Humanizing AI Feedback: Enhancing Self-Efficacy and Creative Agency in Design Education

Artificial intelligence (AI) is increasingly used in higher education, however, little is known about how AI feedback can be designed to support the relational and affective dimensions of studio learning. This mixed-methods pilot study examines an AI feedback system intentionally crafted with empathic and partnership-oriented features to assist senior design students in a studio course. Drawing on social-cognitive theory and feedback literacy, we propose an AI-Empathic Feedback Design model that positions AI as a relational co-participant in studio feedback processes. Twenty final-year design majors used a prototype tool providing qualitative critiques, sentiment-aware prompts, and progress dashboards over a 15-week semester. Pre/post self-efficacy scores increased from 3.40 to 4.10 (Cohen’s d ≈ 1.20), and students engaged in more iterations than a historical cohort. Thematic analysis highlighted enhanced creative confidence, heightened psychological safety, AI as a collaborative partner, and recognized limits of automated critique. While not designed for causal inference, the study shows how empathically framed AI feedback can foster mastery experiences, social persuasion, and low-risk experimentation in design learning.

IntroductionRapid adoption of Artificial Intelligence (AI) in learning has revolutionized learners’ engagement but comprehension of psychological and technological drivers of successful AI-enabled learning remains scarce. This research investigates how students’ perceived agency of AI, usefulness, ease of use, trust, autonomy supporting, and self-efficacy collectively impact students’ self-learning behavior and motivation. Based on Technology Acceptance Model (TAM), Social Cognitive Theory (SCT), and Self-Determination Theory (SDT) theories, our research model predicts an integrated model of motivational and behavioral processes underlying AI adoption in learning settings.MethodsWe adopted and followed a quantitative research design with a structured questionnaire administered among 280 higher education students in Saudi Arabia. We applied Structural Equation Modeling (SEM) using SmartPLS 4 to analyze data.ResultsFindings indicate that students’ perceived agency of AI significantly predicts usefulness, ease of use, and autonomy supporting, while ease of use significantly enhances AI-enabled self-efficacy. Self-efficacy and autonomy supporting significantly impact self-learning motivation, driving self-learning behavior positively. But usefulness and trust in AI failed to influence self-efficacy directly, which reveals cultural and contextual settings.DiscussionThis research adds richness to the fusion of TAM, SCT, and SDT theories in illustrating how AI’s perceived autonomy and usability collectively promote self-directed learning motivation. This research also provides guidelines to educators and system designers to design AI tools that promote learner autonomous settings, usability, and confidence. Future research ought to perform longitudinal and cross-cultural validations to fine-tune theoretically.

The rapid iteration and practical application of generative artificial intelligence (AIGC) have reconstructed the teaching ecology and creative practice logic of vocational digital media art education, and also posed new challenges to the cultivation and development of students' creative agency. As the core literacy of vocational digital media art talents, creative agency is reflected in students' autonomous thinking construction, aesthetic expression, practical exploration and innovative breakthrough capabilities in artistic creation. Taking vocational digital media art education as the research field, this paper combines constructivist learning theory, digital literacy framework and creative development theory to analyze the connotative characteristics and constituent dimensions of students' creative agency in the age of generative AI, explore the dual impacts of AI technology on students' creative thinking and practice, and construct a targeted evaluation system for creative agency. Corresponding cultivation and evaluation implementation strategies are also proposed. This study aims to fill the research gap in the evaluation of students' creative agency in vocational art education under the background of AI-art integration, and provide theoretical reference and practical paths for the teaching reform and talent training of vocational digital media art education.

Artificial intelligence (AI) is increasingly integrated into higher education, yet how different purposes of AI use influence student creativity remains underexplored. In particular, little is known about the mediating role of digital competencies and the moderating role of students' attitudes toward AI. Drawing on Social Cognitive Theory, this study examines how AI use for learning and AI use for entertainment relate to student creativity through digital competencies, and how attitudes toward AI condition these relationships. Data were collected from 271 undergraduate students majoring in Traditional Chinese Medicine in China and analyzed using PLS-SEM and moderated mediation analysis. The results show that both learning-oriented and entertainment-oriented AI use positively relate to digital competencies, which in turn enhance student creativity. Digital competencies fully mediate the relationship between AI use for learning and creativity and partially mediate the relationship between AI use for entertainment and creativity. Moreover, attitudes toward AI play a dual moderating role: positive attitudes strengthen the effect of entertainment-oriented AI use but weaken the effect of learning-oriented AI use on digital competencies. This study contributes to the literature by distinguishing different purposes of AI use, identifying digital competencies as a key explanatory mechanism, and revealing the nuanced role of attitudes toward AI in shaping creativity outcomes. It also offers practical implications for designing AI-supported educational practices in specialized domains such as Traditional Chinese Medicine.

Introduction: The introduction of Artificial Intelligence (AI) in the education sector has sparked a lot of controversy opinions regarding its effectiveness in shaping student learning process and systematic mental growth. While AI has immense potential to transform learning experiences by providing personalised learning, immediate feedback, and access to enormous information, but fears have also been aired that overreliance on artificial intelligence will stifle the problem-solving and critical thinking capabilities of students by providing pre-programmed answers instead of encouraging independent thoughts. Objectives: The purpose of this paper is to rigorously explore the impacts of AI on higher education, with an inspection of how it impacts learning processes, intellectual growth, and the further expectations from students, academics and universities. This research aims to develop AI tools and programs through mixed approaches for the higher education environment that function as collaborative partners rather than just tools. The goal is to establish guiding principles and frameworks for a creative AI practice, incorporating computational tools, benchmarking of successful experiments, and their future applications. The current focus is on presenting early-stage research findings. Methods: Given this research focus on impact of AI in higher education, overviewing several studies, the backgrounds, and research findings achieved through exploratory research to gain a deeper understanding of a topic, develop practical guidelines, and develop more focused research questions for future investigation. Results: By investigating different studies, the examined strategies can be consolidated to address the promise of ethical AI implementation in education and in a manner to enhance and sustain cognitive capabilities among students and other stakeholders while offsetting risks associated with over-reliance and degrading of the human capacity. Conclusions: By embracing AI's potential while upholding core educational values, institutions can ensure AI enhances the learning experience. Successful implementation requires a balanced approach, developing adaptable and culturally sensitive AI tools while investing in teacher training for effective human-AI collaboration. This thoughtful approach considers students' diverse backgrounds and learning preferences, along with the vital role of educators, maintaining human agency, critical thinking, and ethical considerations as central to education. AI in higher education offers significant potential benefits, but responsible integration is key to maximising its positive impact. This approach ensures AI serves educational purposes effectively.

Artificial intelligence (AI) is reshaping modern education, and design education is no exception. Although there have been several reviews on AI in education, there are limited systematic literature reviews and analyses of AI in the field of design education. In order to facilitate the effective integration of AI in design education, this study reviews the current state of AI in design education. The study aims to identify the advantages and challenges of using AI in design education and to explore the potential opportunities that AI can bring to the future of design education. The review analyzed 43 studies on using AI in design education from 2020 to 2024. The findings indicated that the use of AI in design education is rapidly shifting from general AI to generative AI. In particular, image generation has become the most preferred AI technology in design education. In addition, the integration of AI into design education can enhance the design process for students and increase their efficiency. Students could benefit from personalized design learning experiences and strengthen their comprehension and communication. Despite the significant advantages of using AI in design education, there are some challenges, such as ethical and legal issues, response errors and biases, pedagogical challenges, and technological anxiety and overreliance. This study emphasizes that the integration of AI in design education should be supplementary, aiming at enhancing rather than replacing traditional design teaching methods. Received: 26 March 2025 / Accepted: 1 June 2025 / Published: 05 July 2025

Higher education institutions (HEI) are facing fundamental questions regarding students’ use of artificial intelligence (AI) tools in the form of large language model (LLM) based chatbots. Students are already using AI tools to respond to written assignments and exams. Our research question is: What is educators’ standpoint about students’ use of generative AI in higher education? A mixed methods approach was applied for the present study. First, a qualitative investigation was conducted, centered around interviews that revolved around potential consequences (i.e., opportunities, threats, challenges, etc.) and factors related to the educators’ views on AI. Based on the qualitative approach, three propositions were postulated for a narrower quantitative approach, including a larger sample of educators from industrial design (ID) educations at HEIs’ in Europe. The quantitative data was collected through a questionnaire and analyzed using a fuzzy-set qualitative comparative analysis (fsQCA). The findings from the questionnaire supported our proposition about (1) Knowledge about AI leads to seeing opportunities rather than challenges, but not our propositions of (2) Emphasizing skill-focused learning outcomes leads to seeing opportunities rather than challenges, and (3) Use of authentic cases leads to educators’ not emphasizing challenges. This study emphasizes the importance of knowledge about AI for educators.

In contemporary society, Artificial Intelligence (AI) pervades numerous facets of our lives and is likely to impact many sectors and professions, including education. Tertiary-level students in particular face challenges regarding the use of AI for studies and assessment, including limited understanding of AI tools, as well as a lack of deep critical engagement with AI for learning (Shibani et al., 2024). To respond to emerging developments in generative AI, the recent Australian Tertiary Education Quality and Standards Agency (TEQSA) report suggests tertiary-level learning and assessments be designed to foster responsible and ethical use of AI (Lodge et al., 2023). This involves the development of AI literacy among students to engage with AI in critical, ethical ways that aid their learning and not hinder it. Our project aims to narrow the AI literacy gap among students from diverse study backgrounds by providing foundational knowledge and developing critical skills for practical use of AI tools for learning and professional practice, in collaboration with students and academics as part of a Students as Partners (SAP) initiative. Staff bring expertise in AI critical engagement and students bring practical, first-hand experiences of learning in this collaboration, supported by the university’s SAP program. Building on the current UNESCO recommendations for the use of generative AI in education (UNESCO, 2023) and prior theoretical frameworks on AI literacy (Chiu et al., 2024; Ng, et al., 2021; Southworth et al., 2023) we target key skills that higher education students should develop to meaningfully engage with AI. By creating accessible and engaging resources, such as instructional videos and comprehensive guides on generative AI applications like ChatGPT and ways to prompt for enhancing learning, we introduce existing AI tools and teach students to use them effectively, promoting a hands-on learning environment. Using learning design principles, the developed curriculum will be presented in an AI Literacy module on a Canvas site, with supporting instruction workshopped with student participants for evaluation. Student cohorts recruited from diverse disciplines will pilot and assess the effectiveness of the program, and qualitative methods such as focus groups and interviews will be used for evaluation of our intervention and continuous improvements. Findings will inform tertiary students’ current level of AI literacy and the effectiveness of interventions to improve key skills beyond their disciplinary knowledge, better preparing them for life beyond university. Indeed, the implementation of similar AI literacy courses has demonstrated statistically significant improvements in AI literacy and understanding of AI concepts amongst university students (Kong, Cheung, & Zhang, 2021). Our approach underscores the importance of relational engagement in higher education with students as partners (Matthews, 2018) and participatory design with students in a topic that is significant in the current age of AI (Laupichler et al., 2022). The course's flexibility to be accessed directly or embedded into other curricula ensures scalability and broader impact, solidifying the validity of our multifaceted approach. Through utilising relevant research methodologies and learning design principles, we endeavour to create an AI literacy course that is robust, accessible, educational, and engaging to use by tertiary-level students from diverse study backgrounds.

The integration of artificial intelligence into the system of higher education represents a turning point in the process of learning and teaching. The development of artificial intelligence has opened the way to personalized training, automation of administrative tasks and the introduction of innovative training methods. The purpose of the study was to analyze the practical aspects of using artificial intelligence in higher education institutions of Ukraine. It was determined that artificial intelligence is an organized set of information technologies, which makes it possible to perform complex complex tasks. There are three main categories of artificial intelligence: narrow-spectrum artificial intelligence, or Artificial Narrow Intelligence, general artificial intelligence, or Artificial General Intelligence, and artificial superintelligence, or Artificial Super Intelligence. The main educational services provided by artificial intelligence in institutions of higher education are the development and conduct of lectures, seminars and practical classes; teacher counseling; creation of educational programs and electronic courses; development of tasks and simulation of their solution; conducting various educational events; evaluation of the works of education seekers. Some examples of the use of artificial intelligence, in particular chatbots, in the higher education of Ukraine are analyzed and their potential for improving the educational process and forming professional skills is emphasized. An example of the use of GPT-3.5 in the Luhansk Educational and Scientific Institute for teaching foreign languages is presented. Such applications based on artificial intelligence as Thinkster and Duolingo and the main aspects of their use by students of higher education are characterized. Recommendations are provided for the successful implementation of artificial intelligence technologies in higher education.

The integration of artificial intelligence into the system of higher education represents a turning point in the process of learning and teaching. The development of artificial intelligence has opened the way to personalized training, automation of administrative tasks and the introduction of innovative training methods. The purpose of the study was to analyze the practical aspects of using artificial intelligence in higher education institutions of Ukraine. It was determined that artificial intelligence is an organized set of information technologies, which makes it possible to perform complex complex tasks. There are three main categories of artificial intelligence: narrow-spectrum artificial intelligence, or Artificial Narrow Intelligence, general artificial intelligence, or Artificial General Intelligence, and artificial superintelligence, or Artificial Super Intelligence. The main educational services provided by artificial intelligence in institutions of higher education are the development and conduct of lectures, seminars and practical classes; teacher counseling; creation of educational programs and electronic courses; development of tasks and simulation of their solution; conducting various educational events; evaluation of the works of education seekers. Some examples of the use of artificial intelligence, in particular chatbots, in the higher education of Ukraine are analyzed and their potential for improving the educational process and forming professional skills is emphasized. An example of the use of GPT-3.5 in the Luhansk Educational and Scientific Institute for teaching foreign languages is presented. Such applications based on artificial intelligence as Thinkster and Duolingo and the main aspects of their use by students of higher education are characterized. Recommendations are provided for the successful implementation of artificial intelligence technologies in higher education.

Quality feedback is essential for supporting student learning in higher education, yet personalized feedback at scale remains costly. Advances in learning analytics and artificial intelligence now enable the automated delivery of personalized feedback to many students simultaneously. At the same time, recent feedback research increasingly emphasizes learner-centered approaches, particularly the role of feedback literacy—students' varying capacities to engage with and benefit from feedback. Despite growing interest, few studies have quantified how feedback literacy affects students' perceptions of feedback, especially in technology-supported contexts. To address this, we examined (1) students' perceptions of personalized, detailed feedback generated via learning analytics and (2) how feedback literacy moderated these perceptions. In a randomized field experiment, teacher education students (N = 196) participated in a week-long computer-supported collaborative learning task on cognitive activation in the classroom. Both groups received automated, personalized feedback: the control group received basic feedback on task completion, while the experimental group received detailed feedback on group processes and the quality of their collaborative statement. The highly informative feedback significantly improved perceptions of feedback helpfulness, enhanced learning insights, and supported self-reflection and self-regulation. Feedback literacy partially moderated these effects, influencing perceptions of feedback helpfulness and motivational regulation.

The rapid expansion of artificial intelligence (AI) in higher education has intensified concerns regarding autonomy, control, and ethical engagement with digital technologies. While prior research has primarily addressed AI adoption through acceptance- and performance-oriented frameworks, less is known about how students’ experiences of digital agency shape the psychological mechanisms underlying their intention to use AI. Grounded in digital agency theory and expectancy–value perspectives, the present study examines a moderated mediation model explaining students’ intention to use AI in higher education. Using survey data from 673 university students, a conditional process analysis (PROCESS Model 59) was conducted to test whether sense of negative agency (SONA) predicts intention to use AI indirectly through perceived value of AI, and whether this pathway is conditioned by sense of positive agency (SOPA). Performance control was examined as a parallel mediator to assess the role of self-regulated learning mechanisms. Results revealed a significant moderated mediation effect. Specifically, perceived value of AI mediated the relationship between negative agency and intention to use AI only at moderate to high levels of positive agency. When SOPA was low, negative agency was unrelated to perceived value and intention. In contrast, at higher levels of SOPA, negative agency was positively associated with perceived value of AI, which in turn strongly predicted intention to use AI (b = .83, p < .001). Performance control did not emerge as a significant mediator, indicating that AI adoption decisions were not driven by self-regulated performance mechanisms. These findings suggest that students’ engagement with AI is guided primarily by value-based cognitive evaluation rather than by performance regulation. Experiences of reduced control do not necessarily inhibit AI adoption; instead, when integrated through positive agency, they may foster reflective meaning-making and intentional AI use. The study highlights the ethical relevance of digital agency in AI adoption and underscores the importance of agency-aware approaches to AI integration in higher education.

The rapid advancement of artificial intelligence (AI) is reshaping industrial workflows and workforce expectations. After its breakthrough year in 2023, AI has become ubiquitous, yet no standardized approach exists for integrating AI into engineering and computer science undergraduate curricula. Recent graduates find themselves navigating evolving industry demands surrounding AI, often without formal preparation. The ways in which AI impacts their career decisions represent a critical perspective to support future students as graduates enter AI-friendly industries. Our work uses social cognitive career theory (SCCT) to qualitatively investigate how 14 recent engineering graduates working in a variety of industry sectors perceived the impact of AI on their careers and industries. Given the rapid and ongoing evolution of AI, findings suggested that SCCT may have limited applicability until AI technology has matured further. Many recent graduates lacked prior exposure to or a clear understanding of AI and its relevance to their professional roles. The timing of direct, practical exposure to AI emerged as a key influence on how participants perceived AI’s impact on their career decisions. Participants emphasized a need for more customizable undergraduate curricula to align with industry trends and individual interests related to AI. While many acknowledged AI’s potential to enhance efficiency in data management and routine administrative tasks, they largely did not perceive AI as a direct threat to their core engineering functions. Instead, AI was viewed as a supplemental tool requiring critical oversight. Despite interest in AI’s potential, most participants lacked the time or resources to independently pursue integrating AI into their professional roles. Broader concerns included ethical considerations, industry regulations, and the rapid pace of AI development. This exploratory work highlights an urgent need for collaboration between higher education and industry leaders to more effectively integrate direct, hands-on experience with AI into engineering education. A personalized, context-driven approach to teaching AI that emphasizes ethical considerations and domain-specific applications would help better prepare students for evolving workforce expectations by highlighting AI’s relevance and limitations. This alignment would support more meaningful engagement with AI and empower future engineers to apply it responsibly and effectively in their fields.

As artificial intelligence (AI) technologies rapidly integrate into higher education, they impose increasing demands on the teaching approaches and digital competence of physical education teachers. However, the relationship between physical education teachers’ behavioral intention to use AI and their digital competence remains underexplored. This study focuses on college physical education teachers and examines the relationship between their intention to use AI and their digital competence. Grounded in Social Cognitive Theory (SCT) and the Unified Theory of Acceptance and Use of Technology (UTAUT), the study proposes a structural equation model incorporating behavioral intention, self-efficacy, social influence, and digital competence, with gender as a moderating variable. A questionnaire survey was conducted among 479 physical education teachers from ten universities in mainland China, and the model was tested using AMOS and SPSS. The results indicate that teachers’ behavioral intention to use AI is positively associated with their self-efficacy, perceived social influence, and digital competence, with both self-efficacy and social influence serving as significant mediators. Furthermore, self-efficacy is positively related to social influence, while gender does not exert a significant moderating effect on any of the proposed paths. This study contributes to the integrated application of SCT and UTAUT in the context of physical education in higher education and offers theoretical and practical implications for enhancing digital competence and promoting intelligent transformation among college physical education teachers.

This paper discusses principles and practices that can optimize artificial intelligence (AI) teaching and learning from the perspectives of leading organizational change and by reimaging learning activities with AI as a collaborative partner. Based on the Kowch's participatory teaching and learning (PTL) principles for networked organizations, the authors analyze the integration of new talent patterns emerging more agile interdisciplinary education systems connected through information and technologies, and propose principles for collaboration through machines as AI participants and principles for designing new education systems where the teams with AI can thrive. Taking a different angle from the traditional linear education system design tasks and education institution redesigns, the proposed principles assume more time for education and training leaders to take stronger leadership roles the creation of better teams with AI augmentation. We offer principles for designing education institutions that are capable of adapting with these innovations and we also offer principles for designing these next generation learning environments. Finally by zooming in on instruction and AI, we use Activity theory to imagine better inclusions of social and cultural components with AI as an important, emerging, and unscripted new partner.