Search Results (4,243)

Generative artificial intelligence (GenAI) is reshaping higher education and early-career work, raising questions about how universities can support pedagogically sustainable career readiness. This study examines undergraduate students’ perceptions of AI, automation, entry-level employment, and perceived preparedness for an AI-augmented labor market. Survey data were collected from 153 undergraduate students. The questionnaire examined awareness of AI and automation, perceived risks to traditional entry-level work, anxiety and perceived preparedness regarding post-graduation employment, skill priorities, and desired institutional support. The findings indicate substantial awareness of AI-related change, with many students expecting routine junior tasks such as data entry, basic research, report generation, customer support, and simple coding-related work to be affected. However, confidence in academic preparation was weaker and more uncertain. Students emphasized human-centered capabilities, including critical thinking, creativity, communication, and problem solving, alongside AI literacy and practical exposure to digital tools. The study identifies an awareness-preparedness gap and argues that higher education institutions should strengthen GenAI-era curriculum design, AI-authentic assessment, experiential learning, career guidance, and ethical AI literacy to support perceived preparedness and sustainable career readiness. Full article

Much like virtual reality (VR) some years ago, generative artificial intelligence (GenAI) as a general term has recently seen an explosion in interest as educational technology. Although VR in education has received significant attention over the past decade, its widespread adoption in classrooms has not materialized, and similar patterns may repeat for large-scale use of GenAI in middle and secondary school education. Both technologies share issues related to basic concepts, usability, perceived usefulness, and sustainable mass deployment in educational settings. This means that significant financial, environmental and human resources might be spent with very little overall benefit for most students. We discuss solutions for achieving more efficient and productive outcomes in secondary education and suggest avenues for further meaningful research. Full article

The rapid integration of digital technologies into education has transformed electronic textbooks (e-textbooks) from static replicas of printed books into interactive, multimedia-rich learning environments. This bibliometric literature review synthesizes findings from 89 peer-reviewed studies published between 2000 and 2024, retrieved from IEEE Xplore, ACM Digital Library, ScienceDirect, and SpringerLink. Following the Kitchenham and Charters methodology and complemented by bibliometric analyses, this review is guided by the overarching research question (RQ): How are e-textbooks conceptualized, designed, and deployed to support learning outcomes while promoting accessibility and equity? This guiding question is complemented by three additional RQs: (1) How has the field of e-textbooks developed over time? (2) Which countries are leading research related to interactive textbooks, considering quantitative indicators of publications/country? (3) What evidence exists regarding the contribution of e-textbooks to learning outcomes? The analysis reveals a clear trajectory from early static digitization toward intelligent platforms incorporating adaptive feedback, personalization, and analytics. Well-designed e-textbooks using multimedia learning principles demonstrate significant potential to enhance conceptual understanding, learner engagement, and motivation—especially in K–12 contexts. However, the field remains challenged by definitional inconsistencies, the uneven application of accessibility standards, and the geographic concentration of research output in North America and East Asia. These disparities highlight an urgent need to align digital textbook development with broader global education goals. By foregrounding accessibility, inclusion, and equitable access, e-textbooks can play a transformative role in advancing Sustainable Development Goal 4 (Quality Education) by ensuring inclusive, engaging learning opportunities, and SDG 10 (Reduced Inequalities) by reducing educational disparities across diverse learner populations. This review concludes by calling for interdisciplinary frameworks that integrate cognitive science, human–computer interaction, and artificial intelligence to guide the design of the next generation of interactive, accessible, and pedagogically robust e-textbooks—tools that are not only technologically innovative, but also aligned with global commitments to equitable and sustainable education for all. Full article

Achieving the Sustainable Development Goals (SDGs) requires education that extends beyond formal schooling and fosters inclusion, lifelong learning, and social participation. Informal learning communities remain underexplored in this respect. This study examines how members of the speedcubing community perceive its educational and social potential in relation to SDG 4, 5, 10, and 11. The case was chosen for its open participation, intergenerational interaction, and the absence of age and gender categories in competitions. Anonymous questionnaires concerning 112 speedcubers, recruited in Poland, were completed by the speedcubers, by their parents or guardians, or jointly. Responses were analysed using descriptive statistics, non-parametric tests with effect sizes, and correspondence analysis by experience, gender, and respondent role. The community was rated as easy to enter (71.4% positive on five-point scales) and as accessible through free online resources (94.6%), with strong affirmation of its intergenerational character (93.8%) and culture of mutual respect (89.3%). Perceived equality of opportunity did not differ between female and male speedcubers, in contrast to patterns reported for other mind sports. Respondents also saw potential for engaging older adults socially. In formal education, potential was recognised above all in extracurricular clubs, without significant subgroup differences. These perceptions suggest that inclusive informal learning communities may complement formal education in supporting the SDGs. Full article

The introduction of Generative Artificial Intelligence (GenAI) in higher education can foster pedagogical innovation, thereby contributing to quality education (SDG 4) and advancing university sustainability. However, students must possess the necessary knowledge and use it appropriately. In this context, it is important to consider variables such as academic program, age, and gender when implementing measures to reduce inequalities (SDG 10) and promote gender equality (SDG 5). This research aims to analyze knowledge and use of GenAI among university students in Education, Architecture, and Engineering programs, identifying challenges that hinder innovative practices. A quantitative study was conducted with 410 university students, based on data collected through a questionnaire, which were analyzed according to academic and sociodemographic variables. The students demonstrate moderate levels of knowledge ($\overline{X}$ = 3.55/5) and use ($\overline{X}$ = 3.29/5) of GenAI, with both constructs positively correlated (r = 0.46). However, knowledge scores are significantly higher (p < 0.001). Differences are identified in academic and sociodemographic variables. Engineering students and those in the final years of their bachelor’s or master’s degree programs obtain the highest scores. Predominantly, male students show higher values than female students. Lower scores are observed for knowledge among 18–20-year-old students and for use among those aged 44–74. Full article

This study aimed to identify the ecological literacy education needs of pre-service teachers in Türkiye. A qualitative approach was adopted. Data were collected from 23 pre-service teachers, selected through convenience sampling, using an open-ended questionnaire developed by the researchers. The data were analyzed through inductive content analysis. Findings revealed that participants primarily conceptualized ecological literacy in terms of environmental knowledge and environmental protection while placing limited emphasis on systems thinking, human–nature relationships, and action-oriented competencies. Although the participants demonstrated general awareness of the causes of ecological problems, they also exhibited misconceptions regarding fundamental ecological concepts. They predominantly prioritized global environmental issues, with little or no reference to political–economic dimensions of ecological crises or local environmental problems. The participants identified teacher education programs as an important source of ecological literacy development and emphasized the value of nature-based, practice-oriented learning experiences. By empirically documenting these needs within the Turkish context, this study extends the theoretical distinction between environmental and ecological literacy and offers novel insight into an underexplored context. Practically, findings inform teacher education curricula, suggesting that ecological literacy education should move beyond a knowledge-focused approach toward one integrating systems thinking, critical thinking, local contexts, political–economic dimensions, and collective action competencies. Full article

This study explored whether participation in a digitally mediated sustainability literacy intervention was associated with short-term improvements in sustainability-related learning outcomes across two contrasting contexts: Portugal (stability) and Myanmar (polycrisis). Using a Most-Different Systems Design, 152 university students participated in a four-week Digital Sustainability Literacy Online Course, with food systems and responsible consumption providing a practical context for integrating environmental, social, economic, and governance dimensions of sustainability. A mixed-methods approach combined pre- and post-intervention assessments using the Sustainability Consciousness Questionnaire, operationalized through Knowledge, Attitudes, Self-reported Practices, and Self-reported Explanatory Efficacy (KAP + E), alongside engagement measures informed by ARCS and EGameFlow. Quantitative data were analyzed using paired- and independent-samples t-tests and mixed-design ANOVAs, while qualitative responses were examined using deductive thematic analysis. Significant pre–post improvements were observed across all four outcomes in both cohorts. Contextual differences were observed for knowledge and attitudes, while a significant Time × Context interaction for explanatory efficacy indicated greater gains in Portugal. Qualitatively, environmental and social dimensions predominated in both cohorts, while governance was more prominent in Portugal. Overall, participation was associated with short-term gains across multiple sustainability-related outcomes, while explanatory efficacy showed greater contextual sensitivity. Given the absence of a control group and complete confounding of the country with the stability/polycrisis condition, the findings should be interpreted as comparative associations rather than causal effects of the intervention or polycrisis. Full article

As artificial intelligence (AI) becomes increasingly embedded in education and society, cultivating critical AI literacy has become a key objective of sustainable higher education, enabling learners to engage with AI responsibly, critically, and throughout lifelong learning. However, empirical evidence on how innovative pedagogical approaches foster critical AI literacy remains limited. This quasi-experimental study evaluated a critical thinking-oriented flipped instructional model in an AI general education course. Following data screening, the final analytic sample consisted of 82 undergraduate students (53 in the Experimental Group and 29 in the Control Group). Closely aligned pretest and posttest forms assessed critical AI literacy across knowledge, skill, and attitude dimensions. Between-group gain-score comparisons showed that the Experimental Group demonstrated greater improvement in overall critical AI literacy than the Control Group (Cohen’s d = 1.12, 95% CI [0.63, 1.60]). Greater gains were also observed for knowledge (d = 0.54), skill (d = 0.84), and attitude (d = 1.21), with the standardized between-group difference descriptively largest for attitude. These findings suggest that the critical thinking-oriented flipped instructional model is a promising approach for cultivating critical AI literacy in higher education and may have broader implications for sustainable higher education. Full article

The growing use of artificial intelligence (AI)-based language technologies in learning, assessment, writing support, and speech-enabled educational platforms has made linguistic representation an increasingly important dimension of sustainable and inclusive education. This exploratory mixed-methods study examines how transcribed forms of Turkish dialects are processed within task-appropriate natural language processing (NLP) environments and how their digital representation is interpreted by linguists and dialect speakers. The study combines a spoken-language corpus compiled from 100 speakers in 14 provinces across Türkiye’s seven geographical regions with spaCy- and Stanza-based morphosyntactic outputs, an exploratory BERTurk-based semantic-similarity component, and semi-structured interviews. The corpus contains approximately 60 h of recordings, 120 pages of transcripts, and 3500 annotated structures. Descriptive task-specific observations indicate recurrent processing difficulties involving compound tense forms, non-canonical word order, dialect lexicon, discourse particles, and pragmatically marked expressions. For the POS configurations examined, descriptive POS agreement was higher on standard written Turkish (SWT) reference material (89–91%) than on transcribed dialect data (60–65%). The qualitative material indicates that non-recognition may be interpreted as cultural invisibility, reduced trust in digital tools, and possible pedagogical misalignment. These educational and social dimensions are presented as potential implications rather than directly tested outcomes. The study conceptualizes digital linguistic sustainability through three interrelated dimensions: technical robustness in processing Turkish dialects, the continuity of culturally embedded linguistic knowledge, and educational inclusion. Full article

The Sustainable Development Goals (SDGs) aim to create a sustainable future by considering environmental, social, and economic elements from a holistic perspective. Instilling these goals in individuals through education is critically important for increasing awareness of sustainable development and translating this awareness into behavior. This study aims to examine how non-governmental organizations (NGOs) contribute to the implementation of the Sustainable Development Goals (SDGs) through educational projects conducted in collaboration with schools. While the projects involved various educational stakeholders, including students, teachers, school administrators, and parents as project beneficiaries, the qualitative data for this study were collected through semi-structured interviews with representatives of 17 NGOs responsible for planning and implementing these projects. The interview data were analyzed using inductive qualitative content analysis. The findings show that SDG projects significantly contribute to developing environmental awareness, sustainable thinking, and responsible behavior in students. However, the success of the projects varies depending on the level of stakeholder participation in the educational environment, i.e., the active involvement and collaboration of relevant individuals in the process. The research findings indicate that strengthening stakeholder participation and more regular integration of sustainability-themed projects into educational curricula are necessary to promote the dissemination of sustainable development goals through education. Full article

Quality assurance in Initial Teacher Education (ITE) has evolved from compliance-oriented approaches toward more comprehensive frameworks emphasizing continuous improvement, evidence-informed decision-making, and educational sustainability. Nevertheless, across Latin America, persistent fragmentation between accreditation processes, institutional data systems, and teacher education practices continues to limit universities’ capacity to address complex organizational and territorial challenges. This study aimed to develop an empirically informed conceptual framework for evidence-based quality assurance in ITE. A convergent mixed-methods design was employed, integrating a quantitative longitudinal component based on publicly available official secondary data covering 1743 students enrolled in teacher education programs at a regional Chilean university between 2020 and 2025 with a qualitative longitudinal documentary analysis of accreditation, institutional, regulatory, and territorial evidence covering the 2012–2025 period. Both components were analyzed independently and subsequently integrated through convergent triangulation during the interpretative phase. The quantitative findings revealed a heterogeneous student population characterized by predominantly female enrollment, differentiated academic trajectories, territorial concentration with additional geographic diversity, and descriptive variation in retention across teacher education programs. Documentary evidence identified recurrent strengths in institutional purpose, curricular development, and territorial engagement, alongside persistent challenges related to information integration, organizational workload, evidence-informed curricular improvement, and academic capacity. The convergence and complementarity of these findings informed the development of the Evidence-Based Excellence Management Model (EBEMM), which integrates student entry characteristics, institutional processes, observed academic outcomes, quality-assurance evidence, and territorial context within a continuous-improvement framework. The EBEMM is presented as an empirically informed conceptual framework rather than a formally validated causal, predictive, or operational model. Future multi-institutional research should examine its structural relationships, evaluate its potential predictive applications where appropriate, and assess its external applicability. Full article

Education for sustainable development (ESD) requires pedagogies that connect global sustainability goals with disciplinary, cultural, and professional contexts. Using repeated Q methodology, this study compared how 31 Chinese STEM undergraduates perceived a two-semester course activity integrating instruction and oral practice with the generative artificial intelligence (GenAI) conversational agent Doubao (app version 7.2.0). Participants completed identical 60-statement Q-sorts at two post-exposure occasions—after one semester (T1) and after two semesters (T2). Principal component extraction followed by varimax rotation produced three-factor solutions explaining 50% of variance in the rotated Q-sort solution at T1 and 45% at T2. A rule-based loading audit identified 28 defining Q-sorts at T1 and all 31 at T2. T1 viewpoints were Autonomous Cultural Translators, GenAI-Scaffolded Linguistic Pragmatists, and Career-Oriented Global Communicators; T2 viewpoints were Independent GenAI-Cautious Rehearsers, Critical Cultural Negotiators, and Iterative GenAI-Supported Revisers. Cross-time factor-array correlations were low to moderate (r = −0.053 to 0.452), and participant assignments were dispersed across the solutions. The pattern describes occasion-specific viewpoint configurations and selective GenAI appraisal rather than measured competency growth or a causal developmental trajectory. Without a pre-exposure baseline, a comparison group, or direct learning-outcome measures, the study cannot establish educational effectiveness or GenAI’s independent effect. Full article

Against the backdrop of Emerging Engineering Education (3E) construction, promoting practical teaching reform in mechanical majors is key to cultivating high-level innovative talents. Problems in the practical courses of mechanical majors are identified in this study, such as a disconnect between industrial demands and teaching content, fragmented and isolated practical teaching links, and insufficient innovation ability training. To address these, a “Five-in-One, Four-Stage Progressive” teaching model for mechanical innovation practice courses is proposed. Driven by industrial demands, the systematic cultivation of students’ engineering practice and innovation abilities is achieved through four progressive stages: “Classroom Foundation Building, Scientific Research Enhancement, Competition Verification, and Graduation Project Application.” To verify the model’s effectiveness, a quasi-experimental pretest–posttest design was employed, and the ability development of 30 participating students (experimental group) and 30 non-participating students (control group) was assessed. Results indicate that the improvement in core abilities such as complex engineering problem analysis and innovative scheme design was significantly greater for the experimental group than for the control group. Good applicability and effectiveness are demonstrated by this model in cultivating students’ abilities to meet the demands of sustainable industrial development. A reference is also provided for practical teaching reform in mechanical majors under the Emerging Engineering Education context. Full article

Against the backdrop of sustainable education and the digital transformation of higher education, AI assistants on online learning platforms have become important tools for facilitating students’ access to learning resources, providing immediate feedback, and supporting self-directed learning, thereby creating new opportunities for more effective and sustainable learning experiences. However, the psychological mechanisms linking AI assistant intelligence to students’ innovative behavior remain unclear. Based on Social Cognitive Theory (SCT), this study examines how AI assistant intelligence on online learning platforms relates to higher education students’ innovative behavior through academic self-efficacy and academic procrastination and further explores the moderating role of AI literacy. Using a two-phase time-lagged design, the study collected data through online questionnaires from students at Chinese universities, yielding a final matched sample of 503 students. The findings show that AI assistant intelligence is positively associated with both academic self-efficacy and academic procrastination, but it has no significant direct effect on innovative behavior. Importantly, these two mediators operate in opposing directions: academic self-efficacy transmits a positive indirect effect, whereas academic procrastination transmits a negative indirect effect on innovative behavior. In addition, AI literacy strengthens the positive relationship between AI assistant intelligence and academic self-efficacy while weakening its positive relationship with academic procrastination. These findings reveal the dual psychological pathways through which AI assistant intelligence shapes students’ innovative behavior, extend Social Cognitive Theory in intelligent learning environments, and offer practical implications for leveraging AI-assisted learning to foster students’ innovative capacity and inform the sustainable integration of AI in higher education. Full article

To investigate the potential of generative artificial intelligence (Gen AI) in sustainable engineering education and laboratory instruction, this study used the direct shear test in soil mechanics as a case study. A total of 112 third-year undergraduate students majoring in hydraulic engineering were assigned to traditional and AI-assisted groups. Their performance in experimental operation, data processing, report writing, presentation of results and problem solving was compared using grade statistics, classroom observations and interview data. The results showed that the AI group generally outperformed the traditional group in Experimental operation, data analysis, report completeness, presentation and defense, and overall scores. The analysis showed that Gen AI can function as a form of cognitive scaffolding in conceptual explanation, data processing, report structuring, and error analysis. Specifically, it can provide stage-specific prompts for understanding, procedural support, and feedback during the learning process, thereby helping students complete experimental learning tasks. However, the qualitative corpus included documented cases of overreliance on Gen AI, including uncritical acceptance of generated outputs and alteration of discrepant data without adequate verification; these behaviours were not tabulated at the pair level, so their prevalence cannot be estimated. These findings suggest that the integration of Gen AI into soil mechanics laboratory instruction should be grounded in teacher guidance, disciplinary knowledge support, data verification awareness, and standardized tool-use practices. These findings also offer implications for the responsible use of Gen AI in sustainable engineering education and for the development of students’ AI literacy, awareness of data authenticity, and responsible technology use competencies. Full article