Exploring Higher Education Faculty Insights on Generative AI in Creative Courses

Purpose Generative AI is changing every facet of life, including higher education. Educators are not immune to this disruption, which challenges their traditional mindsets. This paradigm shift requires critical reflection for educators to unlearn and relearn generative AI’s possibilities. This study explores how higher education educators must approach unlearning traditional teaching practices to adopt generative AI effectively. Design/methodology/approach The study applies a conceptual approach to explore using organizational unlearning theory as a pathway for meaningful generative AI integration. The conceptual integration of organizational unlearning and digital maturity is adopted to map multiple stages for higher education educators to integrate GenAI as an assistant. Findings Five nonlinear maturity stages are proposed for higher education educators to adopt practically: (i) foundation awareness, (ii) educational experimentation, (iii) strategic implementation, (iv) transformational practice, and (v) educational ecosystem architects. Each stage reflects on recognition activities, unlearning levels, and key actions to realize generative AI’s integration. Research limitations/implications The conceptual approach requires empirical exploration to formalize the maturity framework in higher education. Practical implications The maturity stages provide a framework for progressive integration by starting with low-stakes GenAI experiments. The five stages require extensive introspection of how generative AI influences operational and transformational stages to architect an innovative higher education environment. Originality/value Organizational unlearning is an underexplored concept in higher education. This study provides a conceptual framework for initiating higher education unlearning in the generative AI era.

As generative AI (GenAI) becomes increasingly embedded in higher education, students face contradictory narratives about its use as it is often presented either as a risk to academic integrity or as a valuable educational resource (Luo, 2025). Navigating these tensions can be particularly complex for students from equity groups, who already contend with challenges in adapting to university learning environments (Stokes, 2024). While institutional policies attempt to regulate GenAI use, these often lack the nuance needed to guide context-sensitive decision-making (Corbin et al., 2025). This presentation outlines an action research project that investigated how dialogic teaching approaches can support students in developing critical GenAI literacies through collaborative exploration and reflection. The research took place within an Australian university’s Enabling Education program and was grounded in the principles of Dialogic Pedagogy which is a critical framework rooted in reciprocal communication, shared meaning-making, and the co-construction of knowledge (Shor & Freire, 1987). Rather than imposing rules, this pedagogical model encourages transparency, inquiry, and inclusive negotiation of acceptable practices. Encouraging students, who are new to university, to become active agents supports them to build their critical AI literacy (Toncelli, et al., 2025; Wang & Wang, 2025). This approach shifts the emphasis from compliance to empowerment, allowing students and educators to engage in meaningful dialogue about the ethical and effective use of GenAI in their learning. Using a critical participatory action research methodology (Kemmis et al., 2014), the study collected data across three foundational courses: language and literacy, digital literacy, and information literacy. In the language and literacy unit, students explored the use of GenAI tools in developing their academic writing. Through classroom activities and reflective discussions, students examined GenAI-generated feedback on their work, considering how such input could enhance their language development while identifying its limitations and ethical implications. In the digital and information literacy courses, activities focused on the broader societal and epistemological impacts of GenAI. Students investigated how AI technologies influence knowledge creation, information access, and authorship. Educators facilitated discussions that encouraged students to evaluate when and how GenAI might be used responsibly in their coursework, particularly in creative and research-based projects, without compromising academic ownership. Findings from this project highlight the value of dialogic approaches in Enabling Education programs to help students take ownership of their learning. Students’ initial uncertainty shifted to confidence in sharing their experiences of GenAI. Further, this approach created a safe space for open discussion enabling co-construction of knowledge about critical AI literacy. By centering student voice and agency, this model offers a compelling alternative to top-down enforcement of AI-related policies. It suggests that fostering critical engagement with GenAI builds students’ confidence and skills and strengthens trust between educators and learners. This work contributes to emerging conversations around AI literacy in higher education by proposing a framework that is both inclusive and responsive to the real-world complexities of student experience. It underscores the importance of pedagogical strategies that are participatory, flexible, and grounded in shared inquiry, particularly for students navigating the margins of academic culture.

Interprofessional collaboration in higher learning education is widely acclaimed as a critical prerequisite to achieving greater quality and coherence in the facilitation of teaching and learning. The COVID-19 pandemic not only presented higher education with challenges but also allowed all academics to rethink the assumptions about teaching and learning strategies that promote student engagement and academic success across the faculties. Interprofessional reflection is important because it provides opportunities for innovation and new teaching-learning strategies that meet the current climate of higher learning, which promotes student engagement and academic success. The aim is to explore and synthesise literature regarding effective teaching and learning strategies that encourage student engagement and academic success across faculties in higher education. Arksey and O'Malley's five-stage framework served as the basis for the scoping review adopted by the review. Three databases were searched for sources: CINHAL, Scopus, and Medline. Only English full-text written articles across various research designs published between 2016-2023 were part of the inclusion criteria for the review. As a result, four themes emerged: the use of technology as a teaching and learning modality, work-integrated learning for theory practice integration, university teacher-centric approach, and student-centric approach. In conclusion, across faculties, there is a focus on integrating technology in the teaching and learning process, using various pedagogies to meet the needs of different students. Moreover, there is a need to encourage educators to adopt a growth mindset, involving students actively in the learning process. This approach will encourage students to develop critical thinking, become self-directed learners, and fully engage within their academic communities.

In this article, we investigate the critical intersections of AI, academic integrity, and disability in the context of a large undergraduate course. Our aim was to adapt the course to respond to generative AI (GenAI) to avoid entrenching barriers for students, and instead teach them how to use GenAI tools in ways that deepen their learning and uphold academic honesty. Grounded in disability justice and access pedagogies, we outline five design goals centered on guidelines for AI usage, education on responsible AI use, revised assessments, support for teaching assistants (TAs), and accessible materials. These activities are detailed in our methodology. In our findings, we provide a critical reflection of the course adaptation, taking up issues such as varying levels of familiarity with GenAI, students’ capacity to engage with course changes, resistance to GenAI, instructors’ relational shifts to AI, and feelings of demoralization among the teaching team. We conclude by offering practical recommendations for educators, calling for learning communities to view this disruption as an invitation to listen to disabled students.

Generative AI (GenAI) has attracted a surge of attention from higher education constituents after OpenAI released ChatGPT in November 2022. While numerous articles discuss applications and perceptions of GenAI in higher education, no comprehensive review has considered commonalities and differences among various educational stakeholder groups and contexts. In this review, we synthesize the applications, capabilities, and perceptions of GenAI in higher education to provide stakeholders (i.e., students, instructors, researchers, staff, and administrators) with insights into this topic to facilitate GenAI integration in higher education. We reviewed 50 relevant empirical articles published from January 2023 to April 2025 on GenAI in higher education. Our findings demonstrate how GenAI has already been applied and present its potential for implementation across teaching and learning, research, and student affairs in higher education. Among various stakeholders in higher education, students hold a more open and positive attitude toward this rising technology, while instructors and researchers hold mixed attitudes toward GenAI usage, and administrators tend to hold an open but cautious attitude toward GenAI implementation. Addressing common stakeholder concerns and needs, we outline institutional strategies for responsible GenAI integration, including launching GenAI learning hubs, formalizing license agreements, redefining academic originality, and implementing pilot programs.

Generative AI is reshaping the enterprise technology landscape, offering intelligent automation, insight generation, and contextual understanding capabilities that redefine how businesses handle data. Enterprise data management (EDM) - once constrained by rigid architectures, manual processing, and fragmented governance - can now evolve into a dynamic, self-improving ecosystem through the integration of generative AI. With organizations generating petabytes of data from operations, customer interactions, supply chains, and IoT devices, the need for scalable and intelligent data handling systems has never been greater. Generative AI models, including large language models (LLMs) and multimodal transformers, provide new tools for data ingestion, cleansing, integration, transformation, synthesis, and summarization. By applying generative AI to enterprise data workflows, companies can enhance metadata enrichment, automate data cataloging, improve data lineage tracking, and simplify data governance. These capabilities increase data discoverability, trust, and compliance—core principles of modern data management. Additionally, generative AI supports natural language querying, automates report writing, and generates synthetic data for training and simulation, boosting data availability and operational speed. While generative AI brings immense promise, it also raises concerns around hallucination, model transparency, data privacy, and regulatory compliance. Ensuring responsible AI adoption requires rigorous validation, bias mitigation, and alignment with existing data governance policies. Nonetheless, enterprises that embrace generative AI can unlock superior decision-making, improve productivity, and democratize data access across technical and non-technical users. This white paper explores the opportunities, challenges, architectural considerations, and best practices for embedding generative AI into enterprise data management. Through industry examples and forward- looking analysis, it offers a roadmap for transforming data operations and maximizing enterprise intelligence in the era of AI. Keywords: Generative AI, Enterprise Data Management, LLMs, Data Governance, Metadata, Data Cataloging, Synthetic Data, Data Lineage, Natural Language Processing, Responsible AI

Generative AI has drawn significant attention from stakeholders in higher education. As it introduces new opportunities for personalized learning and tutoring support, it simultaneously poses challenges to academic integrity and leads to ethical issues. Consequently, governing responsible AI usage within higher education institutions (HEIs) becomes increasingly important. Leading universities have already published guidelines on Generative AI, with most attempting to embrace this technology responsibly. This study provides a new perspective by focusing on strategies for responsible AI governance as demonstrated in these guidelines. Through a case study of 14 prestigious universities in the United States, we identified the multi-unit governance of AI, the role-specific governance of AI, and the academic characteristics of AI governance from their AI guidelines. The strengths and potential limitations of these strategies and characteristics are discussed. The findings offer practical implications for guiding responsible AI usage in HEIs and beyond.

Generative AI's growing use in higher education research requires strong protocols for responsible use. This need arises from the potential for misuse and the current uncertainty around ethical concerns and intellectual property. The lack of clear rules about openness in AI use, along with the “black box” nature of many AI systems, raises worries about reproducibility and the possibility of biased or fake results. This paper focuses specifically on generative AI tools (e.g., LLMs like ChatGPT, research‐specific platforms like Elicit/SciSpace). The paper presents the ETHICAL protocol (i.e., E stablish your purpose, T horoughly explore options, H arness the appropriate tool, I nspect and verify output, C ite and reference accurately, A cknowledge AI usage transparently, and L ook over publisher's guidelines), a detailed guide designed to direct researchers in the ethical and responsible inclusion of generative AI in their work. The protocol was created through a multi‐step process, including a scientometric review of current trends, a systematic review of researcher experiences, and a policy analysis of 74 documents from various stakeholders (authorities, universities, publishers, and publication manuals). This analysis shaped the creation of a seven‐heading, nine‐item checklist covering key aspects of responsible AI use, from setting clear research goals to checking outputs and openly acknowledging AI help. The ETHICAL protocol gives practical examples and detailed explanations for each item, highlighting the importance of AI literacy and careful choice of suitable tools. It also stresses the vital need for checking AI‐generated content to lessen the risk of errors and made‐up information (“hallucinations”). The resulting protocol offers a practical and easy‐to‐use guide for researchers, encouraging responsible AI practices and upholding academic integrity. The ETHICAL protocol offers a helpful tool for managing the complex area of AI in research, ultimately leading to more open, reliable, and ethically sound scholarly work. Its broad acceptance could greatly improve the responsible use of AI in higher education, building trust and furthering knowledge growth.

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 article explores the sudden shift in higher education that was brought about by the generative artificial intelligence (GAI), a technology that is reshaping pedagogical, evaluating, research and administrative spheres. While GAI holds great promise with the benefits of personalized instruction, improved operational efficiency, expanded accessibility, and innovation, at the same time it creates deep ethical concerns that include issues related to academic integrity, systemic bias, lack of transparency, privacy breaches, accountability, and loss of trust and equity. Contemporary universities are therefore under increasing pressure to establish by far rigorous, principled governance systems that prudent directions for the responsible use and deployment of GAI technologies: Based on current scholarship, the forth study provides a critical examination of some of the key ethical dilemmas, institutional responses, and issues of governance paradigms that have transpired in the intersection of GAI integration ostracism in academic settings. Using the literature review methodology in the form of narrative, analysis can synthesize an existing scholarly discourse and identify key governors dimension(s) necessary for responsible implementation. The results suggest that ethical governance needs to move beyond ad hoc rules focused on academic malpractices and instead adopt a broad institutional model based on transparency, accountability, fairness, privacy, human oversight and constant refining of policies. In response to this, a pragmatic ethical governance structure organized around institutional leadership, policy formulation, conscientious procurement, stakeholder engagement, assessment reform, capacity development, monitoring mechanism, and iterative evaluation is proposed in the article. It concludes that the practice of proactive governance is essential in ensuring the alignment of technological innovativeness with the fundamental values of education as well as human dignity ensuring that GAI can help enhance the educational quality and at the same time protect the trust and inclusion while maintaining its integrity and institutional credibility.

While Generative AI is reshaping higher education, its role in tourism-focused Virtual Exchange remains underexplored. This study examines the impact of AI on student interaction, academic performance, and engagement in a pilot Virtual Exchange between universities in Spain and Ireland. Drawing on Conversation Theory, Virtual Exchange is conceptualized as a triadic process involving students and AI, moving from interaction to shared understanding and collaborative action. Using a mixed-methods approach with qualitative emphasis, the study explores how AI tools shaped the learning experience, highlighting both opportunities and challenges. Grounded in frameworks of women’s empowerment in tourism, internationalization of the curriculum, intercultural competence, and digital pedagogy, the findings deepen understanding of AI’s potential and limitations in Virtual Exchange contexts. The study also offers practical guidance for educators on developing AI literacy, promoting ethical awareness, and enhancing student collaboration in online international learning environments.

The aim of this study is to examine the adoption of Generative AI (GenAI) tools among faculty in higher education using a revised Unified Theory of Acceptance and Use of Technology (UTAUT) model. A quantitative survey was conducted with 244 faculty members from eight Saudi universities, and the data were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS. The results show that Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), and Behavioral Intention (BI) significantly influence the adoption of GenAI tools. Among these, PE and EE had the strongest positive impact on BI, stressing the importance of user-friendly design and faculty training, while SI had a weaker but still meaningful effect. Gender and academic position were also found to moderate adoption behaviors, indicating differences across faculty groups. This study extends the UTAUT model by introducing new moderating factors and provides empirical evidence from Saudi Arabia. It further recommends intuitive system design, technical support, training, and the development of an AI-friendly academic culture to promote effective integration of GenAI in higher education.

The creative industries are shifting rapidly with the emergence of generative AI technologies. These transitions provide new opportunities for young people entering higher education in the creative industries, while perpetuating existing patterns of extraction and marginalisation under late capitalism. This article turns to the algorithmic arts in search of counter-practices that disrupt instrumental applications of AI within creative industries and higher education. Bringing the philosophy of technology into conversation with education and the digital arts, the authors identify three tactics to inform educational design with AI: (1) continuous mattering between algorithms and world; (2) playing with algorithmic assemblages; and (3) ethics-in-formation. The article builds on these critical tactics to inform the development of AI-powered software for navigating education and work futures in Southeast Asia. By situating generative AI within broader assemblages of human relationships with algorithms, the article offers critical interpretations of AI and its situated design implications for the creative industries and higher education.

With the rapid evolution and widespread adoption of Generative AI (GenAI), there has been a recent surge in research on students’ perceptions and usage of these tools for learning. However, a critical need remains to understand how students from various demographic groups perceive and utilize GenAI for educational purposes. This research aims to fill this gap by evaluating students’ use of GenAI, comfort level, perception of readiness, benefits and challenges, and demographic differences in their perceptions. Through an online survey of 482 students across diverse institutions of higher education primarily within the United States, the findings indicate a significant majority of students feel comfortable using GenAI tools and recognize their potential to enhance productivity and academic success. However, students also reported concerns related to GenAI use, including concerns about academic integrity, overreliance on AI, and data privacy and security. The analysis highlights differences in the subscales of perceived readiness (GenAI Comprehension; GenAI Utilization and Proficiency), benefits (Impact of GenAI; GenAI Empowerment), and challenges (Negative Impact of GenAI; GenAI Limitation) across demographic groups, including student classification, enrollment status, and institution types. Findings suggest a critical need for the integration of AI literacy into curricula to address opportunities and challenges posed by GenAI in higher education, and this study calls for additional research to explore evolving perceptions of GenAI use over time and its long-term impact on higher education.

This publication explores the potential and actual use of generative AI (GAI) in business education at Arcada University of Applied Sciences, with a particular focus on the teacher’s role in integrating GAI into course modules. Research suggests that GAI can enhance teaching and student learning by offering personalized and engaging experiences. The aim of the study is to contribute to a deeper understanding of AI in education and to promote knowledge about the responsible integration of AI into business education. A further objective is to develop teaching practices that prepare students to engage with the technology responsible in their future professional lives.We investigate teachers’ perceptions of AI and their reflections on working with it. Additionally, we describe our collaborative work on these issues over the course of a year. During spring and autumn 2024, we conducted research into our own teaching practices in relation to AI within our teaching team. In parallel, we collected data from workshops where current AI practices, tools, challenges, and educational needs were discussed.The project also provides insights into how AI can be integrated across various areas of business education and lays the foundation for future research on optimizing AI use in educational contexts.We identify challenges related to safety, bias, and academic integrity. Finally, we discuss future trends and the evolving role of teachers in an educational landscape where AI is embedded in the learning environment. A balanced use of AI is recommended, and continued work is needed to ensure responsible, reliability, and ethical integration.The publication aligns with strategic goals concerning sustainable digital solutions and responsible AI. We argue that the publication contributes to the broader discourse on high-quality, sustainable, and responsible education. Our project supports Sustainable Development Goals 4 and 11, and peer learning has been central throughout our process.