Inscitech Education

Student Engagement in Artificial Intelligence-Based Learning: An Analysis of Behavioral, Emotional, and Cognitive Engagement

2025-02-14T10:12:37+07:00

This study aims to describe the level of student engagement in Artificial Intelligence (AI)-based learning by examining three dimensions of engagement: behavioral, emotional, and cognitive. The respondents were students of the English Education Study Program at Universitas Muhammadiyah Purwokerto. Data were collected through a Likert-scale questionnaire (1–5) and analyzed descriptively. The results showed that student engagement was at a high level across all three dimensions. The average scores were 12.80 (out of 15) for behavioral engagement, 12.67 (out of 15) for emotional engagement, and 21.09 (out of 25) for cognitive engagement. These findings indicate students’ readiness to respond to the integration of AI in learning. Further research is recommended to analyze these aspects using a correlational quantitative approach or causal models such as Structural Equation Modeling (SEM) to explore the relationships between engagement and a broader range of factors.

The Future of Learning Student Perceptions on the Integration of Artificial Intelligence in Vocational Technical Education

2025-02-07T10:15:04+07:00

The rapid advancement of Artificial Intelligence (AI) has brought transformative changes across various sectors, including education. In vocational education, AI presents opportunities to enhance learning efficiency, content personalization, and interactivity. However, successful integration of AI in learning environments requires a comprehensive understanding of student perceptions, particularly in technical vocational contexts. This study aims to explore the perceptions of students in the Automotive Vocational Education Program at Universitas Ahmad Dahlan regarding the adoption of AI in learning. The research employs a descriptive quantitative approach involving 55 students as respondents. Data were collected using a Likert-scale questionnaire covering eight key indicators of AI adoption: learning efficiency, ease of use, content personalization, overall benefits, long-term relevance, social impact, interactivity, and engagement. The analysis was conducted using descriptive statistical methods, including mean and standard deviation calculations. The results reveal that students generally hold positive perceptions toward AI integration in learning. The highest mean score was found in content personalization (4.55), followed by ease of learning (4.53) and general benefits of AI (4.53). However, the indicator on social impact of AI showed slightly lower agreement and higher variation (mean = 4.35; SD = 0.85), suggesting some student concerns regarding ethical and societal implications. This study concludes that students are optimistic about AI’s role in shaping the future of vocational education. The findings highlight the importance of supporting policies, infrastructure, and digital literacy to foster effective AI integration. Further studies are recommended to explore deeper insights using mixed methods and broader respondent samples across institutions.

Mapping the Knowledge Landscape: A Bibliometric Analysis of Research Trends in Entrepreneurship Education

2025-02-16T12:35:23+07:00

Entrepreneurship education has gained increasing global attention as a critical driver of innovation, youth employment, and economic resilience. Despite the proliferation of research in this area, there remains a lack of comprehensive understanding regarding its intellectual structure, thematic evolution, and research gaps. This study conducts a bibliometric analysis of 992 scholarly articles on entrepreneurship education published between 2000 and 2023 and indexed in the Scopus database. Using VOSviewer software, the analysis maps co-authorship networks, keyword co-occurrences, and thematic clusters to reveal dominant research trends and emerging areas of interest. The results show a significant increase in publication output, particularly after 2018, reflecting heightened academic and policy engagement with entrepreneurial learning. Five major thematic clusters were identified: (1) entrepreneurial intention and behavior, (2) entrepreneurship in higher education, (3) digital and social entrepreneurship, (4) gender and inclusivity, and (5) pedagogical innovations. The findings highlight a shift in focus from traditional economic outcomes toward social impact, digital transformation, and inclusive practices. The study also uncovers regional imbalances in research contributions, with developed countries dominating the discourse. While bibliometric methods offer valuable macro-level insights, limitations include the exclusion of non-Scopus sources and the lack of qualitative depth. Future research should address these gaps through comparative and context-sensitive approaches, particularly in underrepresented regions. This study contributes to a more systematic understanding of entrepreneurship education and provides strategic directions for researchers, educators, and policymakers aiming to develop more inclusive and adaptive entrepreneurial learning ecosystems.

Directions of Curriculum and Learning Models Changes in The Digital Era

2025-02-20T12:36:19+07:00

The research background is related to the lack of a good and in-depth mapping regarding the direction of changes, opportunities, and challenges related to the development of curriculum and learning models in the digital era. This study aims to conduct a comprehensive systematic literature review related to the direction of curriculum changes and learning models in the context of the digital learning era. This research adopts a systematic literature review approach. The study begins with collecting literature from databases and continues with the screening process, concluding with the literature analysis process to identify patterns, trends, and key findings related to the research topic. The results of the study describe that trends and developments in curriculum and learning models lead to technology-based learning, competency-based curriculum, and adaptive learning. Furthermore, technology plays a role in creating accessibility, personalized learning, and innovation. Additionally, developments face challenges such as digital divide and data security. On the other hand, developments also present opportunities such as learning flexibility, unlimited access, and the development of innovative learning models. Finally, factors influencing these developments include technological changes, social changes, and labor market needs, changes in student needs and learning preferences, as well as educational regulations and policies. Recommendations for further research include conducting in-depth analysis regarding solutions to address the gap in technology learning practices and considering social and cultural aspects in the development of digital curriculum.

Measuring the Effectiveness of Entrepreneurship Education: A Meta-Analytic Examination of Its Influence on Entrepreneurial Interest

2025-02-27T12:55:52+07:00

This study conducts a comprehensive meta-analysis to examine the overall effect of entrepreneurship education on students’ entrepreneurial intention in higher education contexts. Amid growing emphasis on fostering entrepreneurial mindsets among university students, existing empirical findings have yielded mixed and inconsistent results, necessitating a systematic synthesis. A total of 26 primary studies published between 2012 and 2024 were analyzed, comprising diverse cultural and educational settings. The meta-analytic estimation revealed a statistically significant and strong average effect size of 0.674 (p = 0.026; 95% CI = 0.082–1.265), indicating that entrepreneurship education positively and meaningfully influences students’ intention to engage in entrepreneurial activities. A test of heterogeneity (p < 0.001) confirmed substantial variation across studies, suggesting that contextual factors such as teaching approaches, entrepreneurial ecosystems, and student profiles moderate the impact of entrepreneurship education. Given this heterogeneity, a random-effects model was employed to ensure more accurate estimation. The findings imply that institutions should design context-sensitive and experiential entrepreneurship curricula, support entrepreneurial learning environments (e.g., student incubators), and involve practitioners as mentors to enhance learning outcomes. Theoretically, the study is grounded in Ajzen’s Theory of Planned Behavior and supported by social learning and entrepreneurial intention theories. Practically, the results provide evidence-based justification for expanding entrepreneurship education initiatives. This research also identifies gaps in current literature and proposes directions for future studies, particularly regarding the long-term effects and qualitative aspects of entrepreneurship education. Overall, the study reinforces the strategic value of entrepreneurship education as a catalyst for cultivating entrepreneurial intention in higher education.