’s own knowledge production. Related recent work, such as Lloyd’s proposal for community standards of epistemic responsibility in human-AI collaboration and Nieminen and Ketonen’s account of epistemic agency in assessment, suggests the field is converging on this framing from multiple directions.

For a sector gripped by anxiety over AI-fueled cheating, hallucinated citations, and unreliable detection tools — problems documented in studies ranging from Elsayed’s analysis of misinformation risks to Deep and colleagues’ evaluation of AI detectors — the study offers a refreshingly constructive agenda. Instead of policing students, it proposes equipping them: teach prompt literacy as a gateway skill, build critical AI literacy as the engine of honest evaluation, and redesign assessments so that the process of thinking is visible and valued. The authors are careful to note the limits of their design; a cross-sectional survey captures associations at one point in time and cannot establish causation, and the sample, drawn entirely from Indian institutions, may not generalize everywhere. Still, with 649 respondents and a rigorously validated model, the evidence is hard to ignore.

As generative AI becomes as unremarkable in seminar rooms as the calculator once was, the question facing higher education is no longer whether students will use these systems but whether their use will be honest, critical, and educationally meaningful. This study suggests the answer can be engineered — not through code, but through curriculum. If universities take its findings seriously, the future of AI in education may look less like an arms race between detectors and deceivers, and more like a classroom where the prompt, the critique, and the assignment all work together to make knowing itself transparent.

Subject of Research: The roles of prompt literacy, critical AI literacy, and assessment design in fostering epistemic transparency among university students using generative AI.

Article Title: Developing Epistemic Transparency in AI-Supported Higher Education: Roles of Prompt Literacy, Critical AI Literacy, and Assessment Design

Article References: N., A., Nayak, K. M., Divyashree, M. S., & Jain, U. (2026). Developing Epistemic Transparency in AI-Supported Higher Education: Roles of Prompt Literacy, Critical AI Literacy, and Assessment Design. Frontiers of Digital Education, 3(4), Article 28. https://doi.org/10.1007/s44366-026-0102-2

Image Credits: AI Generated

DOI: 10.1007/s44366-026-0102-2

Keywords: generative artificial intelligence, higher education, prompt literacy, critical AI literacy, epistemic transparency, assessment design, PLS-SEM, academic integrity, AI literacy, educational research, Developing, Epistemic

The study, conducted by Muhammad Shaban Rafi of Riphah International University and Lubna Khalil of the University of the Punjab, applied the Artificial Intelligence Assessment Scale developed by Perkins and colleagues in 2024 to evaluate the appropriate role of generative AI across the successive stages of dissertation writing. Rather than treating AI as either a threat or a miracle, the framework allowed the researchers to distinguish between tasks that can be fully delegated to machines, tasks that require a human–AI partnership, and tasks that must remain exclusively human. The results challenge both the alarmist narrative that AI will hollow out doctoral scholarship and the utopian claim that it can write a dissertation on command.

Where the technology shone most clearly was in the mechanics of writing. Participants reported that generative AI could be used without reservation to improve grammar, sentence structure, and overall coherence, enhancing the clarity and efficiency of their prose. For many doctoral students, particularly those writing in a second language, this mechanical support removed a persistent barrier that had previously consumed hours of revision. By offloading surface-level editing to the machine, students said they could redirect their energy toward deeper intellectual engagement with their arguments, literature, and data, a shift that supervisors in the study viewed as a legitimate pedagogical gain rather than a shortcut.

The second major application involved data analysis. Doctoral candidates described using generative AI to help analyze large qualitative datasets by defining a coding frame, identifying recurring trends, and conducting sentiment analyses. In the social sciences and humanities, where interviews, open-ended survey responses, and archival texts can generate overwhelming volumes of material, the ability of large language models to propose initial coding categories and flag patterns offered a practical starting point. Crucially, the participants did not describe the AI as replacing their analytical judgment. Instead, the machine-generated coding frames served as provisional scaffolding that researchers then tested, refined, and validated against their own close reading of the data.

A third productive territory was argument structuring. Students reported using generative AI to organize literature reviews, suggest logical arrangements of sentences and paragraphs, and generate counterarguments that stress-tested their claims. The capacity of these models to simulate an opposing viewpoint proved especially valuable in the humanities, where a dissertation’s strength often depends on anticipating objections. By prompting the system to challenge a thesis statement or identify gaps in a literature synthesis, candidates effectively gained a tireless sparring partner available at any hour, one that could surface alternative framings the writer had not considered.

Yet the enthusiasm had sharp limits. Participants were emphatic that generative AI should be limited or prohibited in areas demanding critical reasoning, originality, and cultural context. The core intellectual contributions of a dissertation, formulating research questions, interpreting findings within their disciplinary traditions, and producing genuinely novel insights, were judged to require human intelligence at their center. Supervisors in particular warned that AI-generated content may lack accuracy and contextual depth, producing text that reads fluently but fails under scholarly scrutiny. The study highlights the well-documented problem of hallucination in natural language generation, in which models confidently produce plausible-sounding but false claims, a hazard especially dangerous in academic contexts where fabricated citations or mischaracterized sources can constitute serious misconduct.

This is where the concept of prompt literacy enters the picture. The researchers argue that the quality of output from generative AI is not a fixed property of the technology but a function of how skillfully the user frames the request. Prompt engineering has emerged in recent years as a recognized digital competence, with studies describing it as a new twenty-first-century skill and cataloging systematic techniques, from chain-of-thought prompting, which guides models through explicit reasoning steps, to retrieval-augmented generation, which grounds model outputs in external source documents. The study positions prompt literacy as an essential component of broader AI literacy, encompassing the ability to specify role, context, constraints, and desired output format in a prompt, and to iteratively refine instructions when the first response falls short.

The pedagogical implications are significant. If the difference between a vague, hallucination-prone response and a precise, useful one lies in the prompt, then universities face a choice about whether to teach these skills explicitly or leave students to learn by trial and error. The study responds by providing a scale for the appropriate use of generative AI at each stage of doctoral writing, along with ready-to-use prompts developed as part of the research. Such resources suggest a model of AI integration in which institutions define clear boundaries, students develop the technical and critical competence to work within them, and assessment practices evolve to distinguish between acceptable assistance and academic dishonesty. The ethical dimension is central: the study received ethical approval under professional research ethics codes, and its framework is explicitly designed for the ethical integration of AI in educational assessment.

The findings arrive amid a rapidly expanding body of research on generative AI in higher education, including studies of students’ prompt patterns, hermeneutic approaches to prompt optimization, and surveys of hallucination in language models. What distinguishes this study is its focus on the doctoral dissertation, the genre in which the stakes of originality are highest and the tension between efficiency and authenticity most acute. By grounding its conclusions in the lived experience of students and supervisors rather than speculation, it offers a pragmatic middle path: generative AI as a powerful tool for mechanics, data handling, and argument testing, governed by human judgment, validated at every step, and wielded by researchers who understand both what to ask and what the machine cannot be trusted to answer.

For the current generation of doctoral candidates, the message is double-edged. The technology can genuinely save time and elevate the quality of dissertation writing, but only for those who invest in the literacy to use it well. As generative AI becomes embedded in the infrastructure of research, the study suggests that the most important qualification for the twenty-first-century scholar may not be the ability to write without AI, or even with it, but the ability to converse with it critically, skeptically, and skillfully, keeping human intelligence, as the authors insist, at the core of the scholarly enterprise.

Subject of Research: The applications, limitations, and prompt literacy requirements of generative AI in doctoral dissertation writing

Article Title: Generative AI in Doctoral Dissertation Writing: Applications, Limitations, and the Need for Prompt Literacy

Article References: Rafi, M. S., & Khalil, L. (2026). Generative AI in Doctoral Dissertation Writing: Applications, Limitations, and the Need for Prompt Literacy. Frontiers of Digital Education, 3(2), Article 14. https://doi.org/10.1007/s44366-026-0088-9

Image Credits: AI Generated

DOI: 10.1007/s44366-026-0088-9

Keywords: generative AI, doctoral dissertation writing, prompt literacy, AI Assessment Scale, human–AI collaboration, AI literacy, higher education, academic writing, prompt engineering, hallucination, ethical use of AI, social sciences and humanities