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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Dehasen Mengabdi Indonesian Journal of Innovation Science and Knowledge Social Sciences Journal EduTech Journal
Ahmad Tohir
Sekolah Tinggi Keguruan dan Ilmu Pendidikan Al Islam Tunas Bangsa
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Augmented Reality in Learning: Its Impact on the Understanding of Abstract Concepts Arimbi Pamungkas; Rahayu Soraya; Ahmad Tohir
EduTech Journal Vol. 2 No. 3 (2026): JET - JANUARY
Publisher : PT. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/xjs9bx55

Abstract

This study investigates the effect of immersive simulation-based learning on students’ empathy development compared to video-based learning and non-technology instruction. A randomized controlled trial with a pre-test–post-test design involving three groups was employed. A total of 120 undergraduate students aged 18–25 years were randomly assigned to a virtual reality (VR) simulation group, a video-based learning group, and a control group. Empathy was measured as a latent construct consisting of cognitive and emotional dimensions, adapted from established empathy theory. Data were analyzed using repeated measures ANOVA and Structural Equation Modeling–Partial Least Squares (SEM–PLS). The results indicate that the VR simulation group demonstrated a significantly greater increase in empathy compared to the other groups. Structural modeling further revealed that immersive simulation had a stronger positive effect on empathy than video-based learning. These findings suggest that immersive learning environments effectively support empathy development through integrated cognitive and emotional engagement
Evaluating the Effectiveness of Technology-Based Learning through an Evidence-Based Education Framework Arimbi Pamungkas; Rahayu Soraya; Ahmad Tohir
EduTech Journal Vol. 2 No. 3 (2026): JET - JANUARY
Publisher : PT. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/4zkfbq42

Abstract

The accelerated adoption of technology-based learning in the post-pandemic era has encouraged educational institutions to integrate various digital innovations into instructional practices. However, increased technology use does not consistently translate into measurable learning gains, highlighting the need for evidence-based evaluation. This study aims to evaluate the effectiveness of technology-based learning through an evidence-based education framework using a Systematic Literature Review (SLR) approach. Literature searches were conducted in reputable academic databases with inclusion criteria emphasizing empirical quality and pedagogical relevance. A total of 32 empirical studies were selected and analyzed through thematic and comparative synthesis. The findings indicate that technology-based learning tends to produce positive learning outcomes when supported by evidence-informed instructional design, alignment between learning objectives, activities, and assessment, and the use of objective learning outcome measures. In contrast, technology implementations that merely substitute traditional methods without strong pedagogical grounding yield limited effects. This study concludes that the effectiveness of technology-based learning is conditional rather than universal and must be evaluated within an evidence-based education framework. The findings underscore the need to shift from innovation-driven technology adoption toward pedagogical decision-making grounded in robust empirical evidence.
Analisis Kinerja Pendamping Sosial PKH dalam Pelaksanaan Program di Provinsi Lampung Mohammad Khairul Idaman; Ahmad Tohir
Jurnal Dehasen Mengabdi Vol 4 No 2 (2025): September-Februari
Publisher : Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jdm.v4i2.9674

Abstract

The Family Hope Program (PKH) is a form of conditional social assistance that has proven effective in improving the quality of life of poor families in various regions of Indonesia. In Lampung Province, this program plays a strategic role in poverty alleviation efforts while strengthening community social resilience, particularly in rural areas that still face limited access to basic services. The policy direction of the PKH Lampung 2025–2030 program focuses on building independent families through increasing economic capacity, education, and health. The program's primary target group is nearly 400,000 Beneficiary Families (KPM), with priority given to households with school-age children, pregnant women, the elderly, and people with disabilities. This research uses a case study approach, emphasizing the implementation strategy of the PKH Lampung 2025–2030 program, which integrates the use of data, technology, and multi-stakeholder collaboration. A SWOT analysis is applied to assess the program's strengths, weaknesses, opportunities, and threats, particularly in practice in Tanjung Harapan Village, Central Lampung. Field studies demonstrate the success of empowering beneficiary families (KPM) through synergy with social services, entrepreneurship training, and the development of cooperative-based sewing and garment businesses marketed through digital platforms. The research confirms that the role of PKH social facilitators significantly contributes to increasing the program's effectiveness, although challenges remain in human resource capacity and cross-sector coordination. Therefore, strengthening competencies through tiered training, providing proportional incentives, integrating economic empowerment programs, a digitalization roadmap, and participatory monitoring are crucial steps. With this strategy, the effectiveness of PKH in Lampung can be sustained while reducing the community's dependence on aid.
Technology-Based Learning Enhances Self-Regulated Learning: Systematic Literature Review Ahmad Tohir; Arimbi Pamungkas; Agnes Fibriana Kurniawati
Social Sciences Journal Vol. 3 No. 2 (2026): August
Publisher : Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/sosj.v3i2.2019

Abstract

The global transformation of the digital education ecosystem demands a high level of independent learning from students to navigate expanding academic autonomy. This systematic literature review aims to identify, evaluate, and synthesize the effectiveness of implementing cutting-edge instructional technologies on improving students' self-regulation strategies across academic levels. This research approach adopted the rigorous PRISMA selection protocol by extracting purely primary empirical literature from the Scopus database covering publications from 2016 to 2026, resulting in fifteen final articles for in-depth thematic analysis. The analysis findings demonstrate that the presence of adaptive learning platforms, artificial intelligence generative feedback, gamified virtual reality devices, and smart e-books successfully trigger a surge in students' metacognitive capacity and positive affect. These digital devices consistently replace the role of manual instructor supervision by providing instant cognitive scaffolding that can calibrate task difficulty based on students' actual performance track records. The study concludes that the modern technology ecosystem is not simply a passive repository of material but has evolved into a proactive co-regulatory agent that fundamentally transforms academic cognitive load into long-term intrinsic persistence. The novelty of this research rests on a theoretical unpacking of the psychological interactions of humans and machines, providing conclusive evidence of how personalized digital algorithms are able to hijack the brain's gratification system to catalyze self-discipline, while providing a crucial guiding framework for designing hybrid pedagogical curricula in the future.
MODEL INTEGRASI GENERATIVE AI DALAM INSTRUCTIONAL DESIGN UNTUK PEMBELAJARAN PERGURUAN TINGGI Ahmad Tohir; Agnes Fibriana Kurniawati; Parwito Parwito; Agus Riyanto
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 4 (2026): August 2026 (1)
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i4.7210

Abstract

Abstract: The integration of Generative AI into higher education instruction depends not only on technological availability but also on lecturers' AI-related pedagogical competence and institutional support in guiding its use within the instructional design process. This study aims to analyze the impact of lecturers' AI-pedagogical competence and institutional support on the quality of Generative AI integration and instructional design quality, while also examining the mediating role of Generative AI integration quality. A quantitative approach with an explanatory cross-sectional design was employed. Data were collected from 300 lecturers and analyzed in stages using IBM SPSS Statistics and SmartPLS 4. The research model comprises four reflective constructs—lecturers' AI-pedagogical competence, institutional support, Generative AI integration quality, and instructional design quality—measured via 60 indicators using a five-point Likert scale. The analysis encompassed assessments of data quality, common method bias, the measurement model, and the structural model, as well as examinations of direct and indirect effects via bootstrapping. The results indicate that AI-pedagogical competence positively influences both the quality of Generative AI integration and instructional design quality. Institutional support positively affects the quality of Generative AI integration but does not show a significant direct effect on instructional design quality. Generative AI integration quality positively influences instructional design quality. Furthermore, Generative AI integration quality mediates the relationship between AI-pedagogical competence, institutional support, and instructional design quality. These findings suggest that Generative AI should be understood not merely as a technology used in instruction, but as an integration mechanism linking lecturers' capabilities and institutional support to the quality of instructional design. The study offers implications for developing lecturers' AI-pedagogical competence and strengthening institutional support to ensure that Generative AI integration is carried out systematically, pedagogically, and responsibly. Keywords: Generative AI; AI-pedagogical competence; institutional support; quality of AI integration; learning design; instructional design; higher education.   Abstrak: Integrasi Generative AI dalam pembelajaran perguruan tinggi tidak hanya bergantung pada ketersediaan teknologi, tetapi juga pada kompetensi pedagogis dosen dan dukungan institusi dalam mengarahkan penggunaannya ke dalam proses desain pembelajaran. Penelitian ini bertujuan menganalisis pengaruh kompetensi pedagogis-AI dosen dan dukungan institusi terhadap kualitas integrasi Generative AI serta kualitas desain pembelajaran, sekaligus menguji peran mediasi kualitas integrasi Generative AI. Penelitian menggunakan pendekatan kuantitatif dengan desain explanatory cross-sectional. Data diperoleh dari 300 dosen dan dianalisis secara bertahap menggunakan IBM SPSS Statistics dan SmartPLS 4. Model penelitian mencakup empat konstruk reflektif, yaitu kompetensi pedagogis-AI dosen, dukungan institusi, kualitas integrasi Generative AI, dan kualitas desain pembelajaran, yang diukur melalui 60 indikator dengan skala Likert lima poin. Analisis meliputi pengujian kualitas data, common method bias, model pengukuran, model struktural, pengaruh langsung, dan pengaruh tidak langsung melalui bootstrapping. Hasil penelitian menunjukkan bahwa kompetensi pedagogis-AI berpengaruh positif terhadap kualitas integrasi Generative AI dan kualitas desain pembelajaran. Dukungan institusi berpengaruh positif terhadap kualitas integrasi Generative AI, tetapi tidak menunjukkan pengaruh langsung yang signifikan terhadap kualitas desain pembelajaran. Kualitas integrasi Generative AI berpengaruh positif terhadap kualitas desain pembelajaran. Selain itu, kualitas integrasi Generative AI memediasi hubungan kompetensi pedagogis-AI dan dukungan institusi dengan kualitas desain pembelajaran. Temuan ini menunjukkan bahwa Generative AI tidak cukup dipahami sebagai teknologi yang digunakan dalam pembelajaran, tetapi sebagai mekanisme integrasi yang menghubungkan kapasitas dosen dan dukungan institusi dengan kualitas instructional design. Penelitian ini memberikan implikasi bagi pengembangan kompetensi pedagogis-AI dosen dan penguatan dukungan institusional agar integrasi Generative AI dapat dilakukan secara sistematis, pedagogis, dan bertanggung jawab. Kata kunci: Generative AI; kompetensi pedagogis-AI; dukungan institusi; kualitas integrasi AI; desain pembelajaran; instructional design; pendidikan tinggi.
AI-Supported Learning Design untuk Meningkatkan Self-Regulated Learning Mahasiswa Ahmad Tohir; Arimbi Pamungkas; Agnes Fibriana Kurniawati
Indonesian Journal of Innovation Science and Knowledge Vol. 3 No. 4 (2026): IJISK 2026
Publisher : Fakultas Pendidikan Ilmu Keguruan, Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ijisk.v3i4.802

Abstract

Perkembangan kecerdasan buatan dalam pendidikan membuka peluang untuk membangun lingkungan belajar yang lebih adaptif, personal, dan responsif terhadap kebutuhan mahasiswa. Pemanfaatan AI belum secara otomatis membentuk kemampuan mahasiswa dalam mengatur tujuan, strategi, pemantauan, dan refleksi belajar sehingga diperlukan desain pembelajaran yang mengintegrasikan AI dengan mekanisme Self-Regulated Learning secara sistematis. Penelitian ini bertujuan menganalisis pengaruh AI-Supported Learning Design terhadap Self-Regulated Learning mahasiswa setelah mengendalikan kemampuan awal. Penelitian menggunakan pendekatan kuantitatif dengan desain kuasi-eksperimental pretest-posttest control group. Sebanyak 60 mahasiswa dibagi secara proporsional ke dalam kelompok eksperimen dan kontrol, masing-masing 30 mahasiswa. Instrumen penelitian terdiri atas 25 item Self-Regulated Learning dengan skala Likert lima tingkat. Analisis dilakukan menggunakan statistik deskriptif, uji asumsi, paired samples t-test, dan ANCOVA. Hasil penelitian menunjukkan peningkatan rata-rata Self-Regulated Learning sebesar 17,700 poin pada kelompok eksperimen dan 5,533 poin pada kelompok kontrol. ANCOVA menunjukkan perbedaan signifikan antar kelompok setelah Pre_Total dikendalikan, F(1,57) = 183,461, p < 0,001, partial η² = 0,763. Adjusted mean kelompok eksperimen sebesar 92,590, sedangkan kelompok kontrol sebesar 80,377. Novelty penelitian terletak pada pengembangan AI sebagai komponen desain pembelajaran yang secara terstruktur mendukung penetapan tujuan, perencanaan, monitoring, regulasi strategi, umpan balik, dan refleksi. Temuan menegaskan bahwa nilai pedagogis AI bergantung pada kualitas desain pembelajaran yang mengarahkan teknologi untuk memperkuat regulasi belajar mahasiswa.
Co-Authors Agnes Fibriana Kurniawati Agus Riyanto Arimbi Pamungkas Mohammad Khairul Idaman Parwito Rahayu Soraya Rahayu Soraya