Gen Z and Generative AI: Shaping the Future of Learning and Creativity

There is a heightened concern over undergraduate students being over‐reliant on Generative AI and using it recklessly. Reliance behaviours describe the frequencies and ways that people use AI tools for tasks such as problem‐solving, influenced by individual factors such as trust and AI literacy. One way to conceptualise reliance is that reliance behaviours are affected by the extent to which learners consciously evaluate the relative performance of AI and humans, suggesting the potential impacts of critical thinking on reliance. This study, thus, empirically investigates the relationship between critical thinking and reliance behaviours. Critical thinking includes disposition and skills. However, limited empirical studies have investigated how critical thinking influences learners' reliance behaviours when solving problems with Generative AI. Hence, the current study conducted path analyses to investigate how critical thinking is associated with reliance behaviours and how it mediates the effect of individual factors on reliance behaviours. We collected 808 survey responses on critical thinking disposition and skills, reliance behaviours (a self‐developed and validated scale, including reflective use, cautious use, thoughtless use, and collaborative use), trust towards AI, and AI literacy from undergraduates after a problem‐solving task with Generative AI. The results indicate that (1) critical thinking is positively associated with the collaborative, reflective, and cautious use of Generative AI, suggesting that these three types of use of Generative AI could be considered desirable behaviours in human–AI problem‐solving; (2) trust positively predicts thoughtless use; (3) critical thinking can offset the influence of trust on collaborative, reflective and cautious use; and (4) critical thinking can amplify the influence of AI literacy on reflective, cautious and collaborative use. This study contributes new insights into understanding the role of critical thinking in fostering desirable reliance behaviours, including reflective, cautious and collaborative use, and provides implications for future interventions when applying Generative AI for problem‐solving. Practitioner notes What is already known about this topic? Generative AI tools can potentially enhance problem‐based learning (PBL) by supporting brainstorming and solution refinement. Reliance behaviours in human‐AI collaboration are influenced by factors such as trust in AI and AI literacy. Strategy‐graded reliance emphasizes the reasoning process leading to reliance behaviours, focusing on thoughtful engagement with AI tools, and this cognitive process can be captured by critical thinking. What this paper adds? Critical thinking is positively associated with the reflective, collaborative, and cautious use of Generative AI. Critical thinking mediates the effects of trust and AI literacy on reliance behaviours, amplifying reflective, cautious and collaborative use while mitigating the thoughtless use of Generative AI. The study introduces a nuanced understanding of reliance behaviours by applying a strategy‐graded framework, emphasising cognitive engagement rather than a purely outcome‐based understanding of reliance behaviours. Implications for practice and/or policy Educational interventions could consider critical thinking when integrating AI tools in problem‐solving contexts. Students' trust in AI needs to be balanced with critical thinking skills to reduce overreliance and enhance thoughtful engagement with AI tools.

Generative AI (GAI) is reshaping the future of work in architecture by introducing innovative ways for humans to interact with technology, transforming the design process. In education, GAI offers students immersive environments for iterative exploration, enabling them to visualize, refine, and present design concepts more effectively. This paper investigates how GAI, through a structured framework, can enhance the learning of design tasks in elaborating interior design proposals, and preparing students for the evolving professional landscape. Drawing on the platform Midjourney, students explored concepts, material moodboards, and spatial compositions, simulating professional scenarios. Each student was assigned a real client and tasked with developing tailored design solutions, guided by client and tutor feedback. This approach demonstrates how GAI supports the development of future-oriented skills, directly linking education to the technological shifts in professional practice (Araya, 2019). The study adopts a practice-based methodology, documenting the outcomes of an interior design workshop where students employed GAI tools to develop client-specific proposals. Students engaged in role-playing, meeting their assigned clients face-to-face to gather requirements, acting as junior architects. They analyzed client feedback to inform the design phase, after which they used a structured framework for better using GAI to iteratively refine their proposals. By generating AI-assisted visualizations of spatial configurations and materials, students developed final design solutions that aligned with client expectations. Data from GAI iterations, client feedback, and tutor evaluations were used to assess how effectively AI tools contributed to producing professional-quality designs (Schwartz et al., 2022). Two research questions frame this investigation: (1) How does Generative AI enhance students' ability to create client-specific interior design solutions, from concept generation to final visualization, within a structured educational framework? (2) How does the integration of GAI tools impact the teaching of iterative design processes in architecture, particularly in preparing students for the future of work in the profession? The findings reveal that GAI significantly improved students' design outcomes by enabling them to visualize and refine their proposals based on real-world scenarios. GAI facilitated the exploration of current trends and supported the creation of material moodboards and space visualizations. The iterative nature of AI tools allowed students to better grasp the relationships between spatial configurations, design choices, and client needs. Their final proposals, incorporating AI-generated outputs, were praised for their conceptual clarity and technical precision, reflecting how AI-driven processes can transform traditional workflows (Burry, 2016). This study illustrates the transformative potential of GAI in architectural education, particularly in fostering dynamic human-technology interactions. By leveraging AI, students maintained control over outputs while transforming abstract concepts into client-ready designs. Moreover, the iterative feedback loop enabled by GAI promoted a more adaptive and responsive learning process, giving students real-time insights into their design decisions. These insights reflect broader changes in the future of work, where AI-driven tools will become integral to professional practice. Future research could explore expanding GAI’s role in more complex design stages, such as schematic design and development, building on the benefits observed in this study.

This study explores the integration of generative AI (GenAI) tools in the professional development of English language teachers (ELT) using the Technological Pedagogical Content Knowledge (TPACK) framework. Subscribing to narrative inquiry research design, in-depth interviews were conducted with four educators in Kathmandu Valley, Nepal, in August 2024 to understand their experiences integrating GenAI in teaching practices. Four themes were identified using thematic analysis—enhanced teaching skills and methods, professional growth as a continuous learning process, GenAI dependency, and ethical challenges and coping mechanisms for further discussion. The findings disclose that GenAI enhances pedagogical strategies by providing personalized learning resources, dynamic classroom activities, and automated feedback, which foster student engagement and teacher adaptability. Participants noted that GenAI supports continuous professional growth by offering real-time insights to refine instructional methods and address diverse learner needs. However, challenges such as technical skill gaps, ethical concerns about data privacy, and the risk of over-reliance on AI, which may hinder critical thinking and teacher-student rapport, were identified. The study emphasizes the need to balance GenAI's technological benefits with human-centric pedagogy, underlining the importance of ethical guidelines, institutional training, and collaborative peer learning to reduce the dependency and algorithmic biases by aligning GenAI integration with TPACK principles—harmonizing technological, pedagogical, and content knowledge—the research advocates for structured support systems to empower educators in using AI responsibly. The implications call for policy frameworks prioritizing AI literacy, equitable access, and mindfulness practices to sustain professional development while preserving the irreplaceable role of human interaction in education.

The rapid rise of Generative AI (GenAI) tools is reshaping conversations about assessment and feedback in higher education. While much institutional attention focuses on detection, compliance, and academic integrity (Cotton et al., 2024), this presentation shifts the lens to educators and how they are actually using GenAI in assessment practice. We present findings from a grant-funded initiative at UNSW that explores educator-led innovation through a Postcards of Practice approach. The Postcards of Practice are one-page, practice-based narratives where educators document their use of GenAI tools. These postcards highlight applications including formative feedback generation, student prompting literacy, assessment redesign, and co-creation with AI. They reveal how educators are experimenting with GenAI to support student learning while navigating ethical concerns, transparency, and pedagogical alignment. Our study uses a qualitative interpretive methodology, combining thematic analysis of the postcards with follow-up interviews. The analysis draws on theoretical frameworks including feedback literacy (Carless & Boud, 2018), dialogic assessment (Nicol, 2010), and new paradigm feedback design (Winstone & Carless, 2020). We also apply institutional and national GenAI guidelines (Liu & Bridgeman, 2023; Perkins, 2023) to surface shared values such as authenticity, inclusivity, and responsible innovation that guide educators’ decisions. The aim of this study is to explore how educators are experimenting with GenAI in assessment and feedback, and to capture their emerging practices and reflections through the Postcards of Practice initiative. The central research question guiding this work is: How are educators integrating GenAI into assessment and feedback, and what opportunities, challenges, and support needs arise from these practices? This work advances Technology Enhanced Learning (TEL) by providing empirical insights into how GenAI is actually integrated at the coalface of teaching. Educators describe how GenAI supports more frequent, personalised feedback and builds student agency in learning. At the same time, they raise concerns about over-reliance, AI hallucination, and the need for clear pedagogical scaffolding. These reflections point to the need for professional development that is discipline-sensitive, responsive, and grounded in practice. The postcard approach also functions as a professional learning intervention. It prompts reflection, encourages cross-disciplinary dialogue, and helps build a local community of practice around GenAI use. Through this model, we demonstrate an innovative and scalable method of capturing and supporting TEL innovation in real time. The findings suggest GenAI is prompting a rethinking of assessment: from summative, compliance-driven models to more transparent, formative, and student-centred designs. Educators begin to embed feedback literacy, ethical AI use, and critical prompting into their teaching, with clear implications for program-level assessment and graduate capability development. To strengthen clarity, we propose a concise diagram mapping the emerging practices captured in the postcards against the theoretical frameworks of feedback literacy, dialogic assessment, and new paradigm feedback design. This visual representation illustrates how practical insights align with, extend, or challenge these frameworks, making the study’s contribution accessible across diverse tertiary contexts. This proposal offers exemplary innovation in TEL by foregrounding bottom-up, practice-led experimentation with GenAI. It is grounded in strong theoretical frameworks and applicable across diverse tertiary contexts. The Pecha Kucha format will present key insights through rich visual storytelling, including excerpts from the postcards themselves. We conclude by proposing future directions for research and institutional strategy, including how to embed GenAI into assessment ecosystems in ways that enhance learning, uphold integrity, and empower educators to lead digital transformation from within.

This study examines how generative AI (GAI) impacts primary students’ in-depth learning, focusing on critical thinking and prior knowledge. A quasi-experiment involved 163 sixth-graders divided into three groups: a control group (lecture-based instruction) and two experimental groups using GAI as a cognitive tool (materials generation) or thinking tool (critical analysis), in which 126 participants successfully completed all the tests and were included in the analysis. ANOVA revealed the thinking-tool group and cognitive-tool group both outperformed the control group in in-depth learning, which was reflected by the knowledge transfer. Hierarchical regression showed students’ critical thinking skills and use of generative artificial intelligence significantly contributed to their in-depth learning, while prior knowledge did not. Further analysis found that significant interaction effects existed between the use of generative artificial intelligence and critical thinking skills, while no significant interaction was found between the use of generative artificial intelligence and students’ prior knowledge. In sum, critical thinking amplified GAI’s impact, while prior knowledge showed no interaction. The results suggest GAI enhances deep learning when integrated with critical thinking, reducing reliance on prior knowledge. Educators should prioritize fostering critical thinking to maximize GAI’s benefits. The findings underscore the need for pedagogical designs that balance GAI’s cognitive support with metacognitive skill development.

Aim/Purpose To address the gap in students’ effective use of generative AI tools, this paper presents a framework to introduce university students to the principles and practices of prompt engineering – the art and science of crafting precise and purposeful inputs to guide LLMs in generating accurate and useful outputs. This paper aims to equip students with strategies to interact meaningfully with AI chatbots for academic success. Background Generative AI tools, like ChatGPT, are widely adopted in educational settings, yet many students lack the skills to harness their full potential. This paper introduces prompt engineering as a critical competency for students to develop both technical proficiency and critical thinking. Methodology The paper provides a structured framework for teaching prompt engineering in university courses. It draws on existing literature, practical applications, and pedagogical strategies to guide educators in integrating generative AI effectively into their university courses. Contribution This paper contributes to the body of knowledge by presenting a comprehensive framework for teaching prompt engineering. It highlights prompt engineering’s role in enhancing AI literacy and preparing students for technology-driven academic and professional environments. Findings Prompt engineering enhances students’ ability to generate precise and relevant outputs from AI tools by supporting student development of communication strategies tailored to large language models. This guide introduces essential concepts and skills that facilitate effective interaction with AI chatbots. Structured instruction in prompt engineering helps to foster critical thinking, problem-solving, and reflective interaction – key competencies for navigating an AI-driven environment. Additionally, integrating prompt engineering into education improves AI literacy, enabling students to tackle complex tasks and apply AI tools effectively across various disciplines. Recommendations for Practitioners Educators should integrate structured, prompt engineering instruction into their courses, emphasizing its interdisciplinary applications. Scaffolded learning will help students develop competency in applying prompt engineering techniques and strategies. Recommendations for Researchers Future studies should explore the long-term impact of prompt engineering instruction on academic performance and professional readiness. Additionally, research should examine its effectiveness across diverse disciplines. Impact on Society Teaching prompt engineering equips students with essential AI literacy skills, fostering responsible and innovative use of AI in academic, professional, and societal contexts. This contributes to a workforce better prepared for the challenges of the AI era. Future Research Further research should examine the integration of multimodal AI tools alongside prompt engineering to assess how combined approaches can enhance learning outcomes. In addition, studies should investigate the effective-ness of various instructional designs to identify best practices for promoting student engagement and skill development. Exploring discipline-specific and pedagogically meaningful student use cases will also be essential to guiding the thoughtful integration of AI tools across diverse educational contexts.

This study evaluates the efficiency and accuracy of Generative AI (GAI) tools, specifically ChatGPT and Gemini, in comparison with traditional academic databases for industrial engineering research. It was conducted in two phases. First, a survey was administered to 101 students to assess their familiarity with GAIs and the most commonly used tools in their academic field. Second, an assessment of the quality of the information provided by GAIs was carried out, in which 11 industrial engineering professors participated as evaluators. The study focuses on the query process, response times, and information accuracy, using a structured methodology that includes predefined prompts, expert validation, and statistical analysis. A comparative assessment was conducted through standardized search workflows developed using the Bizagi tool, ensuring consistency in the evaluation of both approaches. Results demonstrate that GAIs significantly reduce query response times compared to conventional databases, although the accuracy and completeness of responses require careful validation. A Chi-Square analysis was performed to statistically assess accuracy differences, revealing no significant disparities between the two AI tools. While GAIs offer efficiency advantages, conventional databases remain essential for in-depth literature searches requiring high levels of precision. These findings highlight the potential and limitations of GAIs in academic research, providing insights into their optimal application in industrial engineering education.

Background and Aim: The integration of Generative AI (GenAI) in scholarly works presents transformative opportunities alongside ethical, cognitive, and institutional challenges. This qualitative study investigates the real-world manifestations of effective Generative AI adoption through the Generative AI Integration in Scholarly Works (GAIS) framework, aiming to explore how scholars operationalize AI tools while addressing critical concerns such as ethical integrity, equity, and governance. Materials and Methods: Adopting an interpretivist paradigm, the study conducted focus group discussions involving 15 scholars drawn from two graduate institutions. Data were analyzed using thematic analysis to identify key dimensions of Generative AI integration in scholarly works. Results: Six central themes emerged: (1) Productivity and Creativity (efficiency vs. over-reliance risks); (2) Ethical Integrity (transparency, plagiarism, authorship); (3) Equity (access disparities, algorithmic bias); (4) Personalized Learning (adaptive knowledge scaffolding); (5) Cognitive Trade-offs (efficiency vs. critical thinking erosion); and (6) Institutional Governance (policy gaps in privacy and oversight). Findings highlight Generative AI’s potential to augment scholarship but reveal tensions in ethical accountability, access inequality, and academic self-efficacy. Conclusion: Significant access disparities persist, intensifying academic inequities. While Generative AI aids personalized learning and knowledge construction, its over-reliance risks diminishing critical thinking and self-efficacy. Institutional governance remains inconsistent, highlighting an urgent need for clear policies, structured oversight, and inclusive training. Practical recommendations include (a) institutional policies on Generative AI ethics, (b) cross-disciplinary Generative AI literacy programs, and (c) inclusive governance models. Future research should pursue longitudinal studies on GAIS evolution and broader stakeholder engagement to ensure equitable and sustainable AI adoption in scholarly works.

Generative AI is transforming the educational landscape by offering new ways for students and educators to engage in personalized, adaptive learning. Unlike traditional tools, generative AI enables students to access a vast repository of information, interact with content in real-time, and generate responses, which collectively support individualized learning pathways. This study explored the role of generative AI, particularly ChatGPT, in students’ self-directed learning (SDL) process. College students (n=15) from science-oriented programs were purposively sampled to be interviewed. Findings revealed that students used AI to enhance efficiency in completing tasks, generate content, and engage in deeper learning experiences. Students reported that AI tools, such as ChatGPT, helped break down complex subjects, provided instant feedback, and allowed them to manage learning at their own pace. These features supported autonomy, motivation, and competence, core components of SDL, by enabling students to make independent learning choices and confidently tackle challenging content. Student narratives illustrated how generative AI aided in organizing study material, understanding science topics, and even learning to troubleshoot code, which supported mastery of complex science program skills. The findings also suggested that AI tools contributed to active learning, as students engaged more meaningfully with content, enhancing their analytical and problem-solving abilities. The integration of generative AI in education may shape future pedagogical approaches, enabling educators to promote personalized and adaptive learning environments that support students' intrinsic motivation, SDL, and critical thinking.

This study explores the impact of generative AI-enabled cognitive offload instruction on the development of critical thinking skills in English essay writing among first-year university students. A quasi-experimental design was employed, comparing traditional instruction with an AI-augmented pedagogy that delegated lower-order writing tasks to generative AI tools, allowing students to focus on analysis, evaluation, and reflection. Over 12 weeks, 240 participants engaged in structured writing cycles involving AI brainstorming, individual critique, peer-AI co-revision, and reflective journaling. Results revealed that the AI-enabled cognitive offload group demonstrated significantly greater improvements in standardized critical thinking assessments and produced higher-quality essays in terms of logical coherence, evidence use, and originality. Mediation analysis indicated that cognitive offloading behavior partially explained the relationship between AI use and critical thinking gains. The findings suggest that when generative AI is integrated into pedagogy through deliberate scaffolding, it can enhance rather than hinder higher-order thinking. This study highlights the importance of balancing technological efficiency with instructional strategies that promote active engagement, metacognitive reflection, and collaborative learning. It offers practical implications for educators seeking to incorporate AI tools without compromising the development of essential cognitive skills, proposing that structured cognitive offload instruction can serve as an effective approach to fostering critical thinking in second-language writing contexts.

Generative AI (GenAI) tools such as ChatGPT, Gemini and Copilot have created concerns in academia, particularly after the launch of ChatGPT. GenAI and AI have been the buzz words and academics are discussing about the possibilities of its positive and negative impacts on educations and research. Recently, studies have been conducted on the influence of GenAI tools in education and research. With the above concerns and the impact of GenAI, grounded on Vygotsky's Zone of Proximal Development (ZPD) as a theoretical lens, this study explores how English language teachers integrate GenAI tools to enhance teaching and learning. Particularly, this study explores the integration of GenAI tools in English language teaching and learning, focusing on teaching efficiency, student engagement, personalized learning, and writing skills, subscribing to exploratory research methods grounded on semi-structured interviews. The findings of the study affirmed the positive impact of GenAI tools on teaching efficiency, students’ engagement, and writing skills. The results indicated that GenAI positively influences teaching efficiency and student engagement in learning. The implications of this research highlighted the potential of GenAI tools to create a more intelligent and personalized learning environment for English language teaching that benefits both educators and learners.

Generative artificial intelligence, enabled through sophisticated machine learning frameworks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), is disrupting the creative landscape within many of the creative industries, opening a new set of tools for content creation. Therefore, this article investigates the disruptions generative models present across multiple aspects of creative production, focusing on the influences of art, music, literature, and design. In the visual arts, generative AI produces original artworks that blend aesthetics and styles that extend conventional notions of creativity; in music, AI-composition tools allow musicians to compose original musical works and experiment with entirely new genres. In literature and storytelling, the same generative processes offer AI systems the ability to propose narrative frameworks and other textual elements that may instigate human writers to take these concepts further with a narrative or may stand on their own as original content. In design, generative algorithms support product development all the way down to quickly prototyping new products. The paper includes specific case studies or outlines of the application of generative AI tools to proposed projects, a discussion of effectiveness with the respective generative model, and the potential limitations generative models present for each creative area. The paper will also reflect on the threats generative AI may pose, the potential to redefine human creativity, originality, and ownership, implications for the potential decision-making capabilities of these systems, and if there may be consequences for society. In all these considerations, there are opportunities for and more significant implications of the potential of generative AI to assist and expand human creative possibilities throughout the entire creative process.

The rapid development and availability of Generative AI (GenAI) tools, such as ChatGPT, offer new possibilities for academia, yet they also raise concerns for academic integrity. Chapelle's (2024) seminal work on GenAI’s implications serves as an impetus for this study, which explores teaching academic integrity in the context of AI use. Chapelle frames GenAI as an ‘inflection point’, calling for a re-evaluation of pedagogical strategies to support students in understanding its technological and ethical implications. This article presents findings from a mixed-methods study at a university in Cyprus, where ESL undergraduates in academic writing classes engaged with ChatGPT. Participants with B2/C1 English proficiency from diverse cultural backgrounds were assessed through pre- and post-intervention evaluations and qualitative interviews. A semester-long intervention incorporated activities to enhance academic writing skills and comprehension of ethical AI use, focusing on critical thinking, proper citation, and policy adherence. Findings indicate that students demonstrated increased AI knowledge and awareness of academic integrity. This study contributes to the discourse on integrating GenAI in academia, providing insights into effective instructional practices and addressing ethical considerations in academic writing. It offers guidance for educators and institutions aiming to integrate GenAI responsibly, ensuring academic integrity remains central in second language education.

This study explores the factors influencing the adoption of Generative AI text-to-image tools in the creative industry, using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. The objective is to assess how brand recognition and trust, alongside performance expectancy, effort expectancy, facilitating conditions, and social influence, shape the behavioral intention to use Generative AI tools. While previous research has emphasized the importance of UTAUT constructs in technology adoption, the influence of brand equity factors remains underexplored. This study bridges this gap and provides insights to enhance adoption strategies. Standardized questionnaires were used, incorporating UTAUT constructs and brand-related variables such as Brand Recognition and Brand Trust. The sample consisted of individuals working in the creative industry in the US and Spain, with 208 valid responses. The survey was distributed through creative online communities. Partial Least Squares Structural Equation Modeling was employed to validate the hypotheses, ensuring reliable and valid results. Key findings indicate that performance expectancy, facilitating conditions, and brand trust positively influence the behavioral intention to use Generative AI tools, while brand recognition negatively influences behavioral intention. Social influence and effort expectancy did not present statistically significant results. These insights contribute to developing effective adoption strategies for Generative AI in the creative industry.

Integrating generative AI into digital multimodal composition: A study of multicultural second-language classrooms