The study, published in Discover Education by Alexander M. Sidorkin, examined a single 16-week semester of a course in child and adolescent development. Thirty-six students enrolled, most of them juniors and seniors at a regional public university serving a substantial Hispanic-serving and first-generation population. Most arrived with no prior coursework in qualitative research, no familiarity with academic publishing conventions, and little experience writing extended scholarly manuscripts. Yet the course required each of them to produce a full-length qualitative research manuscript of at least 5,000 words and to submit it to a peer-reviewed journal, with confirmation of submission as part of the final assignment. This was not a simulation of professional work; it was the work itself.

The pedagogical gamble was that generative AI could absorb the procedural burden that historically made such ambitious assignments impractical for novices. Authentic assessment, the idea that students should be evaluated on tasks resembling real professional practice, has struggled for decades with a built-in tension: authentic tasks demand competencies students have not yet developed, and the scaffolding needed to bridge that gap often dilutes the authenticity it is meant to support. Sidorkin’s argument is that a capable language model changes this calculus. A student who previously could not construct a conventional methods section or navigate disciplinary terminology can now obtain that support on demand, shifting the locus of difficulty away from routine production and toward decisions about evidence, interpretation, and methodological fit.

Crucially, the course did not treat AI as a threat to be policed. Students used a custom course-specific tool, the CHDE 111 Class Companion, a configured GPT that offered navigation, concept clarification, assignment protocols, and rubric-based draft review while explicitly instructed to support learning without providing completed assignments. They were also free to use ordinary ChatGPT or other major platforms without limitation, and the syllabus placed responsibility for the quality, veracity, and originality of the final product squarely on the student. For most assignments, students submitted complete logs of their AI conversations alongside their work, making part of the human-machine interaction visible for feedback and analysis.

To measure whether unrestricted AI access compressed student outputs, the study applied three rule-based indicators to the 33 final manuscripts. The Methods Specificity Index, a 0-to-6 scale scoring the presence of six methodological components such as a named qualitative approach, a concretely specified data source, sampling logic, analytic procedure, ethical safeguards, and a rationale connecting method to question, showed dramatic upper-bound compression. The median score was the maximum of 6, nearly 70 percent of manuscripts received the full score, and 97 percent scored either 5 or 6. Methodological specification, in other words, had become something AI scaffolding could reliably deliver.

The other two indicators told a different story. Claim-Evidence Coupling, the proportion of interpretive statements locally accompanied by specific evidentiary support such as a quotation, citation, or reference to the study data, ranged from 0.000 to 0.750 with a median of 0.333. Theory-Interpretation Linkage, the proportion of interpretive statements that explicitly invoked the manuscript’s own theoretical framework, ranged from 0.000 to 0.917 with a median of 0.444. Both remained substantially dispersed across the corpus. Students differed enormously in whether their interpretive claims were tethered to actual evidence and whether theory genuinely informed their analysis, dimensions that AI can assist with but cannot resolve, because warranting a claim depends on the actual data and argument of each individual project.

This differential pattern is the study’s central empirical finding. Compression appeared where structural scaffolding suffices; dispersion persisted where situated judgment is required. The theoretical framework behind the study, drawing on the five-dimensional authentic assessment model of Gulikers and colleagues, holds that when professionals routinely use AI, an assessment that categorically excludes it may actually resemble professional work less faithfully. Student contribution, in this view, is not independent text production but the direction, evaluation, integration, and revision of work produced within a human-AI system, a capacity the study terms extended executive cognition, paired with discerning thinking, the ability to judge AI output for substance and fit rather than equating fluency with quality.

Detailed comparison of four contrasting students, two from the top and two from the bottom of the course-points distribution, illuminated what those differences look like in practice. The stronger students orchestrated their AI use: they specified tasks precisely, supplied information the model could not infer, such as which social media discourse threads belonged in the analysis, repeatedly asked the Companion to audit sections for coherence, and pushed through multi-turn revision cycles until the conceptual fit was right. One student rejected a Companion-generated framework summary as misaligned with her research question and iterated across several turns until satisfied. The weaker students, by contrast, engaged in sparse, one-directional interactions, accepting template language and structural outlines without constraining them to the specifics of their actual studies. Their manuscripts could look competent at a glance, but theme statements were thin on evidence and theory was named rather than deployed.

The study is candid about its limits. It is a single-semester case study without a comparison group, so it cannot isolate AI orchestration from prior preparation, effort, or research aptitude. The three indicators are heuristic measures applied with the assistance of ChatGPT rather than validated instruments, and the instructor, course designer, tool designer, analyst, and author are all the same person, a concentration of roles that creates real confirmation-bias risk despite safeguards such as fixed rules and independent case selection. The process evidence also covers only a late slice of the semester, so it cannot show whether orchestration practices developed through instruction or simply differed among students from the start.

Even with those caveats, the implications are significant for anyone designing courses in the AI era. The study suggests that authentic assessment can remain viable and diagnostically useful when instructors explicitly distinguish between dimensions AI can scaffold reliably and dimensions where student judgment remains consequential, then align tasks, feedback, and rubrics accordingly. It also suggests that AI orchestration itself should become an instructional object, modeled and taught rather than assumed, and that when AI can generate prose quickly, the bottleneck shifts toward reading, evaluating, and revising it. Whether the pattern transfers across disciplines and institutions remains an open empirical question, but the Sacramento case demonstrates that giving every student a powerful AI assistant did not make them interchangeable. The tool was common; the judgment was not.

Subject of Research: Authentic assessment and student judgment in an AI-integrated undergraduate qualitative research methods course

Article Title: Authentic assessment in an AI-integrated qualitative research methods course

Article References: Sidorkin, A. M. (2026). Authentic assessment in an AI-integrated qualitative research methods course. Discover Education, 5(1), Article 1153. https://doi.org/10.1007/s44217-026-02273-4

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02273-4

Keywords: authentic assessment, generative AI, qualitative research methods, undergraduate education, evaluative judgment, human-AI collaboration, assessment design, rubric, extended executive cognition, pedagogy, case study, academic writing

The research, published open access on 11 October 2026, tackles a persistent gap in early childhood pedagogy. Life education—the umbrella term for teaching that helps children understand existence, loss, mortality, and the value of living—is widely recognized as important, yet classroom practice often stalls because adults lack the tools to open the conversation. Direct questioning about death can frighten children or invite rehearsed, adult-shaped answers. Low’s approach inverts the dynamic: instead of asking children to translate their inner world into adult language, the method asks adults to learn to read the symbolic languages children already use.

Expressive arts, as deployed in this study, differ from conventional art instruction in a crucial way. The goal is not technical skill or aesthetic product but the externalization of subjective experience. Children engage in sensory exploration with diverse artistic media, and the resulting works—paintings, constructions, tactile pieces—are treated as evidence of meaning-making rather than as objects to be graded. This distinction matters because the psychological literature has long suggested that pre-verbal and symbolic processing dominates early childhood cognition. A child who cannot define mortality in a sentence may nonetheless represent it vividly in a darkened color field, a broken line, or an empty shape.

To investigate this, Low designed and implemented a structured series of expressive arts-based life education activities and then analyzed them through two interrelated dimensions. The first dimension concerned the children themselves: their subjective interpretations and emotional expressions around life and death as these surfaced in artistic creation, with particular attention to the symbolic meanings embedded in color, line, form, and multisensory experiences. The second dimension concerned the adults and the environment: how teachers’ pedagogical actions, the design of materials, and the configuration of learning spaces shaped children’s participation, the depth of their expression, and their processes of constructing meaning.

The data collection was deliberately triangulated across three streams. Low maintained reflective journals throughout the implementation, capturing the researcher’s own observations and decisions as the activities unfolded. Interviews were conducted with collaborating teachers, providing an external professional perspective on how children responded and how the sessions could be adjusted. Finally, the children’s artistic works and the documentation of their creative processes formed the third evidentiary base, allowing the analysis to track not only what children produced but how their symbols evolved over time. A systematic thematic analysis was then applied to this combined corpus, extracting recurring patterns rather than relying on anecdote.

From this analysis, the study synthesizes two major strands of findings. The first strand identifies interpretive dimensions through which children express life-related meanings in expressive arts creation. In practical terms, this means the research offers a framework for reading children’s work: color choices, gestural qualities of line, formal structures, and the integration of sensory experience are treated as interpretable channels of communication about existence, loss, and continuity. The second strand is prescriptive. Low distills the implementation experience into three key principles for running expressive arts-based life education activities: teacher facilitation, openness of materials, and a process-oriented pedagogical approach.

Each of the three principles carries technical weight. Teacher facilitation refers to the adult’s role as an enabler rather than an instructor—a figure who scaffolds the creative environment, responds to children’s emerging symbols without imposing interpretations, and holds the emotional space steady when difficult themes surface. Openness of materials means that art supplies and sensory resources are presented without predetermined outcomes, so that a lump of clay or a palette of paint can become whatever the child’s inner processing requires rather than what a lesson plan dictates. Process orientation, the third principle, shifts the evaluative center of gravity from finished artwork to the act of creation itself, recognizing that the therapeutic and educational value lies in the externalization and exploration, not in the artifact.

The study’s ethical architecture reflects the delicacy of its subject. The research was approved by the Human Research Ethics Committee of National Cheng Kung University under approval number NCKU HREC-E-114-0587-2, and it was conducted in accordance with the Declaration of Helsinki, Taiwan’s Human Subjects Research Act, and the institutional requirements of National Cheng Kung University. Written informed consent was obtained from the parents or legal guardians of all minor participants. Children themselves were informed about the purpose and procedures of the study, and their participation was voluntary and explicitly decoupled from academic performance or evaluation. Participants retained the right to withdraw at any time without penalty, confidentiality was protected throughout, and support arrangements were available in case any child experienced discomfort—a protocol that models the kind of care the pedagogy itself advocates.

Why does this matter beyond the classroom? Death education remains one of the most contested areas of childhood curriculum worldwide, with many school systems deferring it entirely or reducing it to biology lessons. Yet children encounter loss—of pets, of relatives, of stability—long before any curriculum addresses it. Research of this kind suggests that the developmental window for helping children process these experiences symbolically is not only open but actively demanding of attention. By demonstrating that expressive arts can serve as a non-verbal and symbolic pathway through which children explore and articulate their experiences, the study gives educators a defensible, evidence-based alternative to silence.

The findings also carry implications for how teachers are trained. If the interpretive dimensions identified in the study are to be used in real classrooms, educators need to develop a literacy in children’s symbolic expression—learning, for instance, that a recurring visual motif may signal an ongoing process of meaning-making rather than mere decoration. Low’s dual focus on children’s interpretations and on teachers’ pedagogical actions acknowledges that the two are inseparable: the depth of a child’s expression depends substantially on whether the surrounding adults and materials permit it. The study is candid about practical considerations and limitations in applying expressive arts within educational settings, and it positions its three principles as a starting framework rather than a finished prescription. As expressive arts gain traction in wellbeing-focused education globally, this research offers both a theoretical map of what children’s symbols may mean and a grounded set of implementation principles for bringing life education out of the realm of the unsayable and into the realm of the creatable.

Subject of Research: Expressive arts as a symbolic, non-verbal method for children's life education on life and death

Article Title: Beyond words: symbolic interpretation and implementation principles of expressive arts in children’s life education

Article References: Beyond words: symbolic interpretation and implementation principles of expressive arts in children’s life education. (n.d.). https://doi.org/10.1038/s41599-026-09169-6

Image Credits: AI Generated

DOI: 10.1038/s41599-026-09169-6

Keywords: expressive arts, life education, children, death education, symbolic interpretation, early childhood education, art therapy, meaning-making, teacher facilitation, pedagogy, multisensory learning, qualitative research

The research team, led by Liam Ó Riada with Unai Pascual, Esther Turnhout and Julia Neidig, applied discourse analysis to the historical record of Spanish education policy. Discourse analysis, in this context, is a method for examining how language constructs reality: which concepts are elevated as self-evident truths, which are marginalized, and which actors are granted authority to define what education is for. Rather than treating policy texts as neutral descriptions of reform, the researchers read them as battlegrounds where different worldviews compete. This approach draws on political theory holding that dominant ideas are never permanent; they can be destabilized by crises and dislocations, creating openings for alternative visions to enter the mainstream.

The analysis identified three distinct periods in Spanish education policy. The first, spanning roughly 1975 to 1990, coincided with Spain’s transition to democracy after four decades of dictatorship. During this era, policy discourse was animated by emancipatory aspirations: education was framed as a vehicle for freedom, democratic citizenship and social justice. The legacy of authoritarian schooling made it politically urgent to imagine classrooms as spaces where critical thinking and participation could flourish. This period, the authors argue, contained genuine affinities with what scholars now call transformative sustainability education—an approach that emphasizes transdisciplinary learning, experiential and embodied pedagogies, and the capacity of learners to question and reshape the systems around them.

That emancipatory moment, however, did not last. From about 1990 to 2018, the study documents the consolidation of a neoliberal consensus in Spanish education policy, one heavily shaped by the discourse of the Organisation for Economic Co-operation and Development. The OECD and similar actors have long promoted a human-capital framing of education, in which schools are evaluated primarily by their contribution to economic competitiveness and employability. Under this logic, the purpose of education narrows to generating human capital: measurable skills that can be fed into labor markets. Standardized assessment, international rankings and performance indicators became the dominant instruments of governance, and the creative, critical and transdisciplinary pedagogies that sustainability transformation requires were progressively marginalized.

The technical heart of the study lies in showing how this economistic framing operated discursively. Human-capital discourse did not merely coexist with other visions of education; it absorbed and reinterpreted them. Even concepts with emancipatory origins, such as lifelong learning or educational quality, were redefined in market-oriented terms. The researchers describe this as a hegemonic process, in which one worldview achieves such dominance that its assumptions become common sense, rendering alternatives difficult even to articulate. For advocates of sustainability education, this meant that arguments about ecological crisis, social justice or intergenerational responsibility struggled to find legitimate space within a policy conversation organized around productivity and competitiveness.

The third period begins in 2019 and continues to the present, and it is here that the analysis becomes most striking. A sequence of dislocatory events—including large-scale student climate mobilizations, growing public awareness of ecological breakdown and political turbulence within Spain—destabilized the previously settled consensus. Sustainability emerged as a central signifier in education policy, culminating in a new education law that explicitly legitimizes transdisciplinary, experiential and embodied pedagogies. For the first time in the country’s democratic history, the legal architecture of Spanish schooling formally recognizes that learning for sustainability must go beyond transmitting facts about the environment and instead engage students in transformative, experience-based practice.

The authors are careful, however, not to declare victory. Their analysis shows that the discursive opening of the recent period remains constrained by entrenched neoliberal governance. The market-oriented logics built into assessment regimes, funding structures and institutional incentives did not disappear when sustainability entered the policy vocabulary. This creates a real risk of co-optation: the possibility that sustainability itself will be reframed as another form of human capital—a set of green skills for a green economy—rather than as an invitation to question the economic assumptions that drove ecological crisis in the first place. The study suggests that whether the current opening leads to genuine transformation or to a rebranded version of the old consensus depends on struggles still underway.

One of the most consequential findings concerns how change actually happens. The researchers found that dislocatory events alone are insufficient; what matters is whether different struggles for educational reform can be linked together into a broader coalition. When movements for climate action, pedagogical innovation and democratic schooling connect their demands, they can collectively push elements of transformative sustainability education into law and policy. Conversely, when reform efforts remain fragmented, the resilient machinery of market-oriented governance tends to absorb or dilute them. This insight travels well beyond Spain: it offers a strategic lesson for educators and policymakers in other countries navigating similar tensions between economic and ecological imperatives in their school systems.

The Spanish case also illustrates a broader tension in global sustainability politics. International organizations have increasingly embraced education as a tool for achieving the Sustainable Development Goals, yet the same institutions often promote the human-capital frameworks that the study identifies as a barrier to transformative pedagogy. The result is an ambivalence at the heart of international education policy: sustainability is celebrated in declarations while the governance instruments that shape classrooms daily continue to reward standardization and measurable economic outcomes. The Spanish experience shows that national politics can partially break from this pattern, but only when domestic coalitions are strong enough to translate crisis into legal and institutional change.

What emerges from this research is neither optimism nor fatalism, but a precise map of where leverage lies. The discursive history of Spanish education demonstrates that dominant framings are historically contingent rather than natural, that crises can crack them open, and that coalitions can push transformative visions into binding policy. At the same time, it warns that vocabulary is not victory: a law that mentions sustainability can still be implemented through competitive, market-driven governance that hollows out its meaning. For anyone concerned with how education systems might help societies navigate the ecological transition, the Spanish record offers both a warning and an invitation—the door to transformative education is open, but only sustained political struggle will keep it that way.

Subject of Research: Discourse analysis of Spanish education policy and openings for transformative sustainability education

Article Title: From generating human capital to transformative education: discursive struggles in Spanish education policy and openings for sustainability

Article References: Ó Riada, L., Pascual, U., Turnhout, E., & Neidig, J. (2026). From generating human capital to transformative education: discursive struggles in Spanish education policy and openings for sustainability. PLOS Sustainability and Transformation, 5(8), e0000267. https://doi.org/10.1371/journal.pstr.0000267

Image Credits: AI Generated

DOI: 10.1371/journal.pstr.0000267

Keywords: education policy, sustainability transformation, Spain, discourse analysis, human capital, OECD, neoliberalism, transformative education, climate crisis, pedagogy, PLOS, policy reform

The strategy at the heart of the study is known as KWHLAQ, an extended version of the familiar KWL (Know–Want–Learned) chart that many readers will remember from school. The original KWL tool prompts learners to activate prior knowledge, set questions, and consolidate what they have learned. The modified KWHLAQ framework adds three further steps: identifying how the learner will find the needed information, planning how the new knowledge can be applied in practice, and generating new questions that arise from the learning cycle. Each letter of the acronym corresponds to one stage of an iterative inquiry loop, turning passive note-taking into an active, self-monitoring process of questioning, searching, evaluating, and reflecting.

The research team, led by Yasmeen Mohamed Mohamed Shehata of the Nursing Education Department at Alexandria University, together with colleagues from Alexandria University, Prince Sattam Bin Abdulaziz University, and Helwan University, set out to test whether this framework could move the needle on two competencies that educators consider essential for high-acuity clinical work: critical thinking and self-directed learning. Critical care nursing demands rapid, defensible decisions under pressure, where a misjudged interpretation of ventilator data or a subtle change in a patient’s condition can have immediate consequences. Students who cannot evaluate evidence, weigh alternatives, and direct their own learning are at a distinct disadvantage both in training and at the bedside.

To test the intervention rigorously, the researchers employed a quasi-experimental, nonequivalent control group pretest–posttest design. One hundred undergraduate nursing students in their third semester, all enrolled in the Critical and Emergency Nursing Department at Alexandria University’s Faculty of Nursing, participated during the 2025 academic session. Fifty students were assigned to the study group, which received a twelve-week structured intervention built around the KWHLAQ framework, while the remaining fifty formed a control group that received standard lecture-based instruction. Because randomization was not used, the researchers applied statistical covariate adjustment to account for baseline differences between the groups, a standard approach in quasi-experimental educational research.

Both groups completed two validated instruments before and after the intervention period. The Critical Thinking Questionnaire, or CThQ, with an internal consistency of Cronbach’s alpha of 0.80, measured multiple dimensions of critical thinking, including interpretation, analysis, and evaluation of information. The Self-Directed Learning Instrument, or SDLI, also with an alpha of 0.80, assessed four subscales covering the learner’s ability to set goals, seek resources, monitor progress, and evaluate outcomes independently. Using the same measures in both groups at both time points allowed the team to compare change over time and isolate the effect of the teaching strategy.

The results were unambiguous. After covariate adjustment, the study group demonstrated significantly higher post-intervention critical thinking scores than the control group, with a p-value below 0.05 and a Cohen’s d of 0.67, an effect size in the moderate-to-large range. The study group improved significantly in overall critical thinking, with a mean difference of 5.66 points (p < 0.01), while the control group’s scores actually declined over the same period. The most dramatic difference appeared in the evaluating subscale of the CThQ, where the intervention group posted a mean difference of 4.50 points (p < 0.001) and an effect size of Cohen’s d = 2.10 — an exceptionally large gain by educational research standards, and one made more notable by the fact that students receiving conventional instruction moved in the opposite direction.

Self-directed learning showed an even stronger response. The study group improved by a mean difference of 23.06 points on the SDLI (p < 0.001), corresponding to a Cohen’s d of 2.45, and the gains were significant across all four subscales of the instrument. In practical terms, students who had spent twelve weeks working through the KWHLAQ cycle did not merely absorb more content; they became measurably better at organizing their own learning — identifying what they needed to know, planning how to find it, applying what they learned, and reflecting on remaining gaps. That shift from passive recipient to active manager of knowledge is precisely the behavior that lifelong clinical learning requires, particularly in critical care, where guidelines, technologies, and evidence bases evolve continuously.

The authors argue that the strength of the KWHLAQ approach lies in its structure and simplicity. The framework requires no expensive technology, no additional staffing, and no wholesale curriculum redesign; it can be embedded into existing courses as a recurring scaffold around lectures, case discussions, and clinical assignments. Its iterative design also aligns with established learning theory: by asking students to articulate prior knowledge and explicit questions, the strategy works within what educational psychologists call the zone of proximal development, the space between what a learner can do alone and what they can achieve with appropriate support. By closing each cycle with application and new questions, it encourages the reflective reasoning that underpins sound clinical judgment.

The study was approved by the Research Ethics Committee of the Faculty of Nursing at Alexandria University (Approval No. AU-20-9-380) and conducted in accordance with the Declaration of Helsinki, with all participants giving informed consent and retaining the right to withdraw at any time without adverse consequences. The research received no external funding, and the authors declare no competing interests. The article was received on 12 May 2026, accepted on 13 August 2026, and published on 28 September 2026 under a Creative Commons Attribution 4.0 license, making the full methods and findings freely available to educators worldwide.

For nursing schools facing rising enrollment, acute workforce shortages, and curricula already crowded with technical content, the appeal of a twelve-week, low-cost intervention with effects of this magnitude is considerable. The authors conclude that integrating this structured, replicable pedagogical framework into nursing curricula may promote active learning, reflective reasoning, and the clinical decision-making competencies essential for high-acuity practice. As with any quasi-experimental study conducted at a single institution, broader replication across different settings and student populations will be needed to confirm generalizability, and the published version is subject to further editorial refinement. But the core message is already compelling: sometimes the most powerful upgrade to a demanding professional curriculum is not new technology at all, but a better set of questions.

Subject of Research: Effect of the modified KWHLAQ learning strategy on critical thinking and self-directed learning in critical care nursing education

Article Title: Enhancing critical thinking skills and self-directed learning among critical care nursing students: a quasi-experimental study on the impact of the modified KWHLAQ strategy

Article References: Shehata, Y. M. M., Hendawi, N. E., Atta, M. H. R., Aly, M. A., Nagy, A. S. A., & Younis, A. A. A. (2026). Enhancing critical thinking skills and self-directed learning among critical care nursing students: a quasi-experimental study on the impact of the modified KWHLAQ strategy. BMC Nursing, 25(1), Article 895. https://doi.org/10.1186/s12912-026-05254-y

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05254-y

Keywords: critical thinking, self-directed learning, nursing education, KWHLAQ strategy, critical care nursing, quasi-experimental study, problem-based learning, clinical decision making, Alexandria University, BMC Nursing, pedagogy, intensive care

Challenge-Based Learning, or CBL, has become one of the most widely adopted pedagogical frameworks in higher education worldwide. Rooted in constructivist and experiential learning theory, it flips the traditional classroom: instead of receiving content from lecturers, students work in teams to identify authentic real-world problems, investigate them collaboratively, and design solutions using digital tools relevant to their disciplines. The approach typically unfolds in three iterative phases, engage, investigate, and act, originally conceptualised through Apple’s ACOT2 initiative. Empirical studies across engineering, computer science, entrepreneurship, and sustainability education have linked CBL to gains in critical thinking, creativity, digital literacy, perseverance, and communication, making it an increasingly strategic element of institutional missions aligned with the Sustainable Development Goals.

Yet adoption of CBL remains strikingly uneven. Resource-constrained environments, particularly in Sub-Saharan Africa, have seen few applications and even fewer contextually grounded studies of their impact. The researchers behind the new study argue that this gap mirrors broader structural inequalities in global higher education, where models originating in the Global North are often transplanted without adequate consideration of local pedagogical cultures, infrastructural constraints, or political-economic realities. Universities in the region face systemic pressures to produce digitally skilled graduates for emerging labour markets, but they contend with limited digital infrastructure, constrained funding, rigid curricula, and insufficient faculty development, all of which constrain innovation and experiential learning.

To address this gap, the team developed the Multi-competence Learning (MCL) programme through an institutional cooperation project called GeoICT4e, which brought together eight universities from Finland and Tanzania to support the digital transformation of geospatial and ICT education. MCL builds on CBL’s foundations but diverges in deliberate ways shaped by Tanzanian realities. It supports students less familiar with self-directed learning, treats geospatial and digital innovation competencies as core outcomes reflecting African employment needs, and integrates stakeholder co-creation and feedback in settings where access to data, technology, and mentorship is uneven. The programme targets four competence domains: problem-solving in space and time, geospatial data and technology skills, entrepreneurial and innovation skills, and climate, sustainability, and resilience skills.

Implemented during 2022 and 2023, MCL ran as a structured eight-week campaign across ten cohorts at five Tanzanian universities, engaging 226 students, 116 male and 110 female, from more than 20 study programmes spanning geography, geospatial sciences, business, information technology, environmental sciences, forestry, and the social sciences. Students tackled locally relevant umbrella challenges including water resource management, urban waste management, and land use planning. The research design was ambitious: weekly surveys tracking learning progress yielded 699 responses, a comprehensive end-of-course survey captured 140 students, and 40 semi-structured group interviews with 20 student teams provided qualitative depth, all embedded within a Participatory Action Research framework and ethnographic observation.

The results are emphatic. Between 89 and 95 percent of students agreed or strongly agreed with all six indicators of learning support in the final survey, including mentorship, DigiCampus online materials, and confidence in project progress. Students reported growing proficiency with open-source geospatial tools such as QGIS, Google Earth Pro, KoboToolbox, and ODK, with roughly 62 percent identifying geospatial tools as a major learning gain and 48 percent reporting new digital or ICT skills such as coding, prototyping, or app development. The programme produced mobile applications, interactive maps, and infographics, and for many students it offered a first exposure to an online learning environment, fundamentally changing how they interacted with course materials and applied digital tools to mapping, data collection, and analysis.

Beyond technical competence, the data reveal profound personal and interpersonal growth. In open-ended responses, 44 percent of students identified problem-solving or critical thinking as a key skill gained, while 22 percent highlighted systems thinking or the ability to conceptualise complex problems. Forty-one percent explicitly named teamwork and collaboration as a central gain, describing how interdisciplinary teams fostered creativity, empathy, and collective problem-solving even when differences in perspectives and technical levels caused friction. Students described a trajectory from hesitation to agency: initially reluctant to speak up, many ended the programme chairing meetings, coordinating tasks, and steering projects toward milestones. One student captured the shift plainly, reporting new confidence in framing complex problems, formulating relevant questions, and working across mentors, peers, and stakeholders to design solutions.

The learning culture itself emerged as transformative. In contrast to traditional lecture-based courses, students experienced MCL as open, dialogical, and psychologically safe, valuing the freedom to ask questions and learn through experimentation without fear of criticism, a condition known to foster inquiry and risk-taking. The social dimension mattered too: informal interactions such as shared meals and team-building activities built trust and a sense of belonging that sustained engagement. Remarkably, 88 percent of students expressed willingness to continue their projects beyond the programme, requesting additional financial support, more extensive technical training, stakeholder engagement opportunities, and improved digital infrastructure to do so.

But the study is equally candid about the obstacles. Internet connectivity problems were cited by 46 percent of students in open-ended responses, financial constraints by 38 percent, difficulty obtaining datasets by 27 percent, and time pressure from the programme’s short duration by 24 percent. Technical difficulties and inconsistent mentor availability affected smaller but significant shares. Students struggled initially with unfamiliar software, and the intensive pace compressed the time available for prototyping and testing, limiting the depth of final outputs. Most tellingly, weekly survey participation collapsed from 210 students in week two to just 51 by week seven, a pattern the authors interpret as a critical equity concern suggesting that some students disengaged due to infrastructural, financial, or logistical barriers rather than lack of interest.

The authors are careful to acknowledge limitations, including reliance on self-reported data, the absence of a control group, the eight-week programme’s short duration, and potential researcher involvement bias. Even so, the implications are far-reaching. The Tanzanian case demonstrates that adapted challenge-based frameworks can advance inclusive, practice-oriented learning in resource-limited settings, but only with strategic investment in digital infrastructure, faculty development, flexible curricula, and policy reform. The researchers call for embedding such models into national higher education agendas, longer programme durations, and future research on long-term impacts on graduate employability and civic engagement. As African universities grapple with how to prepare students for digital futures, this study offers both a proof of concept and a sober checklist of what must change first.

Subject of Research: Student learning experiences and outcomes in an adapted Challenge-Based Learning programme across five Tanzanian universities

Article Title: Student learning experiences in challenge based learning across five Tanzanian universities

Article References: Timbuka, M., Käyhkö, N., Kasvi, E., Tengström, T., Msilanga, M., Järvi, A., Ismail, M., Mbwilo, B., Mbise, M., Babere, N., & Hooli, L. (2026). Student learning experiences in challenge based learning across five Tanzanian universities. Discover Education, 5(1), Article 1079. https://doi.org/10.1007/s44217-026-02240-z

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02240-z

Keywords: Challenge-Based Learning, Multi-competence Learning, Tanzania, higher education, geospatial education, Sub-Saharan Africa, student engagement, digital divide, pedagogy, experiential learning, ICT skills, educational innovation

The technical core of the review traces how foundation models actually work and why they represent a break from earlier educational AI. These models are built on the transformer architecture, first described in 2017, which uses attention mechanisms to weigh relationships between every element of an input sequence. Pre-trained on vast corpora of text, images, audio, and video, models such as BERT, the GPT series, Llama, PaLM, and BLOOM acquire general-purpose capabilities that can then be adapted through instruction tuning and reinforcement learning from human feedback. The review emphasizes that this pre-training plus adaptation paradigm is what allows a single model to draft lesson plans, generate quiz questions, score essays, and explain mathematical reasoning, tasks that previously required separate, narrowly engineered systems.

Multimodality emerges as a particularly important trend for classrooms. Vision-language models such as BLIP-2, Flamingo, CogVLM, and MiniGPT-4 can interpret diagrams, handwritten work, and photographs of physical experiments, while audio-language models like Pengi and CLAP extend understanding to spoken language and sound. For younger learners who cannot yet type fluently, and for subjects like geometry, chemistry, and music where content is inherently visual or auditory, the review suggests that multimodal foundation models could finally deliver the long-promised vision of AI tutors that see and hear what a student sees and hears. Benchmarks such as CMMU and CMMMU, designed to test Chinese multimodal question understanding across school disciplines, illustrate how researchers are beginning to measure these capabilities against actual K-12 content.

Personalized learning stands out as the application with the most direct classroom impact. By combining foundation models with educational data mining and learning analytics, systems can model what an individual student knows, recommend learning paths, and adjust the difficulty and framing of explanations in real time. The review notes that recommender systems, long used to suggest resources in e-learning platforms, gain new power when a large language model can explain why a particular exercise was chosen, converse about a student’s confusion, and generate fresh practice items on demand. Research on concept-aware learning path recommendation and on bringing generative AI to adaptive learning points toward tutors that respond to the learner rather than forcing the learner through a fixed sequence.

Automated assessment is another area undergoing rapid change. The review surveys work showing that large language models can generate multiple-choice questions, reading comprehension exercises, and even full examination papers, with comparative studies finding that GPT-4-generated questions in programming education can approach the quality of human-crafted items. Essay scoring is advancing too, with researchers exploring how models can produce not just a grade but a rationale, mimicking the multi-trait judgments of human raters. Yet the authors are careful to flag the risks: studies questioning whether GPT-4 alone is sufficient for reliable essay grading, and concerns that generative AI undermines online exam integrity, show that automated assessment demands rigorous evidence-centered design and human oversight before it can be trusted at scale in schools.

For teachers, the review describes foundation models as collaborators rather than replacements. Systems for lesson planning can draw on decades of instructional design principles, generating plans aligned with established frameworks, while tools such as Tutor CoPilot demonstrate a human-AI approach in which the model supplies real-time expertise to the human tutor mid-session. Pre-service teachers studying AI-generated hints in online mathematics learning reported generally positive perceptions, suggesting that models can scaffold the difficult early years of teaching. The review also highlights domain-specific educational models such as EduChat, a chatbot purpose-built for intelligent education, as evidence that the field is moving beyond generic chatbots toward systems tuned to classroom norms and safety requirements.

The technical challenges the review catalogs are formidable. Hallucination, the tendency of models to produce fluent but false statements, is especially dangerous for children who lack the background knowledge to detect errors, motivating research such as the Woodpecker system for correcting multimodal hallucinations. Mathematical reasoning remains a known weakness, with studies probing whether ChatGPT truly understands place value and whether chain-of-thought prompting and self-consistency techniques can make multi-step problem solving reliable. Retrieval-augmented generation, which grounds model outputs in verified documents, and tool-augmented reasoning frameworks such as ReAct and ChatCot offer partial remedies, but the review stresses that benchmark results, including evaluations on MMLU and dedicated math benchmarks like MathEval, show performance varies widely across subjects and question types.

Equally important are the pedagogical and developmental questions. The review grounds its analysis in learning theory, invoking Piaget’s stages of cognitive development, Vygotsky’s zone of proximal development, Dewey’s experiential learning, and self-determination theory’s account of intrinsic motivation. A model tuned for adult self-learners may undermine a ten-year-old’s motivation by simply supplying answers, whereas productive failure research suggests students often learn more by struggling before receiving help. Age-appropriate instructional strategy, the review argues, is not a cosmetic layer but a fundamental design constraint: hints must be calibrated, reading levels matched to stages described in reading development research, and engagement fostered rather than eroded. The authors identify motivation and engagement as critical open issues that pure capability benchmarks do not measure.

Looking forward, the review sketches a research agenda for the coming years. It calls for rigorous evaluation of foundation models against curriculum standards, development of safeguards for child safety and data privacy, and hybrid workflows in which teachers retain pedagogical authority while models handle content generation, feedback, and administrative load. The global landscape it surveys, spanning OpenAI’s GPT-4, Meta’s Llama 3 family, Google’s Gemma, Alibaba’s Qwen series, and Chinese systems from Baichuan, ChatGLM, and iFLYTEK’s AutoSpark, indicates that educational AI is now an international race, with open-weight models lowering the barrier for schools and researchers to build customized tools. The authors’ central message is one of disciplined optimism: foundation models have demonstrated exceptional performance across domains, but realizing their promise in K-12 education will depend on sustained collaboration between AI engineers, learning scientists, and classroom teachers, ensuring that the technology serves the developing minds it is meant to support rather than the other way around.

Subject of Research: Applications of large-scale foundation models in K-12 education

Article Title: Current Trends and Future Prospects of Large-Scale Foundation Model in K-12 Education

Article References: Zhu, Q., Wang, M., Zhang, T., & Huang, H. (2025). Current Trends and Future Prospects of Large-Scale Foundation Model in K-12 Education. Frontiers of Digital Education, 2(2), Article 22. https://doi.org/10.1007/s44366-025-0059-6

Image Credits: AI Generated

DOI: 10.1007/s44366-025-0059-6

Keywords: foundation models, K-12 education, large language models, multimodal AI, personalized learning, automated assessment, educational technology, intelligent tutoring, hallucination, pedagogy, GPT-4, learning analytics

The research team, led by Shuiling Lu of Wuzhou University together with colleagues at UCSI University and Zhengzhou Normal University, followed PRISMA 2020 guidelines to sift the literature published between January 2015 and May 2025. Starting from 1,964 database records drawn from Web of Science, Scopus, Sage, Springer Link and JSTOR, and applying a search string that combined problem-based learning terms with creativity and higher-education keywords, the team ultimately retained fifteen peer-reviewed studies for systematic review. Only six of those studies, all quasi-experimental designs with pretest-posttest control groups, provided the complete statistical data needed to enter the meta-analysis.

The pooled result from those six studies was eye-catching. Compared with traditional instruction, problem-based learning was associated with a large improvement in creativity outcomes, yielding a pooled Hedges’ g of 1.64 with a 95 percent confidence interval running from 0.96 to 2.32. The overall effect was statistically significant, with a Z-statistic of 4.72 and a p-value below 0.001. In every one of the six studies, the problem-based learning group outperformed the control group on creativity measures, though the magnitude of the advantage varied considerably from one context to another.

That variation is where the caution begins. The heterogeneity statistic, I-squared, came in at 88.5 percent, meaning that the overwhelming majority of the variability between studies reflects real differences in contexts, interventions and measurements rather than random noise. The Q-test confirmed this, returning a value of 43.29 with five degrees of freedom and a p-value below 0.001. With only six studies available, the authors could not run formal subgroup analyses or meta-regression to statistically disentangle those sources of variance. They describe their pooled estimate as an upper-bound figure, potentially inflated by methodological weaknesses, and frame the primary contribution of their work not as a single number but as a demonstration that positive effects appeared consistently across diverse settings.

Methodological quality was a persistent concern. Using the ROBINS-I tool for non-randomized studies, two independent reviewers rated all fifteen included studies as carrying serious or critical risk of bias. The main culprits were inherent to educational research: the absence of randomization, which leaves the confounding domain at serious risk, and the impossibility of blinding students and instructors to the teaching method they were receiving. Missing outcome data and selective reporting, by contrast, were consistently rated low risk. A funnel plot showed slight asymmetry, hinting that small negative studies may be under-represented in the literature, though with only six points the plot has limited reliability.

The individual study effects tell a story of their own. Problem-based learning in engineering education produced the largest single effect, a Hedges’ g of 2.83, though that figure was converted from a reported Cohen’s d because raw standard deviations were unavailable, introducing uncertainty. Writing education yielded a g of 2.52, while PBL combined with digital mind-mapping in mathematics produced a moderate-to-large g of 1.40. Two engineering studies reported comparable effects of 0.99 and 1.41, and the smallest effect, 0.75, came from a dance-based intervention, suggesting that topic relevance, facilitator expertise and implementation fidelity all moderate outcomes. The authors stress that these subgroup comparisons are purely descriptive and hypothesis-generating, since no statistical comparisons between variants were possible.

One of the review’s most striking findings concerns how creativity itself is measured, and how differently that happens across disciplines. In arts and humanities contexts such as fashion design, collage making, dance and writing, assessors foregrounded aesthetic originality, narrative coherence, emotional connection and self-expression. In science and engineering settings, by contrast, creativity was judged through solution feasibility, technical viability, collaborative problem-solving and inventiveness in tackling authentic design problems. This pattern supports the domain-specificity view of creativity, treating it not as a single unified construct but as something that manifests differently depending on disciplinary expectations, a distinction educators must respect when designing both activities and assessment rubrics.

The measurement problem runs deeper still. The included studies operationalized creativity through markedly different instruments, from the Torrance Tests of Creative Thinking to self-report Likert scales and researcher-developed rubrics, each capturing distinct facets of fluency, flexibility, originality and usefulness. Two of the six meta-analyzed studies relied on self-report scales, which research suggests are vulnerable to social desirability bias and may correlate only moderately with demonstrated creative performance. Notably, the studies reporting the largest effects tended to use self-report measures or researcher-developed rubrics, raising the possibility that measurement characteristics inflated the observed magnitudes. None of the included studies employed widely validated, standardized creativity instruments with established psychometric properties, and all follow-ups were short-term, with none exceeding six months, leaving the durability of any creativity gains unknown.

Despite these caveats, the review identifies three mechanisms that appear to amplify problem-based learning’s effect on creativity. First, integrating the arts introduces non-verbal modes of expression, allowing students to externalize tacit knowledge through concept mapping, collage and dance that is difficult to articulate in words alone. Second, digital augmentation through platforms such as MATLAB, Genially and gamification tools creates interactive environments that scaffold creative risk-taking and sustain motivation, as seen in studies of matrix algebra and gamified mathematics training. Third, hybrid models combining problem-based learning with project-based learning generate synergistic gains across the so-called 4C competencies of creativity, collaboration, communication and critical thinking. The authors note these mechanisms are not mutually exclusive, though because most interventions bundled multiple components, the unique contribution of each cannot yet be isolated.

For educators and curriculum designers, the practical message is that problem-based learning shows genuine promise as a creativity-oriented strategy in higher education, particularly when enriched with digital tools or arts integration, but recommendations must remain tentative pending stronger validation. The authors call for future research built on randomized controlled designs, validated standardized creativity assessments and delayed post-tests to establish both causality and durability of effects. They also highlight gaps in the evidence base: disciplinary concentration in engineering and STEAM, sparse representation from the liberal arts and natural sciences, an exclusively undergraduate population, and incomplete reporting of key moderators such as intervention duration and facilitator expertise. Until such rigorous studies arrive, the dramatic effect size of 1.64 should be read not as a guarantee but as an encouraging, preliminary signal that when students are handed real problems to solve together, their creative capacities may genuinely flourish.

Subject of Research: The effect of problem-based learning on creativity outcomes in STEAM higher education

Article Title: Enhancing creativity in STEAM higher education through problem-based learning: a systematic review and meta-analysis

Article References: Lu, S., Phang, S. M., Judge, S. K., Liu, Y., & Fu, K. (2026). Enhancing creativity in STEAM higher education through problem-based learning: a systematic review and meta-analysis. Discover Education, 5(1), Article 1075. https://doi.org/10.1007/s44217-026-02125-1

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02125-1

Keywords: problem-based learning, creativity, STEAM education, higher education, meta-analysis, systematic review, pedagogy, divergent thinking, digital technology, arts integration, quasi-experimental design, educational assessment

The research, led by Sujal Manohar and Eden Noah Gelgoot with colleagues including Alex Walker, Nora McCarthy and Margaret S. Chisolm, set out to answer a question that has lingered around arts and humanities initiatives in medicine: how exactly do these activities enable learning and reflection? While the value of the arts and humanities in medical education has been increasingly recognized in recent years, with the potential to cultivate important clinical attributes, much of the literature has focused on outcomes rather than process. Less is known about how a processfolio actually functions as a vehicle for learning, and even less about the individual, lived experiences of the learners who create one. The Johns Hopkins team designed their study to close that gap, treating the students not as subjects to be measured but as narrators of their own educational journey.

Methodologically, the study took a deliberately qualitative approach. The researchers employed a cross-sectional design anchored in Interpretative Phenomenological Analysis, or IPA, a methodological framework that aims to uncover how individuals make sense of their personal and social world. IPA is well suited to questions of lived experience because it asks researchers to engage in a double hermeneutic: participants are interpreting their own experiences, and the researchers are then interpreting those interpretations. In this case, the data came from three sources. First, the researchers analyzed the completed processfolios themselves, the summative course artefacts in which students had assembled their work. Second, they examined the students’ final written reflections. Third, they conducted individual semi-structured interviews with each participant, allowing them to elaborate on what the processfolio had meant to them.

The sample was small but remarkably complete. All five fourth-year medical students who finished the museum-based course chose to participate in the research study. That total participation is significant in qualitative research of this kind, because it means the findings capture the full cohort rather than a self-selected fragment of it. It also reflects the voluntary, trust-based nature of the study, which was reviewed by the Johns Hopkins University Institutional Review Board and deemed exempt research under reference number IRB00365270. Because the study was classified as exempt, the institutional review board determined that it did not include informed consent procedures or fall under specific guidelines for research with human subjects. The exemption also signals the educational, non-interventional character of the work: the researchers were studying artefacts and reflections that already existed as part of the course.

From their analysis, the researchers distilled four Group Experiential Themes, the shared patterns of meaning that emerged across the five participants’ accounts. The first theme, contemplating the journey of reflection, growth and insight, speaks to the processfolio’s core function: it makes learning visible over time. Rather than submitting a single polished product at the end of a course, students accumulate artefacts that trace how their thinking evolved week by week. Looking back across that accumulation, participants could see their own intellectual and emotional trajectory in a way that conventional assessments rarely allow. The processfolio became, in effect, a mirror held up to the learning process itself, letting students contemplate not just what they had learned but how they had learned it.

The second theme, exploring personal and emotional growth, points to a dimension of medical training that is often squeezed out by the demands of scientific coursework. Medical students are routinely taught to compartmentalize emotion, yet the capacity to recognize and process one’s own emotional responses is increasingly understood as central to physician wellbeing and patient care. In the museum-based course, engagement with art opened a space where emotional responses were not only permitted but expected to be examined. The processfolio gave students a private, structured repository for that examination, and the act of assembling it encouraged them to connect their experiences in the gallery to their emerging identities as physicians. Participants reflected on their development as medical students and future physicians, a conclusion the authors highlight as central to the study’s purpose.

The third theme is perhaps the most clinically resonant: communicating in clinical contexts, with attention to the importance and complexity of both verbal and non-verbal communication. Art museums are, in a sense, laboratories of observation. Standing before a painting or sculpture, students must describe what they see, tolerate ambiguity, and recognize that others may perceive the same object differently. These are precisely the skills that translate to the bedside, where a physician’s reading of a patient’s posture, expression and silence can matter as much as any laboratory value. The processfolio documented this translation, capturing how students moved from describing artworks to thinking about how they describe patients, and how much of clinical communication happens without words at all.

The fourth theme, acknowledging the complex history of medicine and advocating for better practices, reveals a critical, even activist dimension of the experience. Museums and medicine share entangled histories, and engagement with artistic and historical material prompted students to confront uncomfortable truths about how their profession has treated patients, particularly those from marginalized communities. Rather than treating the arts and humanities as a decorative supplement to clinical training, this theme shows them functioning as a lens for professional critique. Students did not simply absorb the history of medicine; they interrogated it, and the processfolio preserved that interrogation as a record of their evolving ethical commitments. The authors frame this as learners advocating for better practices, a striking outcome for a course that takes place far from any hospital ward.

The study is candid about its limitations and about the fact that the processfolio is not a frictionless tool. Learners shared challenges related to its use, an acknowledgment that matters for educators considering adoption. A processfolio demands sustained reflective labor across a course, and students accustomed to the metrics of biomedical assessment may find the open-ended, self-documenting format unfamiliar or burdensome. The authors also emphasize that the research was conducted at a single institution with a small sample size, which constrains how far the findings can be generalized. Five students at one medical school cannot speak for all learners everywhere, and the authors explicitly call for future research in this area to test and extend what they observed.

Even with those caveats, the study offers a rare, granular look inside an arts and humanities intervention that is often praised in the abstract but seldom examined from the learner’s point of view. By combining analysis of the processfolios and written reflections with in-depth interviews, and by interpreting that material through the lens of Interpretative Phenomenological Analysis, the researchers have shown how a single pedagogical artefact can carry a student through contemplation of their own growth, exploration of their emotional life, refinement of their communicative instincts and confrontation with their profession’s history. The work was published open access on 1 October 2026, received on 11 April and accepted on 10 September of that year, with Manohar and Gelgoot serving as co-first authors. Chisolm’s contribution was supported through her direction of the Paul McHugh Program for Human Flourishing at Johns Hopkins. As medical schools worldwide continue to weave the arts and humanities into their curricula, this study suggests that the humble processfolio, a folder of artefacts and reflections assembled over four weeks in a museum, may be one of the most quietly powerful instruments for shaping the physicians of tomorrow.

Subject of Research: Use of processfolios in arts and humanities-based museum education for reflective learning in medical students

Article Title: Exploring the use of processfolios in a museum-based course for medical students

Article References: Manohar, S., Gelgoot, E. N., Walker, A., McCarthy, N., & Chisolm, M. S. (2026). Exploring the use of processfolios in a museum-based course for medical students. BMC Medical Education. https://doi.org/10.1186/s12909-026-10394-5

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10394-5

Keywords: medical education, arts and humanities, processfolio, museum-based education, reflection, Interpretative Phenomenological Analysis, medical humanities, qualitative research, Johns Hopkins, experiential education, pedagogy, communication skills

The paper, written by David Perl-Nussbaum and Noah D. Finkelstein of the University of Colorado Boulder, builds on a distinction drawn by education researchers Justin Reich and Jeffrey Dukes between adoption technologies and arrival technologies. Adoption technologies, such as Peer Instruction, Tutorials in Introductory Physics, the Learning Assistant program, and interactive simulations like PhET, followed a familiar and well-studied sequence: researchers designed them with learning goals in mind, evaluated them rigorously, and only then made them available to faculty alongside evidence of their effectiveness. Generative AI followed the opposite path. ChatGPT entered schools through sudden, widespread, largely spontaneous use by students and teachers, promoted heavily by technology companies, before institutions had formed any response at all and before researchers could assess its risks and benefits for learning.

The authors acknowledge that earlier outside technologies did disrupt classrooms, but they note that calculators and mobile phones appeared on roughly ten-year iteration cycles, giving educators time to adapt between generations of hardware. Generative AI offers no such interval. Frontier models are gaining new capabilities on timescales of months rather than years, and the 2026 AI Index report finds that AI capabilities are still accelerating rather than plateauing. AI systems also exhibit what researchers call a jagged frontier: they handle some tasks well and fail at others that seem similar, and the boundary between the two shifts with every model release. A tool introduced in the first week of a semester may behave in meaningfully different ways by week fourteen.

This creates what the authors identify as the central dilemma of AI-era reform. Institutions cannot wait for best practices to emerge, because student learning is already being reshaped by AI use, campus policies, and individual faculty experimentation, with or without deliberate institutional involvement. Yet neither can they responsibly scale innovations that remain unvalidated. Banning generative AI is not tenable, and neither is uncritically embracing it. Existing models of institutional change, the authors argue, presuppose exactly the kind of stable, well-evidenced innovation that arrival technologies lack. Rogers’ classic Diffusion of Innovations theory, which underpins many STEM change efforts, explicitly describes adoption as an uncertainty reduction process, and it works only when uncertainty can actually be reduced through accumulated evidence.

To address this gap, the framework identifies six dimensions along which prior change models must be adapted, three concerning the tools at the center of reform and three concerning the people involved. The first tool dimension is the evidence base. Because direct evidence for effective AI-based pedagogy does not yet exist in recognizable form and may not for years, the authors call for humble inquiries grounded in intellectual humility: surfacing what faculty are already trying, documenting what appears to work and for whom, and sharing emerging cases without overclaiming their generalizability. Institutions should fund experimentation cycles and provide access to AI with guardrails, without making long-lasting pedagogical or financial commitments. The authors also flag an equity concern, noting that scaling unevidenced practices risks widening existing gaps, since learners with greater resources and digital literacy are best positioned to benefit from early-stage efforts.

The second dimension, rate of change, leads to one of the framework’s most consequential design implications: reform should be organized around pedagogical approaches rather than specific tools. Where past initiatives like the Science Education Initiative could reasonably build repositories of stable course materials, AI-driven change should build repositories of approaches and example cases that survive the replacement of any particular model or platform. These approaches should draw on the durable evidence base from interactive engagement research, including what is known about student engagement, authentic disciplinary practice, and epistemology, none of which becomes obsolete when a new model is released. This reframing also shifts institutional investment away from tool procurement, such as particular grading systems or tutoring agents, toward sustained support for faculty inquiry and iteration. Course policies, the authors add, should be developed through ongoing dialogue between instructors and students and revisited as tools evolve, though they caution that iteration cannot excuse institutions from coherent principles around ethics and academic integrity.

The third tool dimension concerns scope. Unlike clickers or tutorials, which were purpose-built by educators with learning theory embedded in their design, generative AI is a general-purpose technology developed outside education and appropriated for classroom use. Some of its most common applications, producing finished work with minimal effort, stand in direct tension with the purposes of learning. AI is also simultaneously a mediator and an object of learning: students must learn to use it productively, and the disciplinary practices being taught are themselves being reshaped by it. The authors argue that faculty therefore need institutional support to educate students beyond disciplinary content, addressing ethics, agency, and the role of AI in students’ professional futures, and that flexible general tools are a more durable strategy than dedicated platforms that carry obsolescence risk.

On the human side of the framework, the authors document a profound shift in faculty agency. In past reforms, faculty opted in voluntarily, motivated largely by intrinsic interest in student learning. With generative AI, the decision to engage has effectively been made for them: their courses have already been transformed by unsupervised student use, whether faculty are aware of it or not. The disruption is also highly uneven, hitting large introductory writing and computational courses far harder than laboratory or graduate courses, so a framework that treats all faculty as equally affected risks irrelevance for some and under-resourcing for others. The authors recommend mapping disruption through structured interviews and investing in support structures analogous to the Science Education Initiative’s discipline-based teaching fellows, but with a crucial redefinition of the role. Because expertise in AI-based educational practice is largely absent, change agents cannot serve as brokers of known best practices; they must instead act as facilitators of collective inquiry. The authors specifically caution against overvaluing AI tool expertise, warning that change agents drawn from industry or vendor contexts may be implicitly oriented toward promoting adoption rather than critically evaluating it, and arguing that pedagogical and disciplinary expertise is more durable.

The final dimension repositions students from the endpoint of reform to genuine participants in shaping it. Survey data consistently shows that students lead in AI use, are actively seeking guidance on responsible use, and often cite respect for their instructors’ policies as a reason for not using AI to produce finished work. The authors found a structured early-semester survey of students’ AI use to be a valuable entry point for collaborative discussions about norms. They illustrate the framework through a case study of a six-session faculty workshop series in the CU Boulder physics department in Spring 2026, involving nineteen faculty across the series. In one session, survey data from roughly 350 students revealed that checking the correctness of solutions was among their most frequent AI uses, prompting faculty to test actual homework solutions in chatbots and examine which prompting strategies encouraged reflection rather than shortcuts. The authors are careful to note the framework’s limitations: it is theoretical and not yet empirically tested, it draws primarily from U.S.-based change initiatives, and the AI landscape itself continues to evolve. They present it not as exhaustive or final, but as a call to action, urging institutions to move forward with humility and intention into an uncertain landscape that has already arrived.

Subject of Research: A framework for adapting STEM higher education institutional change models to generative AI

Article Title: A framework for institutional change in the age of AI

Article References: Perl-Nussbaum, D., & Finkelstein, N. D. (2026). A framework for institutional change in the age of AI. International Journal of STEM Education, 13(1), Article 60. https://doi.org/10.1186/s40594-026-00649-4

Image Credits: AI Generated

DOI: 10.1186/s40594-026-00649-4

Keywords: generative AI, STEM education, institutional change, higher education, arrival technology, interactive engagement, faculty development, change agents, student partnership, educational reform, intellectual humility, pedagogy

The study was conducted by Vassilis A. Failadis, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, and Vassilis P. Plagianakos of the University of Thessaly in Greece, and appeared online on September 11, 2026. Rather than surveying the sprawling field of artificial intelligence in education at large, the authors deliberately narrowed their focus to lesson planning, an essential component of a teacher’s daily workload and a core element of professional preparation. Following the search-appraisal-synthesis-analysis framework, known as SALSA, the researchers examined 53 recent publications and organized their findings around four themes: the quality and pedagogical value of AI-generated plans, the challenges and limitations of the technology, teachers’ attitudes and perceptions, and possible improvements and future directions. This focused lens matters, because lesson planning sits at the intersection of content knowledge, curriculum requirements, classroom context, and student needs, making it a revealing test case for what generative models can and cannot do.

The technical verdict on output quality is nuanced. Across the reviewed studies, large language models are frequently praised for producing well-organized plans with clear instructional formats. Platforms such as ChatGPT can reliably follow common instructional models, arranging activities into recognizable stages such as brainstorming, group discussion, guided exploration, presentation, and evaluation. In structural terms, the models have absorbed the conventions of lesson design from their training data and reproduce them fluently. Yet the empirical evidence consistently shows that these plans provide only a general framework and require substantial adaptation before they can function in a real classroom. The plans look like lessons; they do not yet behave like lessons, because they lack the embedded awareness of particular students, curricula, and teaching conditions that experienced educators bring to every plan they write.

Four recurring limitations emerge from the literature with striking consistency. The first is limited pedagogical depth: the teaching practices suggested by large language models tend to promote procedural knowledge rather than conceptual understanding, offering little support for complex learning processes such as reasoning, reflection, and justification. The second is weak differentiation, since the plans rarely include strategies for individualization or alternatives for students with different learning profiles, a shortcoming that becomes especially pronounced in specialized contexts such as special education. The third is unrealistic time planning. One study found that a plan designed by an AI for a single hour would require at least three hours in practice, with the models assigning very short durations to essential tasks while allocating generous time to reminder stages. The fourth is inconsistent output quality, with high variance reported even within identical prompt groups; outputs generated from the same prompt were found to differ by at least five rubric elements, a level of variability that undermines reliability in any professional workflow.

These findings lead the authors to an important practical conclusion about how teachers should interact with the technology. Producing a usable plan from a large language model is not a one-step process but an iterative one, in which the quality of the generated material depends partly on the clarity and specificity of the prompts. This reframes lesson planning with AI as a skill in its own right, blending prompt engineering with pedagogical expertise. A vague request yields a generic skeleton; a precise, context-rich prompt can yield something considerably more useful. But even the best outputs remain drafts that demand professional refinement, because no prompt can fully encode the tacit knowledge a teacher accumulates about how a particular class learns, stalls, and responds.

The review also paints a detailed picture of how teachers themselves perceive these tools. Most educators acknowledge the usefulness of artificial intelligence in lesson planning while simultaneously expressing reservations about its pedagogical adequacy. Teachers commonly use AI to generate content, yet many still prefer to work collaboratively with colleagues and emphasize the need for clear institutional policies to regulate its use. Some see the models as a helpful aid but do not believe they can replace a teacher’s pedagogical judgment; many agree that AI can assist with the initial organization of a lesson while stressing that the final responsibility for adapting and applying the plan lies with the teacher. Intriguingly, the authors observed a generational divide: younger educators tend to be more open to using AI, while more experienced teachers remain cautious, a difference the researchers attribute to the influence of professional experience and familiarity with classroom practice on teachers’ perspectives.

To move the field forward, the review identifies four main directions that appear repeatedly in the literature. The first is teacher training and AI literacy, ensuring that educators understand what these systems can actually do and develop the ability to adapt and assess the content they produce. The second is prompt engineering and reflective use, since the pedagogical value of generated content depends directly on how clear, precise, and well-targeted the prompts are. The third is policy development and ethical guidelines, with clear rules needed to guarantee responsible and educationally appropriate deployment of AI tools in schools. The fourth is model improvement and pedagogical alignment, calling for enhanced features and greater flexibility in instructional design, including guided interfaces and options that allow teachers to supply contextual information about their students, curriculum, and constraints. Together, these directions sketch a roadmap in which the technology and the profession evolve in tandem rather than in competition.

The review is equally candid about what the research itself still does not know, identifying four significant gaps. Most existing studies remain theoretical or rely on teachers’ opinions without examining what happens when AI-generated plans are actually applied in classrooms. Most are built on qualitative observations, interviews, or questionnaires, with limited direct comparisons between teacher-created and machine-generated plans. The literature is also geographically concentrated, with the United States and Türkiye appearing most frequently, raising questions about how findings transfer to other educational systems. Finally, little attention has been paid to the processes through which teachers interact with AI systems during planning, leaving the human-machine workflow largely unexplored. Recent empirical work nonetheless offers telling hints: one study found that although teacher-created plans were rated higher in quality overall, teachers were unable to reliably distinguish them from AI-generated ones, while another found that refined AI-generated mathematics lesson plans received high evaluation scores and in several cases outperformed teacher-created plans, even though teacher-generated procedures aligned more closely with actual classroom practice. The gap between perceived and actual quality is clearly a live scientific puzzle.

The overall conclusion is measured rather than sensational. Large language models can serve as useful tools for lesson planning, but they are not yet capable of replacing the teacher’s pedagogical role. Their outputs are usually well-structured, organized, and goal-oriented, yet they tend to fall short in adaptation, differentiation, and pedagogy, the dimensions that determine whether a plan actually teaches anyone anything. As the authors put it, these systems should currently be viewed as support for lesson planning rather than as ready-made planning tools, and should not yet be considered a substitute for pedagogical expertise; their use makes sense when they function as support tools rather than as replacements for professional judgment. For a technology often portrayed as poised to automate teaching outright, the evidence points to a more grounded future: AI as a fast, tireless drafting assistant, and the teacher as the indispensable editor who knows exactly which students are sitting in the room.

Subject of Research: The use of large language models for lesson planning in education and their pedagogical limitations

Article Title: Large language models in lesson planning: Useful aid, but not yet ready to replace teachers

Article References: Large language models in lesson planning: Useful aid, but not yet ready to replace teachers. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: large language models, lesson planning, generative AI, ChatGPT, education technology, pedagogy, teacher attitudes, prompt engineering, AI literacy, narrative review, differentiation, Frontiers of Digital Education