The participants were medical and nursing interns undergoing emergency medicine rotations and were organized into interdisciplinary teams composed of one physician and two nurses. All participants completed a pre-test and learning-needs assessment and received a brief One-Minute Preceptor session before being randomly assigned to either a control group or an intervention group. The control group received the preceptor session followed by structured problem-based learning. The intervention group received simulation-based training integrated into the problem-based learning framework, followed by Plus–Delta reflective debriefing. The simulation scenarios represented resource-limited primary care settings and emphasized teamwork and key treatment procedures.

Knowledge acquisition was evaluated through structured written examinations administered before and after training. Both groups improved significantly from baseline, with post-training differences of p<0.05. The intervention group also achieved significantly greater score improvements than the control group. Treatment implementation and teamwork were areas with relatively low baseline scores, and both improved markedly after training, particularly in the intervention group. Before training, participants reported the greatest uncertainty about the clinical manifestations of anaphylactic shock, cited by 42.6%, and treatment strategies, cited by 40.7%. During simulation-based formative assessment, approximately two-thirds of participants rated their own performance lower than the instructors did.

The findings support simulation-integrated problem-based learning as an educational approach for improving knowledge associated with treatment implementation and teamwork in anaphylactic shock management. The authors described the model as potentially feasible for emergency education in resource-limited primary care settings because it provides opportunities for experiential practice and reflective learning. The study was limited to 54 interns in emergency medicine rotations, and its reported endpoint was knowledge measured by written examinations rather than clinical performance or patient outcomes. The abstract also did not report longer-term retention, comparison of individual simulation components, or the extent to which the educational model could be implemented across different primary care settings. Accordingly, the results establish an association between the combined intervention and greater knowledge gains, without demonstrating effects on patient care.

Subject of Research: Randomized evaluation of simulation-integrated problem-based learning for interns’ anaphylactic shock management knowledge

Article Title: Integrating simulation-based training into a problem-based learning framework to improve knowledge of anaphylactic shock management: a randomized educational intervention study

Article References: Chen, J., Ruan, Z., Zhang, M., Lin, J., Lin, W., Zhou, Y., Du, Y., Liu, Y., Zheng, X., & Chen, X. (2026). Integrating simulation-based training into a problem-based learning framework to improve knowledge of anaphylactic shock management: a randomized educational intervention study. BMC Medical Education. https://doi.org/10.1186/s12909-026-10601-3

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10601-3

Keywords: anaphylactic shock, simulation training, problem-based learning, medical education, nursing education, interprofessional teams, knowledge assessment, emergency medicine, primary care, Integrating, simulation-based, training

Participants were enrolled in two pre-existing sections of a management course. One section received a 14-week problem-based learning intervention based on authentic community pharmacy scenarios, while the other received conventional lecture-based instruction covering the same core content. The researchers assessed critical thinking disposition and social problem-solving at baseline and after the intervention using validated instruments. Quantitative data were examined with mixed-design analysis of variance, which accounted for change over time and the interaction between time and group. The study did not randomly allocate students, and the groups were not equivalent at baseline on the critical thinking disposition measure, limiting interpretation of the between-group pattern.

The researchers also conducted semi-structured interviews with students in the intervention group and analyzed the accounts using reflexive thematic analysis. Students described problem-based learning as an active, experience-based process that increased their engagement and supported the transfer of theoretical knowledge to community pharmacy practice. They also reported developing more systematic and multifaceted approaches to problem-solving. These accounts provided qualitative context for the pre-test and post-test results, but they did not establish that the teaching method alone produced the measured improvements. The authors characterized the quantitative findings as an association between the structured intervention and gains in critical thinking disposition and social problem-solving.

The study’s scope was limited to a modest sample of final-year students in two intact class sections at one institution, and its findings were confined to a semester-long management course and authentic community pharmacy scenarios. The researchers said larger studies with more comparable groups are needed to evaluate the comparative effectiveness of problem-based learning in pharmacy curricula. Within those limits, the combined results suggest that a sustained, scenario-based course may engage students and help them connect classroom knowledge with problem-solving in community pharmacy contexts. The authors associated these experiences with areas relevant to patient counselling, decision-making and practice readiness, while emphasizing that the study does not demonstrate improved patient outcomes or prove that problem-based learning was superior to conventional instruction.

Subject of Research: A mixed-methods study of problem-based learning and pharmacy students’ critical thinking and social problem-solving

Article Title: Enhancing critical thinking and social problem-solving through problem-based learning: a mixed-methods study among pharmacy students

Article References: Ulutas Deniz, E., & Kürüm, F. A. (2026). Enhancing critical thinking and social problem-solving through problem-based learning: a mixed-methods study among pharmacy students. BMC Medical Education. https://doi.org/10.1186/s12909-026-10420-6

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10420-6

Keywords: pharmacy education, problem-based learning, critical thinking, social problem-solving, community pharmacy, mixed-methods research, student engagement, quasi-experimental study, Enhancing, critical, thinking, social

The research, published in Frontiers in Education, examined whether Professional Learning Communities, or PLCs, are associated with mathematics teachers’ implementation of Problem-Based Learning, a learner-centred pedagogy that Ghana’s Ministry of Education and the Ghana Education Service have written into recent curriculum reforms. PBL asks students to construct mathematical understanding by grappling with authentic problems through inquiry, collaboration, and critical thinking, while teachers act as facilitators rather than transmitters of procedures. The catch is that this shift demands far more of teachers than individual effort: it requires sustained opportunities to design lessons together, analyse student thinking, and build shared confidence in unfamiliar instructional territory.

Led by Rose Ampomah, with Benjamin Adu Obeng and Francis Ohene Boateng, the team adopted an explanatory sequential mixed-methods design. In the first phase, they administered a structured questionnaire to 86 mathematics teachers and school administrators across three public Senior High Schools in the Atwima Kwanwoma District of the Ashanti Region, retaining 83 valid responses for quantitative analysis. The sample included 66 mathematics teachers and 20 administrators, among them heads of department, assistant headmasters, headteachers, and PLC coordinators. In the second phase, the researchers conducted semi-structured interviews with 15 mathematics teachers, five purposively selected from each school, to explain the statistical patterns in depth.

The questionnaire measured PLC participation through 25 items spanning five dimensions drawn from DuFour’s influential model: shared mission, vision and values; collective responsibility for student learning; collaborative culture; collective inquiry; and continuous improvement. A separate 10-item scale assessed PBL implementation, covering teachers’ use of authentic problems, student inquiry, collaborative learning, facilitation, mathematical reasoning, and real-life applications. All items used a five-point Likert scale. Exploratory factor analysis supported the intended structure, with all 25 PLC items loading most strongly on their theoretically intended factor and the PBL items forming a single dominant factor. Internal consistency was acceptable to good, with Cronbach’s alpha of .811 for the PBL scale, and Harman’s single-factor test suggested common method bias was not a substantial concern.

The headline numbers were high across the board. Teachers reported high PLC participation overall, with a mean of 3.47 on the five-point scale, and high PBL implementation, with a mean of 3.48. Shared Mission, Vision and Values recorded the highest dimension mean at 3.50, while Collective Inquiry was lowest at 3.45. But the study’s central finding came from a multiple linear regression: the five PLC dimensions jointly predicted PBL implementation with R² = .424, F(5, 77) = 11.345, p < .001. In plain terms, PLC participation accounted for roughly 42 percent of the variance in how thoroughly teachers embraced problem-based instruction — a large effect, with observed statistical power exceeding .99.

Not every dimension carried equal weight. Collective Responsibility for Student Learning emerged as the strongest individual predictor, with a standardized coefficient of β = .317, p = .001, followed by Shared Mission, Vision and Values (β = .278, p = .004) and Collective Inquiry (β = .263, p = .005). Collaborative Culture and Continuous Improvement, by contrast, did not significantly predict PBL implementation once the other dimensions were controlled. The researchers offer a telling interpretation: collaboration alone does not automatically translate into instructional change. Teachers’ descriptions of PLC collaboration in the interviews centred largely on logistical activities — sharing lesson plans, instructional materials, and navigating the Annual Pedagogical Plan — rather than sustained, pedagogically focused inquiry oriented towards PBL. Purposeful collaboration aimed at improving student learning, the findings suggest, matters more than frequent interaction.

The interviews illuminated how this worked in practice. Teachers described weekly PLC meetings as spaces for collaborative lesson planning, professional dialogue, and reflective practice that built their confidence in designing authentic mathematical problems and facilitating group work, inquiry activities, and student presentations. One teacher explained that colleagues shared teaching resources during PLC meetings and worked together to develop materials when they lacked them. Participants also highlighted exposure to technological tools such as GeoGebra and Maple Learn as a valuable route through which PLCs strengthened their instructional repertoire. These accounts align with Wenger’s Community of Practice theory, which holds that professional competence develops through mutual engagement in a joint enterprise rather than isolated individual learning.

Yet the study is equally candid about the barriers. Teachers identified inadequate instructional time, limited resources, difficulty developing authentic problems, and students’ weak critical thinking abilities as constraints on PBL implementation. The PLCs themselves faced structural and motivational hurdles: many teachers lacked laptops needed to participate fully, some colleagues did not see the value of PLC participation, coordinators were sometimes unfamiliar with the new curriculum, and schools lacked shared projectors and computers for disseminating ideas. One teacher put it bluntly, noting that most teachers do not have the laptops that help them participate in PLC activities, while another called for monitoring and sensitisation because many teachers do not see the essence of the meetings.

The authors are careful about what these cross-sectional, self-report findings can and cannot show. Because both predictors and outcome came from the same questionnaire at a single time point, the regression results should be interpreted as associations rather than causal effects, and the findings from one district may not generalise to all Senior High Schools in Ghana. The researchers also acknowledge that some non-mathematics-teacher administrators’ responses to the PBL scale should be read with caution. Future work, they suggest, should employ longitudinal or experimental designs and investigate additional factors such as teacher self-efficacy, pedagogical knowledge, school leadership, and resource availability.

Still, the implications are hard to ignore. For school leaders, the message is that PLC activities should be deliberately structured around shared instructional goals and collective accountability rather than administrative or examination-focused agendas. For the Ghana Education Service and curriculum policymakers, the study warns that rolling out PBL-oriented reforms without corresponding investment in purposeful PLCs, adequate instructional time, and resources is unlikely to yield the intended classroom change. As Ghana’s mathematics results continue to fluctuate, the evidence points to a deceptively simple lever: teachers who learn together, with shared goals and collective responsibility, are far better equipped to teach mathematics in ways that students can actually use.

Subject of Research: Education Research

Article Title: The role of professional learning communities in teacher implementation of problem-based learning in teaching senior high school mathematics

Article References: Ampomah, R., Adu Obeng, B., & Ohene Boateng, F. (2026). The role of professional learning communities in teacher implementation of problem-based learning in teaching senior high school mathematics. Frontiers in Education, 11, Article 1960615. https://doi.org/10.3389/feduc.2026.1960615

Image Credits: AI Generated

DOI: 10.3389/feduc.2026.1960615

Keywords: professional learning communities, problem-based learning, mathematics education, Ghana, teacher collaboration, Frontiers in Education, role, professional, learning, communities, teacher, implementation

The framework, called Simulation-Focused Problem-Based Learning (SF-PBL), was embedded in a 15-week undergraduate Dynamic System Modeling course for 53 second-year B.Tech Mechatronics Engineering students during January to April 2026. It rests on a four-phase computational pipeline: System Identification, Mathematical Modelling, Computational Simulation, and Validation and Communication. Unlike classical problem-based learning, where the learning artefact is a diagnostic report, and unlike capstone design, where a physical prototype takes centre stage, SF-PBL makes the hand-derived mathematical model and its computational verification the core intellectual product. Students selected their own real engineering systems with no prescribed list, from humanoid and hexapod robots to rocket landing simulations, self-balancing platforms, automotive shock absorbers, and digital twins of DC motors.

Each of the 17 student teams progressed through escalating deliverables. In weeks 1 to 4, they identified degrees of freedom, kinematic constraints, and generalised coordinates for their chosen system. In weeks 5 to 9, they drew free body diagrams by hand and applied Newton’s Second Law or the Lagrangian formulation to derive coupled equations of motion, converting the second-order system to first-order state-space form. In weeks 10 to 13, they implemented the model in Python 3.10 using SciPy’s solve_ivp solver, generated time-domain response plots of position, velocity, and energy, and tuned PID gains for controlled systems, with all code version-controlled on GitHub. The final two weeks were devoted to validation against analytical benchmarks such as energy conservation and limit cases, and to defending the work publicly.

That defence took place at a Mini Project Exhibition on 25 April 2026, judged by a six-member external expert panel spanning five professional domains: manufacturing production engineering, patent and innovation consultancy, external mathematics academia, Siemens Industry 4.0 and digital twin expertise, and communication and behavioural science. No judge was affiliated with the institution. The rubric allocated 20 points each to problem formulation, mathematical rigour, simulation accuracy, validation quality, and communication, and the combined panel mean was 82.62 out of 100 with a standard deviation of 9.98 and a range from 53 to 96, confirming genuine discrimination across project quality. Composite reliability across the six judges was high, with Cronbach’s alpha of 0.829, and Kendall’s coefficient of concordance of 0.448 showed statistically significant agreement in rank-ordering despite the judges evaluating distinct professional constructs.

The quantitative gains were broad-based. Conceptual knowledge, measured by five multiple-choice items on degrees of freedom, the Lagrangian, pendulum natural frequency, viscous damping, and Newton’s rotation law, rose from a pre-course mean of 1.96 out of 5 to 3.28, t(52) = 11.32, p < .001, d = 1.20. Hake’s normalised gain of 0.50 places the course in the medium interactive-engagement band, in the upper half of that category. In Hake’s landmark 1998 survey of 62 introductory physics courses, only 14 of 48 interactive-engagement courses exceeded this threshold. The score distribution shifted from strongly left-skewed, with 52.8 percent of students scoring 2 out of 5 and no student achieving a perfect score, to right-skewed, with 22.6 percent achieving 5 out of 5 after the course.

Every one of the ten self-efficacy items improved significantly at p < .001. The two largest gains appeared on PID controller tuning, d = 2.22, and Newton’s equations of motion derivation, d = 2.14, precisely the competencies most directly activated by the modelling and simulation phases. The smallest gain, state-space conversion at d = 1.46, still exceeds Cohen’s large threshold and reflects the most cognitively demanding junction in the pipeline: translating coupled non-linear equations into first-order form before numerical implementation. The authors note that the very narrow pre-survey standard deviation of 0.21 reflects baseline range restriction, which mathematically amplifies standardised effect sizes, and they point to the absolute gain of 0.88 points, with a 95 percent confidence interval of 0.75 to 1.00, as the most practically informative metric: a reliable cohort-wide shift from Neutral toward Agree on domain-specific competency statements.

Perhaps the most timely finding concerns artificial intelligence. Eighty-five percent of students reported using AI tools, with ChatGPT the most common, alone or combined with GitHub Copilot. But the usage was strikingly phase-dependent: AI adoption concentrated in Phase 3, the Python simulation coding and debugging stage, while mathematical derivation remained predominantly human-led. Nearly 40 percent of students estimated that 50 to 75 percent of their derivation was completed without AI, and a further 22.6 percent reported 75 to 100 percent without AI. The rubric explicitly required hand-drawn free body diagrams and hand-derived equations, scored by the external mathematics judge, creating a curricular incentive to complete the intellectual core without AI and use it only to accelerate the implementation of understood mathematics. The 15 percent who used no AI tools reported equivalent breakthrough themes, an important equity signal for institutions with variable AI access.

Qualitative reflections triangulated the numbers. The dominant breakthrough theme, reported by 37.7 percent of students, was the moment when simulation output matched the analytical solution. One student wrote that the instant the simulation plot matched the hand-derived equations, they finally understood what the equations meant physically. The second-ranked theme, understanding PID tuning from first principles at 22.6 percent, aligns with the highest-gain self-efficacy item, providing cross-strand validation of the framework’s core mechanism. Satisfaction with the problem-based experience was high and remarkably consistent, with a total mean of 3.74 and a standard deviation of only 0.30, suggesting the framework was experienced reliably rather than showing high inter-individual variation.

The course was designed under Outcome-Based Education principles with backward design from NBA Programme Outcomes to five Course Outcomes, each assigned a Bloom’s taxonomy level from Apply to Create. Course Outcome attainment evidence showed the largest direct gain on Lagrangian mechanics, rising 62.3 percentage points on the corresponding MCQ item, while Newton’s equations reached near-ceiling post-intervention attainment of 96.2 percent. A noteworthy discrepancy emerged for kinematics and free body diagram construction, where direct MCQ attainment remained modest at 39.6 and 30.2 percent while indirect self-efficacy attainment was strong near 70 percent, identifying these foundational competencies as targets for additional scaffolding in future implementations.

The authors are careful about limitations. The single-group pre-post design without a concurrent control condition means gains cannot be exclusively attributed to the framework; maturation and assessment familiarity remain plausible alternatives. The sample comes from a single institution, programme, and cohort in the Indian engineering education context, AI usage data is self-reported, and outcomes were captured at one post-point immediately following the exhibition. They call for multi-institution replication with quasi-experimental designs, longitudinal follow-up at six and twelve months, and investigation of the derivation-to-simulation breakthrough as a targetable threshold concept. Even so, the study offers a replicable, empirically evaluated, accreditation-compliant model whose central pedagogical logic, derive first, simulate second, validate third, is broadly applicable across thermodynamics, fluid mechanics, structural analysis, and control systems, and whose emergent human-AI complementarity may serve as a template for AI integration policy in STEM education.

Subject of Research: A computationally-integrated simulation-focused problem-based learning framework for engineering dynamics: design, implementation, and mixed-methods evaluation in an OBE-aligned undergraduate course

Article Title: A computationally-integrated simulation-focused problem-based learning framework for engineering dynamics: design, implementation, and mixed-methods evaluation in an OBE-aligned undergraduate course

Article References: Nanade, S., Dash, D., Sarkar, S., & Anne, K. (2026). A computationally-integrated simulation-focused problem-based learning framework for engineering dynamics: design, implementation, and mixed-methods evaluation in an OBE-aligned undergraduate course. Frontiers in Education, 11, Article 1948391. https://doi.org/10.3389/feduc.2026.1948391

Image Credits: AI Generated

DOI: 10.3389/feduc.2026.1948391

Keywords: engineering dynamics, problem-based learning, computational simulation, self-efficacy, Outcome-Based Education, AI-assisted learning, authentic assessment, STEM education, computationally-integrated, simulation-focused, problem-based, learning

The study, conducted by Musa Kallah Saidu and Md Abdullah Al Mamun of the Department of Technical and Vocational Education at the Islamic University of Technology in Gazipur, Bangladesh, set out to map the landscape of research on student engagement in flipped and blended learning environments. The authors performed a systematic search of the Scopus and Web of Science databases, two of the most comprehensive scholarly indices available, which together yielded 649 candidate publications. After removing duplicates and screening out studies that did not meet their eligibility criteria, the researchers settled on 45 articles published between 2007 and 2024 for detailed qualitative analysis. That span captures the full arc of the field, from the early experiments with inverted classrooms before the smartphone era to the pandemic-driven explosion of hybrid teaching models.

The methodological rigor of the review matters. Systematic reviews of this kind follow established protocols, and the authors anchored their search strategy in the widely recognized PRISMA framework, the preferred reporting standard for systematic reviews and meta-analyses. By restricting their databases to Scopus and Web of Science, they ensured strong journal coverage across science, technology, engineering, and mathematics education research, though the choice also means that relevant work indexed elsewhere may have been missed. The analysis examined each included study across several dimensions: the disciplinary scope, the theoretical frameworks used, the research methodologies employed, the technological tools deployed, the instructional strategies at the heart of each intervention, the dimensions of engagement measured, and the outcomes reported.

At the conceptual core of the review is the idea that student engagement is not a single thing. Building on the influential framework of Fredricks and colleagues, which distinguishes behavioral, emotional, and cognitive engagement, the review also considered more recent extensions, including agentic engagement, the extent to which students actively shape and contribute to their own learning activities. This multidimensional view matters because blended learning interventions often affect these dimensions differently. A student may dutifully watch a pre-recorded lecture, showing behavioral engagement, while remaining emotionally flat and cognitively passive. Conversely, a well-structured problem-solving session may ignite all three dimensions at once. The review’s findings suggest that the design of the learning environment, not the technology itself, determines which dimensions of engagement are activated.

The technical findings are strikingly consistent. Across the 45 studies, three factors emerged as the strongest drivers of engagement: technology integration, pre-class preparation, and active classroom learning. The flipped classroom model, in which students encounter new content independently before class and use class time for collaborative problem-solving, exemplifies all three. In a typical flipped science or engineering course, students watch short instructional videos or work through online modules before the session, then arrive ready to tackle laboratory exercises, peer instruction, design challenges, or problem-based activities with the instructor present. The review found that when this cycle is executed well, students engage more deeply with the material and achieve higher academic performance than in traditional lecture-based formats.

Concrete examples from the reviewed literature illustrate how this plays out across disciplines. In general chemistry, combining pre-class preparation with collaborative in-class activities improved both engagement and success rates. In second-year science laboratory courses, flipping the laboratory itself, so students arrive having already studied the procedures and theory, freed precious bench time for genuine inquiry rather than step-following. In engineering programming courses, integrating the flipped model with problem-based learning and peer instruction helped students navigate notoriously difficult introductory material. In mathematics, longitudinal case studies of large flipped barrier courses documented sustained performance gains. The review also highlighted the role of emerging technologies, including adaptive learning platforms and even AI-assisted tools such as guided use of ChatGPT within blended environments, which studies have linked to improvements in self-regulated learning, higher-order thinking skills, and knowledge construction.

Yet the review is candid about the friction points. Inadequate facilities, low student motivation, and limited teacher readiness remain stubborn obstacles. Flipped learning places a heavy demand on students to prepare before class, and when that preparation does not happen, the entire model collapses into a traditional lecture with extra homework. Instructors, meanwhile, must invest considerable time in producing quality pre-class materials and redesigning class sessions around active learning, skills that many faculty members have never been formally trained in. Institutions in resource-constrained settings face additional hurdles, from unreliable internet access to inadequate devices and classroom layouts built for passive listening. These challenges echo findings from the broader literature on emergency remote teaching during the pandemic, which distinguished sharply between hastily improvised online instruction and deliberately designed blended pedagogy.

What distinguishes successful implementations, according to the review, is a cluster of strategies that work together. Collaborative learning puts students in groups where peer accountability and discussion drive engagement. Adaptive platforms tailor content difficulty and pacing to individual learners, reducing the frustration that causes disengagement. Problem-based activities give abstract scientific and engineering concepts immediate purpose, answering the perennial student question of why the material matters. Learning analytics add another layer: by monitoring students’ navigational patterns and participation within learning management systems, instructors can identify disengaging students early and intervene before failure becomes entrenched. The reviewed studies suggest these tools are most powerful when feedback loops are iterative, with course design adjusted continuously in response to what the data reveals about how students actually behave.

The authors argue that the evidence points toward a comprehensive model for sustaining engagement in science and engineering education, one that integrates four elements simultaneously: innovative pedagogy, accessible technology, iterative course design, and robust assessment practices. No single ingredient is sufficient on its own. A beautifully produced video library is worthless without active classroom sessions to use it. Cutting-edge technology is useless if students lack access or instructors lack confidence. And assessment must reward the deeper learning that these approaches aim to produce; if examinations still reward rote memorization, students will rationally revert to cramming. The review’s synthesis suggests that when all four elements align, flipped learning consistently fosters deeper learning and higher performance, whereas partial or superficial adoption yields inconsistent results.

For educators, administrators, and researchers, the implications are practical and immediate. Universities redesigning post-pandemic curricula should treat blended and flipped learning not as a temporary contingency but as a mature pedagogical strategy with a substantial evidence base, provided they invest in faculty development, infrastructure, and course design support. Future research, the authors note, should build on this foundation to refine and validate comprehensive engagement models across diverse institutional and cultural contexts. As science and engineering education continues to absorb new technologies, from learning analytics dashboards to generative artificial intelligence, the central lesson of this review remains clear: engagement is designed, not downloaded, and the classrooms that thrive will be those that treat technology as an enabler of active, student-centered learning rather than a substitute for it.

Subject of Research: Student engagement in flipped and blended learning environments in science and engineering education

Article Title: Student engagement in science and engineering education through blended learning: a review

Article References: Saidu, M. K., & Mamun, M. A. A. (2026). Student engagement in science and engineering education through blended learning: a review. SN Social Sciences, 6(9), Article 372. https://doi.org/10.1007/s43545-026-01669-1

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01669-1

Keywords: blended learning, flipped classroom, student engagement, science education, engineering education, systematic review, technology integration, pedagogy, higher education, learning analytics, problem-based learning, active learning

The intervention was deliberately multicomponent. Before class, students in the intervention arm received a package of video, audio, and text materials along with training questions designed to prepare them for the session ahead. In class, the usual lecture was replaced by question-based, problem-based-learning-oriented discussion, in which students worked through clinical and conceptual problems rather than listening to a monologue. Targeted microteaching sessions were also woven in, giving students short, focused teaching experiences intended to reinforce specific skills and concepts. The comparison group, meanwhile, continued with the traditional lecture model, allowing the researchers to contrast the two approaches under real academic conditions rather than in an artificial laboratory setting.

The study design was a non-randomized, cluster-allocated quasi-experimental trial. Six pre-existing academic subgroups of second-year dental students served as the units of allocation: subgroups A through C received the traditional lecture model, while subgroups D through F received the blended active-learning model. Because intact groups rather than individual students were assigned to the teaching conditions, the researchers treated subgroup-level comparison as the principal basis for inferential interpretation, while student-level analyses were described as descriptive or exploratory. This distinction matters enormously for how the findings should be read, and the authors were unusually candid about its consequences.

Knowledge was assessed one week after the teaching intervention using a five-question multiple-choice quiz randomly drawn from a reviewed and piloted 15-item question bank, with questions selected at a comparable intended difficulty level. Importantly, all students completed the course quiz irrespective of whether they took part in the research, because the quiz formed part of routine academic assessment. Research participation status played no role in calculating final course grades; consent governed only whether individual assessment data could be used in the study. This separation of routine assessment from research participation was designed to minimize assessment bias and to protect students from any academic consequence of joining or leaving the study.

A total of 194 valid post-test scores were available, evenly split at 97 students per teaching model. In the principal subgroup-level analysis, the mean of the three blended subgroup means was 4.47 out of 5, compared with 3.73 for the three traditional subgroup means, a difference of 0.74 points. However, the statistical test associated with this comparison, a Welch t-test, yielded a value of 1.55 with 2.27 degrees of freedom and a p-value of 0.247, with an approximate 95 percent confidence interval stretching from -1.10 to 2.58. With only three clusters per arm, the estimate was highly imprecise and, as the authors state plainly, not statistically conclusive.

Student-level descriptive analyses told a more encouraging story. The blended group showed a higher mean score than the traditional group, 4.47 plus or minus 0.68 versus 3.70 plus or minus 1.57 out of 5. The narrower spread of scores in the blended arm is itself noteworthy, suggesting that the active-learning model may have lifted weaker students in particular, since the traditional group’s standard deviation of 1.57 was more than double that of the intervention group. But the authors are careful to note that these student-level observations did not account for the subgroup-level allocation, and therefore cannot by themselves establish a causal effect of the teaching model.

Perceptions collected from the intervention group added a third layer of evidence. Among 88 respondents who completed the piloted perception questionnaire, 90.9 percent considered the blended model useful, 86.4 percent reported better engagement, and 78.4 percent preferred the question-based model over the lecture format. These are strikingly high approval figures for an educational intervention, and they align with a broader international literature suggesting that students respond positively to flipped classrooms and problem-based approaches when those approaches are well structured and supported with quality pre-class materials.

The authors are refreshingly honest about the limitations of their work. Because only six intact subgroups were included, the effective sample size at the cluster level was tiny, which is why the confidence interval around the principal estimate was so wide. Baseline knowledge was not measured, meaning the researchers could not confirm that the two arms started from equivalent levels of understanding, a critical weakness in any non-randomized comparison. The intervention itself was multicomponent, combining flipped materials, discussion, and microteaching, so even a confirmed effect could not be attributed to any single ingredient. For these reasons, the authors conclude that the findings do not establish a causal effect, and they call for further studies with more clusters, baseline assessment, and longer follow-up.

Why does this matter beyond one dental school in Iraq? Medical and dental education worldwide is undergoing a slow but persistent transformation away from passive lecturing toward active learning, and institutions considering such reforms need evidence about what works, for whom, and at what cost. This study used only routinely available educational resources, including Google Classroom, Google Forms, Microsoft Excel, IBM SPSS Statistics, and instructor-prepared video, audio, and PowerPoint teaching materials, receiving no specific grant from any funding agency. That low-cost, low-tech profile makes the model potentially replicable in resource-constrained settings, where expensive simulation laboratories and dedicated educational technologists are simply not available.

The study also offers a methodological lesson for educational researchers. Cluster-allocated designs are often unavoidable in schools, where students are already organized into fixed groups and randomizing individuals would contaminate the comparison. But with so few clusters, statistical power collapses, and even seemingly large differences in mean scores can be compatible with chance. The Sulaimani team’s decision to foreground the subgroup-level analysis, rather than leaning on the more flattering student-level numbers, is an example of statistical transparency that other education researchers would do well to emulate. It would have been easy to headline the 0.74-point advantage and the 90.9 percent approval rating; instead, the paper’s conclusion emphasizes imprecision and the absence of causal proof.

The ethical architecture of the study was also carefully constructed. Approval was granted by the Ethics Committee of the College of Dentistry, University of Sulaimani, and the research was conducted in accordance with the Declaration of Helsinki. All research procedures, from recruitment and written informed consent through post-test data collection and questionnaire administration, occurred after ethical approval, between June 30 and August 14, 2026. The investigators acknowledged that they had teaching or assessment roles in relation to the participating students when consent was obtained, and they took steps to firewall research participation from grading decisions. Faculty investigators did not have access to students’ detailed course-grade records, formal grading remained with the course instructors, and research data were coded and reported only in aggregate form.

For dental educators watching this space, the practical takeaway is one of cautious optimism. The blended model was popular with students, produced descriptively better and more consistent quiz performance, and can be implemented with everyday digital tools. But the definitive question, whether such a model genuinely causes improved learning when compared fairly with traditional lectures at scale, remains open. Larger trials with more clusters, baseline knowledge measurement, and longer-term follow-up, ideally tracking students into their clinical years, are the logical next step. Until then, the Sulaimani study stands as a promising, honestly reported data point in the ongoing effort to make dental education as active, engaging, and effective as the profession it serves demands.

Subject of Research: Blended active-learning models in undergraduate dental education

Article Title: Quasi-experimental trial of flipped classroom with microteaching and PBL-oriented discussion in dental education: student outcomes and perceptions

Article References: Talabani, R. M., Mahmood, M. A., Abdalrahim, R. H., Amin, A. A., Ali Mahmood, T. M., & Noori, A. J. (2026). Quasi-experimental trial of flipped classroom with microteaching and PBL-oriented discussion in dental education: student outcomes and perceptions. BMC Medical Education. https://doi.org/10.1186/s12909-026-10620-0

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10620-0

Keywords: dental education, flipped classroom, problem-based learning, microteaching, active learning, quasi-experimental study, undergraduate dental students, medical education, oral histology, student perceptions, blended learning, cluster allocation

The study, published in Discover Education, focused on sixty-two second-year computer science education students drawn from two government-owned universities in a developing African context. Programming is notoriously difficult to teach; introductory courses are frequently marked by frustration, disengagement and dropout, because learners must simultaneously absorb an unfamiliar non-natural language, its syntax and semantics, and the abstract art of decomposing problems into algorithms. In many Nigerian institutions, instruction still relies on traditional lecturing, an approach the authors argue has done little to improve the learning experience. The team instead paired problem-based learning, a constructivist method in which students collaboratively analyse authentic problems before writing code, with structured access to Gemini.

The design was deliberately rigorous for a classroom setting. Because intact classes could not be randomly shuffled without disrupting academic schedules, the researchers used a non-equivalent control group pre-test–post-test design. Twenty-nine students received Gemini-enhanced problem-based learning while thirty-three followed the same problem-based sequence without AI support. Crucially, the control condition was not passive lecturing: both groups covered the same curriculum, completed equivalent tasks, received equal instructional time and were assessed with identical rubrics. The only meaningful difference was that the experimental group could consult Gemini as a scaffold, while the control group relied on course materials, peer collaboration, manual debugging and instructor feedback.

Over eight weeks, the intervention followed a carefully scripted progression. Week one established baselines using a thirty-one-item Multi-Dimensional Measure of Trust and a thirty-five-item computer programming achievement test. Subsequent weeks moved from basic syntax through algorithm design and pseudocode, data structures, modular functions and a regression computation, culminating in a collaborative capstone project. In the experimental group, Gemini was introduced only after students had attempted to interpret each problem and draft an initial solution, a rule designed to prevent the chatbot from replacing independent reasoning. Students then used the tool to explain unfamiliar syntax, critique algorithms, diagnose errors, generate test cases and review function structures, recording whether each suggestion was accepted, modified or rejected. Gemini never completed graded work or assigned marks.

The quantitative results were striking. On the pre-test, the two groups were statistically indistinguishable, with means of roughly twenty-three and twenty-four points. By the post-test, the Gemini-enhanced group averaged 73.90 against 57.37 for the control group, a difference that produced an effect size of nearly 0.79 in the analysis of variance. Because variance differed between groups, the team supplemented conventional analysis of covariance with HC3 robust estimation and bias-corrected bootstrapping. The adjusted mean difference held at 16.42 points with a 95 percent confidence interval of 14.16 to 18.67, confirming that the achievement gap was not an artefact of unequal spread or influential outliers.

Trust told a parallel story. Measured across four dimensions drawn from McKnight’s framework, performance trust, moral trust, ethical trust and transparency, trust scores climbed from around 1.6 on a five-point scale at baseline to above 4.2 in the Gemini group, while the control group rose only to the mid-2s. The analysis of covariance confirmed that instructional condition, not gender, drove the difference: gender itself and the condition-by-gender interaction were both statistically nonsignificant. That finding matters in a context where women remain under-represented in computing and where scepticism about technology adoption is often framed through a gendered lens. Here, female students in the AI-supported group actually posted the largest trust gains, rising 2.68 points on average compared with 1.41 for their counterparts without AI.

To understand why the intervention worked, the researchers interviewed eight experimental-group students, four men and four women, using inductive thematic coding. Participants described Gemini as valuable across the full arc of programming work: clarifying problems, developing algorithms, debugging code and enabling self-directed learning. One interviewee linked the approach to improved problem-solving, algorithm skills and structured program design; another said it motivated engagement in coding classes and described natural-language interaction as increasing their enthusiasm for coding. Others associated trust with transparent prompts, responsible data handling and clearly defined lines of responsibility between what the AI generates and what is expected of the student, suggesting that trust coexisted with human oversight rather than blind acceptance.

The authors ground these findings in an unusual theoretical pairing, combining Bourdieu’s concept of habitus, the idea that dispositions are formed through social and educational experience, with McKnight’s technology-specific trust framework. Their interpretation is that trust is not merely a pre-existing personality trait but something that can be cultivated through structured pedagogical encounters with AI. When instructors explained how Gemini worked, how prompts shaped outputs and how data were protected, students appeared to calibrate their reliance on the tool rather than either rejecting it or deferring to it uncritically. The nonsignificant gender effects further suggest that socially formed dispositions did not uniformly determine outcomes in this sample.

The researchers are candid about the limits of their evidence. Intact classes, non-random assignment, different institutions and facilitators, a small qualitative sample of volunteers, and self-reported trust all constrain causal claims, and the study does not establish that problem-based learning is superior to lecturing or that Gemini alone produced the gains. All four quoted interviewees were favourable, and one closed-ended ethical concern went unexplored. Still, for educators and policymakers weighing whether generative AI belongs in formal curricula, the study offers a compelling template: embed the tool inside active, problem-centred pedagogy, require students to reason first, verify AI output against course materials, and make transparency and accountability explicit. Under those conditions, the authors conclude, carefully structured AI support may meaningfully complement programming education, and trust in the machine may be something teachers can actually teach.

Subject of Research: The effect of AI-integrated problem-based learning on programming achievement and student trust in artificial intelligence in higher education

Article Title: Exploring trust in artificial intelligence in a problem-based computer programming course

Article References: Omeh, C. B., Ezeamuzie, N. O., & Olelewe, C. J. (2026). Exploring trust in artificial intelligence in a problem-based computer programming course. Discover Education, 5(1), Article 1172. https://doi.org/10.1007/s44217-026-02200-7

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02200-7

Keywords: generative AI, Google Gemini, trust in AI, problem-based learning, programming education, computer science education, quasi-experimental study, Nigeria, higher education, gender differences, educational technology, achievement

Most efforts to train teachers in health professions education concentrate on observable skills: how to facilitate a small group, how to ask open-ended questions, how to give feedback. The team behind the new study argues that this focus misses something fundamental. Drawing on Fred Korthagen’s well-known onion model of teacher development, they point out that a teacher’s behaviour sits at the surface, while deeper layers — beliefs about learning, a sense of professional identity, and an underlying mission — shape whether those behaviours are ever genuinely enacted. A tutor can be trained in facilitation techniques, the model implies, but if their beliefs remain teacher-centred, the techniques will ring hollow in the tutorial room.

The researchers designed a workshop specifically to reach those inner layers. It lasted just two hours and was organised into three parts. In the first, identification and formation, tutors reflected on questionnaire items describing student-centred beliefs, identity, and mission, and considered how such inner qualities take shape. In the second, tension exploration, participants examined realistic critical incidents — vignettes of challenging teaching situations — in small groups, discussing the tensions these episodes revealed between student-centred ideals and everyday practice. In the third, actualisation planning, each teacher drafted a personal plan for developing their own student-centred beliefs, identity, and mission in their actual work context.

The study enrolled 26 tutors, purposively sampled from two medical schools in Indonesia, each with at least one year of tutoring experience in problem-based learning curricula. Problem-based learning was a natural setting for the investigation, because the approach depends heavily on tutors who genuinely believe that small-group discussion, student-driven questioning, and collaborative knowledge construction drive deep learning. The participants attended the workshop, completed evaluation questionnaires, and took part in group interviews about their experience.

To measure change, the researchers used the Student-Centred Perspective of Teachers questionnaire, an instrument that probes six domains: environment, behaviour, competency, belief, identity, and mission. Participants completed it before and after the workshop. The largest self-reported increases appeared in the inner domains the workshop targeted. Identity showed the biggest shift, with a mean change of plus 0.27 on the scale and a 95 percent confidence interval of 0.09 to 0.45. Mission followed with a mean change of plus 0.22 (confidence interval 0.06 to 0.38), beliefs with plus 0.19 (0.01 to 0.36), and competencies with plus 0.17 (0.02 to 0.32). Crucially, the confidence intervals for these four domains did not cross zero, suggesting the gains were unlikely to be statistical noise.

By contrast, the outer layers of the onion moved less. Self-reported scores for teaching behaviour and for the perceived institutional environment showed smaller and less consistent changes. That pattern is consistent with the workshop’s design logic: a two-hour reflective session can plausibly sharpen awareness of what one believes and why one teaches, but it cannot reorganise a curriculum, lighten clinical workloads, or transform institutional culture. The researchers themselves flag this asymmetry as a key finding rather than a failure.

The group interviews added texture to the numbers. Participants attributed their self-reported shifts to three elements of the workshop: reflecting on the critical incidents, sharing opinions and experiences with fellow tutors, and creating an actualisation plan. The vignettes were described as realistic and relevant to daily work, which helped teachers see concrete challenges and opportunities rather than abstractions. Discussing questionnaire items with peers opened insights that solitary reflection might not have produced, and the act of writing a personal plan converted vague intentions into specific commitments. At the same time, participants identified environmental barriers — clinical duties among them — that they perceived as obstacles to developing student-centred inner aspects, no matter how motivated they felt leaving the room.

The study’s limitations are clearly acknowledged by its authors. It was a single-group, pre-post design with no control group, and the questionnaire was administered immediately after the workshop, so the results capture perceived awareness rather than demonstrated behaviour change. Self-reported gains can fade, and there is as yet no evidence that the tutors who attended actually teach differently weeks or months later. The sample was also confined to two Indonesian medical schools with problem-based learning curricula, which limits generalisability to other settings and teaching formats. The team is explicit that longitudinal and behaviourally oriented evaluation is still needed before any strong claims can be made.

Even with those caveats, the findings carry practical weight for medical schools and other professional education programs. Faculty development is expensive and time-pressured, and teachers in clinical settings often cannot release more than a couple of hours for training. If a brief, incident-based workshop can reliably raise teachers’ awareness of their own beliefs, identity, and mission — the layers that determine whether student-centred methods are embraced or merely performed — then institutions may have a low-cost lever for cultural change, provided they pair it with structural support. The study also suggests a reframing of what counts as faculty development: not just skill transmission, but guided self-examination.

The research, approved by the Ethics Committee of Abdul Wahab Sjahranie Hospital in East Kalimantan and conducted under the Declaration of Helsinki, was published open access on 12 October 2026. Its central message is deceptively simple: to change how teachers teach, it may help to start by changing how teachers see themselves. Whether a two-hour glimpse inward can seed lasting transformation remains the question the authors themselves are urging the field to test next.

Subject of Research: Faculty development workshop targeting medical teachers' student-centred beliefs, identity, and mission in problem-based learning

Article Title: Enhancing teachers’ awareness of student-centred beliefs, identity, and mission: an exploratory mixed-methods evaluation of a critical-incident workshop

Article References: Leatemia, L. D., Susilo, A. P., Dolmans, D. H., & Compen, B. (2026). Enhancing teachers’ awareness of student-centred beliefs, identity, and mission: an exploratory mixed-methods evaluation of a critical-incident workshop. BMC Medical Education. https://doi.org/10.1186/s12909-026-10577-0

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10577-0

Keywords: faculty development, problem-based learning, medical education, teacher beliefs, teacher identity, critical incidents, Korthagen onion model, mixed methods, Indonesia, student-centred teaching, BMC Medical Education, workshop evaluation

Computational thinking, a concept rooted in the work of Seymour Papert and famously articulated by Jeannette Wing in 2006, describes a set of cognitive skills that include breaking problems into parts, recognizing patterns, abstracting essential features and designing step-by-step algorithms. These skills are increasingly viewed as foundational for the digital era, applicable far beyond programming. Unplugged activities are widely regarded as a practical entry point because they foreground algorithms, the core of computational thinking, in a tangible form. According to embodied cognition theory, knowledge is constructed through sensorimotor experience, so physically acting out a sorting algorithm or guiding a classmate who plays the role of a robot may anchor abstract concepts in the body before students ever meet a programming language.

The scale of the underlying literature is striking. The researchers searched five major databases, including SCOPUS, Web of Science, ERIC and the ACM Digital Library, and identified 2,469 records. After removing 1,119 duplicates and screening titles, abstracts and full texts against strict inclusion criteria, only 50 studies survived. Conference papers, preprints and book chapters were excluded because they often report preliminary findings lacking the methodological detail required for rigorous synthesis. The final corpus was appraised with the Joanna Briggs Institute checklists: 64 percent of the studies were judged to be of high methodological quality and 36 percent of moderate quality, with none rated low. Even so, the authors caution that heterogeneity in designs, sample sizes and outcome measures prevented any meta-analysis.

Where is this research happening? Overwhelmingly in early and mainstream education. Seventeen studies focused on primary schools and eight on pre-primary settings, with another twenty in secondary or combined levels. The pattern makes developmental sense: younger children, still transitioning from concrete to abstract reasoning, benefit from play-based and embodied activities, while secondary students are more often assessed with structured pre- and post-tests such as the Bebras challenge or the Computational Thinking Skill Test. Forty-seven of the fifty studies took place in mainstream classrooms. Only three were conducted in inclusive or special-education settings, a figure that sits awkwardly beside the frequent claim that unplugged methods, being low-cost and technology-free, offer equal access to all learners, including those with disabilities.

The activities themselves fall into recognizable families. Paper-based exercises such as the Treasure Hunt, in which students draw step-by-step maps to a hidden prize, were the most common format, appearing in seventeen studies. Board games featured in ten, including tools like BoCaGa, which teaches coding concepts through cards and physical manipulation, and FlowBlocks, where learners connect blocks to construct logical paths. Six studies used purely physical, kinesthetic tasks, and a handful employed story-based designs or unplugged programming simulations. The most popular instructional frame was problem-based learning, used in 23 studies, followed by collaborative learning in 22 and game-based learning in 13. Notably, most studies blended several approaches, suggesting that unplugged activities function best as components of mixed instructional designs rather than standalone methods.

The reported outcomes cluster into three categories: performance skills, soft skills and attitudes. Algorithmic thinking dominated, cited in 23 studies, with decomposition and abstraction each appearing in eight. On the soft-skill side, problem-solving and collaboration led the tally, reported in 21 and 16 studies respectively. Attitudinal outcomes, particularly motivation, satisfaction and engagement, were consistently highlighted across activity types. From the perspective of self-determination theory, this makes intuitive sense: tangible, screen-free tasks may satisfy learners’ needs for autonomy and competence while lowering the technology anxiety that some students bring to coding. The authors stress, however, that these frequencies describe the focus of the literature, not proof of effect sizes or comparative effectiveness.

One of the review’s most interesting findings concerns the factors that supposedly influence performance. Gender and age were the most frequently examined variables, yet the evidence is strikingly inconsistent. Four studies found no significant gender differences, while others reported male advantages in specific contexts such as drawing or algorithmic tasks, and one found that boys’ scores actually declined as question difficulty increased. Age-related findings were similarly contradictory, with some studies emphasizing benefits for young children and others for lower-secondary students. The authors conclude that neither gender nor age acts as an independent predictor; observed differences likely depend on task type, difficulty and instructional context, variables that most studies failed to test systematically.

The three inclusive studies offer tantalizing but limited evidence. The INFACT program, designed for neurodiverse learners including children with ADHD and autism, combines tactile puzzles, embodied logic games and role-play under Universal Design for Learning principles, and was co-designed with educators and therapists. A separate study built physical, multimodal activities for students with mild disabilities and low working memory, reporting improved problem-solving and social interaction. A third explored unplugged programming for learners with visual impairments, finding that tactile and audio stimulation supported engagement and autonomous learning. These are promising designs, but three heterogeneous studies cannot establish effectiveness, and the authors are explicit that improved accessibility should not be conflated with proven learning gains.

The review also exposes a curricular blind spot. Twenty-seven studies were situated in computer science and eight in STEM or STEAM education, while art, English and social sciences appeared in exactly one study each. This concentration suggests unplugged activities are treated mainly as a gateway to programming rather than as a transversal skill. The authors argue that embedding computational thinking across the curriculum, through storytelling in the humanities or algorithmic modeling in biology, could help generalize these skills and potentially narrow gender and socioeconomic gaps in digital literacy. Yet without standardized outcome measures across domains, it remains unknown whether unplugged integration produces comparable learning in different subjects.

For educators and policymakers, the message is one of cautious optimism. Unplugged activities appear to be a flexible, low-cost and engaging way to introduce computational concepts, and the short-term evidence on skills, collaboration and motivation is broadly positive. But the field suffers from small samples, short interventions, inconsistent assessment tools and poor reporting of implementation fidelity, and it has barely begun to test its own inclusive rhetoric. The authors call for longitudinal studies, standardized measures, comparisons between unplugged and plugged modalities, and, above all, robust empirical work with learners with disabilities that distinguishes clearly between accessibility, participation, social inclusion and actual learning effectiveness. Until then, the cardboard-and-crayons revolution in computing education remains a promising hypothesis awaiting its proof.

Subject of Research: Unplugged classroom activities for developing computational thinking, examined through a systematic review with an inclusive education perspective

Article Title: A systematic mapping review and narrative synthesis of unplugged activities for developing computational thinking from an inclusive perspective

Article References: Avramidou, A., & Kazanidis, I. (2026). A systematic mapping review and narrative synthesis of unplugged activities for developing computational thinking from an inclusive perspective. Discover Education, 5(1), Article 1155. https://doi.org/10.1007/s44217-026-02303-1

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02303-1

Keywords: computational thinking, unplugged activities, inclusive education, systematic review, algorithmic thinking, board games, embodied cognition, special education, STEM education, problem-based learning, digital literacy, classroom interventions

The scale of the undertaking reflects both the popularity of the practice and the difficulty of pinning it down. Following the established scoping review frameworks of Arksey and O’Malley and Levac and colleagues, and reporting in accordance with the PRISMA-ScR guidelines, the team searched PubMed, Embase, Scopus, and CINAHL from their inception through October 2025. Two reviewers independently screened titles, abstracts, and full texts, and data on terminology, instruments, implementation, and outcomes were extracted using a predefined codebook. The search strategies were deliberately broad, capturing not only the term peer evaluation but also its frequent synonyms, peer assessment, peer feedback, and peer appraisal, crossed with the full vocabulary of collaborative learning, from teamwork and group work to leadership and contribution. That terminological sprawl turned out to be one of the study’s central findings in itself.

The headline numbers reveal a discipline heavily skewed toward one corner of the health professions. Medicine accounted for 77.2 percent of the included studies, dwarfing the representation of nursing, pharmacy, dentistry, and the allied health fields. Most research was situated within team-based learning, which featured in 30.4 percent of studies, or problem-based learning, at 22.8 percent. Strikingly, only 10.1 percent of studies involved interprofessional settings, despite the fact that modern healthcare delivery is fundamentally interprofessional and that accreditation bodies across medicine, nursing, and pharmacy increasingly demand graduates who can collaborate across disciplinary boundaries. The evidence base, in other words, has grown up in single-profession classrooms while the clinical world it prepares students for looks very different.

When the researchers examined the tools used to gather peer evaluations, the picture became even more fragmented. A majority of studies, 60.8 percent, relied on custom-developed instruments created ad hoc by the investigators themselves, rather than established, validated measures. Only 8.9 percent of studies used the Comprehensive Assessment of Team Member Effectiveness, known as CATME, one of the best-researched peer evaluation instruments available, and an equal proportion used the Koles method. Other established tools, including the Maastricht Peer Activity Rating Scale, the Professional Associate Ratings, the Team Performance Scale, and the Peer Evaluation Scale for Team-Based Learning, appeared only sporadically. The consequence is a literature in which nearly every study measures something slightly different with something slightly different, making meaningful comparison across institutions, disciplines, and decades extraordinarily difficult.

The theoretical grounding of the field proved equally thin. A remarkable 83.5 percent of the included studies referenced no explicit theoretical framework at all, meaning that most peer evaluation schemes are designed and deployed without a stated account of why the practice should work, what psychological or educational mechanisms it engages, or what outcomes it should be expected to produce. In an era when educational interventions are increasingly expected to articulate their theoretical basis, this finding suggests that peer evaluation has spread largely through tradition and institutional habit rather than through theory-driven design. The authors argue that this absence of frameworks is a key reason the literature has accumulated breadth without accumulating coherence.

The formats of evaluation were more consistent, though still revealing. Numerical rating scales dominated, appearing in 78.5 percent of studies, and were frequently paired with narrative comments, which featured in 65.8 percent. This combination reflects a practical consensus of sorts: numbers provide gradable, comparable data, while free-text comments give students the qualitative feedback that many educators consider the real point of the exercise. Beneath the format variation, however, the review found genuine convergence on content. Across the five decades of studies, the constructs being assessed settled into a recognizable cluster: professionalism, communication, teamwork, and contribution to the group’s work. Whatever the instrument, educators appear to agree on what qualities matter when students judge one another.

Where the field falters, according to the review, is in the details of implementation. Peer evaluation was most often formative, used to help students improve rather than to assign grades, in 49.4 percent of studies, yet in roughly one-third of studies the purpose of the evaluation was simply not specified. More troubling still, key implementation parameters were frequently underreported. Whether evaluations were anonymous or identified, when they occurred relative to the collaborative activity, whether they carried grading stakes and how high those stakes were, who could see the feedback that was generated, and whether students received any training in how to evaluate their peers were all commonly left unstated. These are not trivial omissions. Research on peer assessment has long suggested that anonymity, stakes, and training can substantially influence the accuracy and candor of student judgments, and without knowing these parameters, readers cannot interpret the reported results or replicate the practices.

The review’s conclusions are captured in a phrase that is likely to circulate widely in education circles: peer evaluation in health professions education is not underused but under-specified. The practice is everywhere, embedded in team-based learning courses, problem-based curricula, clinical rotations, and interprofessional exercises across the globe. What is missing is the shared infrastructure that would allow the accumulated experience to add up to knowledge. Inconsistent terminology means that studies of peer evaluation, peer assessment, and peer feedback may or may not describe the same activity. Reliance on custom instruments means that results cannot be benchmarked against one another. Limited theoretical grounding means that failures and successes alike are difficult to explain. And incomplete reporting means that even well-designed studies cannot serve as reliable templates for educators seeking to adopt best practices.

Importantly, the authors stop short of calling for a single mandated instrument. Standardization imposed from above, they suggest, would ignore the legitimate diversity of contexts in which peer evaluation operates, from a problem-based tutorial in medicine to a pharmacy team-based learning session to an interprofessional simulation. Instead, the field would benefit from three more modest commitments: shared definitions that clarify what is meant by peer evaluation and how it differs from adjacent practices, a common core of assessed constructs building on the convergence the review documented around professionalism, communication, teamwork, and contribution, and minimum reporting standards ensuring that future studies specify their timing, anonymity, stakes, feedback visibility, and training procedures. For the thousands of educators who deploy peer evaluation every semester, and for the students whose grades and feedback increasingly depend on it, the message of this review is both sobering and actionable. The practice has earned its place in health professions education over more than fifty years of use; the task now is to give it the conceptual and reporting rigor that its ubiquity demands.

Subject of Research: Student peer evaluation practices in health professions education

Article Title: A scoping review of student peer evaluations in health professions education

Article References: Haslam, E. A., Carlson, R., Williams, D. M., & Noel, Z. R. (2026). A scoping review of student peer evaluations in health professions education. BMC Medical Education. https://doi.org/10.1186/s12909-026-10567-2

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10567-2

Keywords: peer evaluation, peer assessment, peer feedback, health professions education, team-based learning, problem-based learning, collaborative learning, medical education, scoping review, interprofessional education, assessment instruments, reporting standards