Aug 2026· Smart Design Policies· Vol 3, pp. 45-64· 0 citations
TL;DR
A policy-oriented posthuman framework for interpreting AI integration in architectural pedagogy and translating it into responsible design education principles is developed, which clarifies the theoretical relevance of posthuman pedagogy for AI-supported architectural education.
Abstract
Architectural education is being reshaped as Artificial Intelligence (AI) challenges human-centered conceptions of creativity, authorship, and knowledge production. However, current discussions on AI-supported architectural education remain largely focused on tool adoption, productivity, creativity, and student perception, while the policy implications of AI for curriculum design, studio governance, assessment, educator training, and ethical accountability remain underdeveloped. Addressing this gap, the study develops a policy-oriented posthuman framework for interpreting AI integration in architectural pedagogy and translating it into responsible design education principles. The study adopts a two-stage review design that combines a conceptual framing review of posthuman pedagogy with a systematic synthesis of empirical and pedagogical studies on AI, computational design, and architectural education published between 2010 and 2025. The review identifies three interrelated dimensions of AI-supported posthuman learning: distributed agency, in which design intelligence is shared across students, educators, AI systems, datasets, interfaces, materials, and studio environments; situated knowing, in which AI becomes pedagogically meaningful only when embedded in reflective, material, and context-sensitive design inquiry; and ethical entanglement, in which authorship, bias, accountability, originality, dependency, and environmental responsibility become core educational concerns. Based on these findings, the paper proposes ecological intelligence as a design education policy principle: the capacity to think, design, evaluate, and act responsibly within interconnected human, technological, material, environmental, and institutional systems. The contribution of the study is twofold. First, it clarifies the theoretical relevance of posthuman pedagogy for AI-supported architectural education. Second, it translates this theoretical perspective into a policy-oriented pedagogical framework that can inform curriculum development, studio pedagogy, assessment criteria, and ethical governance in architectural education.
Generative artificial intelligence (GenAI) has entered education faster than the theoretical and methodological frameworks used to evaluate it. This critical integrative review asks a more consequential question than whether GenAI ‘works’: under what pedagogical conditions can it augment learning without displacing learner agency, epistemic responsibility, and higher-order cognition? A purposive corpus of 56 distinct sources was synthesized, spanning foundational theories of experiential, sociocultural, situated, distributed, social-cognitive, cognitive-load, self-regulated, and technology-mediated learning; established technology-acceptance models; artificial-intelligence-in-education and AI-literacy frameworks; policy guidance; and empirical, systematic-review, and meta-analytic evidence published through 2026. The synthesis indicates that GenAI is most defensibly conceptualized as a fallible cognitive artifact embedded in a distributed learning system rather than as an autonomous epistemic authority. Positive effects on achievement, motivation, engagement, creativity, and higher-order thinking are increasingly supported, but they are conditional on task design, scaffolding, interaction quality, verification practices, AI literacy, and sustained human oversight. Evidence for metacognitive improvement remains weaker, while unstructured use can promote cognitive offloading, overconfidence, dependency, and integrity risks. To integrate these findings, this article proposes the Human–AI Pedagogical Agency Framework (HAPAF), composed of five interacting layers: epistemic positioning, learner agency, interaction design, verification, and governance. The framework reframes effective GenAI integration as an agency-preserving pedagogical design problem rather than a tool-adoption problem.
A. Haro-Sarango· Multidisciplinary Latin Amer...· 0 citations
Generative artificial intelligence (GenAI) is rapidly transforming pedagogical practices in higher education by generating explanations, feedback, simulations, learning resources, and dialogic prompts. Existing AI frameworks in education predominantly conceptualize AI through functional roles, such as tutoring, assessment-centric models, institutional governance principles, or learner literacy perspectives. However, higher education institutions often regard GenAI as a complementary tool while simultaneously framing it as a threat to academic integrity, triggering reactive responses such as prohibition, surveillance, and detection. These framings leave a theoretical gap, offering limited insight into how GenAI redefines pedagogical agency, responsibility, and knowledge work in everyday interactions among instructors, students, and institutional structures. To address this gap, the present study proposes a nested instructor-student-GenAI triadic conceptual model for higher education. The model is derived through a focused integrative interdisciplinary synthesis that brings together literature from higher education, educational technology, learning sciences, instructional design, human-computer interaction, cognitive psychology, policy, ethics, and institutional governance. The model positions GenAI as a bounded didactic-pedagogical mediator operating within a shared didactic mediation space. Higher education institutions are conceptualized as the governance layer that enables, constrains, and legitimizes triadic practice through policies, infrastructure, regulations, and accountability mechanisms, while wider stakeholders shape external expectations. The study further formulates researchable propositions and discipline-sensitive implications to support future empirical validation and responsible GenAI integration in higher education.
Sharmila Rani Moganadas, Freddy Marín-González, Shwu Huey Nun et al.· Frontiers in Education· 0 citations
This theoretical review examines the relationship between Piotr Galperin’s epistemology, the development of human thought, and the incorporation of artificial intelligence (AI) in education. The analysis is organized around five issues: the stage-by-stage formation of mental actions, the zone of proximal development as oriented mediation, the pedagogical role of AI, the ethical and cultural risks of automation, and practical principles for educational design and regulation. The review synthesizes contributions from historical-cultural psychology, activity theory, neuroeducation, ethics of technology, and recent AI-in-education studies in order to argue that AI should not be understood as a substitute for human thinking but as a mediated resource whose value depends on pedagogical intentionality, transparency, and cultural grounding. The paper’s explicit novelty lies in articulating Galperin’s orienting basis of action with current debates on AI design, educational policy, and ethical regulation, thereby moving from philosophical interpretation to concrete pedagogical criteria for practice. From this perspective, the teacher remains a reflective mediator, and education retains its formative responsibility to cultivate autonomy, critical judgment, and consciously regulated action.
Querubín Patricio Flores Núñez, T. Vera-Assaoka, Juan Carlos Huircalaf Diez· International Journal of Eva...· 0 citations
Generative artificial intelligence is reshaping the organization of knowledge, classroom interaction, assessment evidence, and institutional arrangements in ideological and political education (IPE). In this article, IPE refers to a form of higher education that integrates theoretical learning, civic responsibility, and social practice. This conceptual article examines how generative AI changes the conditions under which educational judgment is formed in IPE classrooms. Drawing on educational technology studies, human agency theory, responsible AI governance, and critical AI literacy, it adopts conceptual analysis and theoretical synthesis to develop a four-stage pedagogical redesign model for the generative AI era. The model contains problem generation, negotiated interpretation, evidence verification, and practice transfer. The article identifies four innovation pathways: issue-based knowledge organization, human-AI collaborative dialogue, situated learning environments, and process-based assessment. It also specifies four risk boundaries: knowledge compression, cognitive dependence, relational weakening, and excessive datafication. The main contribution is to argue that generative AI should not be positioned as an autonomous educational subject, but as a conditional medium that supports interpretation, deliberation, and responsible practice under curricular purpose, teacher judgment, transparent rules, critical AI literacy, and institutional safeguards.
It is argued that students' needs matter on their own, the field should start from what students need when deciding how to use AI in design education, and good educational frameworks should be anchored in the learner, not driven by technology.
A conceptual framework offering AI designers, teacher educators, and policymakers a psychologically grounded, empirically mapped, and internationally contextualised basis for developing AI-supported educational systems that advance equity, learner engagement, and meaningful educational transformation is proposed.
Arpana Koul· Review of Artificial Intelli...· 0 citations
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