Volume 1, Issue 1 (2026)

As governments increasingly promote coding and artificial intelligence (AI) in primary education, attention has shifted from policy adoption to the realities of implementation at the school level. This study investigates how elementary schools in Grobogan District, Indonesia, translate national expectations for coding and AI education into everyday teaching practices under the Merdeka Curriculum. Using a qualitative collective case study design, the research combines policy enactment theory, the TPACK framework, and computational thinking as complementary analytical perspectives. Data were generated through interviews with eight teachers, four principals, and twelve students, supported by observations of six classroom sessions and analysis of relevant school and policy documents. The findings show that implementation does not follow a single model. Instead, schools adopt different approaches according to their available resources, teacher competence, and organizational capacity. These approaches include project-based activities using digital coding tools, combinations of unplugged and plugged tasks, integration of unplugged coding concepts into existing subjects, and introductory teacher-led demonstrations. The extent to which the policy was enacted also depended strongly on school leadership and on individual teachers who assumed a leading role in introducing new practices. Differences in teachers’ technological, pedagogical, and content knowledge were evident, with technological competence emerging as the weakest area. Although limited infrastructure restricted the scope of implementation, schools were able to adapt activities to local conditions rather than abandoning the policy altogether. Students showed initial signs of computational thinking, particularly when they decomposed problems, tested alternative solutions, corrected errors collaboratively, and refined their approaches through repeated attempts. Overall, the study indicates that the success of coding and AI education depends not only on curriculum policy, but also on sustained professional development, supportive school leadership, adequate infrastructure, and coordinated assistance at the district level. These findings contribute to discussions on how emerging digital education policies can be implemented more effectively in resource-constrained school systems.

The rapid advancement of artificial intelligence has reshaped social, economic, and technological systems, giving rise to new application areas such as intelligent healthcare, smart homes, autonomous transportation, and data-driven public services. This transformation has also created new expectations for education systems, particularly regarding the early development of AI literacy, computational thinking, and interdisciplinary problem-solving skills. In this context, AI education in primary and secondary schools has become an important strategy for preparing students to participate in future-oriented, innovation-driven societies. This study examines the development, research hotspots, and emerging trends of artificial intelligence education in primary and secondary schools through a bibliometric and visual knowledge mapping approach. Drawing on publications indexed in CNKI and Web of Science, the study analyzes 2,611 papers published between 2014 and 2024. CiteSpace software was used to conduct knowledge map analysis, supported by content analysis to interpret the intellectual structure and thematic evolution of the field.

Artificial intelligence is increasingly shaping educational discourse, yet discussions about its role in schooling often remain limited to practical advantages such as automation, efficiency, personalization, and data-informed decision-making. Such a narrow framing is insufficient for primary education, where teaching is not merely a technical process but also a relational, ethical, and developmentally responsive practice. This study explores the implications of AI for elementary school teachers by examining how it influences the nature of teaching, the construction of knowledge, and the values that guide classroom practice. Adopting a philosophy of science perspective, the study employs a thematic narrative review design. Relevant literature was identified through Scopus, Web of Science, and Google Scholar, resulting in 34 key sources for analysis. These sources were examined through three interrelated philosophical dimensions: ontology, epistemology, and axiology. The analysis shows that AI in primary education should not be understood simply as a neutral instructional tool. Rather, it functions as a socio-technical assemblage composed of generative AI systems, adaptive learning platforms, analytics-based dashboards, and automated feedback mechanisms that shape classroom decisions and alter the distribution of agency between teachers and technologies. From an epistemological perspective, AI changes how educational knowledge is produced, validated, and acted upon by giving algorithmic recommendations a growing role in instructional judgment. However, the findings also suggest that teachers’ epistemic authority does not necessarily diminish when AI is introduced. When teachers possess critical AI literacy, they can interpret, question, contextualize, and pedagogically mediate AI-generated outputs. From an axiological perspective, AI raises significant value-related concerns involving teacher wellbeing, children’s developmental vulnerability, privacy, fairness, and unequal access to digital infrastructure, especially in the Indonesian primary education context. The study concludes that AI is not value-free in primary education. It participates in redefining agency, knowledge authority, and educational priorities in the classroom. Therefore, AI can support meaningful learning only when it remains subordinate to human pedagogical judgment and is embedded within ethical, relational, and child-centered teaching practices. Preserving the moral, relational, and epistemic role of elementary school teachers is essential for ensuring that primary education retains its human foundation in an increasingly algorithmic educational environment.

In an age when law students increasingly study within environments shaped by automated systems and algorithmic assistance, revisiting the historical foundations of legal education is more than an exercise in legal culture. It offers a way to recover the principles, meanings, and formative practices that have shaped legal thought across centuries. Examining how law was taught in periods marked by war, migration, social hierarchy, limited personal freedoms, and restricted access to knowledge allows contemporary legal education to be understood within a broader civilizational and intellectual continuum. This study examines legal education in antiquity through a diachronic, comparative, and evolutionary perspective. It focuses on the content, methods, and institutional forms through which legal knowledge was transmitted in different ancient societies, while also considering the economic, political, and cultural paradigms that shaped these educational models. The civilizations of the Ancient East provide some of the earliest traces of legal instruction, not only through monumental cultural achievements such as Mesopotamian cuneiform writing, Egyptian hieroglyphs, Chinese manuscripts, and Vedic texts, but also through normative traditions associated with the Code of Hammurabi, the Maxims of Ptahhotep, and Confucian moral-political thought. The Greek world later developed a distinctive educational ideal that transformed inherited cultural materials into more abstract systems of knowledge, reasoning, and civic formation. From the Homeric tradition, which Plato regarded as a powerful source of education, to the institutional legacy of Plato’s Academy, Aristotle’s Lyceum, and the Cynosarges, Greek education created enduring models for philosophical, rhetorical, and civic learning. Although legal knowledge in Greek society was often transmitted through close apprenticeship and participation in public life, the systematic teaching of law reached a more organized and lasting form in Roman civilization. The Romans’ enduring contribution lies in transforming legal knowledge into a structured field of technical, practical, and educational formation. Roman law acquired lasting significance not only because of its theoretical and historical importance, but also because of its pedagogical utility in training jurists and shaping legal reasoning. As a foundation of Western legal culture, Roman law represents a decisive stage in the history of legal education, linking ancient methods of instruction with later institutional traditions of legal scholarship and professional formation.

Technological Pedagogical Content Knowledge (TPACK) has become an influential framework for explaining how teachers combine technology, pedagogy, and subject matter knowledge in instructional practice. In Indonesia, scholarly interest in TPACK has increased considerably, particularly in relation to teacher preparation, digital learning media, and technology-supported instruction. However, the existing body of research remains concentrated largely in science and mathematics education, while social studies—especially history education—has received comparatively limited attention. This imbalance is more apparent in the context of equivalency education, where non-formal learners require flexible, contextual, and digitally responsive learning approaches. This study investigates TPACK research trends in Indonesia from 2019 to 2023 and considers their relevance for strengthening history teacher communities in equivalency education. Employing a bibliometric research design, the study analyzed 1,208 Scopus-indexed documents retrieved using the keyword “TPACK.” The analysis examined publication growth, dominant themes, geographical patterns, and the extent to which history education and equivalency education were represented in the research landscape. The findings show that Indonesian researchers contributed substantially to TPACK-related publications during the period studied. Nevertheless, the dominant research themes remained centered on pre-service teacher education, learning media development, and technology integration in science and mathematics. By contrast, studies focusing on history learning, social studies pedagogy, and non-formal education contexts were relatively scarce. This indicates a clear opportunity to extend TPACK research toward history education and to use it as a basis for supporting teacher communities within the equivalency education system. The study argues that TPACK can serve as a strategic framework for improving the digital and pedagogical competencies of history teachers. Through stronger TPACK-based professional development, teachers can design more meaningful, interactive, and technology-enhanced history learning experiences for non-formal learners. The study contributes by mapping Indonesian TPACK research trends, identifying underexplored areas in history and social studies education, and offering practical directions for curriculum development, teacher training, and future research in equivalency education.

How does a field of possibilities become a particular conscious experience, and how do different subjects come to describe a shared world? This conceptual essay develops a multidimensional probability framework that connects these questions without treating speculative metaphysics as established physics. Its strong ontological proposal is that conscious observation participates in the actualization of possibilities across multiple temporal or dimensional domains. A related multiple conscious collapse hypothesis considers whether different subjects could actualize different experiences from a common potential. The essay distinguishes this proposal from a weaker account of context dependent interpretation, which can be investigated without assuming that consciousness changes physical measurement outcomes. Using conceptual analysis and a selective examination of relevant literature, it separates quantum measurement, cognitive probability, subjective time, and social agreement. It introduces a schematic representation of experiential selection, identifies missing definitions in the physical hypothesis, and outlines prospective studies of judgment order, duration estimation, and convergence during dialogue. These studies would assess the psychological component and would not establish the existence of additional dimensions or consciousness induced quantum collapse. The contribution is a set of explicit conceptual distinctions and evidential requirements for interdisciplinary debate. The framework remains provisional, with its strongest claims awaiting an independently specified mechanism and discriminating physical predictions.

Interior layouts shape who meets whom at work, yet the social consequences of everyday design decisions are rarely tested under controlled conditions. This study used a fully computational experiment to examine how five interior decisions in open-plan offices, namely core position, tea point strategy, workstation cluster size, desk screen height and the enclosure of meeting rooms, affect unplanned encounters and visual distraction. A full factorial set of 108 parametric layouts with 72 workstations each was analysed with visibility graphs and simulated with an agent-based model of a four-hour working block, and 60 randomised layouts served as an unseen test set. Most simulated encounters occurred beside desks and were tightly coupled with distraction, whereas encounters away from desks were unrelated to it. High screens reduced distraction by 32% with almost no loss of away-from-desk encounters. A single central tea point produced almost twice as many away-from-desk encounters as two distributed counters while causing the least distraction, larger workstation clusters reduced the share of cross-team contact, and enclosing meeting rooms had negligible effects. Machine learning surrogates trained on conventional visibility descriptors predicted unseen layouts poorly, but adding four simple route and catchment indices raised the explained variance to between 94% and 98% with a regularised linear model. The findings suggest that interior architects should move encounters from desks to shared nodes, and that theory-informed descriptors allow fast and transparent screening of layouts at the sketch stage.

Institutions increasingly rely on algorithmic systems to assess people, allocate opportunities, and justify consequential decisions. Existing debates often ask whether these systems are accurate, fair, transparent, or explainable. This article identifies a distinct philosophical problem: an institution may reach a factually correct outcome while illegitimately replacing a person’s standing to offer relevant reasons with an institutional prediction of what those reasons must be. Through analytical reconstruction, contrastive thought experiments, and normative stress tests, the article develops the concept of epistemic substitution, distinguishes predictive evidence from participatory justification, and proposes a non-substitution principle for decisions affecting persons as accountable agents. It demonstrates that prediction, explanation, and even accurate individualized classification do not by themselves establish that affected persons have been appropriately heard. The resulting framework specifies five conditions for legitimate institutional uptake: access, admissibility, responsiveness, traceability, and revisability. It also distinguishes bounded circumstances in which substitution is permissible from those in which it is not, and derives researchable expectations for public administration, employment, education, and automated peer review. The central conclusion is neither that algorithms should never decide nor that personal testimony should automatically prevail. Rather, decision legitimacy requires institutions to preserve the possibility that an affected person can change the reasons on which a decision is made.