Vol. 4 No. 3 (2026): Indonesian Journal of Pedagogy and Teacher Education

Background: Digital transformation has created new opportunities for integrating technology into entrepreneurship education in Vocational High Schools (SMK). However, technology use does not automatically lead to meaningful entrepreneurial learning experiences.

Aims: This study explores digital pedagogy practices in entrepreneurship education, examines students’ learning experiences, and identifies factors that facilitate or hinder its implementation in vocational schools in South Sumatra.

Methods: This study employed a descriptive-exploratory qualitative design involving 170 students and 34 entrepreneurship teachers from 34 vocational high schools across 17 regencies and cities in South Sumatra. Students served as the primary participants, providing accounts of their learning experiences, while teachers provided supporting information about instructional practices and implementation challenges. Data were collected through semi-structured interviews, classroom observations, and documentation, and were analyzed thematically.

Results: Five primary themes emerged: the use of digital technology in entrepreneurship learning, student learning experiences, engagement and collaboration, entrepreneurial competency development, and contextual enablers and barriers. Students described broader access to learning resources, opportunities for product development and digital marketing, and more independent learning. However, these experiences varied according to digital access, students’ digital skills, teachers’ pedagogical competence, and school support.

Conclusion: Digital pedagogy is most meaningful when technology is integrated into authentic entrepreneurial tasks rather than used solely to deliver content. Effective implementation requires teachers’ pedagogical competence, adequate digital infrastructure, and attention to students’ contextual needs.

Background: Artificial intelligence (AI) has rapidly transformed English language education, yet systematic evidence concerning its influence on learners’ emotional experiences remains fragmented. Existing reviews have primarily emphasized language achievement, specific AI applications, or individual affective constructs.

Aims: This systematic review synthesizes empirical evidence on learner emotions in AI-assisted English as a Second Language (ESL) and English as Foreign Language (EFL) learning, focusing on research characteristics, emotional constructs, and reported affective outcomes.

Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, systematic searches were conducted in Scopus, Web of Science, OpenGrey, and Google Scholar. After applying predefined eligibility and quality-assessment criteria, 39 empirical studies, including 35 peer-reviewed publications and four grey-literature sources, were included in the qualitative synthesis.

Results: The findings indicate that AI-assisted ESL/EFL learning has been implemented across diverse educational settings, language skills, and AI applications across more than ten non-English-speaking countries. The evidence addressed a range of emotional and affective constructs, including foreign language enjoyment, anxiety, boredom, interest, engagement, confidence, well-being, and emotion regulation. Overall, AI-assisted learning was generally associated with more positive emotional experiences and reduced anxiety, although concerns regarding AI-related anxiety, dependence, reliability, and ethical issues were also reported.

Conclusion: The findings indicate that AI-assisted ESL/EFL learning has important affective as well as pedagogical dimensions. Future research should adopt more rigorous and diverse designs to clarify the conditions under which AI can support learners’ emotional well-being alongside language development.

Background: This study examined the effects of family disruption on chemistry performance, drug use, and peer relationships among 100 Senior Secondary School 3 students from two public schools in Akure South LGA, Ondo State, Nigeria.

Aims: The study examined the relationship between family disruption and chemistry performance and drug use, and determined gender differences in peer relationships among students from broken homes.

Method: Using a cross-sectional survey design with multistage sampling, data were collected via the Survey on the Effects of Family Disruption and Chemistry Performance: Drug Use and Peer Relationships (α = 0.80) and analyzed using chi-square, Spearman rho, and t-tests at p < .05 significance.

Results: Results revealed a significant association between broken homes and chemistry performance, χ²(25, N = 100) = 50.05, p = .002, and a strong positive correlation with drug use, rs(100) = .671, p < .001, rejecting H₀₁ and H₀₂. However, no gender differences emerged in peer relationship reactions, t(98) = 0.75, p = .457, accepting H₀₃.

Conclusion: Findings confirm family disruption as a critical risk factor impairing chemistry performance and increasing substance abuse vulnerability among Nigerian secondary students, irrespective of gender. Recommendations include school counselling, academic support, drug-use awareness and prevention for students, parent and caregiver engagement in students' academic and social development, teacher training, and peer mentoring to mitigate these effects and enhance science education equity.

Background: Generative artificial intelligence (AI) is increasingly integrated into higher education, reshaping academic writing practices and student interaction with digital tools. However, the nature of human–AI interaction in academic writing and its implications for learning remain insufficiently synthesized.

Aims: This study systematically reviews AI-assisted academic writing in higher education, focusing on its cognitive, pedagogical, and ethical dimensions. It also develops a context-mediated framework explaining how pedagogical and institutional factors shape educational outcomes.

Methods: A systematic review with qualitative thematic synthesis examined studies published from 2023 to 2025. Searches of Web of Science, Scopus, ERIC, PsycINFO, and PubMed were conducted in January 2026. Following predefined eligibility criteria and structured screening, 20 studies were included.

Results: AI tools can support academic writing through improved linguistic accuracy, more efficient drafting and revision, and greater learner confidence. However, potential risks include reduced cognitive engagement, over-reliance on AI-generated content, and academic integrity concerns. Outcomes appear to depend not on the technology alone but on patterns of human–AI interaction within pedagogical and institutional contexts.

Conclusion: The review proposes a context-mediated framework in which pedagogical guidance, student engagement, and institutional policy condition the educational impact of AI-assisted writing. AI is therefore best understood as a contingent educational tool whose value depends on how responsibly it is integrated into higher education.