Positive and Negative Reshaping of Teaching Practice, Student Creativity, and Learning Outcomes by Artificial Intelligence in University Industrial Design Education

Image-generative artificial intelligence (AI) is increasingly being used in the product design process. In this paper, we present examples of how it is being used and discuss the possibilities of how applications may evolve in the future. We discuss the legal and ethical implications of image-generative AI, including concerns about bias, hidden labor, theft from artists, lack of originality in the outputs, and lack of copyright protection. We discuss how these concerns apply to design education and provide recommendations to educators about how AI should be addressed in the design classroom. We recommend that educators introduce AI as one tool among many in the designer’s toolkit and encourage it to be used as a process tool rather than for generating final design deliverables. We also provide guidance for how educators might engage students in discussions about AI to enhance their learning.

… of artificial intelligence technology on teaching and learning, we conclude that AI positively influences the quality of teaching … the implementation of artificial intelligence in education are …

The advancement of Artificial Intelligence (AI) is reshaping industrial design by enhancing creativity and productivity. This study explores the integration of AI tools—specifically ChatGPT, DALL·E, Midjourney, and Vizcom—into the industrial design process and attempts to apply them in undergraduate industrial design education. By emphasizing the distinction between Pre-AI (divergent phase) and Post-AI (convergent phase) design stages, based on the Design Council's Double Diamond model, the impact of AI-assisted methods on creativity and the design process is examined. Through two case studies involving a Golf Cart Project and a Conceptual Car Project, we analyze the balance between human creativity and AI technology to enhance innovation while maintaining the essential role of human judgment (design decision). The findings offer preliminary insights for transforming design education and practice, highlighting the importance of foundational design skills in the era of AI.

In recent years, the rapid development and the various applications of artificial intelligence technology has been gradually replacing many labor-intensive and creative tasks. Traditional design education in schools invests considerable time in cultivating students' basic skills in using techniques, tools, materials, etc., but overlooks the wave which has been brought by artificial intelligence. Artificial intelligence has surpassed human performance in specific fields and is poised to replace the work of many laborers. As artificial intelligence technology advances, its applications will permeate more areas, including thinking and creative abilities that humans have taken pride in. Design education in universities should re-examine the design values of human designers and artificial intelligence respectively. This redefinition of values implies that the abilities required of designers need to be reframed. Design education in universities should also explore how to integrate artificial intelligence-related technologies into current design curricula. New design courses must manifest the value of human designers. Furthermore, design programs offered by universities should begin planning training courses on collaborative design with artificial intelligence and establish relevant mechanisms for making artificial intelligence an effective tool to assist designers. In addition to redefining the value of designers and planning artificial intelligence-assisted design courses, design departments should monitor industry trends and gather year-by-year feedback to provide students with up-to-date career development advice. We should recognize the crisis that artificial intelligence presents to current design education and address the impact of this wave on design students. Artificial intelligence is progressing toward an irreversible trend, and the growth and transformation of designers will become a new challenge for future design education.

… short, artificial intelligence is transforming product design in many ways. From using machine learning … design tools and improving the efficiency of the design process itself, AI is helping …

… This study aims to analyse the potential of AI in industrial design student activities through … for optimizing teaching activities and cultivating the creativity of industrial design students. …

… Learning in Human Factors Engineering Human factors engineering is a fundamental course in industrial design education … and their applications in design, such as human-computer …

This study investigates the effectiveness of integrating Artificial Intelligence (AI) into industrial design education for enhancing ideation and concept generation. Employing a mixed-method approach, the research includes classroom experiments with basic and UI design, alongside questionnaire surveys to gather quantitative and qualitative data. The study explores how AI supports design processes, the benefits and challenges of its integration, and its impact on teaching outcomes and student learning. Results show AI can significantly enhance creativity and learning efficiency, but also highlights concerns about privacy, over-reliance, and potential biases. The study suggests careful integration of AI with ethical considerations is crucial for effective industrial design education.

With the widespread application of AI image generation technology in higher education design fields, traditional design education models face the necessity of reevaluation. This study aims to explore how design aesthetic features (as objective product attributes) influence designers’ creative thinking and design expression (as subjective capabilities), and accordingly reassess the educational value of foundational design courses in the AI era. Using a comparative experimental method, the research recruited 25 first-year and 25 third-year industrial design students to create product designs using Midjourney, with 36 industrial design experts systematically evaluating the works. Results indicate that design aesthetic features significantly impact design expression more than design thinking, and the two student groups demonstrate notable differences in design element application: novice design students primarily express creativity through intuitive visual elements such as product patterns and product appearance, while advanced students more effectively utilize professional design elements like form contours and material textures, reflecting how design education facilitates students' transition from perceptual cognition to rational analysis. Additionally, the positive correlation between creative thinking and design expression strengthens with deepening design education, indicating a mutually reinforcing relationship. Based on these findings, the paper suggests that foundational design courses in the AI-generated imagery era need repositioning: color and expression courses should shift from basic skill training to high-level theoretical education, creative thinking courses significantly increase in importance, form and material courses maintain core value but need content updates aligned with AI characteristics, while human-computer collaborative design should become a new curricular direction. This study provides an empirical foundation for design education reform in the AI era, emphasizing the importance of understanding design essentials and cultivating innovative thinking.

Introduction With the widespread application of artificial intelligence (AI) technology in design education, exploring how AI tools shape students’ innovative abilities has become increasingly important. Existing technology acceptance models mainly explain the adoption behavior of AI tools but have not examined how technological features influence innovation outcomes through user psychological processes. Methods This study employs a cross-sectional quantitative design to examine how AI tool quality dimensions influence design innovation ability (DIA). Specifically, it tests the pathways through which interaction quality (IQT) and information quality (INQ) affect DIA via satisfaction (SAT) and intention to use (INU). Based on an online survey of 1,016 Chinese industrial design students, PLS-SEM data analysis was employed. Results The study found that both IQT and INQ positively influence SAT. Subsequently, SAT, IQT, and INQ jointly affect INU. INU significantly predicts DIA. Mediating effect analysis confirmed that SAT plays a partial mediating role between the quality dimensions and INU. Discussion Notably, the study reveals a pattern in which information quality exerts stronger effects than interaction quality, and direct functional evaluation outweighs affective mediation, both of which challenge conventional technology acceptance assumptions. The findings extend technology acceptance theory by identifying dual mechanisms, including direct functional and indirect emotional pathways, in AI-assisted creative education.

This study presents an exploration of integrating artificial intelligence tools within a user-centered design framework for industrial design education. The study took place at Iowa State University in the Department of Industrial Design. As AI technologies increasingly impact design practice, design educators face the challenge of preparing students trained in these tools while maintaining focus on human needs and expectations. This study addresses the gap between traditional design methodologies and the evolving AI tools by investigating how AI integration affects student learning outcomes, design process efficiency, and user empathy development. The study examines two industrial design courses taught over consecutive semesters, with 10 students in the first cohort and 15 students in the second cohort. Both groups followed a structured curriculum combining literature review, expert interviews, and practical application using the double diamond design process with integrating tools like ChatGPT, Claude for data analysis, and Newarc.AI for sketch-to-render capabilities. While AI tools significantly enhanced technical execution and iteration speed, students still required a deep understanding of user behavior and human-centered design principles to create meaningful solutions. The research emphasizes that AI tools function as powerful assistants rather than shortcuts; the students absolutely need to maintain critical thinking and compassion-driven design approaches for meaningful outcomes.

… the impact on industrial design (ID) curriculum. An experiment evaluated AI's effectiveness in ID … explored the effectiveness of various image-based AI tools for industrial design and the …

The rapid development of artificial intelligence technology is profoundly reshaping the education ecosystem, while generative AI tools such as ChatGPT and Midjourney are reconstructing the teaching logic of industrial design history. Based on the constructivist learning and design ethics theory, this article proposes three main dimensions of AI empowering the teaching of industrial design history, namely the deep integration of AI technology, the value orientation of curriculum ideological and political values, interdisciplinary knowledge reconstruction, and project-based practice. Compared with the teaching and research project of the “Industrial Design History” course reform at Wuhan University of Technology, this article verifies the potential and limitations of generative AI in historical data processing, situational construction, and critical thinking training, providing a new paradigm of “human-machine collaboration” for design history teaching.

… of artificial intelligence (AI) in the context of artistic expression applied to Industrial Design … Our goal is to compare the impact of integrating AI technologies into design training with …

… –AI collaboration. This study investigates Taiwanese undergraduate industrial design students’ perceptions of AI tools in design … ’ perceptions of AI tools in design learning across key …

Amid the Covid-19 pandemic, design education experienced a significant shift as traditional studio teaching went online. This transition coincided with industry demands for adaptable, technology-proficient graduates prepared to work and collaborate as part of a decentralized workforce. This study examines how design educators in seven countries adapted their post-pandemic studio pedagogy to align with these industry needs. An online survey was used to efficiently reach the wide, geographically dispersed participant pool of educators. Findings indicate a rising acceptance of online technologies in studio teaching. The majority of design educators are now incorporating online elements into their design teaching. Pre-recorded lectures, online feedback and critique sessions, self-paced learning activities, and the use of cloud-based collaboration tools are amongst the most frequently employed methods. Nearly a third of surveyed educators are even considering teaching fully online design courses. This shift reflects a forward-thinking approach aimed at better aligning design education and industry. However, the study also highlights the importance of remaining open to disruptive technologies like generative Artificial Intelligence which is currently reshaping the design industry and work practices.

… students utilize AI tools in a short visual design task and how these experiences relate to self-reported creativity and creative … The primary aim is to understand students’ perspectives on …

This study focuses on Generative Artificial Intelligence (AI) and its transformative impact on design ideation. Generative AI, recognized for its ability to produce a wide array of design alternatives, has become an important tool in design, reshaping traditional methodologies. It facilitates the generation of novel and diverse design forms, acting as a co-creator in the design process. This technology, through machine learning and pattern recognition, analyzes extensive design datasets, enabling the production of innovative solutions. The utilization of generative AI extends beyond replicating AI-provided solutions; it aids in developing and influencing novel concepts, thus fostering original design solutions. This aligns with the concept of ‘reflective practice’ in design, where designers iteratively refine concepts through a dialogue between thought and action. The study employed a quasi-experimental design with 40 design students, randomly assigned to two groups of 20 each. Conducted in two phases, each phase involved a distinct urban furniture design task. In Phase 1, Group A was provided with a text-to-image generating AI tool, while Group B was not. In Phase 2, both groups undertook a similar task without AI assistance. This design exercise allowed for examining the influence of AI on creativity and cognitive load. Design outcomes from both tasks were anonymized and evaluated by experienced professionals using the Creative Product Semantic Scale (CPSS), which measures Novelty, Resolution, and Elaboration and Synthesis. Additionally, the NASA Task Load Index (NASA TLX) questionnaire assessed cognitive load aspects such as mental demand and effort. Findings suggest that generative AI significantly influences the creative design process, enhancing the quality of design outcomes and reducing cognitive load. The AI group demonstrated better performance in both tasks, indicating the impact of AI tools on design skills. This study underscores the potential of AI tools in design education, balancing cognitive load management with creativity enhancement.

… Generative Artificial Intelligence (GenAI) is transforming design, … design courses to enhance student creativity. Through hands-on projects using text/image generation tools for design …

AI has been introduced into design courses to enhance student learning outcomes, foster creativity, provide personalised learning support and improve design productivity. This study integrated AI tools as a facilitative aid in a creative product design course, focusing on examining students' learning outcomes and the creativity of their final products by comparing two groups: the T1 experimental group (AI use) and the T2 control group (no AI use). Students' perspectives of using AI were explored. A total number of 77 undergraduate students majoring in product design at a university in China were recruited for the study. The results indicate that the integration of AI tools into the course significantly enhanced students' learning outcomes and the creativity of their final products. Furthermore, students expressed a positive attitude toward using AI in learning and designing, and a willingness to utilise AI in future design work. Challenges and concerns were also highlighted related to AI use for learning and design.

The integration of artificial intelligence (AI) into education has the potential to revolutionize how students engage in academic activities and tasks. This research empirically analyses the influence of AI on creative ideation within educational settings to validate AI's role in enhancing human creativity since creative tasks, which inherently rely on human intuition, emotion and divergent thinking, may be stifled by systematic AI tools. The study explores whether ChatGPT can aid the creative process or inadvertently limit human creativity with a mixed‐method approach consisting of a randomized controlled experiment with third‐year engineering students in which a total of 728 ideas were obtained, along with a structured survey. The results revealed a predominantly positive perception towards AI‐assisted ideation; nevertheless, concerns were raised about AI's influence on creativity and innovation. While no significant differences in ideation outcomes were observed between the groups that used AI and those that did not, significant differences emerged between students who had prior experience with ChatGPT and those who did not. Qualitative insights provided a nuanced view of student experiences on blended human–AI ideation processes, shedding light on its advantages and disadvantages in educational practices. This research also underscores critical considerations and potential risks associated with the adoption of AI, suggesting that while AI has a place in educational settings, its role should be carefully calibrated to support rather than stifle student creativity and innovation. From the findings, the study provides practical recommendations and best practices regarding the integration of AI tools in education.

Integrating Artificial Intelligence (AI) into learning activities is an essential opportunity to develop students' varied thinking skills. On the other hand, design-based learning (DBL) can more effectively foster creative design processes with AI technologies to overcome real-world challenges. In this context, AI-supported DBL activities have a significant potential for teaching and developing thinking skills. However, there is a lack of experimental interventions in the literature examining the effects of integrating AI into learner-centered methods on active engagement and thinking skills. The current study aims to explore the effectiveness of AI integration as a guidance and collaboration tool in a DBL process. In this context, the effect of the experimental application on the participants’ design thinking mindset, creative self-efficacy (CSE), and reflective thinking (RT) self-efficacy levels and the relationship between them were examined. The participants used ChatGPT and Midjourney in the digital story development process as part of the experimental treatment. The only difference between the control and experimental groups in the digital storytelling process is the AI applications used in the experimental treatment (ChatGPT and Midjourney). In this quasi-experimental method study, participants were randomly assigned to treatment, an AI integration intervention, at the departmental level. 87 participants (undergraduate students) in the experimental group and 99 (undergraduate students) in the control group. The implementation process lasted five weeks. Partial Least Squares (PLS), Structural Equation Modeling (SEM), and Multi-Group Analysis (MGA) were made according to the measurements made at the T0 point before the experiment and at the T1 point after the experiment. According to the research result, the intervention in both groups contributed to the creative self-efficacy, critical reflection, and reflection development of the participants. On the other hand, the design thinking mindset levels of both groups did not show a significant difference in the comparison of the T0 point and the T1 point.

… By examining the differing roles of interaction modes in AI-assisted creativity, this study provides insights for interaction design in digital education, thereby encouraging …

Artificial intelligence (AI) is increasingly embedded in design-based learning because it can accelerate ideation, support rapid iteration, and enable human–AI collaboration; however, a persistent challenge is maintaining an appropriate balance between AI-driven automation and human agency while ensuring that students’ design mindset, creativity, and reflective thinking are genuinely strengthened. This study aimed to examine the perceived effects of AI integration on students’ design mindset, creativity, and critical reflection in higher education. A quantitative cross-sectional design was employed with purposive sampling of 96 university students (predominantly female; mean age ≈20 years) who had used AI tools in learning and design activities; data were collected via an online Likert-scale questionnaire distributed from October to November 2024 and analyzed using descriptive statistics (means and sums). The results indicate that students reported generally moderate-to-positive perceptions of AI’s contribution across all constructs, with overall mean scores suggesting beneficial support for design mindset (M≈2.59) and creativity (M≈2.59), and relatively stronger support for reflection (M≈2.68), particularly in helping students understand their learning/creative processes and learn from mistakes. These findings imply that higher education institutions such as Makassar State University should integrate AI more strategically as a co-creative learning partner, complemented by structured training for both instructors and students to maximize creative and reflective gains while safeguarding human control; overall, AI shows strong potential to enhance design-oriented learning, but deeper implementation and longitudinal evaluation are recommended.

ABSTRACT This study explored the effects of design thinking on the conceptual cognition of artificial intelligence (AI) learning, attitudes toward AI, idea creativity, and the product creativity of AI applications. The concept map indicated that design thinking had a significant effect on the conceptual cognition of AI learning, particularly the relational conjunctions and classes of AI concepts. However, effects of cross-linking and examples were nonsignificant. In addition, it had a significant and positive effect on learning attitudes toward AI, in particular AI input and AI processing. Moreover, design thinking significantly and positively affected the idea creativity of AI applications, particularly the effects of novelty and value. It also had a significant and positive effect on the product creativity of AI applications, particularly functionality and elaboration. However, material novelty, style, idea structure, and product creativity had no significant difference.

… artificial intelligence (AI) on student creative thinking skills and subsequently provide instructors with information on how to guide the use of AI for creative … the quantitative design. There …

This study explores students' perceptions of Artificial Intelligence (AI) in the educational process, focusing specifically on creativity and confidence. As AI technology becomes increasingly integrated into higher education, understanding its impact on students' creative development and their confidence in using AI tools is crucial for shaping effective educational practices. To this end, a comprehensive questionnaire was designed and distributed to higher education students across Latvia, Ukraine, and Spain, resulting in a diverse sample of 89 respondents. The survey collected data on demographic information, general AI usage in education, and students' attitudes towards AI's impact on creativity. To analyse the data, the Kruskal-Wallis test was employed to examine country-based differences in AI usage frequency. The results showed no significant variance (p = 0.448). This finding led to the rejection of the hypothesis that students from EU countries use AI more frequently than those from non-EU countries. Descriptive data analysis revealed that 83% of students felt AI did not limit their creative expression, and 69% reported a positive impact on their ability to generate creative solutions. However, only 47% of students expressed confidence in using AI collaboratively, indicating mixed perceptions about its role in group creative tasks. These results suggest that while students generally view AI as supportive of their creativity, there is a need for increased efforts to enhance confidence in AI's collaborative and creative applications. In light of the escalating significance of AI in educational settings, this study is pivotal in elucidating the optimal integration of AI to nurture students' creative growth and fortify their confidence in the effective utilisation of AI tools. This research makes a significant contribution to the field by offering valuable insights into the evolving role of AI in higher education, emphasising the importance of balanced integration strategies for maximising its potential in the educational sphere.

… IT has a good effect on student learning must be included in classroom education. Art … student-centred approach, and students' passive reception of knowledge must be altered. Students…

… intrinsic mechanics, especially in relationships between students' creativity, self-efficacy, and … deploy artificial intelligence resources, improve the digital literacy of teachers and students, …

… The emergence of artificial intelligence-generated content (AIGC) in … product design instruction, focusing on its advantages, constraints, and consequent influence on students’ design …

This paper investigates how AI-generated designs are transformed into physical prototypes in industrial design education, with a focus on lighting product development. The use of text-to-image generation tools has enhanced the ideation phase by allowing students to explore a wide range of design possibilities efficiently. However, challenges occur when converting these digital concepts into functional physical prototypes, as maintaining design integrity and navigating material and production limitations prove difficult. Through a qualitative case study, involving 20 industrial design students, this research examines a structured four-phase process: (1) ideation through AI tools, (2) transitioning AI-generated images to sketches, (3) refining sketches into 3D models, and (4) developing physical prototypes. A comparative analysis was conducted to assess how closely the final prototypes aligned with the original AI-generated designs in terms of form, function, and material choice. Findings showed that while some prototypes preserved crucial design elements, others required significant adjustments due to material constraints and manufacturing challenges, impacting the final output on prototypes stage. This research highlights the critical role of prototyping in connecting digital concepts with physical products. It emphasizes the need for teaching material selection, production methods, and adaptive design strategies to support students in overcoming real-world challenges.

This study explores the integration of generative AI, particularly textto-image generators, into industrial design education and its role in enhancing creativity during the idea-generation process. Focusing on a lighting product design project, the research examines how students use AI-generated concepts as a starting point and refine them through sketches and 3D models into fully functional designs. Generative AI has significant potential to facilitate creativity by breaking down traditional barriers to ideation and speeding up the concept-generation process. However, turning creative output into practical, manufacturable products can be challenging. The research involved 20 Bachelor of Industrial Design students tasked with transforming AI-generated imagery into functional lighting products. The methodology followed a threephase process: AI-based concept generation, design refinement through sketches, and final 3D modeling. The findings reveal that while AI tools provide creative inspiration, students need to make substantial refinements to ensure the designs are both practical and user-centered. Main adjustments included form simplification, functional enhancements, and material considerations. This highlights the critical role of human intervention in bridging the gap between AI-generated concepts and fully realized, functional products. This research contributes to the growing understanding of how to incorporate generative AI into industrial design education effectively.

Generative Artificial Intelligence is reshaping design education by influencing how students develop ideas and make creative decisions. Unlike earlier digital shifts that mainly changed production methods, generative AI introduces cognitive changes by moving part of ideation and evaluation into human–AI collaboration. This study offers a rare perspective by comparing design educators’ views on integrating AI in El Salvador and Indonesia, with Denmark as a comparison case. Using a qualitative interpretive approach, interviews with nine academics reveal key themes such as AI as a thinking partner, the persistence of manual-first pedagogy and the rising importance of prompt literacy. Findings show that in resource-limited programs, manual-first traditions provide an important foundation for making curatorial judgments about AI-generated ideas, while Denmark educators use a more structured integration aligned with industry demands. Design educators emphasise ethical awareness and critical judgment, framing AI as a catalyst for rethinking creative processes rather than replacing them.

… properly integrated into design pedagogy. Its positive influence on teaching and learning is significant when applied correctly. As one of the latest tools in alternative learning methods, …

… of generative AI tools into industrial design education by examining their role across concept development, product … that generative AI tools hold significant pedagogical potential, not as …

This testimonial article reflects on integrating Generative Artificial Intelligence (GenAI) into design education through three distinct experiences: personal explorations as a designer and educator, collaborative faculty learning in design technology, and integration within an undergraduate digital design course. This paper aims to contribute to discussions on how GenAI tools can support creative and educational practices. It employs a phenomenological approach to document these experiences, assessing the impact of AI on creativity, pedagogical practices, and learning outcomes. The theoretical framework draws on Constructivist Learning Theory, Kolb’s Experiential Learning Theory, Reflective Practice, Technological Pedagogical Content Knowledge (TPACK), and the concept of the democratization of creativity. These theoretical perspectives help to analyze how learners and educators construct knowledge through interaction with AI technologies, iterate through cycles of experimentation, and reflect on their practice. The analysis reveals the transformative role of GenAI in enhancing educational equity and creative engagement while also highlighting ethical considerations such as biases, intellectual property, and the risks of over-reliance. This paper invites educators to critically engage with AI, proposing strategies to integrate these technologies thoughtfully in design education.

Generative Artificial Intelligence (AI) is increasingly reshaping design education by facilitating design workflow and enhancing students’ creativity. However, its application in sustainable design education remains underexplored. This research develops an AI-assisted pedagogical model to examine how generative AI tools can support students’ learning and practice in sustainable design. The model incorporates the EcoDesign Strategy Wheel within the Double Diamond design framework, with generative AI tools providing support during critical stages of the design process. A five-week project-based course involving 24 undergraduate students was conducted to generate quantitative and qualitative insights into the effectiveness of the teaching model and students’ learning experiences. The results suggest that generative AI tools can effectively support various aspects of sustainable design practice, including research, concept generation, material selection, energy use, and distribution consideration. This study further reveals that generative AI helps students rapidly acquire complex interdisciplinary knowledge and develop systems thinking in sustainable design. However, it also poses challenges such as excessive reliance and limited understanding of local cultural contexts. This study provides theoretical and practical support for applying generative AI in sustainable design education, contributing to broader discussions on AI-driven pedagogical innovation.

… design pedagogy, educational psychology, and AI technology… into specific courses, such as logo design, or how it affects … Generative artificial intelligence in product design education: …

This study is based on the background of how artificial intelligence (AI) technology is applied to the field of creativity and design education to improve the design vision, teaching methods, and actual design productivity of practitioners. The purpose of the research is to compare traditional design education and new design education methods combined with AI technology. Taking the Technological Pedagogical Content Knowledge (TPACK) technology integration model as the starting point, a comprehensive evaluation is selected for different types of research to explore the animation design professional courses in design education, the content of students’ perception preferences, and the evaluation of ease of learning so as to conduct research and analyze AI technology. Design new education strategies and practice methods under the background. In the research, a comparative experimental study was conducted on 40 first-year students majoring in animation design. The results show that through online design studio project practice, with personalized project learning guidance, the learning needs of students to show a better trend, and customized learning and project practice content can enhance the learning experience and performance of students. In the future, we can further expand the scope of analysis, include more case studies, and conduct more comprehensive research, including how to deal with the expansion of the platform for students’ learning of design in situations similar to coronavirus disease 2019 (COVID-19) that profoundly affects our lives, and how the project is applied in practice.

… of artificial intelligence (AI) in the conceptual design phase of interior design education, focusing on AI's … Conducted within a design studio course, the research integrates text-to-image …

PurposeThe purpose of this research is to explore the transformative impact of AI-augmented tools on design pedagogy. It aims to understand how artificial intelligence technologies are being integrated into educational settings, particularly in creative design courses, and to assess the potential advancements these tools can bring to the field.Design/methodology/approachThe research adopts a case-study approach, examining three distinct courses within a creative technology curriculum. This methodology involves an in-depth investigation of the role and impact of AI in each course, focusing on how these technologies are incorporated into different creative disciplines such as production design, fine arts, and digital artistry.FindingsThe research findings highlight that the integration of AI with creative disciplines is not just a passing trend but signals the onset of a new era in technological empowerment in creative education. This amalgamation is found to potentially redefine the boundaries of creative education, enhancing various aspects of the learning process. However, the study also emphasizes the irreplaceable value of human mentorship in cultivating creativity and advancing analytical thinking.Research limitations/implicationsThe limitations of this research might include the scope of the case studies, which are limited to three courses in a specific curriculum. This limitation could affect the generalizability of the findings. The implications of this research are significant for educational institutions, as it suggests the need for a balanced interaction between AI's computational abilities and the intrinsic qualities of human creativity, ensuring that the core essence of artistry is preserved in the age of AI.Originality/valueThe originality of this paper lies in its specific focus on the intersection of AI and creative education, a relatively unexplored area in design pedagogy. The value of this research is in its contribution to understanding how AI can be harmoniously integrated with traditional creative teaching methods. It offers insights for educational institutions preparing for this technological transformation, highlighting the importance of maintaining a balance between technological advancements and humanistic aspects of creative education.

ABSTRACT To address the problem of the classroom practice teaching of art and design, this paper proposes an interactive learning method in universities, followed by the use of AI technology to evaluate the quality of art and design teaching. The study aims to achieve the following objectives: 1) To introduce the current research status of art and design and interactive teaching methods used in other countries; 2) To discuss the essential ideas and mechanics of Back Propagation Neural Networks (BPNN) and other standard teaching approaches based on interactive learning; and 3) To input test data into the trained model and compare the obtained results with the evaluation results of experts. The findings of this study indicate that the model used to evaluate art and design instruction is accurate. The proposed interactive learning method is beneficial for art and design majors as it allows them to improve their practical skills and learn more engagingly and effectively. The use of AI technology for evaluation purposes can also improve the quality of art and design education in universities.

AI technology enhances student engagement and communication in art design studios. It provides opportunities for participation, improved academic performance, and increased creativity. Instructors can use interactive learning activities like digital platforms, notebooks, whiteboards, audio files, podcasts, and surveys to enhance collaboration and develop essential skills. Strategies for engaging students include active learning, connecting it to real-world situations, using technology, conducting group activities, creating a positive learning environment, using FAQs, brainstorming, and creating classroom games, visualizations, workshops, exercises, projects, co-curricular activities, competitions, and problem-based learning activities. The research explores how AI can personalize learning experiences and enhance interactive learning through various tools. The study is based on a descriptive-analytical approach, collecting and analyzing data on student engagement in design studios and using different methods and methodologies. Therefore, the study concluded that AI technology has the potential to enhance student engagement and communication, allowing instructors to focus on interacting with students and facilitating discussions. Instructors in the art and design environment should integrate interactive elements into design studios to work with students, especially in design projects and activities.

… pedagogical integration of Skill Studio, an institutional Generative Artificial Intelligence tool developed by Tecnológico de Monterrey to support the instructional design of the new 2026 …

… design to examine how three AI tools, ChatGPT, Gamma and Autopilot, could enhance students’ academic performance. … To evaluate the impact of AI tools on student performance, a …