The concept at the heart of the study is feedback intervention, which the authors define as teachers’ deliberate integration of pedagogical activities designed to enhance students’ feedback literacy. This goes well beyond the classic notion of feedback as evaluative information delivered by a teacher. Drawing on the influential work of David Carless and David Boud, the researchers frame feedback intervention as an effort to engage learners as active participants in the feedback process, capable of appreciating feedback, making evaluative judgments, managing their emotional responses and taking concrete action on the comments they receive. Previous research has catalogued a rich menu of such activities: self-assessment and peer review to sharpen evaluative judgment, reflective journals and interactive cover sheets to stimulate action, exemplar analysis and rubric discussion to build understanding of quality, and feedback dialogues to foster receptiveness and emotional regulation.

What has been missing, the authors argue, is attention to the teachers who must design, sequence, scaffold and adapt these activities in real classrooms. A scoping review by Little and colleagues found that most intervention research has been conducted in health-related fields from a student perspective, leaving teachers’ cognition about feedback intervention largely unexplored. The Chinese university context makes this omission especially consequential. English proficiency in China is gated by high-stakes national examinations such as the College English Test and the Test for English Majors, fostering a result-oriented feedback culture centered on error correction. At the same time, China’s 2020 policy blueprint for deepening educational evaluation reform calls for more process-oriented approaches, creating a tension between long-standing examination practices and emerging policy directions that frames the entire study.

To capture this tension in action, the researchers conducted an exploratory qualitative study with eight university EFL teachers, purposively selected to span novice, mid-career and veteran educators teaching specialized English courses, general college English, or both. Data came from three sources: semi-structured interviews lasting between 37 and 52 minutes, two to four classroom observations per participant, and analysis of artifacts such as annotated student assignments and a peer review guide. The interviews were built around fifteen frequently cited intervention activities, each mapped to the component of student feedback literacy it primarily targets, and presented in a deliberately disrupted order to elicit more reliable responses. The transcripts, collected in Chinese, were analyzed through thematic analysis combining deductive codes drawn from prior literature with inductive codes emerging from the data itself. A second researcher independently coded two transcripts, achieving a percentage agreement of 91.8 percent.

The findings on teacher cognition reveal a telling pattern of selective endorsement. All eight participants acknowledged the necessity and potential benefits of feedback intervention, even though some initially found the term itself unfamiliar, with one teacher describing it as a completely new term and assuming it referred to administrative regulations. When it came to specific activities, the teachers strongly favored those targeting evaluative judgment, affect management and action: feedback dialogues, exemplar analysis, and the explicit specification of task requirements and assessment criteria emerged as the most consistently endorsed practices. Yet activities aimed at helping students simply appreciate feedback, such as explaining its purpose, running feedback workshops, or clarifying students’ active role in the process, were dismissed as impractical or unnecessary. One teacher asked whether students would perceive such activities as redundant, calling them self-evident truths that all students already know, an assumption the authors identify as rarely questioned despite contrary evidence.

Classroom observations told a different and more sobering story. Rather than planned instruction, feedback intervention in these classrooms was overwhelmingly improvised, a reactive response to perceived student needs rather than a deliberately designed component of teaching. One participant admitted that he did not deliberately design feedback interventions when planning his teaching content, believing it came naturally from observing students’ responses. Not a single observation session captured any activity aimed at cultivating students’ appreciation of feedback, confirming the interview data. Meanwhile, the activities actually enacted, such as clarifying assessment criteria and analyzing exemplars, aligned neatly with the institutional obsession with examination passing rates. Most striking was the case of face-to-face feedback dialogues: every participant judged them highly effective, yet almost none conducted them, citing the sheer time they consume. Similarly, the majority of participants avoided peer review altogether, discouraged by past experiences and students’ superficial participation, in which comments amounted to little more than praise.

Yet the study also contains a compelling counter-narrative demonstrating that these failures are not inevitable. One teacher persisted with peer review despite its rocky start, providing students with a structured peer review guide, modeling feedback with sample essays, and requiring revision based on peers’ comments. By the third round, she reported, student critiques had become sharper than her own, and one student observed that seeing others’ weak arguments helped him fix his own. The authors note that this success required deliberate instructional scaffolding, and they connect the broader reluctance around peer critique to culturally situated norms: Confucian-heritage values of hierarchical harmony and the preservation of face, or mianzi, may socialize students to respect teacher authority and avoid openly criticizing peers, making peer feedback particularly fragile without careful training.

The factors shaping this cognition-practice disparity proved to be multidirectional and interdependent, which the authors formalize in an interactive framework of triangular relationships among personal, student-related and contextual influences. On the personal side, teachers’ own histories as EFL learners left lasting marks, with one recalling the humiliation of having error-riddled homework displayed in class and consequently correcting her own students strategically to spare them the same experience. Individual disposition mattered too: teachers with growth mindsets and reflective habits converted professional development into practice change, while others responded to identical training with apathy. Student-related factors cut both ways. One teacher abandoned detailed written corrections after discovering that many students simply ignored them, now reserving advice for those willing to act on it; another scrapped reflective journals mid-semester when student effort proved perfunctory and anonymous mid-term surveys registered dissatisfaction. Notably, classroom observation suggested that student engagement tracked year of study and grade concerns rather than English proficiency itself.

Contextual factors formed the third corner of the triangle. Institutional fixation on CET-4 and TEM-4 passing rates, combined with the absence of supportive policies, meant that feedback intervention was always the second choice when teachers juggled their multiple identities as instructors, researchers and employment tutors. Frequent course reassignments compounded the problem, since teachers only attend to feedback refinement after teaching the same course at least twice. Digital tools offered partial relief, with online correction platforms praised for instant error detection and reduced face-threatening pressure in classes of more than 200 students. The authors argue that the resulting mismatch is not a simple failure of knowing versus doing but a negotiated trade-off under a macro-micro structural contradiction: national policy promotes process-oriented evaluation while institutional incentives still reward examination outcomes. Their prescription is a fundamental reconceptualization of feedback intervention, from incidental practice to intentionally designed instruction, supported by tripartite collaboration among teachers, students and institutions, including professional development, policy incentives and technological scaffolds, so that the activities teachers already believe in can finally reach the classroom.

Subject of Research: Teacher cognition and classroom practices of feedback intervention among Chinese university EFL teachers

Article Title: Teacher cognition and practices of feedback intervention: perspectives from Chinese university EFL teachers

Article References: Teacher cognition and practices of feedback intervention: perspectives from Chinese university EFL teachers. (n.d.). https://doi.org/10.1038/s41599-026-09049-z

Image Credits: AI Generated

DOI: 10.1038/s41599-026-09049-z

Keywords: teacher cognition, feedback intervention, feedback literacy, EFL teaching, higher education, qualitative research, evaluative judgement, peer review, teacher-student dialogue, high-stakes testing, institutional culture, China

The setting was microteaching, a staple of teacher education in which trainees deliver short lessons to their peers and then swap roles as evaluators. Each of 27 third-year undergraduates—specializing in subjects from industrial technology to mathematics and science—delivered a three-minute lesson modeled on a short homeroom activity. Every participant then rated all 26 classmates and wrote a short free-text comment in Japanese about each performance. After excluding self-evaluations, the researchers had 702 paired evaluations, each combining nine five-point ratings (covering voice clarity, speaking speed, facial expression, gaze, gesture, board work, questioning, and content clarity, plus an overall score) with a comment averaging just 41.6 characters.

A crucial design feature shaped the entire analysis: the ratings were collected under a forced distribution. For each item, every evaluator had to assign the top score of 5 to exactly two peers, a 4 to seven peers, a 3 to nine, a 2 to seven, and a 1 to two—a fixed 2–7–9–7–2 shape. This means a rating of 1 or 2 does not indicate objectively poor teaching; it indicates that the evaluator placed that peer in the bottom of their own ranking. The researchers treated the ratings as within-evaluator relative ranks, which gave them an internal benchmark: if rating and commenting were tightly linked, corrective language should cluster precisely where the evaluator’s own low ratings fell.

To measure the comments, the team deliberately avoided large language models and human coding, both of which introduce reproducibility problems. Instead, they built a frozen, rule-based Japanese lexicon and scanned every comment with the morphological analyzer MeCab for five explicit markers: generic praise, concrete instructional referent, learner-oriented mention, explicit feed-forward (improvement-suggestion grammar such as ‘it would be better to’), and explicit criticism. Every reported label can be exactly reproduced from the published dictionaries. As a stress test, the researchers also tried classifying comments by their similarity to Sentence-BERT category prototypes; that probe agreed poorly with the rule-based labels, reinforcing their choice of deterministic rules for such short, praise-heavy text.

The descriptive results confirmed a pattern long noted in the peer-feedback literature: novices praise. Generic praise appeared in 67.1 percent of comments. Explicit feed-forward language was detected in only 14.4 percent of comments under the frozen lexicon and 13.0 percent under a stricter ablation, while explicit criticism appeared in just 6.4 percent and 4.6 percent respectively. The similarity of the frozen and strict rates suggests the scarcity of corrective language is robust rather than an artifact of permissive matching, and an audit of all positive cases found no obvious false positives. Concrete instructional referents ranged from 53.6 to 71.4 percent depending on dictionary breadth, and learner-oriented mentions appeared in 21.4 percent of comments.

The first key finding was a genuine, graded coupling between rating severity and corrective wording. Among the 574 comments accompanying at least one low rating (a 1 or 2 on the eight teaching items), 22.1 percent contained an explicit feed-forward or critical marker, compared with only 10.9 percent of the 128 comments where all items were rated 3 to 5—a difference of +0.118 that a within-evaluator permutation test put at p = 0.008. The corrective-marker rate rose steadily with both the number of low-rated items and the severity of the lowest rating (Spearman ρ = +0.205 and −0.204, each p = 0.0002). In plain terms: the harsher an evaluator’s overall placement of a peer, the more likely their comment carried corrective language.

But the second finding dismantled any hope that this corrective language was targeted. Of the 491 comments that explicitly mentioned at least one of the eight predefined teaching dimensions, only 180 mentioned a dimension the same evaluator had rated low—a 36.7 percent match rate that actually fell slightly below the 39.6 percent chance baseline generated by re-pairing comments with the evaluator’s own low-rated dimension sets (one-sided p = 0.958). When a pre-service teacher named a teaching dimension, it was no more likely than chance to be one they had scored low. Per-item breakdowns told the same story: the difference in same-dimension mention rates between low- and high-rated items was near zero or negative on every item except speaking speed.

Stranger still, even the mentions that did land on a rated-low dimension were usually framed positively. Among those 180 comments, the clause surrounding the mention carried an explicit praise marker more often than a correction marker—55.0 percent versus 26.7 percent at the clause level, and 75.6 percent versus 34.4 percent across the whole comment. The researchers interpret this as ‘loose coupling’: the numerical and textual channels of the same evaluation share a global structure, in which overall severity nudges corrective wording, but the text adds no dimension-specific information beyond the ratings themselves. Notably, a companion analysis of the same cohort found the numerical ratings to be highly halo-structured—a single general impression rather than independent dimension judgments—so weak textual alignment is compatible with how the ratings behave, not paradoxical.

The study has clear limits, which the authors state plainly. It is exploratory, based on a single cohort, with short Japanese comments; the markers capture only explicit surface wording, so implicit or indirectly phrased feedback falls outside the measured construct, and the scarcity rates are lower bounds. Comment length mattered too: the corrective-marker rate climbed from about 8 percent in the shortest length quartile to about 36 percent in the longest, suggesting some of the scarcity reflects brevity rather than disposition alone—though the dimension-targeting failure was not explained by length. Timing also differed: final ratings could be adjusted after re-viewing recorded sessions, while comments were typically written immediately, and no edit histories were recorded.

The practical implications reach both teacher education and the growing field of automated feedback support. For trainers, the message is that ranking peers and articulating usable, dimension-specific suggestions are distinct skills that may need to be scaffolded separately rather than assumed to develop together. For developers of feedback tools, the finding is a caution: systems cannot assume that a low rating will come with detected corrective or dimension-relevant text to build on. The authors suggest future work could test whether prompts or scaffolds help learners convert a noticed weakness into explicit wording, apply the same pipeline to rubric-anchored rating formats, or benchmark large language model classifiers against human codes. For now, the study stands as a precise, reproducible demonstration that what novice evaluators write and what they score are two related but separable acts—one that any effort to automate or improve peer feedback must reckon with.

Subject of Research: The correspondence between numerical ratings and written comment content in pre-service teacher microteaching peer evaluation

Article Title: Peer comment wording in pre-service teacher microteaching tracks rating severity but not the dimensions rated low

Article References: Shirasaka, S., Imagawa, T., & Enokida, S. (2026). Peer comment wording in pre-service teacher microteaching tracks rating severity but not the dimensions rated low. Discover Education, 5(1), Article 1141. https://doi.org/10.1007/s44217-026-02238-7

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02238-7

Keywords: peer feedback, microteaching, pre-service teachers, teacher education, lexicon-based text analysis, natural language processing, forced distribution ratings, feed-forward, evaluative judgement, feedback literacy, permutation test, halo effect

The review’s central conceptual move is to distinguish two kinds of assistance. Instrumental or representational assistance helps learners generate options, translate, format, retrieve, summarise or re-express material. Judgement-bearing assistance goes further: it evaluates correctness, relevance, quality, persuasiveness, ethical acceptability or evidential sufficiency. The two categories can overlap, since even a summary selects what matters, but judgement-bearing assistance is normatively far more demanding because it delegates not only the production of work but the standards by which that work is assessed. A learner may use AI repeatedly yet remain epistemically agentic if the interaction prompts questioning, comparison and revision; another may use it once but treat its answer as decisive in a high-stakes task. The analytic boundary, the authors insist, is not use versus non-use but the relation between assistance and judgement.

To operationalise that boundary, the review proposes six diagnostic criteria separating productive reliance from harmful dependence: contestability, recoverability, transfer, traceability, distributed responsibility and epistemic plurality. Dependence becomes problematic when the relation is difficult to contest, obscures its evidential basis, weakens the learner’s capacity to reconstruct or transfer judgement, or allocates responsibility to people who lack meaningful control. Conversely, frequent AI use can remain productive when it provokes comparison, makes uncertainty visible and leaves the learner more capable of independent and collaborative judgement. The criteria are offered as diagnostic questions for research, design and pedagogy rather than a psychometric scale, and no single criterion is decisive in every context; the pattern matters, as does the importance of the delegated judgement.

The heart of the paper is an analysis of four sociotechnical pathways through which AI affordances and institutional conditions can slide into harmful dependence. The first is fluent authority. Large language models produce grammatically polished, coherent and often confident responses, and fluency is epistemically persuasive because users can mistake ease of processing for reliability. Anthropomorphism, social presence and personalisation can amplify this effect: conversational systems occupy roles associated with human epistemic others, such as respondent, tutor, editor and evaluator, and a supportive tone with apparent memory may encourage the transfer of interpersonal trust to a system that lacks human understanding, responsibility or commitment to the learner. The risk is not error alone but the alignment of error, confidence and immediacy, especially for learners with limited domain knowledge.

The second pathway is frictionless delegation. Generative AI compresses searching, reading, comparing, synthesising and drafting into a single request-response cycle. Cognitive offloading can be adaptive when it frees limited resources for planning, reflection or complex problem solving, but adjacent research on internet search suggests that persistent access to external information can alter memory, confidence and future search behaviour. When intermediate epistemic actions disappear from the workflow, learners may complete tasks without practising the very actions the tasks were intended to develop. Assessment incentives intensify the problem: if institutions reward polished products while leaving process and justification invisible, delegating epistemic work becomes rational, and AI shifts from scaffold to substitute, particularly in feedback and evaluation.

The third pathway is opaque synthesis. Conversational AI often presents an integrated answer without making its source selection, weighting, exclusions or uncertainty inspectable. Traditional search was never transparent or neutral, but it commonly exposed multiple documents, authors and domains, even if learners evaluated them poorly. Conversational synthesis can hide that plurality behind one voice, transforming the learner’s task from selecting among visible sources to recovering the evidential structure of an answer that appears already complete. This can weaken verification while improving the surface quality of the product, making epistemic deficits harder for teachers to detect. It also threatens disciplinary reasoning, since a generic synthesis may flatten the distinct standards by which historical, scientific and philosophical claims are warranted.

The fourth pathway is institutionalised dependence. Universities and schools shape reliance through procurement, platform integration, assessment design, timetabling, policy and professional development, while commercial systems encode objectives concerning engagement, speed, cost and data capture. Once AI is built into learning management systems, writing environments and feedback workflows, it may become the default route into academic work rather than a discrete tool chosen by the learner. Institutionalisation also redistributes authority and responsibility: a student may be held accountable for claims generated through an institutionally licensed but opaque system, a teacher may be expected to police use without access to system logs, and a university may depend on vendor assurances that cannot be independently audited. The review argues that responsibility should track control, knowledge and benefit across learners, educators, institutions and providers, rather than being dumped on the end user.

This institutional dimension brings epistemic justice to the centre of the analysis. Generative systems draw on unequal knowledge infrastructures and may reproduce dominant languages, classifications and perspectives while marginalising local, minoritised or experiential knowledge. Drawing on Miranda Fricker’s account of epistemic injustice and on recent work on formative epistemic injustice, the authors argue that learning arrangements can wrong students by restricting the knowledge, practice and accurate self-assessment through which they develop as knowers. Repeated reliance may also reshape learner identity, fostering a self-conception of being unable to write, understand or judge without AI. Productive reliance should expand participation and capability over time; harmful dependence makes learners and institutions more fragile when the system is absent, changes its terms or fails particular communities.

Against these risks, the review proposes relational epistemic agency as the normative aim: the capacity to question, verify, compare, justify and take responsibility for knowledge claims within human, technological and institutional relations. This is not independence from the machine, and it is not an anti-dependence position. Education has always involved dependence on teachers, peers, texts, instruments and institutions, and ideals of self-sufficient knowing are both unrealistic and exclusionary. The relevant test is functional and developmental: does the human-technology relation enlarge the learner’s capacity to participate in epistemic practice, or merely deliver a product? A calculator supports mathematical agency when the learner understands when and why its operations are appropriate; a generative model supports inquiry when it expands hypotheses, reveals alternatives and prompts verification. The same tools bypass agency when they become non-contestable authorities.

The practical implications are concrete. Designers should provide claim-level provenance where feasible, distinguish retrieved evidence from model-generated synthesis, represent uncertainty through alternatives and explicit unknowns, preserve user control over prompts and outputs, and surface disagreement and culturally diverse sources. Pedagogy should move from policing AI use to teaching AI-mediated judgement, with routines for lateral reading, source triangulation and claim verification, and assignments that require learners to annotate AI responses, identify unsupported assumptions and document why they accepted or rejected particular suggestions. Assessment should make process, judgement and transfer visible through staged drafts, oral defence, source maps and reflective decision logs, without becoming surveillance-heavy. Institutions, meanwhile, should evaluate procurement for provenance, bias, auditability and data governance, and specify which learning outcomes must remain demonstrably human. The empirical agenda that follows is equally clear: researchers must track which epistemic actions are preserved, transformed or displaced, using process evidence such as interaction traces and think-aloud protocols, and must examine organisations and markets, not only learners. The future of knowing, this review suggests, depends less on how often students use AI than on whether the systems and institutions around them are configured so that judgement survives the delegation.

Subject of Research: Epistemic dependence and learner agency in AI-mediated education

Article Title: Epistemic dependence in AI-mediated learning

Article References: Du, Y., & Yuan, Y. (2026). Epistemic dependence in AI-mediated learning. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03294-1

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03294-1

Keywords: generative AI, epistemic dependence, epistemic agency, AI in education, cognitive offloading, epistemic justice, evaluative judgement, large language models, human-AI interaction, assessment, educational governance, AI & Society

The study, led by Zachery Quince of Southern Cross University’s Centre for Learning and Teaching together with Emily Faulconer of Monash University, examined a second-year physics unit serving engineering and education students. Before the term began, the university’s policy had been overhauled, collapsing a five-tier model of permitted AI use into three categories: no use, purpose-specific use, and open use. Because none of the unit’s existing assessments complied with the new rules, the course coordinator was forced to redesign all three tasks, creating a natural experiment in how policy-driven assessment change reshapes student behaviour.

The redesign was revealing in itself. The first assessment, an oral presentation worth 20 percent of the unit, operated under purpose-specific rules: students could use AI for background research, idea generation, slide drafting and script development, but the final recorded presentation had to be their authentic selves, not an AI-generated avatar. The second assessment, a laboratory workbook worth half the grade, and the third, a take-home quiz worth 30 percent, both ran under open-use rules that permitted unrestricted AI engagement, provided students submitted a mandatory declaration describing exactly which tools they used and why.

Those declarations became the study’s raw data. Across the three assessments, students reported more than 120 unique use cases, which the researchers coded into four functional categories: learning, formatting, doing the task, and feedback. The pattern that emerged was striking. In the first assessment, use was dominated by learning and formatting, together accounting for over 95 percent of reported uses, as students brainstormed ideas, checked grammar and structured their presentations. By the final take-home quiz, feedback had exploded to 66.7 percent of all reported uses, with students describing how they checked calculations, verified answers and clarified concepts such as the viability of formulas.

To interpret this shift, the researchers mapped each category onto the SAMR framework, a well-known model that classifies technology integration into four levels: substitution, augmentation, modification and redefinition. Learning and formatting uses sat mostly at the lower levels, where AI merely replaces or improves existing tools without changing the task. Doing the task, where AI generated step-by-step solutions to unfamiliar problems, reached modification and redefinition, the levels where technology genuinely transforms what students can do. Feedback proved the most interesting category of all: it spanned every SAMR level, from simple error-spotting at the bottom to an iterative, student-controlled dialogue at the top, in which students could question, test and immediately apply advice in real time, something no previous feedback mechanism offered at comparable speed or scale.

The performance data added a cautious footnote to the behavioural story. Students who declared AI use scored slightly higher than non-users on all three assessments, with the largest gap appearing in the scaffolded presentation, where users averaged 15.76 out of 20 against 14.80 for non-users, a moderate effect size. But none of the differences reached statistical significance, and the researchers are explicit that this trend should be read as a tentative observation rather than evidence that AI boosts grades. What the data does show is that declared AI use was not associated with poorer achievement in a course where assessments required students to explain reasoning and take final responsibility for their work.

That caveat matters because the wider literature is deeply divided. Some studies report that AI feedback improves performance; others find that heavy ChatGPT use correlates with worse exam results, procrastination and memory loss. Research on high school mathematics has shown that generative AI without guardrails can harm learning, while other work finds stronger outcomes when students use AI to construct knowledge rather than merely execute procedures. The Australian authors argue that much of this contradiction stems from studies conflating fundamentally different behaviours, such as substitution-level use that bypasses cognitive effort with modification-level use that supports it, and from unclear policy contexts that make results impossible to compare.

The study’s central insight is what the researchers call task contingency. Students did not use AI simply because it was available; they used it for the kinds of support each task made useful. A presentation task made planning, language and communication support relevant. A workbook and quiz made checking, clarification and verification relevant. This means a policy label such as purpose-specific or open use can define what is permitted but cannot determine what students will actually do. The assessment design creates the demand. Where a task rewards polished communication, students reach for AI to polish. Where it demands problem-solving, they reach for AI to test their reasoning.

The findings also complicate the academic integrity narrative that dominated early coverage of AI in education. Far from being passive cheaters, students in this cohort reported using AI for preparation, clarification, refinement and verification, not only for answer production. The authors acknowledge the limits of self-reported data, which may be incomplete or strategically framed, and note that the single-marker design and small cohort of 36 students restrict generalisability. Still, they argue that integrity responses built purely on restriction and suspicion are too blunt, and that the real design question is whether an assessment requires students to demonstrate judgement and reasoning that AI cannot simply declare on their behalf.

The practical recommendations are concrete. Where students use AI for feedback, the authors suggest requiring them to highlight the feedback received and explain how it did or did not shape their revisions, making evaluative judgement visible and building feedback literacy. Greater restriction remains appropriate where AI could perform the intended cognitive work without students demonstrating their own understanding. The broader lesson is that generative AI has not invalidated authentic assessment; it has made it urgent. When tools can instantly solve standard textbook problems, educators are pushed toward tasks demanding synthesis, justification and communication, the very skills that define professional practice in physics and engineering. In that sense, the technology that threatened to hollow out assessment may end up forcing higher education to design better ones.

Subject of Research: How changing institutional generative AI policies influence student AI use and academic performance in undergraduate physics assessment

Article Title: Student GenAI use under changing institutional policies: a mixed-methods case study in undergraduate physics

Article References: Quince, Z., & Faulconer, E. (2026). Student GenAI use under changing institutional policies: a mixed-methods case study in undergraduate physics. International Journal of STEM Education, 13(1), Article 61. https://doi.org/10.1186/s40594-026-00651-w

Image Credits: AI Generated

DOI: 10.1186/s40594-026-00651-w

Keywords: generative AI, higher education, physics education, assessment design, SAMR framework, academic integrity, institutional policy, feedback, evaluative judgement, STEM education, authentic assessment, constructive alignment