The timing of the study reflects a broader shift in the engineering profession. As industry moves from the automation-focused vision of Industry 4.0 toward the human-centered ideals of Industry 5.0, employers increasingly expect graduates who can weigh well-being, sustainability, and resilience alongside performance metrics. Education 5.0, the parallel transformation in universities, asks curricula to pair advanced tools such as artificial intelligence and machine learning with an explicit concern for quality of life and long-term social consequences. The authors argue that these competencies cannot be left to develop by accident during a student’s first job. They must be deliberately cultivated in coursework, and the capstone project, where teams tackle real, ill-defined problems over an entire term, is an ideal place to start.

The BPSCm organizes that cultivation around four interrelated elements: artifacts, beliefs and culture, environment, and habits. When students analyze an engineering problem through the model, they are prompted to ask not only whether a technical solution will function, but who will use it, how it fits the social and cultural context, and what conditions might determine whether it is adopted at all. The framework draws on the biopsychosocial tradition, in which biological factors such as human physical characteristics, psychological factors such as cognition and motivation, and sociocultural dynamics such as communication patterns are treated as interacting dimensions rather than separate checklists. Crucially, the model does not claim causal relationships between these dimensions. Instead, it provides a qualitative structure for reflection, helping students notice how a change in one area, say a community’s habits, might ripple through the others and alter the fate of a project.

Methodologically, the study took an unusual and refreshingly honest approach. Rather than measuring student outcomes with tests or surveys, the researchers used an Educational Design Research framework combined with a Self-Study of Teaching Practice methodology. The two course instructors, who are also the study’s authors, kept separate reflective journals, recording one entry after each of roughly sixteen weekly class sessions across two successive academic terms. The first cohort included all 77 enrolled students and the second all 44, but the authors are careful to stress that these numbers describe the classroom context only. Students were never recruited as research participants, and no student assignments, grades, or presentations were collected or analyzed as data. The unit of analysis was the instructors’ own documented observations and interpretations, a design that trades generalizability for a detailed window into pedagogical reasoning.

In the first iteration, the instructors wove the BPSCm into the Technology Transfer Cycle, a phased process running from problem identification and formulation through referential framework building, technological solution development, and three forms of validation: academic, static, and dynamic. Students submitted written reflections analyzing an engineering solution through the model’s four components. The instructors’ journals suggested that students did begin connecting technical decisions with social, cultural, and biological considerations, and a semantic analysis of the journal entries revealed three interconnected domains underpinning engineering competence: education, comprehensive analysis, and skill development. But a persistent problem emerged. Students struggled to apply the framework consistently across project stages and had difficulty translating abstract biopsychosocial dimensions into concrete engineering decisions, particularly when trying to link social and cultural factors with technical variables.

That observation directly shaped the second iteration, where the instructors introduced a graphical representation of the BPSCm as a scaffolding device. Students first performed a context analysis, drawing the actors of a system as boxes, describing the artifacts and tools currently in use, and mapping the relationships among them through the model’s components. They were encouraged to begin from any element of the model to avoid value judgments and reach a more objective interpretation. Then came a solution analysis, in which students reflected on how the system would change once their proposed artifact was implemented, defining general and specific objectives and key variables to evaluate how the target context might transform. The visual structure gave teams a shared language: instructors noted that when students explained their diagrams during presentations, previously hidden interconnections between actors, technical variables, and sociocultural factors became explicit and discussable.

The thematic analysis of the second iteration’s journals surfaced four emerging categories: engineering skills development, analytical skills, formative research, and professional values. Compared with the first cohort, the instructors observed a shift from a broad, experience-based approach toward a more structured and methodical one. Students appeared to break complex problems into manageable components, question their own assumptions, and revise designs iteratively after considering social, cultural, and organizational implications. The instructors documented students moving away from treating solutions as fixed outcomes and toward viewing them as evolving processes open to refinement. They also reported growth in teamwork and research practices, with teams dividing tasks more deliberately, documenting findings systematically, and validating assumptions with data rather than intuition. One instructor reflected that without the model’s analysis, students would not have had the opportunity to address their problems holistically and systemically.

The study is candid about its limits, and those limits matter. Some teams never fully managed to connect the biopsychosocial and cultural dimensions with technical design variables, and a few focused on the visual polish of their diagrams without genuinely engaging with the underlying relationships. Students encountered a learning curve with the graphical aspects of the model, requiring structured workshops and continuous mentoring to apply it coherently. The instructors also noted that inconsistent exposure to the framework across earlier courses created confusion when students reached the capstone. Because the analysis rests entirely on instructor reflections, with the researchers simultaneously serving as teachers, interpretive bias is an acknowledged risk, mitigated only partially by immediate journaling, collaborative discussion, and an external collaborator’s detached perspective on the analysis.

Even with those caveats, the findings carry real weight for anyone thinking about the future of technical education. The instructors’ reflections indicate that a structured conceptual model, especially one made visible through diagrams, can help students move beyond purely technical reasoning and integrate human, social, and environmental considerations into engineering judgment. The approach aligns with Ausubel’s Meaningful Learning theory, which emphasizes active, hands-on engagement that produces durable understanding, and it complements existing tools such as Causal Loop Diagrams and Unified Modeling Language diagrams that already help engineers visualize system behavior. The authors propose extending the BPSCm with an additional analytical layer inspired by System Request, incorporating business need, requirements, value, and constraints to make contextual analysis even more explicit.

What happens next could determine whether this idea spreads beyond a single South American capstone course. The authors call for longitudinal studies that follow students into their careers to see whether biopsychosocial and cultural perspectives persist once the scaffolding is removed, and for implementations in different cultural and educational settings to test the model’s adaptability. They are also explicit that their conclusions are exploratory interpretations rather than direct evidence of changed student learning. Still, the core message resonates far beyond one classroom: engineering solutions are built for specific communities with particular beliefs, habits, and environments, and the engineers of the Industry 5.0 era will need more than code and calculus to serve them. Teaching students to see the whole system, the researchers suggest, may be the most important systems lesson of all.

Subject of Research: Integrating a biopsychosocial and cultural model into engineering capstone education to foster systems thinking

Article Title: Examining systems thinking through a biopsychosocial and cultural perspective in engineering education

Article References: Sabogal-Alfaro, G. P., Feijoo-Garcia, M. A., & Magana, A. J. (2026). Examining systems thinking through a biopsychosocial and cultural perspective in engineering education. Discover Education, 5(1), Article 1138. https://doi.org/10.1007/s44217-026-02257-4

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02257-4

Keywords: systems thinking, engineering education, biopsychosocial model, capstone projects, project-based learning, sociotechnical systems, sociocultural aspects, Educational Design Research, self-study of teaching practice, Industry 5.0, conceptual modeling, learner-centered learning

The research, led by Christian Bokhove with Lucy Hoyes and Ros Hyde, formed the English case within the Erasmus+ project ‘Research in Teacher Education’, in which five teams from four European countries collaborated to help beginning teachers in STEM subjects develop evidence-informed practice. The team redesigned a postgraduate secondary mathematics teacher preparation programme around two complementary theoretical frameworks: a set of six evidence-use mechanisms distilled by Breckon and Dodson from a meta-review of dozens of systematic reviews, and Engeström’s expansive learning cycle, a model in which learners question existing practice, analyse its contradictions, model a new solution, implement it, reflect and consolidate. Crucially, the researchers identified the questioning phase of that cycle as the moment when beginning teachers have the best opportunity to engage with evidence, and they built technology into that phase deliberately.

The team defined evidence broadly, as the best available information on a topic, gathered and interpreted by researchers, practitioners or experts through intentional and systematic procedures, with format and focus varying according to purpose. That breadth matters because, as Gorard and colleagues found in their review of evidence-use in education, most studies in the field have not shown positive results, and it is not always clear whether influential studies are high quality or how their findings should be implemented in schools. Poorly judged implementation, the reviewers warned, can lead to wasted opportunities and even harm. The Southampton redesign therefore focused on building two of Breckon and Dodson’s mechanisms: awareness, meaning positive attitudes towards evidence use, and skills, meaning the ability to access and make sense of evidence.

The programme under revision leads to a postgraduate qualification for secondary mathematics teachers and contains three Master’s level assignments, two of which are specific to mathematics education. The first asks trainees to critically evaluate a mathematics education topic, choosing between mathematical resilience or mathematical misconceptions; the second requires them to design a small research project on that topic, collect empirical data, analyse it and draw conclusions. Support for both assignments was remapped onto the expansive learning cycle, with technology use highlighted at every phase. The aim was not technology for its own sake but technology as a tool for critical evidence-use, which the researchers described as a means to an end.

The questioning phase is where the redesign was most inventive. To ensure all trainees started from a comparable knowledge base, the tutors compiled curated literature lists on the two topics and produced in-depth annotated PDF versions of key articles, complete with critical commentary on conceptual and methodological aspects, alongside screencast video presentations. About a month later, the tutors deliberately challenged that baseline. For the resilience topic, they introduced a psychological article arguing that the claims underpinning Mindset Theory appear stronger than the evidence supporting them, presenting it through an annotated PDF and a screencast recorded by the first author. For misconceptions, they exploited a genuine tension in the literature: one body of work argues that errors should be kept out of instruction, while other studies contend that explicitly including them helps learning. This engineered contradiction mirrors Engeström’s idea that expansive learning begins by criticising or rejecting aspects of accepted practice and existing wisdom.

Using an educational design research approach, the team refined the programme through formative evaluation, expert review within the wider project, and classroom implementation with a cohort of 23 secondary mathematics trainees in 2020-21 and another of around 21 in 2021-22. The researchers then conducted five one-hour voluntary interviews with trainees, transcribed the recordings and coded them through thematic analysis, focusing only on passages about technology. Three themes emerged. The first concerned technology platforms as sources of evidence. Trainees described relying on news websites such as BBC News alongside magazines and research articles, and several stressed the need to consult multiple sources to get, as one put it, a picture of the potential truth, particularly when reports said slightly different things or failed to reference their sources.

The trainees were equally clear about which sources they distrusted. Social media came in for sustained criticism, with one interviewee saying she would not trust anything that came up on platforms like Facebook as evidence, and another describing social media as a channel for potentially harmful misinformation that needs critical challenge. One trainee remarked that she now took everything on the news with a pinch of salt because reports said one thing one day and something different the next. The interviews also revealed a temporal dimension to the problem: recent developments in video editing mean a video can be tampered with, further complicating trust in digital evidence. Trainees preferred documented or hard evidence, such as emails and quantitative data, but acknowledged that qualitative information like student opinions also holds value, even if it is more difficult to measure.

The second theme, challenging evidence, surfaced a tension between critical ideals and classroom practicality. Trainees recognised the need to engage critically with evidence as part of their Master’s level work, but questioned whether complex methods such as large-scale surveys could translate into daily teaching, especially given the time constraints of a busy profession that leaves little room to read everything or conduct proper research. Views on the programme’s two assignments diverged in instructive ways. One trainee said she knew nothing about mathematical anxiety or resilience when she started the first assignment and counted its screencast-supported critical evaluation among the things she had taken from the whole year, while suspecting the more practical data-collection assignment would stay with her less. Another trainee valued collecting his own evidence through small-scale projects, saying it helped him back up what he was doing against external directives. The third theme was more instrumental: trainees described spreadsheets for visualising data, surveys and polls for gathering student feedback, school information systems tracking marks, behaviour and homework, and electronic lesson plans and observation notes serving as evidence of professional practice.

The study’s most forward-looking element is its next redesign, built around generative AI. The authors stress that the human tutors, with decades of teaching experience and deep knowledge of the mathematics education literature, remain an essential ingredient that cannot be understated, but no teacher can have such a knowledgeable other on call throughout a career. As a proof of concept, the team took an evidence grid of critical questions used in Master’s level modules and fed it, together with the mindset-critique article, into NotebookLM, a tool powered by Google’s Gemini large language model. The result was an interactive critical dialogue: the agent could summarise sources, explain the rationale for a study on request, shorten answers to fifty words, identify the article’s biggest limitation, namely that its results mainly apply to university students, and generate study guides, briefing documents and even audio podcasts. The researchers see potential for genAI to support Socratic dialogue between trainee and machine, while cautioning that no data on such use yet exist.

The authors are candid about the study’s limits and about technology’s double edge. With only five interviews from a single country, generalisability is constrained, though the findings align with the wider four-country project. They warn that pre-service teachers are novices who benefit from automaticity in classroom routines, which reduces cognitive load, yet habit formation can also limit growth in teacher effectiveness, so critical thinking about evidence must remain on the radar across a teacher’s whole career rather than only during training. They call for proper field experiments comparing their approach with business as usual. Most pointedly, they caution that technology is no silver bullet: after decades of perennial promise, its effectiveness depends on context, and recent research suggests that cognitive offloading onto AI tools may even erode critical thinking. The lesson, they argue, is neither to dismiss the technology nor to embrace it uncritically, but to weigh its strengths and limitations, which is precisely the questioning stance the programme tries to instil.

Subject of Research: Technology-supported critical evidence-use in pre-service secondary mathematics teacher education

Article Title: Using technology for questioning evidence in pre-service teacher education

Article References: Using technology for questioning evidence in pre-service teacher education. (n.d.). https://doi.org/10.1007/s44322-025-00045-w

Image Credits: AI Generated

DOI: 10.1007/s44322-025-00045-w

Keywords: pre-service teacher education, evidence-informed teaching, expansive learning cycle, generative AI, mathematics education, educational design research, digital literacy, critical thinking, teacher training, evidence-use mechanisms, technology, questioning