Exploring How to Improve Programming Learning Experience with Generative AI Tools: An Integrated Means-End Chain and fsQCA Approach

Reflection is integral to the experiential learning process (Kolb, 1984). Experiential learning supports reflection and transfer of learning, providing foundational skills that contribute to lifelong learning (AAC&U, 2009). Preflection, experience, reflection, and transfer (PERT) operationalize the experiential learning process in teaching (Retallick, 2010). 4-H popularized Do, Reflect, Apply as a reflection cycle for delivering the Experiential Learning Model to learners (Diem, 2001). Together, PERT and the 4-H Experiential Learning Model align to implement Experiential Learning Theory in formal education. Science with Practice (SWP) exemplifies these principles as an experiential learning program and academic course at Iowa State University (Retallick et al., 2009). PERT was used in SWP curriculum design with Do, Reflect, Apply prompts providing structure for student reflection. Students in SWP generate a learning agreement detailing a description of the planned experience and their expectations (preflection). They construct a portfolio including journal entries in the form of biweekly reflections (formative reflection), and a final reflection (summative reflection). The purpose of this study was to examine reflective practice in SWP by determining the breadth and depth of reflection and describing the use of Do, Reflect, Apply to structure reflection in portfolios. Findings document development of reflective practice with an emphasis on the importance of examining types of reflection and the intentional guidance of reflection. In reflective writing, learners effectively analyze and assess their experience, increase their awareness, transfer their learning, and are transformed by their experience. Executing Do, Reflect, Apply as a structure for facilitating reflective practice is an effective strategy for completing the PERT experiential learning process and developing lifelong learning skills.

Scholars investigating customer values and behavior in the field of tourism have used various measurement procedures. This study reviews the application of means-end chain (MEC) theory and its accompanying laddering technique in tourism and hospitality research. MEC theory provides a theoretical framework for scholars to understand consumer behavior. By reviewing 39 publications, this study aimed to comb previous reviews on MEC theory in the tourism industry via a more systematic and integrated review of this literature. We performed bibliometric analysis, which allowed us to establish study clusters, identify key research objects, distinguish interrelations, and excavate collaboration patterns. Content analysis was used to transform crucial results systematically and identify the importance of MEC theory in tourism research, the comprehensive application of multiple models and laddering techniques, and alternative approaches to grouping hierarchical value maps. The conclusion shows that tourism motivation is the most researched theme, and China and Chinese tourists are the most researched region and subjects. This study contributes theoretically and methodologically to the literature related to MEC theory. Theoretically, it bridges the gap of MEC theory used in tourism research and provides theoretical reference for the tourism industry. Methodologically, it also can lead respondents to think about their inner values in response to research questions. Moreover, it can be used alongside other research methods to contribute to more comprehensive and valuable knowledge about tourism.

The year 2030 marks a significant juncture in the evolution of education, where Generative Artificial Intelligence (AI) tools are poised to revolutionize the learning experience. In education society, the importance of generative AI is to improve the accessibility of learning at the global level so that personalized learning experiences can be provided to every learner as per their needs. This chapter explores the multifaceted role of generative AI tools in reshaping educational practices, envisioning a future where these tools foster personalized, adaptive, and engaging learning environments. Generative AI tools, characterized by their ability to create and adapt content autonomously, are instrumental in tailoring educational materials to individual learner needs. This chapter surveys the landscape of generative AI applications in education, including content generation, interactive simulations, intelligent tutoring systems, and dynamic learning pathways. These tools aim to provide adaptive, context-aware learning experiences that cater to diverse learning styles and preferences. The adaptability of generative AI tools extends to the creation of personalized learning pathways. By leveraging data analytics and machine learning algorithms, these tools dynamically adjust content delivery, pacing, and complexity, ensuring that each learner's educational journey is optimized for their unique requirements. The discussion encompasses the potential of generative AI tools to support both formal and informal learning settings. Generative AI tools also play a crucial role in promoting inclusivity in education. By generating diverse and culturally relevant content, these tools contribute to breaking down barriers and addressing disparities in access to quality education. This chapter explores how generative AI can be leveraged to create content that resonates with learners from different backgrounds, fostering a more inclusive educational landscape.

Purpose The purpose of the research is to investigate customer shopping behaviour for Italian market, extra virgin olive oil (EVOO) using the quick response (QR) code. The study uses means-end chain (MEC) theory to explore the connections between product attributes, personal values and consumer preferences. Design/methodology/approach The theory of means-end chain helps to uncover the underlying reasons why consumers choose specific products by examining the links between tangibles. There is a conceptual model developed with the help of MEC. The conceptual framework comprises product information and marketing communication, which constitute the sensory and non-sensory aspects of consumption behaviour, and it is empirically tested by CB-SEM technique. QR code usefulness and loyalty are treated as mediators to evaluate shopping behaviour to purchase EVOO. Findings The study has used MEC theory and has extended this theory. This study found that there is a profound impact of technology (QR code) on the buying behaviour of EVOO for the Italian customers. This study has also found that product information and product communication play a critical role towards shopping behaviour of Italian consumers regarding EVOO related products. Research limitations/implications Innovation and technology adoption, especially the use of QR codes in understanding product features, has been an attractive area for research exploration. This could include details about the product’s origin, production methods and nutritional information. Such technology and innovation can change the business process management landscape of food-product distribution process. Not many studies are available which examine customers’ interest and shopping behaviour using QR codes, especially in the context of EVOO. Thus, this research study has a profound impact on the Italian and other European market. Originality/value In the Italian market, EVOO is more than just a product, it is a symbol of tradition, quality and cultural heritage. EVOO is not just a condiment, it is an essential ingredient that contributes to the overall flavour of dishes. This study explores the usage of QR code for EVOO related products. This is a unique study as there are a very few research studies available in this area of business process management and technology usage related to EVOO products. Moreover, this research study adds values to the body of literature of business process management, technology usage, innovation and food technology. Thus, this study is unique and adds value to the existing literature.

Means–end chain theory proposes that knowledge held in consumer's memory is organized in a hierarchy with concrete thoughts linked to more abstract thoughts in a sequence progressing from means (i.e., product features), to psychological and social consequences, and finally to ends (i.e., fulfillment of personal values). This article proposes several advances in the theory. First, specific buying and consumption situations serve as frames of reference when consumers are thinking about products and alternative features of products and brands. Second, states of psychological imbalance may occur in consumers' minds among linkages retrieved automatically for features–consequences and consequences–values; thus, Heider's balance theory informs means–end chain theory and research. The theoretical and practical usefulness of means–end research increases from asking consumers to name an acceptable alternative to the product and brand used in a recent consumption situation, as well as an unacceptable option, and to describe the features–consequences–values of these options; consequently, Fournier's alternative relationships of consumer–brands (e.g., casual friendships, marriages, enmities) become relevant for means–end chain theory. To examine the propositions empirically, the article describes psychological schemas for four means–end chains that link two consumers' recent lived consumption situations to personal values. © 2004 Wiley Periodicals, Inc.

Purpose Although experiential learning (EL) is well studied, limited research examines how skill acquisition interacts with psychological mechanisms – such as self-esteem – to shape career attitudes, particularly in large-scale, government-backed programs in the Global South. This study investigates how EL participation develops soft and hard skills and how these skills influence career attitudes through self-esteem. Design/methodology/approach Guided by the experiential learning and attribution theories, this quantitative cross-sectional study analyzed survey data from 18,505 Indonesian university students participating in the Magang dan Studi Independen Bersertifikat (MSIB) program, a nationwide certified internship and independent study initiative. Data were analyzed using partial least squares structural equation modeling (PLS-SEM). Findings EL participation significantly enhanced both soft and hard skills. These skills positively influenced students’ career attitudes, with self-esteem partially mediating this relationship. Research limitations/implications Incorporating self-esteem into EL models offers more profound insight into the psychological processes that translate skills into career-oriented mindsets in underrepresented contexts. Practical implications Embedding project-based learning, sustained industry engagement and inclusive access to EL programs can strengthen graduates’ technical and interpersonal skills, boost self-confidence and enhance competitiveness in dynamic labor markets. Originality/value This study proposes a novel integrated framework linking skill acquisition and self-esteem to predict career attitudes – an approach largely absent in prior EL research. While most studies focus on the Global North, this work provides rare, large-scale empirical evidence from the Global South, where institutionalized EL opportunities remain limited.

This paper seeks to establish the impact of university experiential learning programs on small- to medium-sized enterprises while emphasizing the benefit to rural workforce development and sustainable manufacturing practices. Data were collected from diverse partners of Louisiana State University’s experiential learning program over the last 7 years to illustrate the types of recommendations and implementation statistics for sustainable manufacturing practices. The study found that rural enterprises favored the adoption of short-term, high-saving solutions to mitigate the impact of utility costs resulting from geographical isolation, while there was low implementation of long-term, large investment projects. This highlighted the practical feasibility of a project over a focus on long-term sustainability plans, which require significant capital investment, management planning, and employee training. This study outlines a university-led experiential learning program’s engagement through academic–industrial partnerships that serve student development and the economic advancement of small- to medium-sized enterprises. The data can direct future incentive opportunities for sustainability projects that have more immediate payback, to increase the adoption rate in rural facilities. The larger implication provides a framework and validation that can support the development of similar programs for extension and enterprise engagement to impact sustainable manufacturing practices.

The final case study outlined in this chapter examines university learners’ experiences in a mandatory 6-week community-based experiential learning (CBEL) programme aimed at nurturing the adaptive expertise of student educators at our university. Through semi-structured interviews with high performers in the programme, we aim to shine a light on how student teachers are being prepared as future educators during such a learning process. We use this case study to argue that professional knowledge is significant, but not necessarily sufficient for good teaching. Teachers need to position themselves in a wider context through a process of globalization and be able to personalize their classroom from their students’ perspective; in short, adaptive teaching. This also attends to the development of teachers’ knowledge for teaching rather than just focusing on their knowledge of teaching. This case study also explains how and why some student teachers perform better than others in CBEL programmes and stresses the importance of student feedback in the development of such learning opportunities.

In the dynamic field of programming education, integrating artificial intelligence (AI) tools has started to play a significant role in enhancing learning experiences. This paper presents a case study conducted during a foundational programming course for first-year students in higher education, where students were encouraged to utilize generative artificial intelligence programming copilot extensions in their programming IDE and browser-based generative AI tools as supportive AI tools. The primary objective was to observe the impact of AI on the learning curve and the overall educational experience.Key findings suggest that the introduction of AI tools significantly altered the learning experience for students. Many who initially struggled with grasping elementary programming concepts found that AI support made understanding basic programming concepts much easier, enhancing their confidence and skills. This was particularly evident in the reduced levels of anxiety typically associated with early programming learning, as the AI copilot provided a non-judgmental, always-available source for clarifying doubts, including queries that students might hesitate to ask in a traditional classroom setting.Notably, some students leveraged the AI to generate similar exercise problems, reinforcing their understanding and skills. The AI's capability to address basic queries also freed up the instructor's time, allowing for more personalized student guidance in more advanced problems. This shift in the instructional dynamic further contributed to a learning environment where students felt more comfortable engaging with complex topics, thereby reducing the psychological barriers often linked with early-stage programming education.The course's structure, enriched by AI, enabled students to delve into more complex programming constructs earlier than traditional curricula would allow. For instance, students were tasked with simulating basic e-commerce operations, such as user registration, product browsing, and cart functionalities. These practical challenges naturally introduced advanced concepts like external data storage, unit testing, and user interface design, which are typically reserved for more advanced courses. With the help of generative AI programming copilot tools, students at any programming skill level were able to develop nearly functional complex structures. Interestingly, even when their projects were not fully functional, students remained motivated. Instead of feeling discouraged by these imperfect outcomes, they showed resilience and a keen interest in understanding and improving their code. This reaction is a significant shift from traditional learning settings, where unfinished or flawed projects often lead to increased anxiety or a drop in motivation.Furthermore, the AI's proactive suggestions inspired students to explore beyond the curriculum. Advanced learners delved into databases, cryptography libraries in Python, and even more advanced user interface design, ensuring that they remained engaged and challenged. This elementary course, enhanced by generative AI tools, also inspired students to learn other programming languages since they now learned that individual learning is more available with the aid of generative AI.In conclusion, the integration of AI in programming education offers a promising avenue for enhancing both the learning experience and outcomes. This case study underscores the potential of AI to revolutionize traditional teaching methodologies, fostering a more dynamic, responsive, and inclusive learning environment.This paper handles the results, possibilities and challenges of AI empowered education in programming. It also gives practical examples as well as future research perspectives.

The rapid development of generative artificial intelligence (GenAI) has brought opportunities and new challenges to higher education. Students need a high level of self‐regulated learning to adapt to this change. However, it is difficult for students to persist in self‐regulation without guidance. Metacognitive support has a significant advantage in enhancing self‐regulated learning, but fewer studies have explored the effects of its role in GenAI environments. The purpose of this study was to investigate the impacts of metacognitive support on college students' self‐regulated learning and learning experiences in a GenAI environment. A quasi‐experiment was designed in which 68 college students were divided into two groups. The experimental group ( N = 35) received explicit metacognitive support, while the control group ( N = 33) did not receive any metacognitive prompts. The experiment lasted 4 weeks. The study measured students' academic performance, self‐regulated learning ability and learning experiences (including cognitive load and technology acceptance). The results indicate that in the GenAI environment, metacognitive support, while not producing significant between‐group differences in achievement, enhances students' self‐regulated learning abilities particularly in terms of task strategy and self‐evaluation, as well as optimizing their learning experience. The study also found that students were at risk of decreasing their level of self‐regulated learning if they lacked metacognitive support in the GenAI environment. The conclusion points out that GenAI supports learners to accomplish learning tasks while potentially reducing self‐regulated learning effectiveness, and that metacognitive support is key to supporting effective regulation in learners' GenAI environments. This study provides an important theoretical and practical basis for how to better support learners' learning in GenAI environments. Practitioner notes What is already known about this topic SRL is vital for effective learning in digital environments. Generative AI tools, like ChatGPT, can enhance learning but require support. Learners often struggle to apply SRL strategies without guidance. What this paper adds Metacognitive support improves SRL in Generative AI environments. It reduces cognitive load and increases the perceived usefulness of AI tools. Structured support leads to better academic outcomes. Implications for practice and/or policy Teachers should integrate metacognitive support when using AI tools. Teacher training should focus on SRL strategies in tech‐rich settings. Policies should promote ethical and effective AI use in education.

Will the advancement of GAI diminish international students' reliance on Chinese language teachers? Evidence from SEM and FsQCA.

With the advancement of artificial intelligence (AI) and the Internet of Things (IoT), smart clothing, which has enormous growth potential, has developed to suit consumers' individualized demands in various areas. This paper aims to construct a model that integrates that technology acceptance model (TAM) and functionality-expressiveness-aesthetics (FEA) model to explore the key factors influencing consumers' smart clothing purchase intentions (PIs). Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the data, complemented by fuzzy-set qualitative comparative analysis (fsQCA). The PLS-SEM results identified that the characteristics of functionality (FUN), expressiveness (EXP), and aesthetics (AES) positively and significantly affect perceived ease of use (PEOU), and only EXP affects perceived usefulness (PU). PU and PEOU positively impact consumers' attitudes (ATTs). Subsequently, PU and consumers' ATTs positively influence PIs. fsQCA revealed the nonlinear and complex interaction effects of the factors influencing consumers' smart clothing purchase behaviors and uncovered five necessary and six sufficient conditions for consumers' PIs. This paper furthers theoretical understanding by integrating the FEA model into the TAM. Additionally, on a practical level, it provides significant insights into consumers' intentions to purchase smart clothing. These findings serve as valuable tools for corporations and designers in strategizing the design and promotion of smart clothing. The results validate theoretical conceptions about smart clothing PIs and provide useful insights and marketing suggestions for smart clothing implementation and development. Moreover, this study is the first to explain smart clothing PIs using symmetric (PLS-SEM) and asymmetric (fsQCA) methods.

Musculoskeletal disorders are a major occupational health issue for dental professionals. Traditional ergonomics training is ineffective for achieving mastery of its principles. This study evaluated the effectiveness of the experiential ergonomics course incorporating dental operating microscopes, using Kirkpatrick’s levels 1 and 2. This study conducted a pretest-posttest study with a single group of 103 second-year dental undergraduates from Wuhan University, who completed an experiential ergonomic learning program. The course was designed based on Kolb’s cycle, using dental operating microscope as a key tool. Participants completed researcher-developed pre- and post-course questionnaires featuring 5-point Likert scales, multiple-choice, and binary-response items to assess changes in knowledge, attitudes, and satisfaction. Practical skills were evaluated using the Dental Ergonomics Assessment (DEA) rubric. Data were analyzed using paired-sample t-tests, one-sample t-tests, McNemar’s test, and Spearman’s correlation, with a significance level of p < 0.05. Of the 103 enrolled students, 89 completed matched pre- and post-course assessments. The curriculum led to significant improvements in students’ ergonomic knowledge (p < 0.001) and their attitudes toward the importance of ergonomics for pain prevention. Post-course ergonomic performance was favorable (overall DEA score: 1.45), and this objective performance showed a strong negative correlation with the belief in ergonomics’ effectiveness (r = -0.624, p < 0.001). Participant feedback was exceptionally positive, with 100% satisfaction regarding course content. The microscope-based experiential learning course improved dental students’ ergonomic knowledge, attitudes, and postural performance. The strong correlation between competency and conviction supports the view that this approach effectively synchronizes theory with practice.

The rapid adoption of Generative Artificial Intelligence (GAI) in higher education has introduced new ethical challenges, particularly concerning students' academic misconduct. While prior research has linked personality traits to unethical behavior, little is known about how different combinations of personality traits shape students' misuse of GAI. This study integrates the HEXACO model and the Dark Triad framework to examine the configurational effects of personality on GAI-related academic misconduct. A total of 864 university students completed questionnaires. Using fuzzy-set Qualitative Comparative Analysis, we identified multiple configurations leading to both high and low levels of GAI misconduct. No single trait is sufficient to explain GAI-related academic misconduct. Rather, high misconduct consistently emerged from configurations characterized by low Honesty-Humility and Conscientiousness combined with high Machiavellianism or Psychopathy. In contrast, low misconduct was associated with configurations combining high Honesty-Humility, Conscientiousness, and Agreeableness with low levels of Dark Triad traits. This study demonstrates that personality traits interact synergistically rather than independently to shape individuals' ethical or unethical engagement with AI technologies. Moral restraint is sustained by both the presence of virtues and the absence of exploitative tendencies. The findings support the idea that moral integrity and self-regulation constitute foundational safeguards against unethical use of technology. These findings align with self-regulatory theories of academic dishonesty, reinforcing that individuals high in honesty and conscientiousness are less likely to rationalize or justify academic misconduct even when new technological affordances make it easier. The study therefore advances theoretical understanding by integrating personality frameworks within a configurational paradigm and offers practical insights for developing personality-informed ethics education.

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.