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Open research questions in Design Education and Practice

86 unresolved questions extracted from the limitations and future-work sections of 2,496 Design Education and Practice papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • agency over time. Based on the findings of this study, it is recommended that future research further explore how design thinking can be implemented across diverse aesthetic education contexts to cultivate students’ imagination, creative expression, and aesthetic judgment (T. Brown, 2009; Löwgren & Stolterman, 2004). AI-supported art and design learning should be more strategically integrated to stimulate higher levels of creative experimentation and reflective engagement with emerging technologies. Story-driven pedagogical approaches informed by narrative transportation theory (Gerrig, 1993; Green & Brock, 2000) are also encouraged to deepen students’ emotional involvement, aesthetic immersion, and multimodal storytelling experiences. Moreover, educators should emphasize a balanced integration of theoretical foundations—such as Dewey’s (1934) notion of meaningful aesthetic experience—and responsible AI usage, enabling students to connect technological innovation with humanistic design values.

    Integrating Generative AI and Design Thinking in Aesthetic Education: A Narrative-Based Instructional Model in a Digital Culture Course · 2026 · DOI

  • Future research should examine this model across larger, more diverse samples, compare its application across different disciplinary contexts, and investigate how varying levels of AI scaffolding…

    Integrating Generative AI and Design Thinking in Aesthetic Education: A Narrative-Based Instructional Model in a Digital Culture Course · 2026 · DOI

  • AI-generated engineering artifacts in the context of product development under increasing regulatory and interdisciplinary pressures have been defined and classified and the management of such artifacts has been shown. The approach defined engineering knowledge artifacts with explicit origins and validation levels, captured the generative context and validation evidence required for AI assistance, and enforced admission into the lifecycle system, ensuring that only items reviewed and accepted by human experts are improved retrieval and model synthesis. The instantiation of electrifying a bicycle demonstrated that the approach reduces redundant development, improves traceability under regulatory pressure, and updates the SysML v2 baseline without bypassing configuration or change control. Critically, the scientific contribution provides an explicit framework for managing AI-generated artifacts to support trustworthy and traceable AI integration into daily engineering work, by making the origin, validation, and generative context of artifacts auditable. While the process may appear complex, most steps, including the majority of the extraction, networking, and pre-validation of engineering knowledge, are automated, thereby minimizing extra workload for engineers. Networking knowledge in RDF triples enables the codification of complex relationships this can reduce between product aspects, development costs and duration by reducing miscommunication and avoiding redundant parallel efforts. Future research should discuss and evaluate various approaches to generating models based on networked knowledge. Multiple approaches are theoretically usable to generate valid SysMLv2 code in its textual notation, such as prompt engineering, grammar-constrained decoding, function calling, or parsing SysML v2 code from a more established notation in the LLM training data. Which approach produces the best models is the most efficient, and how to evaluate the different approaches must be discussed. improving reusability for future iterations.

    Handling AI-generated knowledge artifacts in generative product engineering · 2026 · DOI

  • - **Olfactory reproduction:** Cross-climate scent fidelity, despite proposed normalization methods, remains an open research challenge. Limitations This white paper acknowledges the following open challenges: - **Sensor fidelity:** Current sensors cannot capture all dimensions of human skill.

    Project LINGXI: The Implicit Archive of Human Civilization — White Paper v1.0 · 2026 · DOI

  • Based on the findings of this study, learning management through design thinking and VR technology should be implemented in engineering drawing courses, particularly in vocational education contexts. Using VR environments to present three-dimensional models and spatial structures can enhance students’ understanding of complex geometric relationships and support the development of engineering drawing ability as an integrated cognitive competence.

    The Impact of Learning Management through Design Thinking and VR Technology to Enhance the Engineering Drawing Ability of the Second-Year Vocational Students · 2026 · DOI

  • This study is a case study conducted within the architecture department of a specific univer- sity, focusing on the development of a hidden curriculum framework and offering new per- spectives. Therefore, the findings are limited to this particular context and do not produce universal results. On the other hand, it proposes a method for analyzing hidden curriculum in architecture education, which is a field that often remains unexamined. Nevertheless, it is expected that employing a larger sample in future research or expanding the scope to include various contexts across multiple universities and countries will generate more com- prehensive contributions to this field. Furthermore, this study examines the hidden curriculum through the lenses of institu- tional discourse, instructor perspective, and student outcomes. While student outcomes are evaluated in this study, cognitive processes and students' perceptions of the hidden cur- riculum are outside its scope. This limitation constrains the evaluation of findings from the students’ perspective. Future studies that incorporate student perspectives will enable a deeper understanding of the hidden curriculum. In this regard, valuable data can be gained by conducting semi-structured interviews with students and utilizing reflective design dia- ries. Additionally, voice-recording and analyzing the feedback sessions during the design studio process will provide new insights into the hidden curriculum from both student and instructor perspectives and will also allow for comparative analysis. Supplementary Information The online version contains supplementary material available at h t t p s : / / d o i . o r g / 1 0 . 1 0 0 7 / s 1 0 7 9 8 - 0 2 6 - 1 0 1 0 2 - w . Acknowledgements We would like to thank all professors for being so kind to interview and to the CoLab Team (Dr. Gaizka Altuna Charterina, Mirza Vranjakovic, Andreas Woyke, Anna-Sophie Hartung, Karolina Rysava, Katerina Tzouvala) for their support throughout this research. Funding No funding was received for conducting this study.

    Implicit and explicit aspects of design learning: unveiling the hidden curriculum · 2026 · DOI

  • While this research demonstrates the toolkit’s capacity to support early-stage value reasoning, several limitations warrant acknowl- edgment. First, the toolkit was developed and evaluated primarily within Western, technology-industry design contexts. Cultural, or- ganizational, and disciplinary differences can shape how values, harms, and ethical reasoning are understood and prioritized. Sys- tematic validation across more diverse contexts is needed to assess whether these mechanisms generalize. Second, our Phase 1 evaluation relied on single-item measures for each dimension, limiting psychometric rigor and construct validity compared to validated multi-item scales. The uniformly high ratings likely reflect the composition of the sample, specifically experienced designers with AI project experience, and may not generalize to the broader range of practitioners who would encounter the toolkit in practice. Future evaluations should develop validated multi-item measures to enable more robust comparison of how tools support reflective depth, value operationalization, and anticipatory harm reasoning. Third, all participants in both phases were design professionals presumably oriented toward reflective practice. Cross-functional teams (e.g., product managers, data scientists, and engineers) who build AI-enabled products and systems may require different scaf- folding or vocabulary, and the productive friction designers found generative may function differently under professional norms that prioritize speed and precision over careful reflection. Evaluation with interdisciplinary teams is therefore an important direction for future research. Moreover, the toolkit does not address the role of datasets in AI concept design, given that training data encodes assumptions about representation, inclusion, and harm that are inseparable from the value implications of AI capabilities. Future it- erations could explore how dataset provenance and curation choices might be surfaced within the AI Capability Library, where capabil- ity definitions implicitly depend on the quality and composition of underlying data. A natural future direction is to extend the toolkit with a com- plementary set of design principles. While the toolkit currently integrates value reflection, harm consideration, and AI capabil- ity grounding into a single proactive framework, its use remains episodic and context-dependent. Design principles would allow designers and product teams to reason about potential harms and ethical trade-offs even when the toolkit itself is not actively in use, reinforcing structured and actionable reasoning throughout early-stage AI concept design at scale.

    Developing an AI Concept Envisioning Toolkit to Support Reflective Juxtaposition of Values and Harms · 2026 · DOI

  • Strong for early-stage ethical consideration; lacks integration with AI capabilities or sustained iterative reflection Consultative; Does not structure negotiation of value…

    Developing an AI Concept Envisioning Toolkit to Support Reflective Juxtaposition of Values and Harms · 2026 · DOI

  • Our study has limitations that should be considered when inter- preting the findings. The 20-minute design activity captures early- stage ideation in a specific context and cannot capture the full process of AI concept development. The limited session time re- duced opportunities to revisit and renegotiate value commitments as concepts developed, precisely when cross-value tensions tend to surface. Asking participants to engage with only a single value, while ecologically valid, further bounded the kind of deep value ten- sion exploration that more extended or multi-value engagements would enable. These choices reflect our scoping toward naturalis- tic early-stage ideation rather than structured value negotiation, and findings should be interpreted accordingly. Nevertheless, un- derstanding how designers approach value integration and harm recognition at this formative stage matters precisely because it establishes foundational direction that constrains or enables possi- bilities in later development phases [97]. Future work could build on this by examining how extended multi-session engagement and iterative reflection influence design outcomes. We also allowed participants to use generative AI tools of their choice to preserve ecological validity and reflect real-world varia- tion in workflows. As a result, tool-specific factors such as inter- action history and model version likely shaped the character and quality of AI outputs participants received. Future work should standardize the AI tools and account for user familiarity levels to isolate tool-specific influences. While all participants reported prior interaction with AI tools, their experiences ranged from end-user engagement to active in- volvement in AI product design, and we did not assess their prior orientations toward AI. Familiarity with AI does not necessarily translate to understanding its inner workings [91], and those who had developed more critical stances through professional expe- rience may have been more attuned to recognizing AI authority effects than those with more uniformly positive associations. Partic- ipants’ varying familiarity with explicit value reflection also likely shaped the depth of engagement visible in our analysis. Future work should examine these dynamics across varying levels of AI familiarity and prior orientation, and could explore how targeted re- sources might deepen designers’ comprehension of AI’s capabilities and constraints. Finally, while this work focused on design practitioners, AI product concept development in practice often involves multidis- ciplinary teams including product managers, data scientists, and others. Building on recent research [11, 35, 40, 43, 44, 96, 100], future work could explore role-specific support mechanisms and resources, moving beyond general collaboration facilitation toward tailored interventions that address each role’s distinct contributions and needs.

    How Designers Envision Value-Oriented AI Concepts with Generative AI · 2026 · DOI

  • 1 Interplay of GenAI and Digital Fabrication in Craft Although prior projects have combined CAM-based design meth- ods with clay printing [5], or applied GenAI in other areas of digi- tal fabrication [65], they have not explored this particular hybrid workflow.

    ClayScape: A GenAI-Supported Workflow for Designing Chinese Style Ceramics with Clay 3D Printing · 2026 · DOI

  • comparative studies, but the lack of a consistent structure for Taken together, standards and comparison. Furthermore, none of these approaches focuses on the underlying organisation of topics, problems, and learning sequences—a problem for understanding how the complex information is decomposed to support learning. is particularly significant that 2.2 Analysing a pedagogical exemplar The study by Vrontissi et al. (2018) is a comprehensive example that provides a clear overview of the topics taught, a sequential breakdown of content delivery and exercises, and an analysis of student outcomes with respect to the content. This level of detail allows the structure of the learning process to be examined more closely, helping to address some of the issues identified in the previous sections transform concept; hence, Vrontissi et al. (2018) adopt a pedagogical approach that emphasises conceptual structural design thinking in architectural education. They state, “the objective is to eventually shift the focus from the actual formal composition of the material construct to the conceptual relational disposition of the inherent structural to structural organisation to a spatial one” (Vrontissi et al., 2018, 9). They achieve this goal by structuring their teaching activity in three distinct steps, which can be summarised as: (1) exploring structural principles, (2) translating the structural principles into an architectural idea, and (3) materialising the architectural idea.

    Teaching technical knowledge in architectural education: a framework for designing and reflecting on teaching activities · 2026 · DOI

  • Taxonomies and tests of human skills ✔ Skills assessments in education ✔ Abilities and skills: Assessing humans and artificial intelligence/robotics systems ✔ An occupational taxonomic approach to assessing AI capabilities Artificial intelligence capabilities and their measures ✔ Identifying artificial intelligence capabilities: What and how to test ✔ Assessing artificial intelligence capabilities ✔ Assessing Natural Language Processing ✔ Common sense skills: Artificial intelligence and the workplace Reflections and a pragmatic way forward ✔ Tasks and tests for assessing artificial intelligence and robotics in comparison with humans ✔ Questions to guide assessment of artificial intelligence systems against human performance ✔ Building an assessment of artificial intelligence capabilities “It enables an understanding of the potential as well as the limits of AI capabilities at a detailed task level so we can describe more precisely how humans and AI are complementary.” Infrastructure, Human Capacity &…

    Investigating generative artificial intelligence’s role in logo design pedagogy: effects on learning experience and outcomes · 2026 · DOI

  • Empirical accounts report AI use for conceptual framing and constraint exploration in early-stage brief development, but systematic evidence comparing outcomes of AI-mediated versus traditional brief development is absent.

    Creativity and artificial intelligence in design · 2026 · DOI

  • In recent years, the new arts call more and more high, the new arts under the background of design professional development research is relatively diverse, provides sufficient theoretical support for research, but involves the development of design professional research is scarce, there is a certain blank, the need researchers to deeper exploration and mining.

    The rational examination of the connotation characteristics and problems of design major under the background of new liberal arts · 2023 · DOI

  • Practical experiences that occur during training, such as those had in internships, are surely interesting in this regard; but the related question of how instructional designers continue to learn and develop professionally, long after formal training and internships have been completed, stands as a unique and under-researched area of scholarship in the field.

    Instructional Design and Professional Informal Learning: Practices, Tensions, and Ironies · 2015

  • They conclude that interest in DBR is increasing and that results provide limited evidence for guarded optimism that the methodology is meeting its promised benefits.

    Design-Based Research · 2012 · DOI

  • This article argues that the emerging paradigm of complexity offers design education the rigour it has been lacking, for this paradigm constructs studio projects not as problems with rational solutions but as systems that need to be explored in order to discover their relational meanings and values – precisely what creativity, balanced with rationality, can accomplish in both Western nations and rapidly developing East Asian nations such as China.

    A New Paradigm for Design Studio Education · 2010 · DOI

  • Edubox is located as a solution for the OUNL and this allows it to offer a viable platform for student use and to use particular implementations for aspects that are not fully determined in the Learning Design specification.

    Developing an approach for Learning Design Players · 2005 · DOI

  • T h e tendencies are: t o substitute something that is meaningful for a meaningless design, or that is lower in the evolutionary series for maturation in drawing; to unify and to “close” the design, to introduce rhythm, symmetry, or conventional propor- tions when these are lacking.

    The Simplification Tendency in Reproducing Designs · 1944 · DOI

  • Without phase-specific risk awareness, practitioners may apply Phase 3 cautions (metric fixation) at Phase 1, where they are insufficient, or fail to recognize Phase 5 risks as cumulative processes that require monitoring across time rather than correction at a single decision point.

    The Integrative Learning Design Framework Revisited: AI Affordances, Risks, and Guardrails for Learning Experience Design Research and Practice · 2026 · DOI

  • The primary limitation of this study is that only 21 design educators are interviewed, and this may not be representative of the IDEs.

    Challenges in understanding, using, and teaching design methods: perspectives of design educators · 2026 · DOI

  • This study demonstrates that guideline-based training can effectively bridge the gap between technological change and human learning during a Computer-Aided Design/Product…

    Using guidelines to train key users: accelerate skills development during a CAD/PDM software transition · 2026 · DOI

  • This finding suggests that when biomimicry is not explored in depth within educational settings or introduced in a comprehensive manner, it may be understood in a relatively superficial way, often reduced to notions such as “imitation” or “copying.

    Biomimicry-Based Design Solutions to Everyday Life Problems by 5th Grade Middle School Students · 2026 · DOI

  • The study's functional taxonomy is structured around the Double Diamond model, but its applicability to other design methodologies and creative domains beyond traditional design practice remains unexplored.

    Creativity and artificial intelligence in design · 2026 · DOI

  • The paper proposes a more-than-human orientation requiring pedagogies attentive to relational co-agency and plural epistemologies, but does not provide concrete examples or frameworks for implementing such pedagogies in design education.

    Creativity and artificial intelligence in design · 2026 · DOI

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86 open questions have been extracted from the limitations and future-work passages of 2,496 Design Education and Practice papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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