This study aimed to analyse the impacts of artificial intelligence (AI) enabled creativity on divergent and convergent thinking among higher education students, focusing on how key factors—cognition, behaviour, interaction, ethics, and emotion—shape creative outcomes. The AI tools integration with academic environments has enhanced how students generate ideas, solve problems, and express originality. However, AI influences on critical cognitive processes and ethical considerations associated with creativity evidence are limited. The research adopts a quantitative approach, surveying a diverse sample of higher education students across various disciplines and institutions. Data were collected using a standardised questionnaire based on a Likert scale, covering variables such as divergence, convergence, metacognition, dependency, risk-taking, feedback, collaboration, transparency, confidence, and implementation. Structural equation modelling (SEM) was used to test the proposed relationships between these constructs. The findings reveal significant positive effects of cognition on emotion and behaviour, and of behaviour on creativity, while ethics and interaction showed complex, partly indirect pathways influencing creative outcomes. The model fit indices confirmed the robustness of the proposed framework, with acceptable values for CMIN/DF, RMSEA, and CFI. The study emphasises the need for educational institutions to design AI-integrated learning environments that promote ethical engagement, emotional well-being, and critical thinking. The results provide actionable insights for curriculum designers, educators, and policymakers seeking to harness AI for fostering student creativity while safeguarding academic integrity.

With women underrepresented, engineering education in India continues to face persistent gender disparities. Despite various reforms and interventions across the decades, the imbalance is still apparent. This study examines gender representation in engineering education in India using both the systematic literature review and quantitative data analysis. The systematic literature review followed the PRISMA guidelines to review peer-reviewed open-access research articles. The review helped to identify the key factors that influence women’s participation in engineering education, such as social, cultural, organizational, and policy-based barriers. For the quantitative data analysis, the authors have referred to the data from the All-India Survey of Higher Education (AISHE) across multiple years (2012–2022). This study aims to understand the trends in female enrollment and graduation in engineering programs. The data was organized, cleaned, and represented. The graphical representation highlights year-wise patterns, progress, and areas where gender gaps persist. With the combined insights from SLR and AISHE data trends, the authors have proposed a conceptual framework that aims to improve female participation. The findings of this study are expected to inform institutions and policymakers on how to make the engineering education system more inclusive for female participants.

Transforming engineering pedagogy is imperative in an era defined by global sustainability challenges. The United Nations’ Sustainable Development Goals (SDGs) offer innovative teaching methodologies by embedding necessary competencies to tackle complex virtual and real-world problems. The paper integrates engineering courses with SDG-6, SDG-7, SDG-9, and SDG-11. By viewing education pedagogy through the lens of the SDGs, this paper positions the shift from the traditional content-based model towards project- and problem-based models that cultivate ethical solutions, critical thinking, interdisciplinary collaboration, and the transformation of scholarly, industrial, and societal perspectives. At a policy level, this transformation entails reforming the curriculum and assessment framework that values both societal impact and technical competence. By viewing engineering education through the lens of the SDGs, this study positions pedagogy as a catalyst for preparing future engineers not only as innovators but also as responsible agents of sustainable global development.

This study aims to critically explore the impact of peer learning on conceptual understanding in online engineering education. By leveraging current peer-reviewed articles, the research provides an extensive analysis of the efficacy, variations, and challenges of peer learning approaches in virtual engineering classrooms. A comprehensive systematic literature review (SLR) methodology is used in the study, adhering to PRISMA for transparent identification, screening, and assessment of suitable studies. The major databases searched for articles were PubMed, Scopus, and Google Scholar. The inclusion criteria were peer-reviewed articles published from 2015 to 2025, in the English language, and aligning with the scope of this study. The selected articles were then analyzed using a thematic analysis approach to identify patterns related to peer learning approaches like peer instruction, collaborative projects, and discussion forums, improving the conceptual gains of engineering students. Comparative findings were drawn from these parameters related to the study. Based on the extensive review and analysis of literature, a conceptual framework is presented in this paper, highlighting the relationships between peer learning approaches and their implications for online pedagogy. Recommendations focused on optimization of peer learning structures for the online environment in engineering and identifying support mechanisms for diverse learning groups. The review also identified research gaps, promoted adaptive peer learning models and longitudinal studies, and significantly contributed to the advancement of inclusive and effective online engineering education.

This study evaluated the developed cost-effective stepper motor laboratory equipment for undergraduate engineering students, addressing the challenges of financial constraints and limited lab access. Utilizing the ADDIE model, the equipment was designed to support basic to intermediate microcontroller-robotics applications through five experiments. Student performance was assessed by comparing traditional practice, simulations (Tinkercad), and the actual lab equipment across three lab activities, revealing significant improvements with the physical setup. For the remaining two experiments, where Tinkercad lacked the necessary components, a mixed-methods approach was employed. Quantitative survey results demonstrated strong agreement and satisfaction, with Cronbach’s alpha exceeding 0.90, confirming reliability. Qualitative thematic analysis, using Braun and Clarke’s 6-step method, highlighted user-friendliness and component-specific features as key strengths. Minor suggestions primarily focused on improving physical design. These findings validated the effectiveness of the developed stepper motor lab equipment in enhancing practical learning and bridging the gap between theoretical knowledge and microcontroller-robotics applications, particularly by overcoming the limitations of simulation-only learning.

This study develops and validates the personal cyber resilience scale (PCRS) to identify resilience profiles and explore differences between Gen Z engineering students and Gen Alpha students nearing higher education. A mixed-methods study (N = 3275) employed EFA, CFA, and K-means clustering, supplemented by qualitative analysis. We developed a robust 32-item, seven-factor PCRS (α = .871) and identified four stable profiles. Profile distribution differed significantly by generation (p = .011) and gender (p < .001), with qualitative data providing context to these differences. The PCRS is an effective tool for measuring youth cyber resilience, and the profiles can inform targeted prevention strategies in educational contexts.

Artificial intelligence (AI) is entering engineering courses rapidly, yet tool-led adoption can weaken assessment validity and academic integrity. This paper presents the E-AIP (Engineering–AI Pedagogy) Framework, which centers three pillars learning Outcomes, Process Evidence, and Integrity & Ethics Guardrails and links them to design levers (AI function, task authenticity, feedback granularity, locus of agency). We define seven constructs, state eight propositions about alignment, validity moderation, authenticity, and agency, and operationalize E-AIP through a compact matrix (AI function × outcome type with required process evidence and guardrails). Two design patterns (CS1 debug-with-defense; circuits param-twins) illustrate classroom use; a lightweight adoption toolkit (two rubrics and an integrity or privacy checklist) supports immediate deployment. Additional patterns and full matrices appear in the online supplement. E-AIP enables instructors to capture AI’s benefits while preserving what scores validly claim to measure.

This study investigates how gamified and blended instructional design affects student engagement and learning outcomes in an undergraduate cybersecurity course. The course was implemented across two academic years using consistent content and delivery, providing a natural context for cohort-based comparison. Participants included third-year (n = 47) and second-year (n = 52) students enrolled in the same course, enabling analysis of academic-level differences in cognitive, behavioral, and motivational outcomes. A mixed-methods approach was used, combining pre/post-test results, learning management systems (LMS)-based behavioral data (e.g., login frequency, task completion, forum activity), and a student perception survey. Both cohorts showed significant learning gains, but behavioral engagement differed. Second-year students exhibited higher LMS activity and stronger motivational responses to gamification, while third-year students completed tasks more efficiently. Forum participation was positively correlated with learning gains, suggesting that interactive engagement is a key factor in performance. The study provides practical insights into how gamified blended learning, supported by digital learning analytics, can be scaled across diverse student profiles. It contributes to emerging technologies in higher education by demonstrating the role of behavioral data in refining instructional design.

This study examines the factors influencing low enrollment in manufacturing, mechatronics, and electronics engineering programs using a mixed-methods approach. A cross-sectional retrospective survey of 578 students surveyed demographics, enrollment influences, and perceptions of program value using a 5-point Likert scale and qualitative analysis of three openended questions using Braun and Clarke’s 6-Step Thematic Analysis. The study findings reveal that Manufacturing Engineering’s (MFE) high response rate but low first-choice preference indicates reliance on reconsideration admissions, while MEE and Electronics Engineering (ECE) attract stronger initial interest. Gender distribution shows MFE and Mechatronics Engineering (MEE) as male-dominated, while ECE is more balanced. Quantitative results highlight strong agreement on program value but lower scores for promotion, particularly for MEE and ECE. Qualitative insights emphasize inadequate program visibility, the need for hands-on learning, and industry exposure. Recommendations include enhancing marketing through social media and alumni networks, integrating experiential learning, modernizing curriculum, and strengthening industry partnerships. These strategies aim to align programs with student expectations, improve enrollment, and enhance career preparation, addressing gaps in awareness and engagement across BulSU’s engineering programs.