Jul 2026· PUPIL International Journal of Teaching Education and Learning· 0 citations
TL;DR
The research outcomes demonstrate that AIAS functions effectively as a learning architecture, aligning academic integrity with instructional design, and offers a replicable model for fashion programs and other disciplines seeking responsible AI integration.
Abstract
The rapid growth of generative Artificial Intelligence (AI) is reshaping how higher education conceptualizes learning, assessment, and pedagogy. Many institutions respond by relying on restrictive policies. Unfortunately, this approach often fails to support meaningful and sustainable education. The objective of this study is to reinterpret the Artificial Intelligence Assessment Scale (AIAS) (Perkins et al., 2024) as a developmental pedagogical framework that enables transparent, ethical, and reflective integration of AI into teaching and learning. Methodologically, the study applies the five-level AIAS model, ranging from AI prohibition to full AI collaboration, in FASH 137: Clothing, Society, and Culture, a General Education course examining the sociocultural meanings of dress. AI integration is scaffolded across multiple assignments, each explicitly aligned with a designated AIAS level. Data are drawn from assignment design, faculty observations, and structured student reflections documenting AI use and learning outcomes. Findings reveal three interrelated pedagogical themes. First, transparency as pedagogical integrity emerges through required “AI Use Notes,” which normalize disclosure and foster academic trust. Second, critical evaluation as human distinction is strengthened as students compare AI-generated insights with their own analyses, reinforcing judgment, creativity, and cultural interpretation. Third, AI as a structured learning partner supports exploration, critique, writing development, and identity reflection without replacing human authorship. The research outcomes demonstrate that AIAS functions effectively as a learning architecture, aligning academic integrity with instructional design. The framework offers a replicable model for fashion programs and other disciplines seeking responsible AI integration. Future research will expand empirical assessment across courses, disciplines, and institutions, examine longitudinal learning impacts, and refine discipline-specific AIAS applications to guide higher education in the AI-driven future.
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.
This article re-examines the role of assessment within the rapidly evolving landscape of artificial intelligence (AI), focusing specifically on differentiated instruction, and highlights the potential of AI to not only streamline the assessment process but also cultivate a more equitable and student-centered learning environment.
Hoai Thu Trinh· VNU Journal of Foreign Studi...· 0 citations
A holistic framework that integrates inquiry-based learning (IBL) with artificial intelligence (AI) to support learning is proposed, arguing that the sustainability of such a system depends on shifting assessment from content mastery to measurable complex thinking skills.
Lori B. Doyle, Jill L. Swisher· Educational Point· 0 citations
Artificial intelligence (AI) is reshaping digital learning environments across higher education, medical training, and K-12 contexts, yet the field lacks a unifying conceptual account linking learner psychology, institutional readiness, and sustainability-oriented outcomes. This systematic literature review (SLR) synthesizes 29 peer-reviewed and conceptual papers spanning AI literacy, AI anxiety, self-regulated learning (SRL), human-AI delegation, algorithmic transparency, and AI-driven institutional transformation. Using a PRISMA-informed selection process, studies were screened for empirical or conceptual relevance to AI in digital learning, yielding a final corpus analyzed for research gaps, objectives, methodological design, data collection approach, and key findings. Results indicate that AI literacy and perceived opportunities consistently predict acceptance and adoption, that anxiety and trust/transparency perceptions act as critical psychological mediators, and that sustainability and organizational-culture factors increasingly shape institutional AI adoption beyond the individual learner level. The review proposes an integrated conceptual framework connecting antecedents (AI literacy, perceived opportunities/challenges, institutional readiness), mediating psychological processes (anxiety, needs satisfaction, trust), and outcomes (acceptance, SRL, work engagement, sustainable innovation). Gaps remain in cross-cultural validation, longitudinal and experimental designs, and integrated governance frameworks, pointing to a research agenda relevant to dissertation work at the intersection of AI, education, and behavioral science.
Bhawna· EPRA International Journal o...· 0 citations
First-year university students’ perceptions of generative AI in academic work are investigated, foregrounding student agency in a Global South context and offering pedagogical and policy implications for responsible AI adoption.
Sharifuzzaman, M. Rahman· Asian Journal of Contemporar...· 0 citations
Although blended learning (BL) represents a transformative educational model, it is unclear how digital transformation (DT) shapes the dynamics of BL, particularly through the integration of artificial intelligence (AI), in higher education. This study examines how artificial intelligence–supported self-regulated learning (AI-SRL), informed by cognitive load theory and AI-driven pedagogy, can enhance BL models to promote equitable, inclusive education and advance sustainable development within the context of ongoing DT. Three hundred and ten higher-education students in the United Arab Emirates (UAE) who participated in BL courses completed an online survey. In addition, 11 faculty members and academic leaders were interviewed. The findings show that the use of AI mediates the relationship between DT and students’ behavioural and academic outcomes – namely, procrastination, heuristic processing, and academic achievement. DT increases the adoption of AI to improve academic performance while simultaneously increasing procrastination and reliance on heuristic (surface-level) processing. AI-SRL mitigates these negative outcomes, reducing procrastination and heuristic processing. Furthermore, AI-integrated pedagogy empowers students to harness the benefits of AI-enhanced DT to improve academic performance while minimising adverse cognitive and behavioural effects. This study provides a nuanced understanding of BL in higher education, showing that the use of AI helps shape student experiences and outcomes.
N. Shaya, Rawan Abukhait, Muhammad Nisar Khattak et al.· Journal of Applied Learning...· 0 citations