That blind spot is what motivated a team at the South Texas Veterans Health Care System (STVHCS) and the University of Texas Health Science Center at San Antonio. In a short report published in the Archives of Dermatological Research, Ruoxuan Su, Brooke A. Burgess, and Joshua L. Owen describe a brief educational intervention designed to turn audiologists into informed observers of the skin they examine every day. The idea borrows deliberately from an established precedent: hairdressers, who see the scalp and neck in detail that physicians rarely do, have been studied for more than a decade as potential facilitators of skin cancer screening. Prior research in Houston salons and rural salons across the United States showed that with targeted education, non-physician professionals could recognize suspicious lesions and encourage clients to seek dermatologic evaluation. Nobody, however, had ever asked whether the same logic applies to the clinicians with arguably the best view of the ear in all of medicine.

The rationale is anatomically straightforward. Audiologists perform otoscopy, fit hearing aids and earmolds, and manipulate the pinna and external auditory canal dozens of times a day. They routinely illuminate the ear canal and the concha with an otoscope light, and they see patients who may not have had a full-body skin examination in years. Veterans, the population served by STVHCS, carry a particularly heavy burden of cumulative sun exposure from outdoor military service and outdoor occupations, making the head and neck an especially high-yield region to monitor. If an audiologist could recognize that a crusted, pearly, or irregularly pigmented lesion on the helix deserves a dermatology referral, the diagnostic pathway for that patient could begin years earlier than it otherwise might.

To test whether such recognition could be taught quickly, the researchers surveyed audiologists at STVHCS before and after an educational session on skin cancer. The numbers were small, as is typical for a proof-of-concept study at a single institution: twelve participants completed the pre-intervention survey and eight completed the post-intervention survey. But the baseline findings were striking. Every single participant reported having received little or no prior skin cancer training, despite spending their professional lives in close visual contact with one of the highest-risk skin sites on the body. At the same time, 58 percent of respondents, seven of the twelve, expressed strong interest in receiving exactly that kind of education. The appetite was there; the training simply had never been offered.

The educational intervention itself was deliberately brief, reflecting the reality that audiologists cannot be expected to complete dermatology coursework on top of their clinical duties. After the session, participants retook a knowledge assessment, and the results were statistically convincing. Overall knowledge scores rose significantly, with a p-value of 0.0023, a threshold that indicates the improvement was very unlikely to be a chance artifact of the small sample. When the researchers broke the results down by lesion type, they found significant gains in the ability to distinguish benign lesions, at p equal to 0.039, and malignant lesions, at p equal to 0.046. Knowledge of premalignant lesions, such as actinic keratoses, did not improve significantly, with a p-value of 0.5, a gap the authors’ data suggest may reflect the inherent subtlety of these lesions or the limited time available to cover them.

Perhaps the most reassuring number in the study concerns referral accuracy. Even before the intervention, the audiologists were already highly reliable at flagging the lesions that matter most: after education, referral accuracy stood at 100 percent for basal cell carcinoma and squamous cell carcinoma, and 94 percent for melanoma. In practical terms, this means that when shown images of the two most common skin cancers and the deadliest one, the trained audiologists essentially never failed to identify them as lesions warranting dermatologic evaluation. For a screening facilitator role, that is precisely the performance profile you want. The goal is not to turn audiologists into diagnosticians; it is to ensure they know which findings should never be ignored, and the data indicate a short session achieves that.

The qualitative feedback from participants reinforced the quantitative results. Audiologists reported a heightened awareness of their potential role in skin cancer detection, greater familiarity with melanoma and its warning signs, and increased comfort with the process of referring patients to dermatology. That last point matters more than it might appear. Referral comfort is often the bottleneck in interprofessional screening schemes: a professional may notice something suspicious but hesitate to act, uncertain whether raising the concern is appropriate or how to route the patient. By explicitly framing dermatology referral as part of the audiologist’s scope, the intervention appears to have lowered that psychological barrier.

The study’s limitations are worth stating plainly. With twelve pre-intervention and eight post-intervention participants at a single veterans’ health care system, the findings are exploratory rather than definitive, and the authors themselves frame the work as identifying gaps and proposing a solution rather than establishing a new standard of care. The survey was anonymous and voluntary, the study was reviewed by the University of Texas Health Science Center at San Antonio institutional review board and determined not to be regulated human research, and no protected health information was collected. Whether the knowledge gains persist over months, whether they translate into real referral behavior in the clinic, and whether they improve patient outcomes such as time-to-diagnosis will require larger, longitudinal studies across multiple centers. The authors report no funding source and no conflicts of interest, and the work was conducted in accordance with the Declaration of Helsinki.

Even so, the implications are hard to dismiss. Skin cancer remains the most common malignancy in the United States, and early detection is the single most powerful lever for reducing morbidity, mortality, and treatment costs. The ear, along with the rest of the head and neck, is a site where lesions are both common and easy to miss, partly because patients cannot see their own ears well and partly because routine medical exams rarely involve a careful inspection of the pinna. Audiologists occupy a unique position at that intersection: they are medically trained, they look at ears under magnification and illumination every working day, and they see a patient population with elevated cumulative UV exposure. The STVHCS study suggests that a modest investment in education, the kind that could be delivered during a staff meeting or an online module, is enough to convert that positional advantage into genuine screening capacity.

The broader lesson may extend beyond audiology. The hairdresser studies that inspired this work demonstrated that non-dermatology professionals can serve as early-warning sensors when given the right tools, and the audiologist data now extend that principle to a clinical discipline with direct, repeated access to a high-risk anatomical site. As health systems search for scalable ways to catch skin cancers earlier, the cheapest interventions are often the ones that repurpose expertise that already exists inside the building. Twelve audiologists in San Antonio may seem like a small starting point, but the finding that a brief educational session can significantly raise skin cancer knowledge and sustain near-perfect referral accuracy for malignant lesions points to a simple, testable idea with obvious room to grow: the ears are already being examined, and now the people examining them know what to look for.

Subject of Research: Skin cancer knowledge and screening behaviors among audiologists following an educational intervention

Article Title: Skin cancer knowledge and screening behaviors amongst audiologists at South Texas Veterans Health Care System: identifying gaps and proposing a solution

Article References: Su, R., Burgess, B. A., & Owen, J. L. (2026). Skin cancer knowledge and screening behaviors amongst audiologists at South Texas Veterans Health Care System: identifying gaps and proposing a solution. Archives of Dermatological Research, 318(1), Article 426. https://doi.org/10.1007/s00403-026-04913-z

Image Credits: AI Generated

DOI: 10.1007/s00403-026-04913-z

Keywords: skin cancer, audiologists, screening, educational intervention, dermatology referral, melanoma, basal cell carcinoma, squamous cell carcinoma, veterans health, early detection, otolaryngology, public health

The study was designed as two linked experiments, each addressing a distinct question. The first asked whether individual differences in empathy predict differences in helping behavior among autistic children. The second asked whether that relationship could be shifted by affective priming, the well-documented psychological phenomenon in which a preceding emotional stimulus colors subsequent judgments and actions. This two-stage architecture matters because it separates a stable trait from a transient state. If helping depended mainly on momentary mood, the second experiment should have revealed it. Instead, the results point to empathy itself as the engine of prosocial action in this population.

Experiment 1 began with measurement. The team administered the Chinese Measure of Empathy and Sympathy, a validated self-report instrument that captures both the cognitive dimension of empathy, understanding what another person feels, and the affective dimension, resonating with those feelings. Rather than treating empathy as a continuous variable alone, the researchers adopted an extreme-groups design, a technique that maximizes statistical contrast by comparing children at the top 30 percent of empathy scores with those in the bottom 30 percent. This approach, discussed in the methodological literature by Preacher and colleagues, trades sample breadth for analytical power, making it easier to detect genuine differences between groups when they exist.

Helping behavior was then assessed with the Block-Dropping Task, a behavioral paradigm in which a child has the opportunity to assist another person, with the amount of assistance quantified as a score. Behavioral measures of this kind are valuable because they bypass some of the reporting biases that plague questionnaires, particularly in children whose self-insight or verbal ability may differ from that of their neurotypical peers. The results were unambiguous. Children in the high-empathy group produced a mean helping score of 3.54 with a standard deviation of 0.51, while the low-empathy group averaged 1.29 with a standard deviation of 1.46. The gap was not only large in magnitude but highly reliable, with a test statistic of Z equal to negative 4.929 and a p-value below 0.001.

Those numbers translate into a clear scientific statement: empathic ability exerts a significant positive predictive effect on the helping behavior of children with autism spectrum disorder. In practical terms, an autistic child who reports greater empathy is substantially more likely to act on it by helping others. The finding aligns with a long line of research in developmental psychology, including classic meta-analytic work by Eisenberg and Miller linking empathy to prosocial behavior, and with the empathy-altruism hypothesis advanced by C. Daniel Batson, which holds that empathic concern triggers genuinely other-oriented motivation. What is new here is the demonstration that this pathway operates robustly within an autistic population, where the empathy deficit has often been assumed to be broad and blanket-like.

That assumption has been eroding for some time. Research by Jones and colleagues in 2010 distinguished between different types of empathy deficits, showing that autistic boys and boys with psychopathic tendencies differ qualitatively in how their empathy breaks down. More recent work by Shalev, Warrier, and Baron-Cohen introduced the concept of empathic disequilibrium, an imbalance between cognitive and affective empathy, as a better predictor of autistic traits than a simple overall deficit. Studies of autistic adolescents by Rieffe and colleagues have documented substantial empathic responding, particularly when the emotional state of another person is made clear. The new study adds a behavioral outcome, helping, to this picture, showing that when empathy is present at high levels in autistic children, it converts into action at measurably higher rates.

Experiment 2 then asked whether the emotional weather could change the forecast. The researchers recruited only the children who had scored in the top 30 percent on the empathy measure, ensuring that every participant possessed high empathic ability, and divided them equally into two subgroups. One subgroup watched cartoon clips designed to induce positive emotion; the other watched clips designed to induce negative emotion. This affective priming procedure draws on a tradition going back to Murphy and Zajonc’s work in 1993, which showed that emotional stimuli presented before a task can shape responses even when the influence operates below awareness. Prior studies in neurotypical populations, including work by Rosenhan and colleagues, have suggested that positive affect can boost altruism, in part by shifting attention outward and increasing the salience of others’ needs.

The priming, however, did nothing. The two groups showed no significant disparity in helping behavior, and the statistical test of the priming effect yielded a Z of negative 1.152 with a p-value of 0.250, far from conventional significance thresholds. For high-empathy autistic children, whether they had just experienced an uplifting or a dispiriting cartoon made no detectable difference to how much they helped. This null result is scientifically meaningful rather than merely disappointing. It suggests that in this population, the empathy-to-helping pathway is comparatively insulated from transient mood states. The trait carries the behavior; the state does not override it. That robustness could reflect the way empathic concern, once established, provides a stable motivational substrate that small laboratory mood inductions cannot easily perturb.

The findings arrive with important caveats that the authors and the broader literature make clear. The extreme-groups design, while powerful, examines the tails of the distribution and cannot by itself characterize the full dose-response relationship between empathy and helping across all autistic children. The mood induction relied on cartoon clips, a gentle manipulation whose effects may simply have been too weak to register, and the study did not include a neutral priming condition in the reported comparison. The datasets, protected for student privacy, are available only from the corresponding author on reasonable request, which limits independent reanalysis for now. The participants were Chinese schoolchildren, and cultural norms surrounding helping and emotional expression may shape both baseline behavior and responsiveness to mood in ways that deserve cross-cultural replication.

Even with those limitations, the implications for education and intervention are concrete. If empathy predicts helping in autistic children, then interventions that cultivate empathic understanding, rather than merely drilling social scripts, may unlock prosocial behavior that has too often been assumed absent. And if momentary mood does not gate that behavior, teachers and caregivers need not engineer emotionally perfect environments to see helping emerge; they need instead to build the underlying empathic capacity. The study’s authors, who include Ze Jia Li, Xuan Lin, and corresponding author Shu Man Wu, frame the work as illuminating the mechanisms of social-behavioral development in autism and informing directions for educational intervention. In a field where autistic children have long been characterized by what they supposedly lack, the more newsworthy finding may be what they demonstrably possess: an empathy-driven inclination to help that persists regardless of whether the moment feels bright or bleak.

Subject of Research: The relationship between empathy, affective priming, and helping behavior in children with autism spectrum disorder

Article Title: A Study on the Influence of Empathy on Helping Behavior of Children With Autism Spectrum Disorder Under Different Affective Priming Conditions

Article References: A Study on the Influence of Empathy on Helping Behavior of Children With Autism Spectrum Disorder Under Different Affective Priming Conditions. (n.d.). https://doi.org/10.1007/s10803-026-07508-9

Image Credits: AI Generated

DOI: 10.1007/s10803-026-07508-9

Keywords: autism spectrum disorder, empathy, helping behavior, affective priming, prosocial behavior, child development, mood induction, Block-Dropping Task, social behavior, educational intervention, developmental psychology, empathic concern

The research, led by Nuril Kusumawardani Soeprapto Putri of Institut Teknologi Sepuluh Nopember and Bina Nusantara University, together with Umi Laili Yuhana, Daniel Oranova Siahaan, and Istiani, addresses a problem familiar to anyone who has worked with large-scale assessment data. Universities routinely collect performance measures at admission, but these numbers are rarely translated into actionable profiles that instructors can use. The authors argue that before artificial intelligence can meaningfully stratify learners, the analytics pipeline itself must be reproducible, leakage-free, and honest about what a statistical cluster does and does not represent. Their framework is explicitly designed so that clusters are treated as operational descriptions of performance patterns, not as naturally discrete or rank-ordered psychological categories.

At the foundation of the approach lies multidimensional item response theory, a psychometric technique that estimates latent abilities from how students answer individual test items rather than relying on raw totals. The researchers extracted ability estimates in three domains: logical reasoning, abstraction, and verbal reasoning. This multidimensional representation matters because two students can arrive at the same overall score through very different routes. One might excel in verbal tasks while struggling with logic, while another shows the reverse pattern. A single aggregate number collapses those differences; a three-dimensional ability vector preserves them and makes richer profiling possible.

To guard against data leakage, one of the most insidious pitfalls in applied machine learning, the cohort was split into a training set of 29,449 students and a held-out test set of 7,364 students. Every transformation, every cluster boundary, and every profile label was derived exclusively from the training partition and then transferred, unchanged, to the test partition. This discipline ensures that the reported performance reflects how the system would behave on genuinely new students rather than on data it has already seen. Many published clustering studies skip this step entirely, fitting and evaluating on the same data and thereby inflating apparent quality. Here, the split is the backbone of the entire validation strategy.

The first level of the framework applied k-means clustering with the number of clusters fixed at four, a choice justified by a composite ranking of three standard internal validation indices: the Silhouette score, which measures how well each point fits its own cluster compared to neighbors; the Davies-Bouldin index, which rewards compact, well-separated clusters; and the Calinski-Harabasz index, which compares between-cluster to within-cluster dispersion. Using multiple indices rather than a single criterion reduces the risk of selecting a cluster count that flatters one metric but fails on others. The resulting four profiles were characterized not by how much ability students had, but by the shape of their ability centroids: a uniformly lower profile, a uniformly higher profile, a logic-dominant profile, and a verbal-abstraction dominant profile.

One of the most striking findings comes from the relationship between those two middle profiles. The logic-dominant and verbal-abstraction dominant groups crossed each other across the three ability dimensions, meaning that neither could be described as simply better than the other. If cognitive performance lay along a single ordinal continuum from low to high, such crossing would be impossible. Its presence demonstrates, in the study’s own data, that student cognition cannot be compressed onto one severity scale. Students are not merely higher or lower performers; they exhibit distinct patterns of relative strengths, and those patterns carry information that intervention designers can exploit.

The second level of the pipeline drilled into the uniformly lower profile, subdividing it into four domain-specific sub-profiles. This stratification is arguably the study’s most practically valuable contribution. Students in the lowest overall group are not interchangeable; some struggle more with logic, others with abstraction or verbal reasoning. By separating these sub-groups, the framework gives educators a way to tailor remediation, for example by directing logic-focused support to one sub-profile and verbal or comprehension-focused support to another, rather than applying a generic intervention to everyone below a cutoff. Differentiated support of this kind has long been an aspiration in higher education; this work offers a scalable computational route toward it.

The statistical evidence for the clustering’s strength was substantial. A PERMANOVA test, a non-parametric analysis of variance suited to multivariate distance data, yielded a pseudo-F statistic of 10,798 and an R-squared of 0.524, indicating that the profile assignment accounted for more than half of the multivariate variance in the ability estimates. Among the three cognitive domains, logical reasoning contributed most strongly to differentiating the profiles, with an effect size of eta-squared equal to 0.81. These are large effects by any convention in the behavioral sciences, and they indicate that the four-way partition captures genuine structure in the data rather than an arbitrary slicing of a homogeneous cloud.

The authors are notably careful about what the final validation step proves. They used hierarchical label recovery to reconstruct profile assignments on the held-out test data, achieving approximately 98 percent accuracy, which demonstrates that the assignment rule transfers reliably to unseen students. Yet they also report that a simple baseline model achieved comparable performance, and they interpret this honestly: the result demonstrates reproducibility of the assignment rule under leakage control, not psychological validation of the clusters as true categories. This distinction matters. The clusters are useful operational summaries, but the framework does not claim that four cognitive types exist in nature. That kind of epistemic restraint is rare and welcome in a field often tempted to overinterpret unsupervised structure.

Broader implications extend beyond the specific dataset. The study received institutional support from the INNOGEN Lab of Bina Nusantara University and was conducted as a retrospective analysis of fully anonymized secondary data, with ethical clearance from the university’s research ethics committee. Because the pipeline is computationally scalable and the labels are interpretable, the approach could in principle be adopted by institutions that already administer multidimensional admission or placement assessments. The framework offers an interpretable and efficient way to convert raw assessment data into profiles that support differentiated educational interventions, while its emphasis on held-out validation and modest claims sets a methodological example for the growing field of learning analytics. As universities increasingly turn to data-driven tools to support struggling students, this work suggests that the most important algorithmic ingredient may be rigor: get the measurement right, split the data honestly, and never mistake a convenient cluster for a psychological fact.

Subject of Research: A reproducible two-level machine learning framework for cognitive profiling and stratification of low-performing students in higher education

Article Title: A reproducible two level clustering framework for profiling cognitive performance and stratifying the lowest performing group in higher education

Article References: Putri, N. K. S., Yuhana, U. L., Siahaan, D. O., & Istiani (2026). A reproducible two level clustering framework for profiling cognitive performance and stratifying the lowest performing group in higher education. Discover Psychology. https://doi.org/10.1007/s44202-026-00888-0

Image Credits: AI Generated

DOI: 10.1007/s44202-026-00888-0

Keywords: cognitive profiling, item response theory, k-means clustering, cluster validation, reproducibility, higher education, learning analytics, psychometrics, data leakage, PERMANOVA, educational intervention, machine learning