Enhancing self‐regulated learning and learning experience in generative AI environments: The critical role of metacognitive support

While industry practices evolve rapidly, marketing education in Australia and New Zealand faces challenges in keeping pace, particularly regarding the adoption of current marketing technologies (Harrigan et al., 2022). Generative AI, exemplified by systems like ChatGPT and DALL·E, has demonstrated benefits for learning (Baidoo-Anu & Ansah, 2023). However, despite its potential, there remains a dearth of practical guidance on effectively incorporating these technologies into marketing courses. This gap persists even as general frameworks for responsible and ethical AI use, such as the Australian Framework for Generative AI in Schools (2023), emerge. As the demand for graduates with generative AI skills grows in the job market, educators must explore innovative pedagogical approaches to bridge this gap. This academic poster presents an innovative application of generative artificial intelligence (GenAI) in the context of teaching digital marketing at the postgraduate level. Its purpose is to bridge the gap between academic theory and industry practice by encouraging educators to integrate AI tools into their curriculum through experiential learning pedagogy (Kolb, 2014), characterized by a learning process whereby knowledge is created through hands-on experiences. The poster exemplifies how various types of GenAI technologies — specifically text-based, image-based, and video-based — can enhance teaching content, tutorial exercises, and assessments within the digital marketing course. The poster showcases examples of how these GenAI tools are integrated in the course content, to guide students in generating innovative ideas for using AI in marketing to gain a competitive edge: Text-based GenAI: Tools like ChatGPT and Gemini can automatically generate search keywords for search engine marketing. By integrating text-based GenAI tools with established marketing technology (MarTech) tools such as Google Ads and Google Ads Keyword Planner, students engage in practical exercises that combine AI-generated initial ideas (e.g., search keywords) with further analysis (e.g., search volume, click-through rates, and bidding costs) using established MarTech tools. This hands-on approach enhances their learning experience and prepares them for real-world applications. Image-based GenAI: Platforms such as DALL·E, Midjourney, and Stable Diffusion enable the creation of custom images for display advertising, enhancing visual communication in marketing materials. Through experiential learning activities, students can explore ideas, seek unusual combinations, and inspire creativity faster with image-based GenAI tools, resulting in a greater variety of display ad materials. Video-based GenAI: Applications like Sora and Synthesia facilitate the production of short video clips suitable for social media marketing (e.g., YouTube Shorts, TikTok). By engaging in dynamic content creation exercises, students learn to streamline content creation, reduce manual work, and save both time and budget, thereby gaining practical skills in social media marketing. By incorporating these GenAI technologies through experiential learning pedagogy, educators can enrich the learning experience, foster critical thinking, and prepare students for the evolving landscape of digital marketing. Future research can study the use of GenAI in marketing education using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2016).

In recent years, generative artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize collaborative learning experiences. This study investigates the role of generative AI in facilitating innovative collaboration among Saudi university students engaged in group projects within digital learning environments. As higher education institutions increasingly adopt digital tools and platforms, understanding how generative AI can enhance creativity, teamwork, and overall learning outcomes becomes paramount.Utilizing a mixed-methods approach, this research employs a comprehensive questionnaire distributed to a diverse sample of Saudi university students. The questionnaire is designed to assess participants’ experiences with generative AI tools, their perceptions of these tools' effectiveness in enhancing collaboration, and the impact on their creativity and problem-solving abilities during group projects. The survey includes both closed-ended questions for quantitative analysis and open-ended questions for qualitative insights, allowing for a nuanced exploration of the role of generative AI in educational settings.The findings reveal that a significant majority of participants (approximately 78%) reported that generative AI tools, such as AI-driven brainstorming applications and collaborative writing platforms, have had a positive influence on their group project experiences. Students highlighted that these tools fostered a more inclusive environment, enabling team members to contribute ideas more freely and creatively. Moreover, 65% of respondents noted improved communication and coordination among team members, attributing this enhancement to the real-time collaborative features provided by generative AI tools.Additionally, qualitative feedback from open-ended questions indicated that students appreciated the ability of generative AI to generate diverse perspectives and ideas, which enriched the brainstorming process and led to more innovative project outcomes. Participants expressed that the use of AI tools reduced the cognitive load associated with project management, allowing them to focus more on creative problem-solving and critical thinking. However, some concerns were raised regarding the potential for over-reliance on AI-generated content, emphasizing the need for a balanced approach that combines human creativity with AI assistance.This study contributes to the growing body of literature on the integration of artificial intelligence in education, particularly in the context of collaborative learning. The implications of these findings suggest that incorporating generative AI tools into the curriculum can significantly enhance the collaborative capabilities of Saudi university students, fostering a culture of innovation and creativity in digital learning environments. As educational institutions continue to navigate the complexities of digital transformation, understanding the potential of generative AI as a catalyst for collaboration will be essential for fostering effective and engaging learning experiences.In conclusion, this research highlights the transformative potential of generative AI in enhancing group projects among Saudi students, offering valuable insights for educators and policymakers aiming to leverage technology for improved collaborative learning outcomes.

An ASEE 2023 paper considered whether student use of Generative Artificial Intelligence (GenAI) to write engineering papers constitutes cheating. It was concluded that it depended on specific circumstances, but it was noted that students might potentially undermine their own learning by relying on tools like ChatGPT to answer questions and compose papers. This paper addresses how to enable student use of these tools in a way that students are not cheating themselves. Educators faced a related issue 20 years ago when calculators were introduced into the classroom. As time passed, instructors realized that allowing students to use calculators allowed them to concentrate on teaching concepts, strategies for problem solving and critical thinking. Calculators allowed more individualized instruction and more use of real-world applications. The introduction of calculators into the classroom stimulated discussion on ethical use of technology in teaching. A similar revolution is occurring with the introduction of Generative Artificial Intelligence tools such as ChatGPT, BARD and many others, and a similar set of opportunities is emerging. A key issue is how to use GenAI tools constructively to encourage critical thinking in the solving of engineering problems. The tools can make it easier to differentiate instruction to meet the diverse needs of students and tailor teaching to students with different levels of skills. Using Generative AI tools can help build students' confidence in their own abilities to function as engineers. For example, students can analyze a problem from a variety of perspectives by varying the prompts they give to their GenAI tool. In this paper, we discuss some actions taken in our classes, including changing the style of assessments, teaching students "prompt engineering" as part of the critical thinking process, teaching students how to use Generative AI effectively in coding, and helping students to acquire the habit of validating outputs from Gen AI tools to eliminate hallucinations and fake references. Students can work at their own pace and explore concepts on their own, as Gen AI tools provide instant feedback. Students can explore a problem from multiple perspectives, including the impact of various ethical considerations by varying their prompts. Using Gen AI to generate simple answers to simple questions could, indeed, cheat students out of the ability to learn important engineering concepts. Using Gen AI to explore alternatives and assess different approaches to an engineering problem, for example asking for code using different Python libraries, can make students better and more effective engineers.

Generative artificial intelligence (GenAI) is rapidly reframing academic practice, yet staff confidence to harness its potential remains uneven. Many university staff indicate that a “lack of familiarity with the technology, uncertainty about its use, or a lack of time to engage, contributed to their reluctance to use AI tools for their work” (McDonald, 2024). Building this confidence demands accessible opportunities that honour diverse starting points, and courageous experimentation which normalises trial-and-error—two of the principles guiding the University of Newcastle’s GenAI response. Our GenAI Tinker Workshops operationalise these principles by offering low-stakes, experimentation-based experiences where educators co-explore AI tools, reflect on ethical dimensions and iterate on real teaching artefacts. Playful, informal learning environments may lower cognitive load and heighten curiosity by reducing the focus on serious workplace implications (O’Hara & Lo, 2025). Tinkering studios, for example, cultivate “permission to fail” that supports creativity and innovation (Henriksen, 2021). The informal, low-stakes format of the sessions promoted engagement, echoing Worth’s (2024) assertion that “AI playgrounds” backed by appropriate guidance can support learning. Responding to TEQSA’s call for structured capacity-building around GenAI (TEQSA, 2024), Learning Design & Teaching Innovation designed monthly Tinker Workshops capped at roughly ten participants. Each 60-minute session invited academics to “tinker” with tools like ChatGPT, NapkinAI Copilot, NotebookLM and Gamma to draft real artefacts (course plans, presentations, podcasts, images, etc) for use in their teaching activities. Facilitators guide the participants through a simple introductory activity and focus on identifying opportunities to promote discussion in the group. Evaluation of the workshops, through informal participant feedback and in-session observations, showed an appreciation for this approach. Participants reported increased confidence not only with the tools but with navigating rapid change more broadly, citing reduced anxiety and a stronger sense of community. Participants noted possible operational benefits identified by exposure to various AI tools (example comments – “this will save me so much time” and “That just took about 20 seconds, it would take me three days to do the same.”). They also developed insights beyond the immediate operational focus of the activity (example comments – “I’d never thought of structuring my course that way, it’s really clear” and “This has really helped me think about how I use images and graphics in my lectures.”). Further, the informal nature of the sessions, with facilitator positioned as fellow learner rather than expert instructor supported honest discussion and debate around the implications (both positive and negative) of GenAI (example comments – “it’s just good to be able to talk about this stuff, and know everyone is in the same boat” and “I kind of knew that not all AI is not bad, but have not had the time to learn more.”). End of session discussion is guided towards awareness of the fallibility of GenAI outputs, and the importance of human expertise in co-creating learning experiences with GenAI. By embedding accessible, iterative, low-stakes Tinker Workshops alongside more traditional GenAI initiatives, we aim to directly tackle the challenge of uneven confidence driven by unfamiliarity, uncertainty and limited time. The format normalises trial-and-error, scaffolds ethical reflection and focuses “AI playground” tinkering on real world use cases—delivering immediate efficiency gains while supporting consideration of broader pedagogical impacts. Participants report reduced anxiety, stronger professional community and increased confidence in decision making around the use of GenAI. In short, structured opportunities to experiment safely are building adaptable educators who can collaborate with AI responsibly, meeting TEQSA’s call for capability-building and strengthening resilient, human-centred learning.

Generative Artificial Intelligence (GAI) can help teachers create innovative educational content and provide a variety of valuable information and resources. In this chapter we present a study whose aim was to analyse and evaluate the perceptions of students regarding educational practices and resources developed with AI tools and used in Portuguese classes. In order to find out what the students think about the potential and challenges associated with integrating GAI technologies into the teaching and learning process, we used a mixed focus (qualitative and quantitative) and collected data through questionnaires and an interview. The data collected reveals that students highlight the transformative potential of GAI in the educational process and its benefits for educational success in the Portuguese lessons. However, they also recognise the potential risks and challenges for students when it comes to using GAI responsibly in the classroom, and the need to know what GAI is and its potential in learning and to learn how to better interact with these generative technologies.

This study investigates the transformative potential of Generative Artificial Intelligence (AI) in enhancing learning experiences and addressing the issue of low attention span among college students. The research focuses on four key components of AI-generated pedagogy: reducing learning challenges, enhancing learning experiences, ethical considerations, and balancing benefits and risks. Utilizing a quantitative survey-based approach, data were collected from 57 college students in the Kottayam District. The findings reveal that students perceive Generative AI as having a positive impact on learning experiences, effectively reducing learning challenges, enhancing the effectiveness of pedagogical strategies, addressing ethical concerns, and balancing benefits and risks. Notably, a strong positive correlation exists between these Generative AI-generated pedagogical components and low attention span, suggesting that Generative AI can effectively address this issue among college students and open new avenues for leveraging technology to improve educational outcomes. The present research contributes to the broader discourse on the effectiveness of technology-enhanced learning, particularly in improving engagement and attention span in educational settings. It underscores the need for further exploration of the role of Generative AI in education, emphasizing the balance of technological, ethical, and pedagogical considerations to maximize AI's benefits in learning environments.

The study aims to analyze the effect of mathematics self-regulated learning (MSRL) assisted by metacognitive support (MS) by reviewing sex differences (SD) in mathematics anxiety (MA). The reason is that MA causes students to have difficulty in learning, so good strategies and approaches are needed. In several studies, there is a relevant relationship between MA, SRL, and MS. This research is quantitative research using Spearman Rho analysis and ordinal regression analysis, with the research population being 3rd-grade students of primary schools in Sidoarjo, East Java. The instruments used in data collection were questionnaires and tests declared valid and reliable. The validity and reliability of the instrument were obtained through content validity assessment and item analysis testing. The results showed a relationship between SRL and MS to MA. There was a positive effect of MS-assisted MSRL in terms of SD on MA. Furthermore, the average student had a positive MS and a high MSRL. The highest MA students are at the moderate level, with a percentage of 79%, and are dominant in the affective aspect. However, when viewed from the SD, the MA showed that female students were more anxious than male students. Thus, the MS-assisted SRL has a good influence when viewed from SD to MA. The recommendation from this research is so that MS and SRL strategies can be implemented optimally. Teachers must prepare and explore the implementation of using the strategies and approach more deeply.

This study dives into how Generative Artificial Intelligence (GenAI) can boost higher-order thinking skills among vocational college students in Guangxi, China. The researchers used a quasi-experimental mixed-methods approach, involving 70 students from information technology and engineering programs. They split the students into two groups: one that experienced GenAI-enhanced interactive teaching and another that followed traditional instruction. The experimental group utilized GenAI in specific roles like “virtual debater” and “decision consultant,” which helped them engage more actively in debates, case analyses, and reflective decision-making. The quantitative results from pre- and post-tests showed a notable improvement in critical thinking, logical reasoning, and reflective skills for the students in the experimental group. Meanwhile, qualitative data gathered from observations, student journals, and interviews highlighted an increase in metacognitive awareness, deeper engagement with problem-solving tasks, and a positive view of AI as a helpful partner in learning. However, there were also concerns about students becoming too reliant on AI tools and the necessity for guided facilitation. The findings indicate that when implemented thoughtfully, GenAI tools can significantly enhance higher-order thinking in vocational education, especially when paired with teaching strategies that promote active learning and reflection. This study underscores the importance of integrating AI ethically and calls for customized instructional designs to ensure fair access and ongoing cognitive growth. These insights add to the ongoing conversation about human-machine collaboration in education and provide valuable guidance for future curriculum innovations in tech-driven learning environments.

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.

The growing scale and complexity of higher education have exposed limitations in traditional academic advising models, which are often manual, reactive, and constrained by limited human resources. As institutions serve increasingly diverse student populations across physical and digital learning environments, there is a critical need for intelligent, scalable, and personalized advising solutions. This paper presents a comprehensive framework for Generative Artificial Intelligence (GenAI)–driven digital academic advising and intelligent student support systems that integrates large language models, predictive analytics, and institutional academic data. The proposed framework enables natural language interaction, personalized academic planning, early risk identification, and career-oriented guidance by leveraging student records, learning management data, and policy knowledge bases. Generative AI enhances advising by providing context-aware, explainable recommendations and supporting scenario-based academic decision-making. To ensure responsible adoption, the framework incorporates ethical AI principles, including transparency, bias mitigation, privacy preservation, and human-in-the-loop governance. A modular, cloud-native, and API-driven architecture is proposed to support scalability and seamless integration with existing Student Information Systems and Learning Management Systems. Experimental insights and related studies indicate that AI-enabled advising can significantly improve student engagement, advising efficiency, and retention outcomes while reducing advisor workload. Overall, the framework demonstrates how generative AI can transform academic advising into a proactive, intelligent, and continuously learning support ecosystem that aligns institutional goals with student success and long-term educational outcomes.

With the rapid popularization of artificial intelligence technology in the field of higher education, college students are increasingly dependent on AI tools such as ChatGPT, automatic writing assistants, and intelligent translators. Behind the convenience and efficiency, a decline trend in students’ core learning abilities such as autonomous learning ability, critical thinking ability, and knowledge construction ability has gradually emerged. This study aims to explore the interactive logical mechanism between college students’ reliance on AI tools and the weakening of their learning abilities, and on this basis, propose practical and feasible educational intervention strategies. Research has found that while AI tools lower the learning threshold, they also weaken students’ cognitive investment and independent thinking abilities, further intensifying their reliance on technology. In this regard, this paper proposes a three-dimensional intervention path based on guided usage, ability compensation, and value reconstruction to achieve the collaborative improvement of students’ technical usage ability and learning ability. This research has certain theoretical value and practical enlightenment significance for solving the structural predicament of higher education in the intelligent era.

The introduction of generative artificial intelligence (GenAI) has been met with a mix of reactions by higher education institutions, ranging from consternation and resistance to whole-hearted acceptance. Previous work has looked at the discourse and policies adopted by universities across the U.S. as well as educators, along with the inclusion of GenAI-related content and topics in higher education. Building on previous research, this study reports findings from a survey of engineering educators on their use of and perspectives toward generative AI. Specifically, we surveyed 98 educators from engineering, computer science, and education who participated in a workshop on GenAI in Engineering Education to learn about their perspectives on using these tools for teaching and research. We asked them about their use of and comfort with GenAI, their overall perspectives on GenAI, the challenges and potential harms of using it for teaching, learning, and research, and examined whether their approach to using and integrating GenAI in their classroom influenced their experiences with GenAI and perceptions of it. Consistent with other research in GenAI education, we found that while the majority of participants were somewhat familiar with GenAI, reported use varied considerably. We found that educators harbored mostly hopeful and positive views about the potential of GenAI. We also found that those who engaged more with their students on the topic of GenAI, both as communi-cators (those who spoke directly with their students) and as incorporators (those who included it in their syllabus), tend to be more positive about its contribution to learning, while also being more attuned to its potential abuses. These findings suggest that integrating and engaging with generative AI is essential to foster productive interactions between instructors and students around this technology. Our work ultimately contributes to the evolving discourse on GenAI use, integration, and avoidance within educational settings. Through exploratory quantitative research, we have identified specific areas for further investigation.

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.

Generative artificial intelligence models like ChatGPT are improving business resilience by improving adaptability, problem-solving, and operational efficiency across sectors. In an era of rapid technological advancement and unexpected disruptions like global pandemics and economic shifts, generative AI is essential for stability and growth. By using adaptive learning, predictive analytics, and customer engagement, ChatGPT and other AI models support resilience strategies. These AI tools help businesses anticipate issues, streamline decision-making, and strengthen supply chains by processing real-time data. ChatGPT boosts customer service quality and speed, helping companies provide excellent service even during crises. By identifying vulnerabilities and proposing proactive solutions, generative AI helps businesses mitigate risks before they occur. The chapter discusses AI's role in workforce transformation, hyper-personalized customer interactions, and AI-driven scenario planning. This study examines recent developments to show how generative AI fosters business resilience and how organizations can strategically implement AI solutions to improve their adaptability and future-proof operations.

The incorporation of generative artificial intelligence in governance, political knowledge, electoral, and democratic processes is essential as the world transitions to a digital paradigm. Numerous nations have adopted Generative AI (GenAI), a disruptive technology that compels electoral bodies to advocate for the integration of such tools into governance, electoral, and democratic processes. Nevertheless, these technologies do not ensure effortless integration or efficient usage owing to intricate socio-cultural and human dynamics. Certain African jurisdictions are ill-prepared for the adoption of these technologies due to factors including underdevelopment, insufficient electrical supply, lack of technology literacy, reluctance to change, and the goals of governing parties. This study examines generative artificial intelligence as a disruptive technology for enhancing governance, political knowledge, electoral processes, and democracy. A mixed-method approach was employed, incorporating surveys and in-person interviews. The analysis of data, debates, and interpretation of findings were grounded in postdigital theory and theme analysis employing an abductive reasoning technique, in alignment with the tenets of critical realism. The study demonstrated that GENAI can influence political knowledge, election processes, and enhance efficiency in government and democracy. Moreover, GENAI, including ChatGPT, can either exacerbate or mitigate societal tendencies that contribute to human division, facilitate the dissemination of misinformation, perpetuate echo chambers, and undermine social and political trust, as well as polarise disparate groups or sets of viewpoints or beliefs. AI offers substantial opportunities but also poses many obstacles, including technical constraints, ethical dilemmas, and social ramifications. The swift progression of AI may disrupt labour markets by automating tasks conventionally executed by people, resulting in job displacement. Implementing AI necessitates significant upskilling and proficiency with digital tools; therefore, governments and organisations must adequately train their personnel to reconcile the disparity between AI's capabilities and users' comprehension. Additionally, there is a requisite for governmental oversight, regulation, and monitoring of AI adoption and utilisation across all facets of its implementation.