Search Results (1,159)

Artificial Intelligence (AI) systems typically inherit biases from their training data, leading to discriminatory outcomes that undermine equity and inclusion. This issue is particularly significant when popular Generative AI (GAI) applications are used in educational contexts. To respond to this challenge, the study evaluates the effectiveness of dialogic reflection-based training for educators in identifying and mitigating biases in AI. Furthermore, it considers how these sessions contribute to the advancement of algorithmic justice and inclusive practices. A key component of the proposed training methodology involved equipping educators with the skills to design inclusive prompts—specific instructions or queries aimed at minimizing bias in AI outputs. This approach not only raised awareness of algorithmic inequities but also provided practical strategies for educators to actively contribute to fairer AI systems. A qualitative analysis of the course’s Moodle forum interactions was conducted with 102 university professors and graduate students from diverse regions of the Dominican Republic. Participants engaged in interactive activities, debates, and practical exercises addressing AI bias, algorithmic justice, and ethical implications. Responses were analyzed using Atlas.ti across five categories: participation quality, bias identification strategies, ethical responsibility, social impact, and equity proposals. The training methodology emphasized collaborative learning through real case analyses and the co-construction of knowledge. The study contributes a hypothesis-driven model linking dialogic reflection, bias awareness, and inclusive teaching, offering a replicable framework for ethical AI integration in higher education. Full article

Large Language Models (LLMs) are increasingly used in higher education, but their pedagogical role in fields like cybersecurity remains under-investigated. This research explores integrating LLMs into a university cybersecurity course using a designed pedagogical approach based on active learning, problem-based learning (PBL), and computational thinking (CT). Instead of viewing LLMs as definitive sources of knowledge, the framework sees them as cognitive tools that support reasoning, clarify ideas, and assist technical problem-solving while maintaining human judgment and verification. The study uses a qualitative, practice-based case study over three semesters. It features four activities focusing on understanding concepts, installing and configuring tools, automating procedures, and clarifying terminology, all incorporating LLM use in individual and group work. Data collection involved classroom observations, team reflections, and iterative improvements guided by action research. Results show that LLMs can provide valuable, customized support when students actively engage in refining, validating, and solving problems through iteration. LLMs are especially helpful for clarifying concepts and explaining procedures during moments of doubt or failure. Still, common issues like incomplete instructions, mismatched context, and occasional errors highlight the importance of verifying LLM outputs with trusted sources. Interestingly, these limitations often act as teaching opportunities, encouraging critical thinking crucial in cybersecurity. Ultimately, this study offers empirical evidence of human–AI collaboration in education, demonstrating how LLMs can enrich active learning. Full article

Growing interest in food sustainability education aims to increase awareness of food distribution systems, environmental degradation, and the connectivity of sustainable and ethical food practices. However, recent scholarship has questioned whether such pedagogical efforts are meaningfully internalized by students or lead to sustained behavioral change. Prior studies document persistent gaps in students’ understanding of sustainability impacts and the limited effectiveness of existing instructional approaches in promoting transformative engagement. To address these concerns, the Food Ethics, Sustainability and Alternatives (FESA) course was implemented with 21 undergraduate and graduate students at Montclair State University (Montclair, NJ, USA). Course outcomes were evaluated using a mixed-methods design integrating qualitative analysis with quantitative measures informed by the Theory of Planned Behavior, to identify influences on students’ attitudes, and a Transtheoretical Model (TTM) panel survey to address progression from awareness to action, administered pre- and post-semester. Qualitative findings revealed five central themes: increased self-awareness of food system contexts, heightened attention to animal ethics, the importance of structured classroom dialogue, greater recognition of food waste, and increased openness to alternative food sources. TTM results indicated significant reductions in contemplation and preparation stages, suggesting greater readiness for change, though no significant gains were observed in action or maintenance scores. Overall, the findings suggest that while food sustainability education can positively shape student attitudes, the conversion of attitudinal shifts into sustained behavioral change remains limited by external constraints, including time pressures, economic factors, culturally embedded dietary practices, structural tensions within contemporary food systems, and perceptions of limited individual efficacy. Full article

Higher education continues to encounter the challenge of redesigning writing pedagogy beyond the rapid adoption of emerging technologies. This challenge is particularly evident in English writing courses, which play a role in developing students’ writing and research skills in universities across the United Arab Emirates (UAE). While generative artificial intelligence (GenAI) tools offer practical affordances for writing instruction, their growing use has also raised concerns about academic integrity, authenticity, and critical engagement. Although early discourse has focused on the risks and potential of GenAI, there remains a clear dearth of frameworks to guide instructors in designing meaningful and engaging writing assignments. This paper introduces Ai-RACE, an adaptable pedagogical framework for designing purposeful and innovative writing assignments. Grounded in classroom-based insights, principles of writing pedagogy, constructivist and multimodal learning theories, Ai-RACE conceptualises assignment design around five interconnected components: AI integration, Relevance, Authenticity, the 4Cs, and Engagement. Employing a design-focused qualitative approach, the study uses classroom implementation and student reflections to examine the implementation of Ai-RACE in writing contexts. Although situated within a specific institutional context, the study offers transferable guidelines for designing writing assignments across international higher education settings. By positioning Ai-RACE as a design heuristic, the study highlights its significance in supporting engagement, critical thinking, writing skills and ethical use of AI, and highlights the importance of rethinking writing pedagogy and the role of professional development in AI- influenced contexts. Full article

Background/Objective: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have received widespread attention as effective obesity treatments. However, limited research has examined the perspectives of patients contemplating GLP-1RAs. This study explored perceptions, motivations, and barriers among individuals considering GLP-1RA therapy for obesity treatment, with the goal of informing patient-centered care and enhancing clinician engagement. Methods: Adults completed surveys and interviews between June and November 2025. In this pragmatic mixed-methods study, both survey and interview questions explored perceived benefits, barriers, and decision-making processes. Qualitative data, describing themes based on the Health Belief Model, were analyzed using Dedoose (version 9.0.107), and quantitative data were analyzed using SAS (version 9.4). Participant characteristics included marital status, income, educational attainment, employment status, insurance status, age, race/ethnicity, and sex. Anticipated length on GLP-1RA medication and selected self-reported health conditions (depression, anxiety, hypertension, heart disease, back pain, joint pain), reported physical activity level, and perceived weight loss competency were also recorded. Results: Among the 31 non-diabetic participants who were considering GLP-1RA medication for weight loss, cost emerged as the most significant barrier. Life course events, particularly (peri)menopause among women over 44, were commonly cited as contributors to weight gain. Participants expressed uncertainty about eligibility, long-term safety, and treatment expectations. Communication gaps were evident, as few participants initiated discussions and clinician outreach was rare, reflecting limited awareness and discomfort around the topic. Conclusions: Findings highlight that individuals considering GLP-1RA therapy face multifaceted emotional, financial, and informational barriers. Proactive, empathetic clinician engagement, through validation of prior efforts, clear communication of risks and benefits, and correction of misconceptions, can support informed decision-making and align treatment with patient goals. Full article

Medical students experience fluctuations in their motivation, influenced by various factors, including curricular rigor, mental health, and institutional factors. Based on Self-Determination Theory (SDT) and the Four Pillars of Academic Engagement (HPEE), this study, conducted at a private Mexican university, examined motivational variation according to academic year, curricular impact, gender differences, and its relationship with mental health. Methods: A quantitative, cross-sectional, descriptive study was conducted using qualitative tools for contextualization (n = 1326). Mann–Whitney U tests, Kruskal–Wallis tests, logistic regression, and psychological network analysis were performed. Results: Motivation showed cross-sectional variation: high in preclinical years 1 and 2, decreasing in clinical years 3 and 4 (p < 0.001), and rebounding in year 6. The reformed curriculum (elective subjects, student-centered active learning) resulted in greater motivation (OR = 10.68, p < 0.001). Women tended to have slightly higher motivation (p = 0.050), higher grade point averages (p < 0.001), but also greater stress (p < 0.001). Network analysis revealed that intrinsic achievement (centrality = 1.11) and curiosity about knowledge (predictability = 84.5%) are the main drivers, while demotivation was linked to the later years. The qualitative part of the study showed altruism/curiosity as the main motivators; mistreatment/workload (demotivators). Conclusions: Motivation is context-sensitive, peaks in the preclinical stage, and recovers with autonomy but is vulnerable during clinical immersion. Autonomy in course selection, active student-centered pedagogies, and gender-sensitive support foster sustained participation. The centrality of intrinsic factors in the network highlights that achievement motivation and knowledge are general and independent motivators. Qualitative data reveal systemic barriers. Stage-specific interventions, such as mentoring, student support programs, and reporting mistreatment, can be crucial for strengthening resilience and performance. Longitudinal and multi-institutional studies are needed to validate the causality and generalizability of this study. Full article

Intelligent recommender systems are essential for identifying at-risk students and personalizing learning through tailored resources. Accurate prediction of student performance enables these systems to deliver timely interventions and data-driven support. This paper presents the application of machine learning models to predict final exam grades in a university-level programming course, leveraging multi-modal student data to improve prediction accuracy. In particular, a recent raw dataset of students enrolled in a programming course across 36 class sections from the Fall 2024 and Winter 2025 terms was initially processed. The data was collected up to one month before the final exam. From this data, a comprehensive set of features was engineered, including the student’s background, assessment grades and completion times, digital learning interactions, and engagement metrics. Building on this feature set, six machine learning prediction models were initially developed using data from the Fall 2024 term. Both training and testing were conducted on this dataset using cross-validation combined with hyperparameter tuning. The XGBoost model demonstrated strong performance, achieving an accuracy exceeding 91%. To assess the generalizability of the considered models, all models were retrained on the complete Fall 2024 dataset. They were then evaluated on an independent dataset from Winter 2025, with XGBoost achieving the highest accuracy, exceeding 84%. Feature importance analysis has revealed that the midterm grade and the average completion duration of lab assessments are the most influential predictors. This data-driven approach empowers instructors to proactively identify and support at-risk students, enabling adaptive learning environments that deliver personalized learning and timely interventions. Full article

Industry 5.0 challenges higher education to adopt human-centred and sustainable uses of artificial intelligence, yet many current deployments still treat generative AI as a stand-alone tool, neurophysiological sensing as largely laboratory-bound, and governance as an external add-on rather than a design constraint. This article introduces Nested Learning as a neuro-adaptive ecosystem design in which generative-AI agents, IoT infrastructures and multimodal deep learning orchestrate instructional support while preserving student agency and a “pedagogy of hope”. We report an exploratory two-phase mixed-methods study as an initial empirical illustration. First, a neuro-experimental calibration with 18 undergraduate students used mobile EEG while they interacted with ChatGPT in problem-solving tasks structured as challenge–support–reflection micro-cycles. Second, a field implementation at a university in Madrid involved 380 participants (300 students and 80 lecturers), embedding the Nested Learning ecosystem into regular courses. Data sources included EEG (P300) signals, interaction logs, self-report measures of engagement, self-regulated learning and cognitive safety (with strong internal consistency; $\alpha /\omega \ge 0.82$), and open-ended responses capturing emotional experience and ethical concerns. In Phase 1, P300 dynamics aligned with key instructional micro-events, providing feasibility evidence that low-cost neuro-adaptive pipelines can be sensitive to pedagogical flow in ecologically relevant tasks. In Phase 2, participants reported high levels of perceived nested support and cognitive safety, and observed associations between perceived Nested Learning, perceived neuro-adaptive adjustments, engagement and self-regulation were moderate to strong ($r=0.41$–$0.63$, $p<0.001$). Qualitative data converged on themes of clarity, adaptive support and non-punitive error culture, alongside recurring concerns about privacy and cognitive sovereignty. We argue that, under robust ethical, data-protection and sustainability-by-design constraints, Nested Learning can strengthen academic resilience, learner autonomy and human-centred uses of AI in higher education. Full article

Cybersecurity education requires practical activities such as malware analysis, phishing detection, and Capture the Flag (CTF) challenges. These exercises enable students to actively apply theoretical concepts in realistic scenarios, fostering experiential learning. This article introduces an innovative pedagogical approach relying on gamification in cybersecurity courses, combining technical problem-solving with human factors such as social engineering and risk-taking behavior. By integrating interactive challenges into the courses, engagement and motivation have been enhanced, while addressing both technological and managerial dimensions of cybersecurity. Observations from course implementation indicate that students demonstrate higher involvement when participating in supervised offensive security tasks and social engineering simulations within controlled environments. These findings highlight the potential of gamified strategies to strengthen cybersecurity competencies and promote ethical awareness, paving the way for future research on long-term cybersecurity learning outcomes. Full article

Universities face a dual challenge: supporting student mental health while equipping staff to respond effectively. To address this, we co-designed and embedded the “You Matter, Prioritize Your Wellbeing” intervention within the university curriculum using a participatory action research framework. The program was developed through co-design workshops and a student needs survey, piloted across six undergraduate courses, and refined into a scalable Facilitator’s Toolkit. Data were collected from co-design workshop participants (n = 23 staff, n = 7 students), student survey respondents (n = 109), academic facilitators’ interview (n = 5), and student post-pilot feedback (n = 61). Purposive sampling was used for co-design workshops, and convenience sampling for both surveys. A mixed-methods approach was employed: qualitative data were analysed using reflexive thematic analysis, and quantitative data using descriptive statistics. Evaluation showed strong student engagement, with 82% planning proactive self-care. Academic facilitators reported enhanced confidence and competence in facilitating wellbeing conversations, valuing the structured approach for normalizing the topic while maintaining professional boundaries. Synchronous delivery and authentic facilitator sharing were perceived as especially impactful. Despite systemic barriers, all facilitators expressed commitment to continued use. This study presents a practical, scalable model for a whole-of-university approach to wellbeing, moving beyond siloed support services to foster a proactive culture of care in higher education. Full article

This mixed-methods research study focuses on the efficacy of virtual exchange (VE) in promoting authentic cross-cultural immersion, critical awareness of social issues, and collective engagement in local and global communities among undergraduate students. The partner institutions in this VE project were a large public US university and a small private university in Hong Kong. Discussions focused on access and opportunity issues in the US and Hong Kong for various communities, leading to a deeper analysis of the distribution of power and privilege in both countries. I analyzed the impact of VE on the US students (n = 45) through pre- and post-test surveys using the Intercultural Sensitivity Scale (ISS), which measures cross-cultural competence and thematic analysis of student artifacts. VE students’ competence significantly increased from pre-test to post-test on the ISS, while the students in a similar course without VE (n = 28) showed no change. Analysis of student artifacts revealed a shift in global awareness, an appreciation of authentic insights about another the culture, a critical understanding of social structures, and a need for collaboration concerning global issues among youth. Overall, VE offered powerful and enriching experiences for students by integrating international immersion into college education courses. Full article

Previous studies on innovative courses have found that information on the elements of sustainable tourism is still lacking. To fill this research gap, this study examined the concept of sustainable tourism by focusing on two innovative courses titled “Seminar on Sustainable Tourism” and “Management and administration of Ecotourism”. Thirteen graduate students taking one of these courses were recruited as respondents for this study. The Zaltman metaphor elicitation technique (ZMET) was used to analyze photographs taken by the respondents and illustrate how graduate school students perceive sustainable tourism. The ZMET surveys were conducted in the 3rd week and 9th week and between the 17th and 18th weeks to represent the first and second semester, respectively. The results of the analysis illustrated that sustainable tourism involves constructs related to the natural environment and caring for wildlife, environmental conservation, sociocultural sustainability, natural and cultural experiences, perspectives on the environment, and government and policy. Four consensus maps were developed regarding environmental concerns, responsible behaviors, learning experiences, and reflections on sustainable tourism. Finally, the author concluded that when graduate students develop ecotourism itineraries, increase their environmental awareness, gain relevant learning experience, and exhibit reflective engagement, they experience positive feelings that benefit their environmental awareness, environmental attitude, sensory emotions, and reflective engagement, ultimately leading to pro-environmental or ecotourism behaviors that may subsequently boost sustainable tourism practices. Full article

This paper reports a design-rationale account of building and deploying a small ecosystem of AI-driven educational conversational agents with distinct pedagogical personas. Two strands target school contexts: (i) Talk to Bill, a historically grounded Shakespeare interlocutor intended to support close reading, contextual understanding, and interpretive dialogue; and (ii) Here to Help, a set of UK GCSE subject- and exam-board-specific tutors designed for formative practice in recognised question formats with feedback and iterative improvement. The third strand comprises six complementary assistants for an undergraduate Human–Computer Interaction (HCI) module, each bounded to a workflow-aligned role (e.g., empathise-stage coaching, study planning, course operations), with guardrails to privilege process quality over answer generation. We describe how persona differentiation is mapped to established learning, engagement, and motivation theories; how retrieval-augmented generation and provenance cues are used to reduce hallucination risk; and what early deployment observations suggest about orchestration, integration, and incentives. The contribution is a transferable, auditable rationale linking theory to concrete dialogue and UI moves for multi-persona tutoring ecosystems, rather than a claim of causal learning gains. Full article

Rapid adoption of digital surveying technologies in construction has highlighted the need for engineering education to equip students with technological competency as well as higher-order problem-solving skills. This experiment explores undergraduate students’ acceptance of emerging surveying technologies and their perceived learning results within a constructivist framework of experiential learning. Thirty-six students in a required construction surveying class interacted with traditional and advanced technologies such as total stations, terrestrial laser scanning, drones, and mobile LiDAR through structured, semi-structured, and unstructured lab activities. Data were gathered based on two post-course surveys: a technology acceptance survey grounded in Unified Theory of Acceptance and Use of Technology (UTAUT) and a self-perceived cognitive learning outcome survey through Bloom’s Taxonomy. Qualitative analysis along with quantitative analysis indicated a gap between technology acceptance and perceived learning gains. Laser scanner had the greatest acceptance scores followed by other advanced tools. Total station (widespread in hands-on lab activities) was perceived to have been most influential in terms of enhancing learning. Lower-order skills were strengthened in structured labs, while higher-order thinking emerged more unevenly in open-ended labs. These findings underscore that the mode of student engagement with technology matters more for learning than the sophistication of the tools themselves. By embedding UTAUT and Bloom’s Taxonomy in an authentic learning environment, this experiment provides engineering educators a mechanism to assess technology-enhanced learning and identifies strategies to facilitate higher-order skills aligned with industry needs. Full article

Sustainable language education necessitates scalable, accessible learning environments that foster long-term learner autonomy and reduce educational inequality. While online courses have democratized access to language learning globally, persistent deficiencies in instructor-student interaction and learner engagement compromise their sustainability. The “face effect,” denoting the influence of instructor facial appearance on learning outcomes, remains underexplored as a resource-efficient mechanism for enhancing engagement in digital environments. Furthermore, effective measures linking psychological engagement to sustained learning experiences are notably absent. This study addresses three research questions within a sustainable education framework: (1) How does instructor identity, particularly facial appearance, affect second language learners’ outcomes and interactivity in scalable online environments? (2) How can digital human technology dynamically personalize instructor appearance to support diverse learner populations in resource-efficient ways? (3) How does instructor identity influence learners’ flow state, a critical indicator of intrinsic motivation and self-directed learning capacity? Two controlled experiments with Japanese language learners examined three instructor identity conditions: real teacher identity, learner self-identity, and idol-inspired identity. Results demonstrated that the self-identity condition significantly enhanced oral performance and flow state dimensions, particularly concentration and weakened self-awareness. These findings indicate that identity-adaptive digital human instructors cultivate intrinsic motivation and learner autonomy, which are essential competencies for lifelong learning. This research advances Sustainable Development Goal 4 (Quality Education) by demonstrating that adaptive educational technology can simultaneously improve learning outcomes and psychological engagement in scalable, cost-effective online environments. The personalization capabilities of digital human instructors provide a sustainable pathway to reduce educational disparities while maintaining high-quality, engaging instruction accessible to diverse global populations. Full article