Optimizing the conceptual design process of animation scenes using generative AI

In design, fixation is a recurring problem with novices where they choose the first solution that they think of for a given problem rather than explore a range of possible solutions. Fixation can result in barriers in design process, restricted search and premature commitment to solutions that may be sub-optimal. Among undergraduate engineering students, design fixation is due to limited prior conceptual knowledge, insufficient knowledge of design heuristics, and disconnected and evolving knowledge base, skills and strategies. One mechanism to overcome design fixation is expansion of problem and solution space where learners explore a variety of aspects related to the design problem such as constraints, assumptions, concepts and principles, before deciding on the solution to be followed. To be able to expand problem and solution space, novice designers require support. We have devised a pedagogical strategy flare-fork which scaffolds students’ problem space and solution space expansion in the flare phase and generation of multiple constraint optimized designs in the fork phase. Flare-fork is a systematic process that supports students to build on their opportunistic ideas to expand design boundaries and subsequently identify trade-offs to generate a variety of solutions tailored to fulfill a combination of constraints and assumptions. The pedagogy brings together design strategies, tools and practices such as rapid ideation, shared visual representation, categorization and techniques to restructure thinking patterns in a collaborative environment. In this paper, we first describe the conceptual design of our proposed pedagogy. We then describe a study to examine how the intervention operationalizing the flare phase of the pedagogy supports students in expanding their problem and solution space.

Generative AI (GenAI) is rapidly transforming creative fields, raising critical questions about its impact on artistic identity and authorship, which also influence art education. While studies have explored GenAI adoption, the diversity of art students’ subjective perspectives on collaborating with these tools remains under-examined. This study uses Q methodology, a mixed-methods approach to investigating subjectivity, to explore the distinct perspectives art students hold regarding their creative identity when collaborating with GenAI. We conducted a Q study with 40 university students from various art-related disciplines (e.g., visual arts, design, music, animation) across different academic levels. Participants sorted 42 statements reflecting diverse viewpoints on GenAI collaboration, authorship, ethics, and creative control. By-person factor analysis revealed five distinct perspectives: (1) Human-centered directors, who treat GenAI as a tool under strong human intention; (2) Authorship guardians, who resist AI co-authorship and prioritise authenticity and ethical boundaries; (3) Conflicted co-creators, who use GenAI but struggle with ownership, disclosure, and earned pride; (4) Pragmatic integrators, who adopt GenAI for workflow efficiency while keeping craft expertise central; and (5) Enthusiastic explorers, who embrace AI’s creative possibilities, open to co-agency and novel expression. These findings offer a nuanced typology of student viewpoints, moving beyond simple acceptance or rejection. We further propose a two-dimensional framework that maps these perspectives according to students’ openness to GenAI and their sharing of creative agency with it. This research contributes empirical insights into how art students negotiate creative identity with GenAI, provides a valuable framework for educators to develop tailored pedagogical strategies addressing diverse student needs and concerns, and offers implications for designing more human-centered GenAI tools for creative practice.

Generative AI (GenAI) tools introduce the risk of student overreliance, in which learners bypass their cognitive effort and depend uncritically on GenAI outputs. Teachers have a crucial role in preventing students’ overreliance on GenAI. This paper suggests teachers’ knowledge for preventing GenAI overreliance based on the Intelligent-TPACK framework. This framework addresses GenAI-specific pedagogical and ethical knowledge with five dimensions: Intelligent-TK, Intelligent-TPK, Intelligent-TCK, Intelligent-TPACK, and Ethics. To prevent overreliance, Intelligent-TK helps teachers recognise GenAI features such as fluency, confidence, and anthropomorphic cues. Intelligent-TPK includes pedagogical strategies that sustain students’ mental effort by using metacognitive prompts and delayed assistance. Intelligent-TCK supports teachers in evaluating and adapting AI-generated disciplinary content so that conceptual accuracy and cognitive engagement are maintained. Intelligent-TPACK allows teachers to combine these knowledge domains into coherent task sequences that promote student reasoning instead of cognitive outsourcing. Ethical knowledge, including transparency, accountability, fairness, and inclusiveness, further strengthens responsible AI use.

Uncritical use of generative AI (GenAI) responses is a major concern among educators as it can hinder knowledge development, creativity, critical thinking and academic misconduct. To mitigate these repercussions, current discussions predominantly focus on changing assessment methods or policing students’ use of GenAI, which greatly shapes students’ GenAI use, for better or for worse. Few studies have examined how different assessment designs impact the way students use GenAI for coursework and the quality of GenAI-assisted writing. This study uncovered the relationships between assessment design and critical thinking in students’ writing through analysing lecturer feedback on 51 postgraduate ChatGPT-assisted students’ assignments across fourteen modules and assessment information related to the assignments. Results revealed that word limits, genres, information about organisational structures, and cognitive domains required by assessments significantly determined students’ critical thinking performance in their disciplinary writing. Based on the results, we suggested (a) setting word limits based on task complexity rather than module credits, (b) designing integrated tasks with varied assessment methods to encourage critical thinking and knowledge development, (c) providing an appropriate amount of structural information to create space for critical thinking and (d) explicitly signalising cognitive domains required by assessments to address GenAI’s impact on writing. We further encourage educators to critically reflect on the existing assessment guidance and practices to design assessments that cultivate critical AI users in an AI-empowered world.

Teacher support significantly influences student engagement and learning outcomes in educational environments that incorporate generative AI (GenAI). Simulation-based learning (SBL) fosters complex skill development but may cause cognitive overload without appropriate instructional support. While GenAI tools provide personalized assistance, their integration into teaching and learning presents pedagogical challenges. Grounded in Self-Determination Theory (SDT), this study investigated the effects of SDT-based teacher support on undergraduates’ psychological need satisfaction, engagement, and learning outcomes in a GenAI-assisted simulation-based learning environment. Eighty university students participated in a GenAI-assisted performance management simulation activity and were assigned to conditions with or without teacher support. Our analysis showed that teacher support: (i) significantly enhanced competence and relatedness satisfaction; (ii) significantly increased cognitive, emotional, and agentic engagement; (iii) significantly improved learning performance, problem solving, and critical thinking; and (iv) did not significantly promote autonomy satisfaction, behavioral engagement, or creativity. This study underscores the essential role of teacher support in improving student engagement and higher-order thinking skills in learning contexts incorporating GenAI.

Recent developments in prompt-based generative AI has given rise to discourse surrounding the perceived ethical concerns, economic implications, and consequences for the future of cultural production. As generative imagery becomes pervasive in mainstream society, dominated primarily by emerging industry leaders, we encourage that the role of the CHI community be one of inquiry; to investigate the numerous ways in which generative AI has the potential to, and already is, augmenting human creativity. In this paper, we conducted a diffractive analysis exploring the potential role of prompt-based interfaces in artists’ creative practice. Over a two week period, seven visual artists were given access to a personalised instance of Stable Diffusion, fine-tuned on a dataset of their work. In the following diffractive analysis, we identified two dominant modes adopted by participants, AI for ideation, and AI for production. We furthermore present a number of ethical design considerations for the future development of generative AI interfaces.

Purpose Creativity as an outcome of our thoughts and actions is a controversial concept that has been discussed by many disciplines in different ways. In the light of cognitive research on creativity, this study aims to deal with the components of the idea generation process specific to interior design education. Based on the study conducted in the interior design studio, this paper introduces the infographic model of design thinking. the main purpose of the model is to summarize how design students initiate the design process, create their own design ideas, and also how they lead design thinking for a spatial solution. Design/Methodology/Approach The cross-disciplinary research paper within the context of the psychological studies on creative cognition consists of a detailed analysis of the design process in the design studio. A case study was conducted in the second-year undergraduate interior design studio at Hacettepe University in order to observe the students’ experiences during one semester. In the wake of this qualitative research, the data obtained from 15 design students selected have been analyzed gradually and a design thinking model has been generated with the findings. Findings In addition to the interpretation of the creativity models from the literature, initial analyses showed that design thinking can be explained in three main stages as preparation, conceptualization, and spatialization that underlie the internal and external process of design thinking in the design studio. With subsequent analyses, these stages have been divided into different strategical layers according to students’ experiences. As a result, the infographic model of the design thinking process is structured based on the evaluations of these components and the design approaches identified. Research Limitations/Implications The data acquired from the case study undertaken by the author were compared so as to identify the similarities and diversities of these processes. More research on different stages of the design process can shed more light on design thinking. In addition to this, qualitative data are based on a small group of students to get detailed information about the process. The proposed model can be adapted for different studies in the context of the design studio with more participants. Practical/Social Implications The proposed model in the research is intended to be used as a content map that shows the alternative ways of thinking in design ideation, and also an analysis method of the design process for future studies. In other words, the paper shows the two-way implications of the design thinking model on design education. One of which is a guide for practical use for design students and the other is an analytical tool for studio instructors or researchers. Originality/Value This study brings to focus on conceptualization and spatialization for creative idea generation in design studio education. On the basis of drawn from creativity models, the paper introduces a new process model that provides an original interpretation of existing models in design. The proposed model differs from previous cognitive studies, as it expands the ideation process with both internal and external operations.

Two mechanisms widely used in the Software Product Lines (SPL) Engineering are the feature model and the conditional compilation. The former models the variability in the problem space and the latter realizes it in the solution space. Even though the research community know that the feature model imposes a number of constraints to the product line implementation, there is a lack of support to co-evolve problem space and solution space. In this paper, we present an exploratory study whether problem space constraints are considered at source code level of #ifdef-based SPL implementations. In order to accomplish our goal, we developed a preliminary approach to check problem and solution spaces in a prototype tool (fclcheck). The results show a lack of realization of feature model constraints while implementing variation points with that mechanism. We also evaluated the scalability of the approach and the recoverability of the tool.

Tensions between software development methodologies and user-centered design (UCD) have always existed, but waterfall methodologies do provide a process context within which UCD methods can be clearly integrated whenever this is required. Popular agile methodologies such as Scrum create different challenges to integrating UCD. However, fitting UCD into agile methodologies will not necessarily result in high software quality. The combined approaches can still have significant design gaps that must be addressed by additional creative design practices. This chapter relates selected historical methodological trends to tensions between software and creative design. To resolve these tensions, innovative software development needs to draw on creative design practices in addition to UCD and agile methods. Specifically, innovative software development needs to draw on three key insights from design research: creative design work co-evolves problem and solution spaces; design materials talk back; and, the best design work is generous in scope and intent. These three insights are used firstly to structure a critique of the Agile Manifesto and secondly to provide the basis for proposing a balanced approach to software development that can appropriately integrate engineering, user-centered and creative design practices.

This study investigates the potential of generative artificial intelligence–based text-to-image models as early-stage decision-support tools in sustainable tiny house design. Within the conceptual design process, the study systematically models key components—formal schema development, material selection, and spatial atmosphere—through a controlled prompt framework that enables structured comparison of design alternatives. Design outputs generated by diffusion-based models, including ChatGPT (DALL·E 3), Copilot, and Gemini AI, are evaluated using a multi-criteria framework encompassing sustainability principles (use of natural materials, compact planning, energy-efficient openings), typological consistency, and overall design quality. To examine how prompt variations shape design outcomes, three analytical dimensions are defined: (1) morphological decisions (plan layout, roof typology, form), (2) material decisions (timber, recycled composites, hybrid systems), and (3) spatial atmosphere (daylighting, color palette, interior warmth). The findings show that text-to-image workflows not only accelerate the generation of design alternatives but also enable a structured interpretation of how different design options align with specified criteria. In this sense, generated outputs function as interpretable design propositions that can be comparatively assessed within a transparent evaluation logic. The results further suggest that generative AI supports designers by externalizing design reasoning, facilitating the exploration, comparison, and justification of early-stage decisions. These results position generative AI as a viable component of next-generation design decision-support systems, particularly in contexts where rapid iteration, multi-criteria evaluation, and the articulation of design reasoning are critical.

<em>The Design Thinking approach as a method of problem solving and product innovation is well known in business, management and entrepreneurship. However, various design thinking models created by individual researcher and corporations often confuse users about which model to use. This research was conducted with the aim of observing the suitability of five selected design thinking models to support the development of startups in a business incubator. The research applies qualitative descriptive method using a case study of PT. Jiwa Gelora Juara (PT-JGJ) startup development at the Bakrie University’s UBpreneur business incubator. It observes the design thinking process in the pre-incubation, incubation and post incubation stages. The research findings show that each design thinking model has its own strengths and weaknesses in a certain situation, but most of the models are more suitable implemented in the new product/process development phase. To support the startup’s growth stage, other management tools are needed.</em>

In the machine learning based on kernel tricks, people often put one variable of a kernel function on the given samples to produce the basic functions of a solution space of learning problem. If the collection of the given samples deviates from the data distribution, the solution space spanned by these basic functions will also deviate from the real solution space of learning problem. In this paper a multikernel-like learning algorithm based on data probability distribution (MKDPD) is proposed, in which the parameters of a kernel function are locally adjusted according to the data probability distribution, and thus produces different kernel functions. These different kernel functions will generate different Reproducing Kernel Hilbert Spaces (RKHS). The direct sum of the subspaces of these RKHS constitutes the solution space of learning problem. Furthermore, based on the proposed MKDPD algorithm, a new algorithm for labeling new coming data is proposed, in which the basic functions are retrained according to the new coming data, while the coefficients of the retrained basic functions remained unchanged to label the new coming data. The experimental results presented in this paper show the effectiveness of the proposed algorithms.

The application of generative AI in enhancing the cultural identity of cities within the context of urban branding presents a novel perspective. Emojis, as vivid, engaging, and easily disseminated communication media, have become a crucial means of interaction in contemporary society. This study, based on a human factors engineering perspective, takes the design of Wuhan city-themed emojis as an empirical case study. Initially, a comprehensive literature review and semi-structured interviews were conducted to collect diverse data from Wuhan residents, tourists, frequent emoji users, and design experts, aiming to gain in-depth insights into the cultural essence and symbolic imagery of Wuhan. Subsequently, grounded theory was employed to perform a three-level coding process, leading to the development of an initial theoretical framework. The Kano model and the Analytical Hierarchy Process (AHP) were then integrated to classify and prioritize user needs through a systematic weighting analysis. Finally, based on the priority of element weights, Midjourney was utilized for design practice and iterative optimization. The results indicate that generative AI can stimulate creativity and foster emotional connections in city-themed emojis, meeting the diverse needs of various application scenarios and enhancing the digital dissemination of Wuhan's city image. This study provides a novel creative approach and theoretical model for city-themed emoji design, offering valuable insights for other cities in digital branding and cultural communication.

Design Thinking embraces challenges, and re-frames problems into opportunities for positive change. Design thinking is interdisciplinary and can be applied to a myriad of situations. There are multiple models of Design Thinking but one of the more popular ones has come out of the d.school at Stanford University (Costa, 2017). This thesis uses Qualitative data which has been collected through multiple research projects. Through exploratory means, I will evaluate the data that is presented in the research studies already conducted to help understand the issue of Design Thinking within education, as it stands today. I will be looking for similarities in findings through the different studies, as well as working to create an educated hypothesis to help propose future data collection and form a curriculum which supports design thinking as a learning aid. Design Thinking can be compared to the scientific method, however it is tailored to the design field and interdisciplinary practices. It is a methodology which helps designers identify the correct problem, ideate creative solutions and prototype and test these new ideas. This system can be learned and adapted by educators into their curriculums to help reach the 21st century student, and prepare them for the jobs of the future. Inter-disciplinary design and group collaboration has become essential in large companies and successful businesses. Which is why in a rapidly changing world it is important to fluidly remodel our approach to education. This thesis proposes that by teaching the Design Thinking framework in a "designerly" manner young students will more likely become well rounded human centered innovators. And more importantly, go on to work collaboratively with positive global change in mind. Teaching this framework to an age group that is rapidly cognitively developing (adolescence) it may help with their problem solving and problem framing abilities. Adolescence is a developmental phase when the brain is changing the way a person thinks and relates to themselves in direct correlation to their environment and their peers. According to brain research and MRI studies, the frontal cortex and amygdala both responsible for decision making and emotional responses are developing at a very fast rate during adolescence. For this reason, there is inherent justification for teaching effective and creative problem solving in a time when they are forming their decision making skills and are most at risk for problem behaviors. Through making courses available that encourage innovation and surveying student responses to Design Thinking and how it impacts their decision making, we can begin to understand the positive change. A few studies on this subject have already been done, however more research is needed to evaluate how design thinking can be applied across the curriculum, and a rigorous inquiry into how we think and why design can help. I will explore the existing research studies that have been conducted around the topic of how Design Thinking can affect the learner. The purpose is to propose the positive impact that teaching design problem solving to adolescents can have. In the studies presented there has been quantitative data that exhibits positive growth and development as a result of students being involved with creative thinking processes. Through using control groups and surveying student responses to Design Thinking and how it impacts their decision making, we can begin to understand the whether or not there are legitimate correlations.

Understanding how communities experience algorithms is necessary to mitigate potential harmful impacts. This paper presents folk theories of text-to-image (T2I) models to enrich understanding of how artist communities experience creative machine learning systems. This research draws on data collected from a workshop with 15 artists from 10 countries who incorporate T2I models in their creative practice. Through refexive thematic analysis of workshop data, we highlight artist folk theories of T2I use, harm, and harm reduction. Folk theories of use envision T2I models as an artistic medium, a mundane tool, and locate true creativity as rising above model afordances. Theories of harm articulate T2I models as harmed by engineering eforts to eliminate glitches and product policy eforts to limit functionality. Theories of harm-reduction orient towards protecting T2I models for creative practice through transparency and distributed governance. We examine how these theories relate, and conclude by discussing how folk theorization informs responsible AI eforts.