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.
Abstract
Generative AI is moving quickly into design education, universities and design schools have already issued guidelines, frameworks, and curricular advice. Across this work, three concerns stand out. The first is building students' AI literacy, the second is the effect of AI on the design profession, the third is redesigning teaching for the new tools. What receives far less attention are the student’s psychological needs, such as the need for autonomy, relatedness, competence when using AI for a design task. We argue that good educational frameworks should be anchored in the learner, not driven by technology. Following a scoping review approach, we read the recent design-education discourse on generative AI from 2022 to 2026 together with the established work on learner experience, psychological needs, and wellbeing in HCI and AI. Together they show a clear pattern. Related works are diverse but pay little attention to what students actually need. When needs do appear, they are used mainly to explain outcomes such as creativity or critical thinking. We argue that students' needs matter on their own, the field should start from what students need when deciding how to use AI in design education. Our contribution is not a new framework but a change of stance that puts the learner's needs first.
As generative AI evolves from an assistive tool to an increasingly autonomous system, higher education faces new challenges for teaching and learning. Emerging research describes the rise of the “ghost student,” who completes coursework without active participation, and the “cognitive debt” that accrues when productive struggle is outsourced to automated systems. These developments expose the limits of reactive, compliance-focused approaches. In response, this conceptual paper argues that purposeful, human-centered AI engagement offers a better path forward. Drawing on constructionist learning theory, we introduce the Instructional Model for Human-Centered Generative AI Engagement, a pedagogical framework designed to help faculty guide students in engaging with generative AI as a thinking partner rather than a shortcut. The model consists of five recursive phases: critical and ethical awareness, prompt literacy, AI-supported learning, reflection and revision, and independent application. Central to its implementation is the Prompt Literacy Cycle, nested within Phase Two, which guides students through iterative prompting, critical interpretation of LLM-generated outputs, and reflective revision. Together, these elements support a shift towards process-oriented assessment while fostering ethical awareness, metacognition, and intentional engagement with LLM-mediated knowledge construction.
A. Miles, Paige Haber-Curran, Khalid H. Arar· Open Praxis· 0 citations
In higher education, multimedia authoring, feedback, analytics and adaptive learning systems are increasingly embedded with artificial intelligence (AI). The impact of these changes on instructional design for personalised learning is examined. Instead of viewing personalisation as automatic optimisation, the review examines four interrelated design challenges: the pedagogical quality of AI-generated multimedia; the validity and fairness of automatic feedback; the interpretability of learning analytics; and the impact on established instructional design processes. Research suggests that AI can broaden the diversity and responsiveness of resources for learning, but evidence of lasting learning gains is more patchy and very much dependent on human oversight, curricula and student agency. The systems may then use non-transparent models or data which are unrepresentative, thus reproducing equity concerns. The review suggests a series of principles for responsible design: pedagogical purpose; cognitive coherence; human verification; contestable personalisation; and proportionate data use. AI should be viewed as a limited design resource instead of an independent system of instruction.
F. Shahbodin, C. K. Mohd, Z. Maksom et al.· International journal of res...· 0 citations
This paper examines the opportunities and risks associated with student-facing conversational artificial intelligence (AI) in primary education. It aims to evaluate how large language models (LLMs) can support personalised learning while identifying developmental, pedagogical and ethical challenges. Rather than treating benefits and risks as discrete factors, the study conceptualises AI as a socio-technical intervention that reshapes relationships between learners, teachers and knowledge.
The paper adopts a conceptual and theory-driven approach, synthesising current literature on AI in education, pedagogical theories and emerging practices in primary classrooms. The analysis is structured through a tension-oriented synthesis, identifying points of alignment and misalignment between AI affordances and core learning processes in primary classrooms. Based on this synthesis, the study develops a set of guiding principles grounded in developmental and educational considerations.
Conversational AI offers significant benefits, including personalised learning support, immediate feedback and reduced teacher workload. However, risks include cognitive offloading, overreliance on AI, misalignment with curriculum goals and ethical concerns such as bias and privacy. The analysis suggests that these are not independent challenges but reflect underlying tensions between technological capabilities and pedagogical requirements.
The study is conceptual and lacks empirical validation. Future research should focus on longitudinal and classroom-based studies to assess the actual impact of AI on primary learners' cognitive and social development. The paper highlights the need for interdisciplinary research bridging education, AI and developmental psychology.
The study proposes a set of guiding principles derived from the identified tensions, emphasising teacher-mediated interaction, developmental calibration of AI use, transparency, curriculum alignment, privacy protection and equity considerations. These principles provide a structured basis for integrating AI in ways that support learning processes while mitigating potential risks.
The adoption of AI in primary education raises concerns about equity, access and digital divides. Without careful implementation, AI may reinforce existing inequalities. Promoting critical AI literacy and ethical awareness among young learners is essential to prepare them for responsible participation in an AI-driven society.
This paper contributes a developmentally informed, tension-based conceptual framework for understanding student-facing AI in primary education. By reframing commonly identified opportunities and risks as interrelated tensions, it offers a more analytically grounded basis for guiding AI integration beyond descriptive or normative approaches.
This study presents an abductive analysis of interview data from 13 Finnish teachers involved in national AI education development projects and conceptualises a potential synergy between the domains where students' self‐determined, informed engagement with AI is placed at the centre of educational efforts in AI education.
Janne Fagerlund, Pekka Mertala, Jukka Lehtoranta et al.· Journal of Computer Assisted...· 0 citations
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.
The Pedagogical Prompting Feedback Cycle is offered as a teacher-oriented way of translating existing instructional expertise into AI-supported practice and presented as a tentative conceptual model rather than a validated framework: it is meant to provoke inquiry and design, and it requires empirical validation across diverse languages, disciplines, and educational settings.
Ali Khodi, Samantha M. Curle, Víctor Parra-Guinaldo· Frontiers in Education· 0 citations